Create conversions to/from NNAPI canonical types
This CL creates the following primary sets of functions:
* V1_X::utils::convert(<canonical_type>) -- Converts a canonical type
to the corresponding HAL version type.
* nn::convert(<V1_X_HAL_type>) -- Converts a HAL version type to the
corresponding canonical type.
* neuralnetworks::utils::hasNoPointerData -- Indicates if the object
contains no pointer-based data that could be relocated to shared
memory.
* neuralnetworks::utils::flushDataFromPointerToShared -- Relocate
pointer-based data to shared memory.
* neuralnetworks::utils::unflushDataFromSharedToPointer -- Undoes
`flushDataFromPointerToShared` on a Request object. More
specifically, `unflushDataFromSharedToPointer` copies the output
shared memory data from the transformed Request object back to the
output pointer-based memory in the original Request object.
It also introduces some other minor utility code, including
makeQuantized8PerformanceConsistentWithP, countNumberOfConsumers,
validate, valid, and validatedConvertToCanonical.
Bug: 160667419
Test: mma
Change-Id: I0732e658c1f4ed40cd122f1ca8581fb40b056757
diff --git a/neuralnetworks/1.0/utils/Android.bp b/neuralnetworks/1.0/utils/Android.bp
new file mode 100644
index 0000000..57a052f
--- /dev/null
+++ b/neuralnetworks/1.0/utils/Android.bp
@@ -0,0 +1,33 @@
+//
+// 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.
+//
+
+cc_library_static {
+ name: "neuralnetworks_utils_hal_1_0",
+ defaults: ["neuralnetworks_utils_defaults"],
+ srcs: ["src/*"],
+ local_include_dirs: ["include/nnapi/hal/1.0/"],
+ export_include_dirs: ["include"],
+ static_libs: [
+ "neuralnetworks_types",
+ "neuralnetworks_utils_hal_common",
+ ],
+ shared_libs: [
+ "android.hardware.neuralnetworks@1.0",
+ ],
+ export_static_lib_headers: [
+ "neuralnetworks_utils_hal_common",
+ ],
+}
diff --git a/neuralnetworks/1.0/utils/OWNERS b/neuralnetworks/1.0/utils/OWNERS
new file mode 100644
index 0000000..e4feee3
--- /dev/null
+++ b/neuralnetworks/1.0/utils/OWNERS
@@ -0,0 +1,11 @@
+# Neuralnetworks team
+butlermichael@google.com
+dgross@google.com
+galarragas@google.com
+jeanluc@google.com
+levp@google.com
+miaowang@google.com
+pszczepaniak@google.com
+slavash@google.com
+vddang@google.com
+xusongw@google.com
diff --git a/neuralnetworks/1.0/utils/include/nnapi/hal/1.0/Conversions.h b/neuralnetworks/1.0/utils/include/nnapi/hal/1.0/Conversions.h
new file mode 100644
index 0000000..8ad98cb
--- /dev/null
+++ b/neuralnetworks/1.0/utils/include/nnapi/hal/1.0/Conversions.h
@@ -0,0 +1,66 @@
+/*
+ * 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.
+ */
+
+#ifndef ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_0_CONVERSIONS_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_0_CONVERSIONS_H
+
+#include <android/hardware/neuralnetworks/1.0/types.h>
+#include <nnapi/Result.h>
+#include <nnapi/Types.h>
+#include <nnapi/hal/CommonUtils.h>
+
+namespace android::nn {
+
+Result<OperandType> convert(const hal::V1_0::OperandType& operandType);
+Result<OperationType> convert(const hal::V1_0::OperationType& operationType);
+Result<Operand::LifeTime> convert(const hal::V1_0::OperandLifeTime& lifetime);
+Result<DeviceStatus> convert(const hal::V1_0::DeviceStatus& deviceStatus);
+Result<Capabilities::PerformanceInfo> convert(const hal::V1_0::PerformanceInfo& performanceInfo);
+Result<Capabilities> convert(const hal::V1_0::Capabilities& capabilities);
+Result<DataLocation> convert(const hal::V1_0::DataLocation& location);
+Result<Operand> convert(const hal::V1_0::Operand& operand);
+Result<Operation> convert(const hal::V1_0::Operation& operation);
+Result<Model::OperandValues> convert(const hardware::hidl_vec<uint8_t>& operandValues);
+Result<Memory> convert(const hardware::hidl_memory& memory);
+Result<Model> convert(const hal::V1_0::Model& model);
+Result<Request::Argument> convert(const hal::V1_0::RequestArgument& requestArgument);
+Result<Request> convert(const hal::V1_0::Request& request);
+Result<ErrorStatus> convert(const hal::V1_0::ErrorStatus& status);
+
+} // namespace android::nn
+
+namespace android::hardware::neuralnetworks::V1_0::utils {
+
+nn::Result<OperandType> convert(const nn::OperandType& operandType);
+nn::Result<OperationType> convert(const nn::OperationType& operationType);
+nn::Result<OperandLifeTime> convert(const nn::Operand::LifeTime& lifetime);
+nn::Result<DeviceStatus> convert(const nn::DeviceStatus& deviceStatus);
+nn::Result<PerformanceInfo> convert(const nn::Capabilities::PerformanceInfo& performanceInfo);
+nn::Result<Capabilities> convert(const nn::Capabilities& capabilities);
+nn::Result<DataLocation> convert(const nn::DataLocation& location);
+nn::Result<Operand> convert(const nn::Operand& operand);
+nn::Result<Operation> convert(const nn::Operation& operation);
+nn::Result<hidl_vec<uint8_t>> convert(const nn::Model::OperandValues& operandValues);
+nn::Result<hidl_memory> convert(const nn::Memory& memory);
+nn::Result<Model> convert(const nn::Model& model);
+nn::Result<RequestArgument> convert(const nn::Request::Argument& requestArgument);
+nn::Result<hidl_memory> convert(const nn::Request::MemoryPool& memoryPool);
+nn::Result<Request> convert(const nn::Request& request);
+nn::Result<ErrorStatus> convert(const nn::ErrorStatus& status);
+
+} // namespace android::hardware::neuralnetworks::V1_0::utils
+
+#endif // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_0_CONVERSIONS_H
diff --git a/neuralnetworks/1.0/utils/include/nnapi/hal/1.0/Utils.h b/neuralnetworks/1.0/utils/include/nnapi/hal/1.0/Utils.h
new file mode 100644
index 0000000..ec8da06
--- /dev/null
+++ b/neuralnetworks/1.0/utils/include/nnapi/hal/1.0/Utils.h
@@ -0,0 +1,63 @@
+/*
+ * 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.
+ */
+
+#ifndef ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_0_UTILS_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_0_UTILS_H
+
+#include "nnapi/hal/1.0/Conversions.h"
+
+#include <android-base/logging.h>
+#include <android/hardware/neuralnetworks/1.0/types.h>
+#include <nnapi/Result.h>
+#include <nnapi/Types.h>
+#include <nnapi/Validation.h>
+
+namespace android::hardware::neuralnetworks::V1_0::utils {
+
+constexpr auto kVersion = nn::Version::ANDROID_OC_MR1;
+
+template <typename Type>
+nn::Result<void> validate(const Type& halObject) {
+ const auto canonical = NN_TRY(nn::convert(halObject));
+ const auto version = NN_TRY(nn::validate(canonical));
+ if (version > utils::kVersion) {
+ return NN_ERROR() << "";
+ }
+ return {};
+}
+
+template <typename Type>
+bool valid(const Type& halObject) {
+ const auto result = utils::validate(halObject);
+ if (!result.has_value()) {
+ LOG(ERROR) << result.error();
+ }
+ return result.has_value();
+}
+
+template <typename Type>
+decltype(nn::convert(std::declval<Type>())) validatedConvertToCanonical(const Type& halObject) {
+ auto canonical = NN_TRY(nn::convert(halObject));
+ const auto version = NN_TRY(nn::validate(canonical));
+ if (version > utils::kVersion) {
+ return NN_ERROR() << "";
+ }
+ return canonical;
+}
+
+} // namespace android::hardware::neuralnetworks::V1_0::utils
+
+#endif // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_0_UTILS_H
diff --git a/neuralnetworks/1.0/utils/src/Assertions.cpp b/neuralnetworks/1.0/utils/src/Assertions.cpp
new file mode 100644
index 0000000..0f00951
--- /dev/null
+++ b/neuralnetworks/1.0/utils/src/Assertions.cpp
@@ -0,0 +1,122 @@
+/*
+ * 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 <android/hardware/neuralnetworks/1.0/types.h>
+#include <nnapi/OperandTypes.h>
+#include <nnapi/OperationTypes.h>
+#include <nnapi/Types.h>
+#include <type_traits>
+
+namespace {
+
+#define COMPARE_ENUMS_TYPES(lhsType, rhsType) \
+ static_assert( \
+ std::is_same_v< \
+ std::underlying_type_t<::android::hardware::neuralnetworks::V1_0::lhsType>, \
+ std::underlying_type_t<::android::nn::rhsType>>, \
+ "::android::hardware::neuralnetworks::V1_0::" #lhsType \
+ " does not have the same underlying type as ::android::nn::" #rhsType)
+
+COMPARE_ENUMS_TYPES(OperandType, OperandType);
+COMPARE_ENUMS_TYPES(OperationType, OperationType);
+COMPARE_ENUMS_TYPES(ErrorStatus, ErrorStatus);
+COMPARE_ENUMS_TYPES(OperandLifeTime, Operand::LifeTime);
+
+#undef COMPARE_ENUMS_TYPES
+
+#define COMPARE_ENUMS_FULL(lhsSymbol, rhsSymbol, lhsType, rhsType) \
+ static_assert( \
+ static_cast< \
+ std::underlying_type_t<::android::hardware::neuralnetworks::V1_0::lhsType>>( \
+ ::android::hardware::neuralnetworks::V1_0::lhsType::lhsSymbol) == \
+ static_cast<std::underlying_type_t<::android::nn::rhsType>>( \
+ ::android::nn::rhsType::rhsSymbol), \
+ "::android::hardware::neuralnetworks::V1_0::" #lhsType "::" #lhsSymbol \
+ " does not match ::android::nn::" #rhsType "::" #rhsSymbol)
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, symbol, OperandType, OperandType)
+
+COMPARE_ENUMS(FLOAT32);
+COMPARE_ENUMS(INT32);
+COMPARE_ENUMS(UINT32);
+COMPARE_ENUMS(TENSOR_FLOAT32);
+COMPARE_ENUMS(TENSOR_INT32);
+COMPARE_ENUMS(TENSOR_QUANT8_ASYMM);
+COMPARE_ENUMS(OEM);
+COMPARE_ENUMS(TENSOR_OEM_BYTE);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, symbol, OperationType, OperationType)
+
+COMPARE_ENUMS(ADD);
+COMPARE_ENUMS(AVERAGE_POOL_2D);
+COMPARE_ENUMS(CONCATENATION);
+COMPARE_ENUMS(CONV_2D);
+COMPARE_ENUMS(DEPTHWISE_CONV_2D);
+COMPARE_ENUMS(DEPTH_TO_SPACE);
+COMPARE_ENUMS(DEQUANTIZE);
+COMPARE_ENUMS(EMBEDDING_LOOKUP);
+COMPARE_ENUMS(FLOOR);
+COMPARE_ENUMS(FULLY_CONNECTED);
+COMPARE_ENUMS(HASHTABLE_LOOKUP);
+COMPARE_ENUMS(L2_NORMALIZATION);
+COMPARE_ENUMS(L2_POOL_2D);
+COMPARE_ENUMS(LOCAL_RESPONSE_NORMALIZATION);
+COMPARE_ENUMS(LOGISTIC);
+COMPARE_ENUMS(LSH_PROJECTION);
+COMPARE_ENUMS(LSTM);
+COMPARE_ENUMS(MAX_POOL_2D);
+COMPARE_ENUMS(MUL);
+COMPARE_ENUMS(RELU);
+COMPARE_ENUMS(RELU1);
+COMPARE_ENUMS(RELU6);
+COMPARE_ENUMS(RESHAPE);
+COMPARE_ENUMS(RESIZE_BILINEAR);
+COMPARE_ENUMS(RNN);
+COMPARE_ENUMS(SOFTMAX);
+COMPARE_ENUMS(SPACE_TO_DEPTH);
+COMPARE_ENUMS(SVDF);
+COMPARE_ENUMS(TANH);
+COMPARE_ENUMS(OEM_OPERATION);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, symbol, ErrorStatus, ErrorStatus)
+
+COMPARE_ENUMS(NONE);
+COMPARE_ENUMS(DEVICE_UNAVAILABLE);
+COMPARE_ENUMS(GENERAL_FAILURE);
+COMPARE_ENUMS(OUTPUT_INSUFFICIENT_SIZE);
+COMPARE_ENUMS(INVALID_ARGUMENT);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(lhsSymbol, rhsSymbol) \
+ COMPARE_ENUMS_FULL(lhsSymbol, rhsSymbol, OperandLifeTime, Operand::LifeTime)
+
+COMPARE_ENUMS(TEMPORARY_VARIABLE, TEMPORARY_VARIABLE);
+COMPARE_ENUMS(MODEL_INPUT, SUBGRAPH_INPUT);
+COMPARE_ENUMS(MODEL_OUTPUT, SUBGRAPH_OUTPUT);
+COMPARE_ENUMS(CONSTANT_COPY, CONSTANT_COPY);
+COMPARE_ENUMS(CONSTANT_REFERENCE, CONSTANT_REFERENCE);
+COMPARE_ENUMS(NO_VALUE, NO_VALUE);
+
+#undef COMPARE_ENUMS
+
+#undef COMPARE_ENUMS_FULL
+
+} // anonymous namespace
diff --git a/neuralnetworks/1.0/utils/src/Conversions.cpp b/neuralnetworks/1.0/utils/src/Conversions.cpp
new file mode 100644
index 0000000..4a58f3b
--- /dev/null
+++ b/neuralnetworks/1.0/utils/src/Conversions.cpp
@@ -0,0 +1,361 @@
+/*
+ * 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 <nnapi/OperandTypes.h>
+#include <nnapi/OperationTypes.h>
+#include <nnapi/Result.h>
+#include <nnapi/SharedMemory.h>
+#include <nnapi/Types.h>
+#include <nnapi/hal/CommonUtils.h>
+
+#include <algorithm>
+#include <functional>
+#include <iterator>
+#include <memory>
+#include <type_traits>
+#include <utility>
+#include <variant>
+
+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 {
+
+using hardware::hidl_memory;
+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>>> convert(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;
+}
+
+} // anonymous namespace
+
+Result<OperandType> convert(const hal::V1_0::OperandType& operandType) {
+ return static_cast<OperandType>(operandType);
+}
+
+Result<OperationType> convert(const hal::V1_0::OperationType& operationType) {
+ return static_cast<OperationType>(operationType);
+}
+
+Result<Operand::LifeTime> convert(const hal::V1_0::OperandLifeTime& lifetime) {
+ return static_cast<Operand::LifeTime>(lifetime);
+}
+
+Result<DeviceStatus> convert(const hal::V1_0::DeviceStatus& deviceStatus) {
+ return static_cast<DeviceStatus>(deviceStatus);
+}
+
+Result<Capabilities::PerformanceInfo> convert(const hal::V1_0::PerformanceInfo& performanceInfo) {
+ return Capabilities::PerformanceInfo{
+ .execTime = performanceInfo.execTime,
+ .powerUsage = performanceInfo.powerUsage,
+ };
+}
+
+Result<Capabilities> convert(const hal::V1_0::Capabilities& capabilities) {
+ const auto quantized8Performance = NN_TRY(convert(capabilities.quantized8Performance));
+ const auto float32Performance = NN_TRY(convert(capabilities.float32Performance));
+
+ auto table = hal::utils::makeQuantized8PerformanceConsistentWithP(float32Performance,
+ quantized8Performance);
+
+ return Capabilities{
+ .relaxedFloat32toFloat16PerformanceScalar = float32Performance,
+ .relaxedFloat32toFloat16PerformanceTensor = float32Performance,
+ .operandPerformance = std::move(table),
+ };
+}
+
+Result<DataLocation> convert(const hal::V1_0::DataLocation& location) {
+ return DataLocation{
+ .poolIndex = location.poolIndex,
+ .offset = location.offset,
+ .length = location.length,
+ };
+}
+
+Result<Operand> convert(const hal::V1_0::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)),
+ };
+}
+
+Result<Operation> convert(const hal::V1_0::Operation& operation) {
+ return Operation{
+ .type = NN_TRY(convert(operation.type)),
+ .inputs = operation.inputs,
+ .outputs = operation.outputs,
+ };
+}
+
+Result<Model::OperandValues> convert(const hidl_vec<uint8_t>& operandValues) {
+ return Model::OperandValues(operandValues.data(), operandValues.size());
+}
+
+Result<Memory> convert(const hidl_memory& memory) {
+ return createSharedMemoryFromHidlMemory(memory);
+}
+
+Result<Model> convert(const hal::V1_0::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)),
+ };
+}
+
+Result<Request::Argument> convert(const hal::V1_0::RequestArgument& argument) {
+ const auto lifetime = argument.hasNoValue ? Request::Argument::LifeTime::NO_VALUE
+ : Request::Argument::LifeTime::POOL;
+ return Request::Argument{
+ .lifetime = lifetime,
+ .location = NN_TRY(convert(argument.location)),
+ .dimensions = argument.dimensions,
+ };
+}
+
+Result<Request> convert(const hal::V1_0::Request& request) {
+ auto memories = NN_TRY(convert(request.pools));
+ std::vector<Request::MemoryPool> pools;
+ pools.reserve(memories.size());
+ std::move(memories.begin(), memories.end(), std::back_inserter(pools));
+
+ return Request{
+ .inputs = NN_TRY(convert(request.inputs)),
+ .outputs = NN_TRY(convert(request.outputs)),
+ .pools = std::move(pools),
+ };
+}
+
+Result<ErrorStatus> convert(const hal::V1_0::ErrorStatus& status) {
+ switch (status) {
+ case hal::V1_0::ErrorStatus::NONE:
+ case hal::V1_0::ErrorStatus::DEVICE_UNAVAILABLE:
+ case hal::V1_0::ErrorStatus::GENERAL_FAILURE:
+ case hal::V1_0::ErrorStatus::OUTPUT_INSUFFICIENT_SIZE:
+ case hal::V1_0::ErrorStatus::INVALID_ARGUMENT:
+ return static_cast<ErrorStatus>(status);
+ }
+ return NN_ERROR() << "Invalid ErrorStatus " << underlyingType(status);
+}
+
+} // namespace android::nn
+
+namespace android::hardware::neuralnetworks::V1_0::utils {
+namespace {
+
+template <typename Input>
+using ConvertOutput = std::decay_t<decltype(convert(std::declval<Input>()).value())>;
+
+template <typename Type>
+nn::Result<hidl_vec<ConvertOutput<Type>>> convert(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(utils::convert(arguments[i]));
+ }
+ return halObject;
+}
+
+} // 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<OperandLifeTime> convert(const nn::Operand::LifeTime& lifetime) {
+ if (lifetime == nn::Operand::LifeTime::POINTER) {
+ return NN_ERROR() << "Model cannot be converted because it contains pointer-based memory";
+ }
+ return static_cast<OperandLifeTime>(lifetime);
+}
+
+nn::Result<DeviceStatus> convert(const nn::DeviceStatus& deviceStatus) {
+ return static_cast<DeviceStatus>(deviceStatus);
+}
+
+nn::Result<PerformanceInfo> convert(const nn::Capabilities::PerformanceInfo& performanceInfo) {
+ return PerformanceInfo{
+ .execTime = performanceInfo.execTime,
+ .powerUsage = performanceInfo.powerUsage,
+ };
+}
+
+nn::Result<Capabilities> convert(const nn::Capabilities& capabilities) {
+ return Capabilities{
+ .float32Performance = NN_TRY(convert(
+ capabilities.operandPerformance.lookup(nn::OperandType::TENSOR_FLOAT32))),
+ .quantized8Performance = NN_TRY(convert(
+ capabilities.operandPerformance.lookup(nn::OperandType::TENSOR_QUANT8_ASYMM))),
+ };
+}
+
+nn::Result<DataLocation> convert(const nn::DataLocation& location) {
+ return DataLocation{
+ .poolIndex = location.poolIndex,
+ .offset = location.offset,
+ .length = location.length,
+ };
+}
+
+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)),
+ };
+}
+
+nn::Result<Operation> convert(const nn::Operation& operation) {
+ return Operation{
+ .type = NN_TRY(convert(operation.type)),
+ .inputs = operation.inputs,
+ .outputs = operation.outputs,
+ };
+}
+
+nn::Result<hidl_vec<uint8_t>> convert(const nn::Model::OperandValues& operandValues) {
+ return hidl_vec<uint8_t>(operandValues.data(), operandValues.data() + operandValues.size());
+}
+
+nn::Result<hidl_memory> convert(const nn::Memory& memory) {
+ const auto hidlMemory = hidl_memory(memory.name, memory.handle->handle(), memory.size);
+ // Copy memory to force the native_handle_t to be copied.
+ auto copiedMemory = hidlMemory;
+ return copiedMemory;
+}
+
+nn::Result<Model> convert(const nn::Model& model) {
+ if (!hal::utils::hasNoPointerData(model)) {
+ return NN_ERROR() << "Mdoel 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)),
+ };
+}
+
+nn::Result<RequestArgument> convert(const nn::Request::Argument& requestArgument) {
+ if (requestArgument.lifetime == nn::Request::Argument::LifeTime::POINTER) {
+ return NN_ERROR() << "Request cannot be converted because it contains pointer-based memory";
+ }
+ const bool hasNoValue = requestArgument.lifetime == nn::Request::Argument::LifeTime::NO_VALUE;
+ return RequestArgument{
+ .hasNoValue = hasNoValue,
+ .location = NN_TRY(convert(requestArgument.location)),
+ .dimensions = requestArgument.dimensions,
+ };
+}
+
+nn::Result<hidl_memory> convert(const nn::Request::MemoryPool& memoryPool) {
+ return convert(std::get<nn::Memory>(memoryPool));
+}
+
+nn::Result<Request> convert(const nn::Request& request) {
+ if (!hal::utils::hasNoPointerData(request)) {
+ return NN_ERROR() << "Request cannot be converted because it contains pointer-based memory";
+ }
+
+ return Request{
+ .inputs = NN_TRY(convert(request.inputs)),
+ .outputs = NN_TRY(convert(request.outputs)),
+ .pools = NN_TRY(convert(request.pools)),
+ };
+}
+
+nn::Result<ErrorStatus> convert(const nn::ErrorStatus& status) {
+ switch (status) {
+ case nn::ErrorStatus::NONE:
+ case nn::ErrorStatus::DEVICE_UNAVAILABLE:
+ case nn::ErrorStatus::GENERAL_FAILURE:
+ case nn::ErrorStatus::OUTPUT_INSUFFICIENT_SIZE:
+ case nn::ErrorStatus::INVALID_ARGUMENT:
+ return static_cast<ErrorStatus>(status);
+ default:
+ return ErrorStatus::GENERAL_FAILURE;
+ }
+}
+
+} // namespace android::hardware::neuralnetworks::V1_0::utils
diff --git a/neuralnetworks/1.1/utils/Android.bp b/neuralnetworks/1.1/utils/Android.bp
new file mode 100644
index 0000000..85a32c5
--- /dev/null
+++ b/neuralnetworks/1.1/utils/Android.bp
@@ -0,0 +1,35 @@
+//
+// 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.
+//
+
+cc_library_static {
+ name: "neuralnetworks_utils_hal_1_1",
+ defaults: ["neuralnetworks_utils_defaults"],
+ srcs: ["src/*"],
+ local_include_dirs: ["include/nnapi/hal/1.1/"],
+ export_include_dirs: ["include"],
+ static_libs: [
+ "neuralnetworks_types",
+ "neuralnetworks_utils_hal_common",
+ "neuralnetworks_utils_hal_1_0",
+ ],
+ shared_libs: [
+ "android.hardware.neuralnetworks@1.0",
+ "android.hardware.neuralnetworks@1.1",
+ ],
+ export_static_lib_headers: [
+ "neuralnetworks_utils_hal_common",
+ ],
+}
diff --git a/neuralnetworks/1.1/utils/OWNERS b/neuralnetworks/1.1/utils/OWNERS
new file mode 100644
index 0000000..e4feee3
--- /dev/null
+++ b/neuralnetworks/1.1/utils/OWNERS
@@ -0,0 +1,11 @@
+# Neuralnetworks team
+butlermichael@google.com
+dgross@google.com
+galarragas@google.com
+jeanluc@google.com
+levp@google.com
+miaowang@google.com
+pszczepaniak@google.com
+slavash@google.com
+vddang@google.com
+xusongw@google.com
diff --git a/neuralnetworks/1.1/utils/include/nnapi/hal/1.1/Conversions.h b/neuralnetworks/1.1/utils/include/nnapi/hal/1.1/Conversions.h
new file mode 100644
index 0000000..d0c5397
--- /dev/null
+++ b/neuralnetworks/1.1/utils/include/nnapi/hal/1.1/Conversions.h
@@ -0,0 +1,45 @@
+/*
+ * 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.
+ */
+
+#ifndef ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_1_CONVERSIONS_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_1_CONVERSIONS_H
+
+#include <android/hardware/neuralnetworks/1.1/types.h>
+#include <nnapi/Result.h>
+#include <nnapi/Types.h>
+#include <nnapi/hal/CommonUtils.h>
+
+namespace android::nn {
+
+Result<OperationType> convert(const hal::V1_1::OperationType& operationType);
+Result<Capabilities> convert(const hal::V1_1::Capabilities& capabilities);
+Result<Operation> convert(const hal::V1_1::Operation& operation);
+Result<Model> convert(const hal::V1_1::Model& model);
+Result<ExecutionPreference> convert(const hal::V1_1::ExecutionPreference& executionPreference);
+
+} // namespace android::nn
+
+namespace android::hardware::neuralnetworks::V1_1::utils {
+
+nn::Result<OperationType> convert(const nn::OperationType& operationType);
+nn::Result<Capabilities> convert(const nn::Capabilities& capabilities);
+nn::Result<Operation> convert(const nn::Operation& operation);
+nn::Result<Model> convert(const nn::Model& model);
+nn::Result<ExecutionPreference> convert(const nn::ExecutionPreference& executionPreference);
+
+} // namespace android::hardware::neuralnetworks::V1_1::utils
+
+#endif // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_1_CONVERSIONS_H
diff --git a/neuralnetworks/1.1/utils/include/nnapi/hal/1.1/Utils.h b/neuralnetworks/1.1/utils/include/nnapi/hal/1.1/Utils.h
new file mode 100644
index 0000000..6f9aa60
--- /dev/null
+++ b/neuralnetworks/1.1/utils/include/nnapi/hal/1.1/Utils.h
@@ -0,0 +1,65 @@
+/*
+ * 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.
+ */
+
+#ifndef ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_1_UTILS_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_1_UTILS_H
+
+#include "nnapi/hal/1.1/Conversions.h"
+
+#include <android-base/logging.h>
+#include <android/hardware/neuralnetworks/1.1/types.h>
+#include <nnapi/Result.h>
+#include <nnapi/Types.h>
+#include <nnapi/Validation.h>
+#include <nnapi/hal/1.0/Conversions.h>
+
+namespace android::hardware::neuralnetworks::V1_1::utils {
+
+constexpr auto kDefaultExecutionPreference = ExecutionPreference::FAST_SINGLE_ANSWER;
+constexpr auto kVersion = nn::Version::ANDROID_P;
+
+template <typename Type>
+nn::Result<void> validate(const Type& halObject) {
+ const auto canonical = NN_TRY(nn::convert(halObject));
+ const auto version = NN_TRY(nn::validate(canonical));
+ if (version > utils::kVersion) {
+ return NN_ERROR() << "";
+ }
+ return {};
+}
+
+template <typename Type>
+bool valid(const Type& halObject) {
+ const auto result = utils::validate(halObject);
+ if (!result.has_value()) {
+ LOG(ERROR) << result.error();
+ }
+ return result.has_value();
+}
+
+template <typename Type>
+decltype(nn::convert(std::declval<Type>())) validatedConvertToCanonical(const Type& halObject) {
+ auto canonical = NN_TRY(nn::convert(halObject));
+ const auto version = NN_TRY(nn::validate(canonical));
+ if (version > utils::kVersion) {
+ return NN_ERROR() << "";
+ }
+ return canonical;
+}
+
+} // namespace android::hardware::neuralnetworks::V1_1::utils
+
+#endif // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_1_UTILS_H
diff --git a/neuralnetworks/1.1/utils/src/Assertions.cpp b/neuralnetworks/1.1/utils/src/Assertions.cpp
new file mode 100644
index 0000000..ba4a388
--- /dev/null
+++ b/neuralnetworks/1.1/utils/src/Assertions.cpp
@@ -0,0 +1,100 @@
+/*
+ * 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 <android/hardware/neuralnetworks/1.1/types.h>
+#include <nnapi/OperandTypes.h>
+#include <nnapi/OperationTypes.h>
+#include <nnapi/Types.h>
+#include <type_traits>
+
+namespace {
+
+#define COMPARE_ENUMS_TYPES(type) \
+ static_assert(std::is_same_v< \
+ std::underlying_type_t<::android::hardware::neuralnetworks::V1_1::type>, \
+ std::underlying_type_t<::android::nn::type>>, \
+ "::android::hardware::neuralnetworks::V1_1::" #type \
+ " does not have the same underlying type as ::android::nn::" #type)
+
+COMPARE_ENUMS_TYPES(OperationType);
+COMPARE_ENUMS_TYPES(ExecutionPreference);
+
+#undef COMPARE_ENUMS_TYPES
+
+#define COMPARE_ENUMS_FULL(symbol, type) \
+ static_assert( \
+ static_cast<std::underlying_type_t<::android::hardware::neuralnetworks::V1_1::type>>( \
+ ::android::hardware::neuralnetworks::V1_1::type::symbol) == \
+ static_cast<std::underlying_type_t<::android::nn::type>>( \
+ ::android::nn::type::symbol), \
+ "::android::hardware::neuralnetworks::V1_1::" #type "::" #symbol \
+ " does not match ::android::nn::" #type "::" #symbol)
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, OperationType)
+
+COMPARE_ENUMS(ADD);
+COMPARE_ENUMS(AVERAGE_POOL_2D);
+COMPARE_ENUMS(CONCATENATION);
+COMPARE_ENUMS(CONV_2D);
+COMPARE_ENUMS(DEPTHWISE_CONV_2D);
+COMPARE_ENUMS(DEPTH_TO_SPACE);
+COMPARE_ENUMS(DEQUANTIZE);
+COMPARE_ENUMS(EMBEDDING_LOOKUP);
+COMPARE_ENUMS(FLOOR);
+COMPARE_ENUMS(FULLY_CONNECTED);
+COMPARE_ENUMS(HASHTABLE_LOOKUP);
+COMPARE_ENUMS(L2_NORMALIZATION);
+COMPARE_ENUMS(L2_POOL_2D);
+COMPARE_ENUMS(LOCAL_RESPONSE_NORMALIZATION);
+COMPARE_ENUMS(LOGISTIC);
+COMPARE_ENUMS(LSH_PROJECTION);
+COMPARE_ENUMS(LSTM);
+COMPARE_ENUMS(MAX_POOL_2D);
+COMPARE_ENUMS(MUL);
+COMPARE_ENUMS(RELU);
+COMPARE_ENUMS(RELU1);
+COMPARE_ENUMS(RELU6);
+COMPARE_ENUMS(RESHAPE);
+COMPARE_ENUMS(RESIZE_BILINEAR);
+COMPARE_ENUMS(RNN);
+COMPARE_ENUMS(SOFTMAX);
+COMPARE_ENUMS(SPACE_TO_DEPTH);
+COMPARE_ENUMS(SVDF);
+COMPARE_ENUMS(TANH);
+COMPARE_ENUMS(BATCH_TO_SPACE_ND);
+COMPARE_ENUMS(DIV);
+COMPARE_ENUMS(MEAN);
+COMPARE_ENUMS(PAD);
+COMPARE_ENUMS(SPACE_TO_BATCH_ND);
+COMPARE_ENUMS(SQUEEZE);
+COMPARE_ENUMS(STRIDED_SLICE);
+COMPARE_ENUMS(SUB);
+COMPARE_ENUMS(TRANSPOSE);
+COMPARE_ENUMS(OEM_OPERATION);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, ExecutionPreference)
+
+COMPARE_ENUMS(LOW_POWER);
+COMPARE_ENUMS(FAST_SINGLE_ANSWER);
+COMPARE_ENUMS(SUSTAINED_SPEED);
+
+#undef COMPARE_ENUMS
+
+#undef COMPARE_ENUMS_FULL
+
+} // anonymous namespace
diff --git a/neuralnetworks/1.1/utils/src/Conversions.cpp b/neuralnetworks/1.1/utils/src/Conversions.cpp
new file mode 100644
index 0000000..7fee16b
--- /dev/null
+++ b/neuralnetworks/1.1/utils/src/Conversions.cpp
@@ -0,0 +1,209 @@
+/*
+ * 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/Types.h>
+#include <nnapi/hal/1.0/Conversions.h>
+#include <nnapi/hal/CommonUtils.h>
+
+#include <algorithm>
+#include <functional>
+#include <iterator>
+#include <type_traits>
+#include <utility>
+
+namespace android::nn {
+namespace {
+
+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>>> convert(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;
+}
+
+} // anonymous namespace
+
+Result<OperationType> convert(const hal::V1_1::OperationType& operationType) {
+ return static_cast<OperationType>(operationType);
+}
+
+Result<Capabilities> convert(const hal::V1_1::Capabilities& capabilities) {
+ const auto quantized8Performance = NN_TRY(convert(capabilities.quantized8Performance));
+ const auto float32Performance = NN_TRY(convert(capabilities.float32Performance));
+ const auto relaxedFloat32toFloat16Performance =
+ NN_TRY(convert(capabilities.relaxedFloat32toFloat16Performance));
+
+ auto table = hal::utils::makeQuantized8PerformanceConsistentWithP(float32Performance,
+ quantized8Performance);
+
+ return Capabilities{
+ .relaxedFloat32toFloat16PerformanceScalar = relaxedFloat32toFloat16Performance,
+ .relaxedFloat32toFloat16PerformanceTensor = relaxedFloat32toFloat16Performance,
+ .operandPerformance = std::move(table),
+ };
+}
+
+Result<Operation> convert(const hal::V1_1::Operation& operation) {
+ return Operation{
+ .type = NN_TRY(convert(operation.type)),
+ .inputs = operation.inputs,
+ .outputs = operation.outputs,
+ };
+}
+
+Result<Model> convert(const hal::V1_1::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,
+ };
+}
+
+Result<ExecutionPreference> convert(const hal::V1_1::ExecutionPreference& executionPreference) {
+ return static_cast<ExecutionPreference>(executionPreference);
+}
+
+} // namespace android::nn
+
+namespace android::hardware::neuralnetworks::V1_1::utils {
+namespace {
+
+using utils::convert;
+
+nn::Result<V1_0::PerformanceInfo> convert(
+ const nn::Capabilities::PerformanceInfo& performanceInfo) {
+ return V1_0::utils::convert(performanceInfo);
+}
+
+nn::Result<V1_0::Operand> convert(const nn::Operand& operand) {
+ return V1_0::utils::convert(operand);
+}
+
+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>>> convert(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;
+}
+
+} // anonymous namespace
+
+nn::Result<OperationType> convert(const nn::OperationType& operationType) {
+ return static_cast<OperationType>(operationType);
+}
+
+nn::Result<Capabilities> convert(const nn::Capabilities& capabilities) {
+ return Capabilities{
+ .float32Performance = NN_TRY(convert(
+ capabilities.operandPerformance.lookup(nn::OperandType::TENSOR_FLOAT32))),
+ .quantized8Performance = NN_TRY(convert(
+ capabilities.operandPerformance.lookup(nn::OperandType::TENSOR_QUANT8_ASYMM))),
+ .relaxedFloat32toFloat16Performance =
+ NN_TRY(convert(capabilities.relaxedFloat32toFloat16PerformanceTensor)),
+ };
+}
+
+nn::Result<Operation> convert(const nn::Operation& operation) {
+ return Operation{
+ .type = NN_TRY(convert(operation.type)),
+ .inputs = operation.inputs,
+ .outputs = operation.outputs,
+ };
+}
+
+nn::Result<Model> convert(const nn::Model& model) {
+ if (!hal::utils::hasNoPointerData(model)) {
+ return NN_ERROR() << "Mdoel 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,
+ };
+}
+
+nn::Result<ExecutionPreference> convert(const nn::ExecutionPreference& executionPreference) {
+ return static_cast<ExecutionPreference>(executionPreference);
+}
+
+} // namespace android::hardware::neuralnetworks::V1_1::utils
diff --git a/neuralnetworks/1.2/utils/Android.bp b/neuralnetworks/1.2/utils/Android.bp
new file mode 100644
index 0000000..a1dd3d0
--- /dev/null
+++ b/neuralnetworks/1.2/utils/Android.bp
@@ -0,0 +1,37 @@
+//
+// 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.
+//
+
+cc_library_static {
+ name: "neuralnetworks_utils_hal_1_2",
+ defaults: ["neuralnetworks_utils_defaults"],
+ srcs: ["src/*"],
+ local_include_dirs: ["include/nnapi/hal/1.2/"],
+ export_include_dirs: ["include"],
+ static_libs: [
+ "neuralnetworks_types",
+ "neuralnetworks_utils_hal_common",
+ "neuralnetworks_utils_hal_1_0",
+ "neuralnetworks_utils_hal_1_1",
+ ],
+ shared_libs: [
+ "android.hardware.neuralnetworks@1.0",
+ "android.hardware.neuralnetworks@1.1",
+ "android.hardware.neuralnetworks@1.2",
+ ],
+ export_static_lib_headers: [
+ "neuralnetworks_utils_hal_common",
+ ],
+}
diff --git a/neuralnetworks/1.2/utils/OWNERS b/neuralnetworks/1.2/utils/OWNERS
new file mode 100644
index 0000000..e4feee3
--- /dev/null
+++ b/neuralnetworks/1.2/utils/OWNERS
@@ -0,0 +1,11 @@
+# Neuralnetworks team
+butlermichael@google.com
+dgross@google.com
+galarragas@google.com
+jeanluc@google.com
+levp@google.com
+miaowang@google.com
+pszczepaniak@google.com
+slavash@google.com
+vddang@google.com
+xusongw@google.com
diff --git a/neuralnetworks/1.2/utils/include/nnapi/hal/1.2/Conversions.h b/neuralnetworks/1.2/utils/include/nnapi/hal/1.2/Conversions.h
new file mode 100644
index 0000000..81bf792
--- /dev/null
+++ b/neuralnetworks/1.2/utils/include/nnapi/hal/1.2/Conversions.h
@@ -0,0 +1,86 @@
+/*
+ * 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.
+ */
+
+#ifndef ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_2_CONVERSIONS_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_2_CONVERSIONS_H
+
+#include <android/hardware/neuralnetworks/1.2/types.h>
+#include <nnapi/Result.h>
+#include <nnapi/Types.h>
+#include <nnapi/hal/CommonUtils.h>
+
+namespace android::nn {
+
+Result<OperandType> convert(const hal::V1_2::OperandType& operandType);
+Result<OperationType> convert(const hal::V1_2::OperationType& operationType);
+Result<DeviceType> convert(const hal::V1_2::DeviceType& deviceType);
+Result<Capabilities> convert(const hal::V1_2::Capabilities& capabilities);
+Result<Capabilities::OperandPerformance> convert(
+ const hal::V1_2::Capabilities::OperandPerformance& operandPerformance);
+Result<Operation> convert(const hal::V1_2::Operation& operation);
+Result<Operand::SymmPerChannelQuantParams> convert(
+ const hal::V1_2::SymmPerChannelQuantParams& symmPerChannelQuantParams);
+Result<Operand> convert(const hal::V1_2::Operand& operand);
+Result<Operand::ExtraParams> convert(const hal::V1_2::Operand::ExtraParams& extraParams);
+Result<Model> convert(const hal::V1_2::Model& model);
+Result<Model::ExtensionNameAndPrefix> convert(
+ const hal::V1_2::Model::ExtensionNameAndPrefix& extensionNameAndPrefix);
+Result<OutputShape> convert(const hal::V1_2::OutputShape& outputShape);
+Result<MeasureTiming> convert(const hal::V1_2::MeasureTiming& measureTiming);
+Result<Timing> convert(const hal::V1_2::Timing& timing);
+Result<Extension> convert(const hal::V1_2::Extension& extension);
+Result<Extension::OperandTypeInformation> convert(
+ const hal::V1_2::Extension::OperandTypeInformation& operandTypeInformation);
+Result<NativeHandle> convert(const hardware::hidl_handle& handle);
+
+Result<std::vector<Extension>> convert(const hardware::hidl_vec<hal::V1_2::Extension>& extensions);
+Result<std::vector<NativeHandle>> convert(const hardware::hidl_vec<hardware::hidl_handle>& handles);
+Result<std::vector<OutputShape>> convert(
+ const hardware::hidl_vec<hal::V1_2::OutputShape>& outputShapes);
+
+} // namespace android::nn
+
+namespace android::hardware::neuralnetworks::V1_2::utils {
+
+nn::Result<OperandType> convert(const nn::OperandType& operandType);
+nn::Result<OperationType> convert(const nn::OperationType& operationType);
+nn::Result<DeviceType> convert(const nn::DeviceType& deviceType);
+nn::Result<Capabilities> convert(const nn::Capabilities& capabilities);
+nn::Result<Capabilities::OperandPerformance> convert(
+ const nn::Capabilities::OperandPerformance& operandPerformance);
+nn::Result<Operation> convert(const nn::Operation& operation);
+nn::Result<SymmPerChannelQuantParams> convert(
+ const nn::Operand::SymmPerChannelQuantParams& symmPerChannelQuantParams);
+nn::Result<Operand> convert(const nn::Operand& operand);
+nn::Result<Operand::ExtraParams> convert(const nn::Operand::ExtraParams& extraParams);
+nn::Result<Model> convert(const nn::Model& model);
+nn::Result<Model::ExtensionNameAndPrefix> convert(
+ const nn::Model::ExtensionNameAndPrefix& extensionNameAndPrefix);
+nn::Result<OutputShape> convert(const nn::OutputShape& outputShape);
+nn::Result<MeasureTiming> convert(const nn::MeasureTiming& measureTiming);
+nn::Result<Timing> convert(const nn::Timing& timing);
+nn::Result<Extension> convert(const nn::Extension& extension);
+nn::Result<Extension::OperandTypeInformation> convert(
+ const nn::Extension::OperandTypeInformation& operandTypeInformation);
+nn::Result<hidl_handle> convert(const nn::NativeHandle& handle);
+
+nn::Result<hidl_vec<Extension>> convert(const std::vector<nn::Extension>& extensions);
+nn::Result<hidl_vec<hidl_handle>> convert(const std::vector<nn::NativeHandle>& handles);
+nn::Result<hidl_vec<OutputShape>> convert(const std::vector<nn::OutputShape>& outputShapes);
+
+} // namespace android::hardware::neuralnetworks::V1_2::utils
+
+#endif // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_2_CONVERSIONS_H
diff --git a/neuralnetworks/1.2/utils/include/nnapi/hal/1.2/Utils.h b/neuralnetworks/1.2/utils/include/nnapi/hal/1.2/Utils.h
new file mode 100644
index 0000000..b1c2f1a
--- /dev/null
+++ b/neuralnetworks/1.2/utils/include/nnapi/hal/1.2/Utils.h
@@ -0,0 +1,70 @@
+/*
+ * 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.
+ */
+
+#ifndef ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_2_UTILS_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_2_UTILS_H
+
+#include "nnapi/hal/1.2/Conversions.h"
+
+#include <android-base/logging.h>
+#include <android/hardware/neuralnetworks/1.2/types.h>
+#include <nnapi/Result.h>
+#include <nnapi/Types.h>
+#include <nnapi/Validation.h>
+#include <nnapi/hal/1.0/Conversions.h>
+#include <nnapi/hal/1.1/Conversions.h>
+
+#include <limits>
+
+namespace android::hardware::neuralnetworks::V1_2::utils {
+
+constexpr auto kDefaultMesaureTiming = MeasureTiming::NO;
+constexpr auto kNoTiming = Timing{.timeOnDevice = std::numeric_limits<uint64_t>::max(),
+ .timeInDriver = std::numeric_limits<uint64_t>::max()};
+constexpr auto kVersion = nn::Version::ANDROID_Q;
+
+template <typename Type>
+nn::Result<void> validate(const Type& halObject) {
+ const auto canonical = NN_TRY(nn::convert(halObject));
+ const auto version = NN_TRY(nn::validate(canonical));
+ if (version > utils::kVersion) {
+ return NN_ERROR() << "";
+ }
+ return {};
+}
+
+template <typename Type>
+bool valid(const Type& halObject) {
+ const auto result = utils::validate(halObject);
+ if (!result.has_value()) {
+ LOG(ERROR) << result.error();
+ }
+ return result.has_value();
+}
+
+template <typename Type>
+decltype(nn::convert(std::declval<Type>())) validatedConvertToCanonical(const Type& halObject) {
+ auto canonical = NN_TRY(nn::convert(halObject));
+ const auto version = NN_TRY(nn::validate(canonical));
+ if (version > utils::kVersion) {
+ return NN_ERROR() << "";
+ }
+ return canonical;
+}
+
+} // namespace android::hardware::neuralnetworks::V1_2::utils
+
+#endif // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_2_UTILS_H
diff --git a/neuralnetworks/1.2/utils/src/Assertions.cpp b/neuralnetworks/1.2/utils/src/Assertions.cpp
new file mode 100644
index 0000000..9d9716a
--- /dev/null
+++ b/neuralnetworks/1.2/utils/src/Assertions.cpp
@@ -0,0 +1,188 @@
+/*
+ * 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 <android/hardware/neuralnetworks/1.2/types.h>
+#include <nnapi/OperandTypes.h>
+#include <nnapi/OperationTypes.h>
+#include <nnapi/Types.h>
+#include <type_traits>
+
+namespace {
+
+#define COMPARE_ENUMS_TYPES(type) \
+ static_assert(std::is_same_v< \
+ std::underlying_type_t<::android::hardware::neuralnetworks::V1_2::type>, \
+ std::underlying_type_t<::android::nn::type>>, \
+ "::android::hardware::neuralnetworks::V1_2::" #type \
+ " does not have the same underlying type as ::android::nn::" #type)
+
+COMPARE_ENUMS_TYPES(OperandType);
+COMPARE_ENUMS_TYPES(OperationType);
+COMPARE_ENUMS_TYPES(DeviceType);
+COMPARE_ENUMS_TYPES(MeasureTiming);
+
+#undef COMPARE_ENUMS_TYPES
+
+#define COMPARE_ENUMS_FULL(symbol, type) \
+ static_assert( \
+ static_cast<std::underlying_type_t<::android::hardware::neuralnetworks::V1_2::type>>( \
+ ::android::hardware::neuralnetworks::V1_2::type::symbol) == \
+ static_cast<std::underlying_type_t<::android::nn::type>>( \
+ ::android::nn::type::symbol), \
+ "::android::hardware::neuralnetworks::V1_2::" #type "::" #symbol \
+ " does not match ::android::nn::" #type "::" #symbol)
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, OperandType)
+
+COMPARE_ENUMS(FLOAT32);
+COMPARE_ENUMS(INT32);
+COMPARE_ENUMS(UINT32);
+COMPARE_ENUMS(TENSOR_FLOAT32);
+COMPARE_ENUMS(TENSOR_INT32);
+COMPARE_ENUMS(TENSOR_QUANT8_ASYMM);
+COMPARE_ENUMS(BOOL);
+COMPARE_ENUMS(TENSOR_QUANT16_SYMM);
+COMPARE_ENUMS(TENSOR_FLOAT16);
+COMPARE_ENUMS(TENSOR_BOOL8);
+COMPARE_ENUMS(FLOAT16);
+COMPARE_ENUMS(TENSOR_QUANT8_SYMM_PER_CHANNEL);
+COMPARE_ENUMS(TENSOR_QUANT16_ASYMM);
+COMPARE_ENUMS(TENSOR_QUANT8_SYMM);
+COMPARE_ENUMS(OEM);
+COMPARE_ENUMS(TENSOR_OEM_BYTE);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, OperationType)
+
+COMPARE_ENUMS(ADD);
+COMPARE_ENUMS(AVERAGE_POOL_2D);
+COMPARE_ENUMS(CONCATENATION);
+COMPARE_ENUMS(CONV_2D);
+COMPARE_ENUMS(DEPTHWISE_CONV_2D);
+COMPARE_ENUMS(DEPTH_TO_SPACE);
+COMPARE_ENUMS(DEQUANTIZE);
+COMPARE_ENUMS(EMBEDDING_LOOKUP);
+COMPARE_ENUMS(FLOOR);
+COMPARE_ENUMS(FULLY_CONNECTED);
+COMPARE_ENUMS(HASHTABLE_LOOKUP);
+COMPARE_ENUMS(L2_NORMALIZATION);
+COMPARE_ENUMS(L2_POOL_2D);
+COMPARE_ENUMS(LOCAL_RESPONSE_NORMALIZATION);
+COMPARE_ENUMS(LOGISTIC);
+COMPARE_ENUMS(LSH_PROJECTION);
+COMPARE_ENUMS(LSTM);
+COMPARE_ENUMS(MAX_POOL_2D);
+COMPARE_ENUMS(MUL);
+COMPARE_ENUMS(RELU);
+COMPARE_ENUMS(RELU1);
+COMPARE_ENUMS(RELU6);
+COMPARE_ENUMS(RESHAPE);
+COMPARE_ENUMS(RESIZE_BILINEAR);
+COMPARE_ENUMS(RNN);
+COMPARE_ENUMS(SOFTMAX);
+COMPARE_ENUMS(SPACE_TO_DEPTH);
+COMPARE_ENUMS(SVDF);
+COMPARE_ENUMS(TANH);
+COMPARE_ENUMS(BATCH_TO_SPACE_ND);
+COMPARE_ENUMS(DIV);
+COMPARE_ENUMS(MEAN);
+COMPARE_ENUMS(PAD);
+COMPARE_ENUMS(SPACE_TO_BATCH_ND);
+COMPARE_ENUMS(SQUEEZE);
+COMPARE_ENUMS(STRIDED_SLICE);
+COMPARE_ENUMS(SUB);
+COMPARE_ENUMS(TRANSPOSE);
+COMPARE_ENUMS(ABS);
+COMPARE_ENUMS(ARGMAX);
+COMPARE_ENUMS(ARGMIN);
+COMPARE_ENUMS(AXIS_ALIGNED_BBOX_TRANSFORM);
+COMPARE_ENUMS(BIDIRECTIONAL_SEQUENCE_LSTM);
+COMPARE_ENUMS(BIDIRECTIONAL_SEQUENCE_RNN);
+COMPARE_ENUMS(BOX_WITH_NMS_LIMIT);
+COMPARE_ENUMS(CAST);
+COMPARE_ENUMS(CHANNEL_SHUFFLE);
+COMPARE_ENUMS(DETECTION_POSTPROCESSING);
+COMPARE_ENUMS(EQUAL);
+COMPARE_ENUMS(EXP);
+COMPARE_ENUMS(EXPAND_DIMS);
+COMPARE_ENUMS(GATHER);
+COMPARE_ENUMS(GENERATE_PROPOSALS);
+COMPARE_ENUMS(GREATER);
+COMPARE_ENUMS(GREATER_EQUAL);
+COMPARE_ENUMS(GROUPED_CONV_2D);
+COMPARE_ENUMS(HEATMAP_MAX_KEYPOINT);
+COMPARE_ENUMS(INSTANCE_NORMALIZATION);
+COMPARE_ENUMS(LESS);
+COMPARE_ENUMS(LESS_EQUAL);
+COMPARE_ENUMS(LOG);
+COMPARE_ENUMS(LOGICAL_AND);
+COMPARE_ENUMS(LOGICAL_NOT);
+COMPARE_ENUMS(LOGICAL_OR);
+COMPARE_ENUMS(LOG_SOFTMAX);
+COMPARE_ENUMS(MAXIMUM);
+COMPARE_ENUMS(MINIMUM);
+COMPARE_ENUMS(NEG);
+COMPARE_ENUMS(NOT_EQUAL);
+COMPARE_ENUMS(PAD_V2);
+COMPARE_ENUMS(POW);
+COMPARE_ENUMS(PRELU);
+COMPARE_ENUMS(QUANTIZE);
+COMPARE_ENUMS(QUANTIZED_16BIT_LSTM);
+COMPARE_ENUMS(RANDOM_MULTINOMIAL);
+COMPARE_ENUMS(REDUCE_ALL);
+COMPARE_ENUMS(REDUCE_ANY);
+COMPARE_ENUMS(REDUCE_MAX);
+COMPARE_ENUMS(REDUCE_MIN);
+COMPARE_ENUMS(REDUCE_PROD);
+COMPARE_ENUMS(REDUCE_SUM);
+COMPARE_ENUMS(ROI_ALIGN);
+COMPARE_ENUMS(ROI_POOLING);
+COMPARE_ENUMS(RSQRT);
+COMPARE_ENUMS(SELECT);
+COMPARE_ENUMS(SIN);
+COMPARE_ENUMS(SLICE);
+COMPARE_ENUMS(SPLIT);
+COMPARE_ENUMS(SQRT);
+COMPARE_ENUMS(TILE);
+COMPARE_ENUMS(TOPK_V2);
+COMPARE_ENUMS(TRANSPOSE_CONV_2D);
+COMPARE_ENUMS(UNIDIRECTIONAL_SEQUENCE_LSTM);
+COMPARE_ENUMS(UNIDIRECTIONAL_SEQUENCE_RNN);
+COMPARE_ENUMS(RESIZE_NEAREST_NEIGHBOR);
+COMPARE_ENUMS(OEM_OPERATION);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, DeviceType)
+
+COMPARE_ENUMS(OTHER);
+COMPARE_ENUMS(CPU);
+COMPARE_ENUMS(GPU);
+COMPARE_ENUMS(ACCELERATOR);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, MeasureTiming)
+
+COMPARE_ENUMS(NO);
+COMPARE_ENUMS(YES);
+
+#undef COMPARE_ENUMS
+
+#undef COMPARE_ENUMS_FULL
+
+} // anonymous namespace
diff --git a/neuralnetworks/1.2/utils/src/Conversions.cpp b/neuralnetworks/1.2/utils/src/Conversions.cpp
new file mode 100644
index 0000000..fed314b
--- /dev/null
+++ b/neuralnetworks/1.2/utils/src/Conversions.cpp
@@ -0,0 +1,502 @@
+/*
+ * 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
diff --git a/neuralnetworks/1.3/utils/Android.bp b/neuralnetworks/1.3/utils/Android.bp
new file mode 100644
index 0000000..279b250
--- /dev/null
+++ b/neuralnetworks/1.3/utils/Android.bp
@@ -0,0 +1,39 @@
+//
+// 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.
+//
+
+cc_library_static {
+ name: "neuralnetworks_utils_hal_1_3",
+ defaults: ["neuralnetworks_utils_defaults"],
+ srcs: ["src/*"],
+ local_include_dirs: ["include/nnapi/hal/1.3/"],
+ export_include_dirs: ["include"],
+ static_libs: [
+ "neuralnetworks_types",
+ "neuralnetworks_utils_hal_common",
+ "neuralnetworks_utils_hal_1_0",
+ "neuralnetworks_utils_hal_1_1",
+ "neuralnetworks_utils_hal_1_2",
+ ],
+ shared_libs: [
+ "android.hardware.neuralnetworks@1.0",
+ "android.hardware.neuralnetworks@1.1",
+ "android.hardware.neuralnetworks@1.2",
+ "android.hardware.neuralnetworks@1.3",
+ ],
+ export_static_lib_headers: [
+ "neuralnetworks_utils_hal_common",
+ ],
+}
diff --git a/neuralnetworks/1.3/utils/OWNERS b/neuralnetworks/1.3/utils/OWNERS
new file mode 100644
index 0000000..e4feee3
--- /dev/null
+++ b/neuralnetworks/1.3/utils/OWNERS
@@ -0,0 +1,11 @@
+# Neuralnetworks team
+butlermichael@google.com
+dgross@google.com
+galarragas@google.com
+jeanluc@google.com
+levp@google.com
+miaowang@google.com
+pszczepaniak@google.com
+slavash@google.com
+vddang@google.com
+xusongw@google.com
diff --git a/neuralnetworks/1.3/utils/include/nnapi/hal/1.3/Conversions.h b/neuralnetworks/1.3/utils/include/nnapi/hal/1.3/Conversions.h
new file mode 100644
index 0000000..43987a9
--- /dev/null
+++ b/neuralnetworks/1.3/utils/include/nnapi/hal/1.3/Conversions.h
@@ -0,0 +1,79 @@
+/*
+ * 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.
+ */
+
+#ifndef ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_3_CONVERSIONS_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_3_CONVERSIONS_H
+
+#include <android/hardware/neuralnetworks/1.3/IPreparedModel.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <nnapi/Result.h>
+#include <nnapi/Types.h>
+#include <nnapi/hal/CommonUtils.h>
+
+namespace android::nn {
+
+Result<OperandType> convert(const hal::V1_3::OperandType& operandType);
+Result<OperationType> convert(const hal::V1_3::OperationType& operationType);
+Result<Priority> convert(const hal::V1_3::Priority& priority);
+Result<Capabilities> convert(const hal::V1_3::Capabilities& capabilities);
+Result<Capabilities::OperandPerformance> convert(
+ const hal::V1_3::Capabilities::OperandPerformance& operandPerformance);
+Result<Operation> convert(const hal::V1_3::Operation& operation);
+Result<Operand::LifeTime> convert(const hal::V1_3::OperandLifeTime& operandLifeTime);
+Result<Operand> convert(const hal::V1_3::Operand& operand);
+Result<Model> convert(const hal::V1_3::Model& model);
+Result<Model::Subgraph> convert(const hal::V1_3::Subgraph& subgraph);
+Result<BufferDesc> convert(const hal::V1_3::BufferDesc& bufferDesc);
+Result<BufferRole> convert(const hal::V1_3::BufferRole& bufferRole);
+Result<Request> convert(const hal::V1_3::Request& request);
+Result<Request::MemoryPool> convert(const hal::V1_3::Request::MemoryPool& memoryPool);
+Result<OptionalTimePoint> convert(const hal::V1_3::OptionalTimePoint& optionalTimePoint);
+Result<OptionalTimeoutDuration> convert(
+ const hal::V1_3::OptionalTimeoutDuration& optionalTimeoutDuration);
+Result<ErrorStatus> convert(const hal::V1_3::ErrorStatus& errorStatus);
+
+Result<std::vector<BufferRole>> convert(
+ const hardware::hidl_vec<hal::V1_3::BufferRole>& bufferRoles);
+
+} // namespace android::nn
+
+namespace android::hardware::neuralnetworks::V1_3::utils {
+
+nn::Result<OperandType> convert(const nn::OperandType& operandType);
+nn::Result<OperationType> convert(const nn::OperationType& operationType);
+nn::Result<Priority> convert(const nn::Priority& priority);
+nn::Result<Capabilities> convert(const nn::Capabilities& capabilities);
+nn::Result<Capabilities::OperandPerformance> convert(
+ const nn::Capabilities::OperandPerformance& operandPerformance);
+nn::Result<Operation> convert(const nn::Operation& operation);
+nn::Result<OperandLifeTime> convert(const nn::Operand::LifeTime& operandLifeTime);
+nn::Result<Operand> convert(const nn::Operand& operand);
+nn::Result<Model> convert(const nn::Model& model);
+nn::Result<Subgraph> convert(const nn::Model::Subgraph& subgraph);
+nn::Result<BufferDesc> convert(const nn::BufferDesc& bufferDesc);
+nn::Result<BufferRole> convert(const nn::BufferRole& bufferRole);
+nn::Result<Request> convert(const nn::Request& request);
+nn::Result<Request::MemoryPool> convert(const nn::Request::MemoryPool& memoryPool);
+nn::Result<OptionalTimePoint> convert(const nn::OptionalTimePoint& optionalTimePoint);
+nn::Result<OptionalTimeoutDuration> convert(
+ const nn::OptionalTimeoutDuration& optionalTimeoutDuration);
+nn::Result<ErrorStatus> convert(const nn::ErrorStatus& errorStatus);
+
+nn::Result<hidl_vec<BufferRole>> convert(const std::vector<nn::BufferRole>& bufferRoles);
+
+} // namespace android::hardware::neuralnetworks::V1_3::utils
+
+#endif // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_3_CONVERSIONS_H
diff --git a/neuralnetworks/1.3/utils/include/nnapi/hal/1.3/Utils.h b/neuralnetworks/1.3/utils/include/nnapi/hal/1.3/Utils.h
new file mode 100644
index 0000000..f8c975d
--- /dev/null
+++ b/neuralnetworks/1.3/utils/include/nnapi/hal/1.3/Utils.h
@@ -0,0 +1,67 @@
+/*
+ * 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.
+ */
+
+#ifndef ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_3_UTILS_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_3_UTILS_H
+
+#include "nnapi/hal/1.3/Conversions.h"
+
+#include <android-base/logging.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <nnapi/Result.h>
+#include <nnapi/Types.h>
+#include <nnapi/Validation.h>
+#include <nnapi/hal/1.0/Conversions.h>
+#include <nnapi/hal/1.1/Conversions.h>
+#include <nnapi/hal/1.2/Conversions.h>
+
+namespace android::hardware::neuralnetworks::V1_3::utils {
+
+constexpr auto kDefaultPriority = Priority::MEDIUM;
+constexpr auto kVersion = nn::Version::ANDROID_R;
+
+template <typename Type>
+nn::Result<void> validate(const Type& halObject) {
+ const auto canonical = NN_TRY(nn::convert(halObject));
+ const auto version = NN_TRY(nn::validate(canonical));
+ if (version > utils::kVersion) {
+ return NN_ERROR() << "";
+ }
+ return {};
+}
+
+template <typename Type>
+bool valid(const Type& halObject) {
+ const auto result = utils::validate(halObject);
+ if (!result.has_value()) {
+ LOG(ERROR) << result.error();
+ }
+ return result.has_value();
+}
+
+template <typename Type>
+decltype(nn::convert(std::declval<Type>())) validatedConvertToCanonical(const Type& halObject) {
+ auto canonical = NN_TRY(nn::convert(halObject));
+ const auto version = NN_TRY(nn::validate(canonical));
+ if (version > utils::kVersion) {
+ return NN_ERROR() << "";
+ }
+ return canonical;
+}
+
+} // namespace android::hardware::neuralnetworks::V1_3::utils
+
+#endif // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_3_UTILS_H
diff --git a/neuralnetworks/1.3/utils/src/Assertions.cpp b/neuralnetworks/1.3/utils/src/Assertions.cpp
new file mode 100644
index 0000000..96d647a
--- /dev/null
+++ b/neuralnetworks/1.3/utils/src/Assertions.cpp
@@ -0,0 +1,218 @@
+/*
+ * 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 <android/hardware/neuralnetworks/1.3/types.h>
+#include <nnapi/OperandTypes.h>
+#include <nnapi/OperationTypes.h>
+#include <nnapi/Types.h>
+#include <type_traits>
+
+namespace {
+
+#define COMPARE_ENUMS_TYPES(lhsType, rhsType) \
+ static_assert( \
+ std::is_same_v< \
+ std::underlying_type_t<::android::hardware::neuralnetworks::V1_3::lhsType>, \
+ std::underlying_type_t<::android::nn::rhsType>>, \
+ "::android::hardware::neuralnetworks::V1_3::" #lhsType \
+ " does not have the same underlying type as ::android::nn::" #rhsType)
+
+COMPARE_ENUMS_TYPES(OperandType, OperandType);
+COMPARE_ENUMS_TYPES(OperationType, OperationType);
+COMPARE_ENUMS_TYPES(Priority, Priority);
+COMPARE_ENUMS_TYPES(OperandLifeTime, Operand::LifeTime);
+COMPARE_ENUMS_TYPES(ErrorStatus, ErrorStatus);
+
+#undef COMPARE_ENUMS_TYPES
+
+#define COMPARE_ENUMS_FULL(symbol, lhsType, rhsType) \
+ static_assert( \
+ static_cast< \
+ std::underlying_type_t<::android::hardware::neuralnetworks::V1_3::lhsType>>( \
+ ::android::hardware::neuralnetworks::V1_3::lhsType::symbol) == \
+ static_cast<std::underlying_type_t<::android::nn::rhsType>>( \
+ ::android::nn::rhsType::symbol), \
+ "::android::hardware::neuralnetworks::V1_3::" #lhsType "::" #symbol \
+ " does not match ::android::nn::" #rhsType "::" #symbol)
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, OperandType, OperandType)
+
+COMPARE_ENUMS(FLOAT32);
+COMPARE_ENUMS(INT32);
+COMPARE_ENUMS(UINT32);
+COMPARE_ENUMS(TENSOR_FLOAT32);
+COMPARE_ENUMS(TENSOR_INT32);
+COMPARE_ENUMS(TENSOR_QUANT8_ASYMM);
+COMPARE_ENUMS(BOOL);
+COMPARE_ENUMS(TENSOR_QUANT16_SYMM);
+COMPARE_ENUMS(TENSOR_FLOAT16);
+COMPARE_ENUMS(TENSOR_BOOL8);
+COMPARE_ENUMS(FLOAT16);
+COMPARE_ENUMS(TENSOR_QUANT8_SYMM_PER_CHANNEL);
+COMPARE_ENUMS(TENSOR_QUANT16_ASYMM);
+COMPARE_ENUMS(TENSOR_QUANT8_SYMM);
+COMPARE_ENUMS(TENSOR_QUANT8_ASYMM_SIGNED);
+COMPARE_ENUMS(SUBGRAPH);
+COMPARE_ENUMS(OEM);
+COMPARE_ENUMS(TENSOR_OEM_BYTE);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, OperationType, OperationType)
+
+COMPARE_ENUMS(ADD);
+COMPARE_ENUMS(AVERAGE_POOL_2D);
+COMPARE_ENUMS(CONCATENATION);
+COMPARE_ENUMS(CONV_2D);
+COMPARE_ENUMS(DEPTHWISE_CONV_2D);
+COMPARE_ENUMS(DEPTH_TO_SPACE);
+COMPARE_ENUMS(DEQUANTIZE);
+COMPARE_ENUMS(EMBEDDING_LOOKUP);
+COMPARE_ENUMS(FLOOR);
+COMPARE_ENUMS(FULLY_CONNECTED);
+COMPARE_ENUMS(HASHTABLE_LOOKUP);
+COMPARE_ENUMS(L2_NORMALIZATION);
+COMPARE_ENUMS(L2_POOL_2D);
+COMPARE_ENUMS(LOCAL_RESPONSE_NORMALIZATION);
+COMPARE_ENUMS(LOGISTIC);
+COMPARE_ENUMS(LSH_PROJECTION);
+COMPARE_ENUMS(LSTM);
+COMPARE_ENUMS(MAX_POOL_2D);
+COMPARE_ENUMS(MUL);
+COMPARE_ENUMS(RELU);
+COMPARE_ENUMS(RELU1);
+COMPARE_ENUMS(RELU6);
+COMPARE_ENUMS(RESHAPE);
+COMPARE_ENUMS(RESIZE_BILINEAR);
+COMPARE_ENUMS(RNN);
+COMPARE_ENUMS(SOFTMAX);
+COMPARE_ENUMS(SPACE_TO_DEPTH);
+COMPARE_ENUMS(SVDF);
+COMPARE_ENUMS(TANH);
+COMPARE_ENUMS(BATCH_TO_SPACE_ND);
+COMPARE_ENUMS(DIV);
+COMPARE_ENUMS(MEAN);
+COMPARE_ENUMS(PAD);
+COMPARE_ENUMS(SPACE_TO_BATCH_ND);
+COMPARE_ENUMS(SQUEEZE);
+COMPARE_ENUMS(STRIDED_SLICE);
+COMPARE_ENUMS(SUB);
+COMPARE_ENUMS(TRANSPOSE);
+COMPARE_ENUMS(ABS);
+COMPARE_ENUMS(ARGMAX);
+COMPARE_ENUMS(ARGMIN);
+COMPARE_ENUMS(AXIS_ALIGNED_BBOX_TRANSFORM);
+COMPARE_ENUMS(BIDIRECTIONAL_SEQUENCE_LSTM);
+COMPARE_ENUMS(BIDIRECTIONAL_SEQUENCE_RNN);
+COMPARE_ENUMS(BOX_WITH_NMS_LIMIT);
+COMPARE_ENUMS(CAST);
+COMPARE_ENUMS(CHANNEL_SHUFFLE);
+COMPARE_ENUMS(DETECTION_POSTPROCESSING);
+COMPARE_ENUMS(EQUAL);
+COMPARE_ENUMS(EXP);
+COMPARE_ENUMS(EXPAND_DIMS);
+COMPARE_ENUMS(GATHER);
+COMPARE_ENUMS(GENERATE_PROPOSALS);
+COMPARE_ENUMS(GREATER);
+COMPARE_ENUMS(GREATER_EQUAL);
+COMPARE_ENUMS(GROUPED_CONV_2D);
+COMPARE_ENUMS(HEATMAP_MAX_KEYPOINT);
+COMPARE_ENUMS(INSTANCE_NORMALIZATION);
+COMPARE_ENUMS(LESS);
+COMPARE_ENUMS(LESS_EQUAL);
+COMPARE_ENUMS(LOG);
+COMPARE_ENUMS(LOGICAL_AND);
+COMPARE_ENUMS(LOGICAL_NOT);
+COMPARE_ENUMS(LOGICAL_OR);
+COMPARE_ENUMS(LOG_SOFTMAX);
+COMPARE_ENUMS(MAXIMUM);
+COMPARE_ENUMS(MINIMUM);
+COMPARE_ENUMS(NEG);
+COMPARE_ENUMS(NOT_EQUAL);
+COMPARE_ENUMS(PAD_V2);
+COMPARE_ENUMS(POW);
+COMPARE_ENUMS(PRELU);
+COMPARE_ENUMS(QUANTIZE);
+COMPARE_ENUMS(QUANTIZED_16BIT_LSTM);
+COMPARE_ENUMS(RANDOM_MULTINOMIAL);
+COMPARE_ENUMS(REDUCE_ALL);
+COMPARE_ENUMS(REDUCE_ANY);
+COMPARE_ENUMS(REDUCE_MAX);
+COMPARE_ENUMS(REDUCE_MIN);
+COMPARE_ENUMS(REDUCE_PROD);
+COMPARE_ENUMS(REDUCE_SUM);
+COMPARE_ENUMS(ROI_ALIGN);
+COMPARE_ENUMS(ROI_POOLING);
+COMPARE_ENUMS(RSQRT);
+COMPARE_ENUMS(SELECT);
+COMPARE_ENUMS(SIN);
+COMPARE_ENUMS(SLICE);
+COMPARE_ENUMS(SPLIT);
+COMPARE_ENUMS(SQRT);
+COMPARE_ENUMS(TILE);
+COMPARE_ENUMS(TOPK_V2);
+COMPARE_ENUMS(TRANSPOSE_CONV_2D);
+COMPARE_ENUMS(UNIDIRECTIONAL_SEQUENCE_LSTM);
+COMPARE_ENUMS(UNIDIRECTIONAL_SEQUENCE_RNN);
+COMPARE_ENUMS(RESIZE_NEAREST_NEIGHBOR);
+COMPARE_ENUMS(QUANTIZED_LSTM);
+COMPARE_ENUMS(IF);
+COMPARE_ENUMS(WHILE);
+COMPARE_ENUMS(ELU);
+COMPARE_ENUMS(HARD_SWISH);
+COMPARE_ENUMS(FILL);
+COMPARE_ENUMS(RANK);
+COMPARE_ENUMS(OEM_OPERATION);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, Priority, Priority)
+
+COMPARE_ENUMS(LOW);
+COMPARE_ENUMS(MEDIUM);
+COMPARE_ENUMS(HIGH);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, OperandLifeTime, Operand::LifeTime)
+
+COMPARE_ENUMS(TEMPORARY_VARIABLE);
+COMPARE_ENUMS(SUBGRAPH_INPUT);
+COMPARE_ENUMS(SUBGRAPH_OUTPUT);
+COMPARE_ENUMS(CONSTANT_COPY);
+COMPARE_ENUMS(CONSTANT_REFERENCE);
+COMPARE_ENUMS(NO_VALUE);
+COMPARE_ENUMS(SUBGRAPH);
+
+#undef COMPARE_ENUMS
+
+#define COMPARE_ENUMS(symbol) COMPARE_ENUMS_FULL(symbol, ErrorStatus, ErrorStatus)
+
+COMPARE_ENUMS(NONE);
+COMPARE_ENUMS(DEVICE_UNAVAILABLE);
+COMPARE_ENUMS(GENERAL_FAILURE);
+COMPARE_ENUMS(OUTPUT_INSUFFICIENT_SIZE);
+COMPARE_ENUMS(INVALID_ARGUMENT);
+COMPARE_ENUMS(MISSED_DEADLINE_TRANSIENT);
+COMPARE_ENUMS(MISSED_DEADLINE_PERSISTENT);
+COMPARE_ENUMS(RESOURCE_EXHAUSTED_TRANSIENT);
+COMPARE_ENUMS(RESOURCE_EXHAUSTED_PERSISTENT);
+
+#undef COMPARE_ENUMS
+
+#undef COMPARE_ENUMS_FULL
+
+} // anonymous namespace
diff --git a/neuralnetworks/1.3/utils/src/Conversions.cpp b/neuralnetworks/1.3/utils/src/Conversions.cpp
new file mode 100644
index 0000000..4c54e3b
--- /dev/null
+++ b/neuralnetworks/1.3/utils/src/Conversions.cpp
@@ -0,0 +1,552 @@
+/*
+ * 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/1.3/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/1.2/Conversions.h>
+#include <nnapi/hal/CommonUtils.h>
+
+#include <algorithm>
+#include <chrono>
+#include <functional>
+#include <iterator>
+#include <limits>
+#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 auto validOperandType(nn::OperandType operandType) {
+ switch (operandType) {
+ case nn::OperandType::FLOAT32:
+ case nn::OperandType::INT32:
+ case nn::OperandType::UINT32:
+ case nn::OperandType::TENSOR_FLOAT32:
+ case nn::OperandType::TENSOR_INT32:
+ case nn::OperandType::TENSOR_QUANT8_ASYMM:
+ case nn::OperandType::BOOL:
+ case nn::OperandType::TENSOR_QUANT16_SYMM:
+ case nn::OperandType::TENSOR_FLOAT16:
+ case nn::OperandType::TENSOR_BOOL8:
+ case nn::OperandType::FLOAT16:
+ case nn::OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
+ case nn::OperandType::TENSOR_QUANT16_ASYMM:
+ case nn::OperandType::TENSOR_QUANT8_SYMM:
+ case nn::OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
+ case nn::OperandType::SUBGRAPH:
+ case nn::OperandType::OEM:
+ case nn::OperandType::TENSOR_OEM_BYTE:
+ return true;
+ }
+ return nn::isExtension(operandType);
+}
+
+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_3::OperandType& operandType) {
+ return static_cast<OperandType>(operandType);
+}
+
+Result<OperationType> convert(const hal::V1_3::OperationType& operationType) {
+ return static_cast<OperationType>(operationType);
+}
+
+Result<Priority> convert(const hal::V1_3::Priority& priority) {
+ return static_cast<Priority>(priority);
+}
+
+Result<Capabilities> convert(const hal::V1_3::Capabilities& capabilities) {
+ const bool validOperandTypes = std::all_of(
+ capabilities.operandPerformance.begin(), capabilities.operandPerformance.end(),
+ [](const hal::V1_3::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";
+ }
+
+ auto operandPerformance = NN_TRY(convert(capabilities.operandPerformance));
+ auto table =
+ NN_TRY(Capabilities::OperandPerformanceTable::create(std::move(operandPerformance)));
+
+ return Capabilities{
+ .relaxedFloat32toFloat16PerformanceScalar =
+ NN_TRY(convert(capabilities.relaxedFloat32toFloat16PerformanceScalar)),
+ .relaxedFloat32toFloat16PerformanceTensor =
+ NN_TRY(convert(capabilities.relaxedFloat32toFloat16PerformanceTensor)),
+ .operandPerformance = std::move(table),
+ .ifPerformance = NN_TRY(convert(capabilities.ifPerformance)),
+ .whilePerformance = NN_TRY(convert(capabilities.whilePerformance)),
+ };
+}
+
+Result<Capabilities::OperandPerformance> convert(
+ const hal::V1_3::Capabilities::OperandPerformance& operandPerformance) {
+ return Capabilities::OperandPerformance{
+ .type = NN_TRY(convert(operandPerformance.type)),
+ .info = NN_TRY(convert(operandPerformance.info)),
+ };
+}
+
+Result<Operation> convert(const hal::V1_3::Operation& operation) {
+ return Operation{
+ .type = NN_TRY(convert(operation.type)),
+ .inputs = operation.inputs,
+ .outputs = operation.outputs,
+ };
+}
+
+Result<Operand::LifeTime> convert(const hal::V1_3::OperandLifeTime& operandLifeTime) {
+ return static_cast<Operand::LifeTime>(operandLifeTime);
+}
+
+Result<Operand> convert(const hal::V1_3::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<Model> convert(const hal::V1_3::Model& model) {
+ return Model{
+ .main = NN_TRY(convert(model.main)),
+ .referenced = NN_TRY(convert(model.referenced)),
+ .operandValues = NN_TRY(convert(model.operandValues)),
+ .pools = NN_TRY(convert(model.pools)),
+ .relaxComputationFloat32toFloat16 = model.relaxComputationFloat32toFloat16,
+ .extensionNameToPrefix = NN_TRY(convert(model.extensionNameToPrefix)),
+ };
+}
+
+Result<Model::Subgraph> convert(const hal::V1_3::Subgraph& subgraph) {
+ auto operations = NN_TRY(convert(subgraph.operations));
+
+ // Verify number of consumers.
+ const auto numberOfConsumers =
+ hal::utils::countNumberOfConsumers(subgraph.operands.size(), operations);
+ CHECK(subgraph.operands.size() == numberOfConsumers.size());
+ for (size_t i = 0; i < subgraph.operands.size(); ++i) {
+ if (subgraph.operands[i].numberOfConsumers != numberOfConsumers[i]) {
+ return NN_ERROR() << "Invalid numberOfConsumers for operand " << i << ", expected "
+ << numberOfConsumers[i] << " but found "
+ << subgraph.operands[i].numberOfConsumers;
+ }
+ }
+
+ return Model::Subgraph{
+ .operands = NN_TRY(convert(subgraph.operands)),
+ .operations = std::move(operations),
+ .inputIndexes = subgraph.inputIndexes,
+ .outputIndexes = subgraph.outputIndexes,
+ };
+}
+
+Result<BufferDesc> convert(const hal::V1_3::BufferDesc& bufferDesc) {
+ return BufferDesc{.dimensions = bufferDesc.dimensions};
+}
+
+Result<BufferRole> convert(const hal::V1_3::BufferRole& bufferRole) {
+ return BufferRole{
+ .modelIndex = bufferRole.modelIndex,
+ .ioIndex = bufferRole.ioIndex,
+ .frequency = bufferRole.frequency,
+ };
+}
+
+Result<Request> convert(const hal::V1_3::Request& request) {
+ return Request{
+ .inputs = NN_TRY(convert(request.inputs)),
+ .outputs = NN_TRY(convert(request.outputs)),
+ .pools = NN_TRY(convert(request.pools)),
+ };
+}
+
+Result<Request::MemoryPool> convert(const hal::V1_3::Request::MemoryPool& memoryPool) {
+ using Discriminator = hal::V1_3::Request::MemoryPool::hidl_discriminator;
+ switch (memoryPool.getDiscriminator()) {
+ case Discriminator::hidlMemory:
+ return createSharedMemoryFromHidlMemory(memoryPool.hidlMemory());
+ case Discriminator::token:
+ return static_cast<Request::MemoryDomainToken>(memoryPool.token());
+ }
+ return NN_ERROR() << "Invalid Request::MemoryPool discriminator "
+ << underlyingType(memoryPool.getDiscriminator());
+}
+
+Result<OptionalTimePoint> convert(const hal::V1_3::OptionalTimePoint& optionalTimePoint) {
+ constexpr auto kTimePointMaxCount = TimePoint::max().time_since_epoch().count();
+ const auto makeTimePoint = [](uint64_t count) -> Result<OptionalTimePoint> {
+ if (count > kTimePointMaxCount) {
+ return NN_ERROR()
+ << "Unable to convert OptionalTimePoint because the count exceeds the max";
+ }
+ const auto nanoseconds = std::chrono::nanoseconds{count};
+ return TimePoint{nanoseconds};
+ };
+
+ using Discriminator = hal::V1_3::OptionalTimePoint::hidl_discriminator;
+ switch (optionalTimePoint.getDiscriminator()) {
+ case Discriminator::none:
+ return std::nullopt;
+ case Discriminator::nanosecondsSinceEpoch:
+ return makeTimePoint(optionalTimePoint.nanosecondsSinceEpoch());
+ }
+ return NN_ERROR() << "Invalid OptionalTimePoint discriminator "
+ << underlyingType(optionalTimePoint.getDiscriminator());
+}
+
+Result<OptionalTimeoutDuration> convert(
+ const hal::V1_3::OptionalTimeoutDuration& optionalTimeoutDuration) {
+ constexpr auto kTimeoutDurationMaxCount = TimeoutDuration::max().count();
+ const auto makeTimeoutDuration = [](uint64_t count) -> Result<OptionalTimeoutDuration> {
+ if (count > kTimeoutDurationMaxCount) {
+ return NN_ERROR()
+ << "Unable to convert OptionalTimeoutDuration because the count exceeds the max";
+ }
+ return TimeoutDuration{count};
+ };
+
+ using Discriminator = hal::V1_3::OptionalTimeoutDuration::hidl_discriminator;
+ switch (optionalTimeoutDuration.getDiscriminator()) {
+ case Discriminator::none:
+ return std::nullopt;
+ case Discriminator::nanoseconds:
+ return makeTimeoutDuration(optionalTimeoutDuration.nanoseconds());
+ }
+ return NN_ERROR() << "Invalid OptionalTimeoutDuration discriminator "
+ << underlyingType(optionalTimeoutDuration.getDiscriminator());
+}
+
+Result<ErrorStatus> convert(const hal::V1_3::ErrorStatus& status) {
+ switch (status) {
+ case hal::V1_3::ErrorStatus::NONE:
+ case hal::V1_3::ErrorStatus::DEVICE_UNAVAILABLE:
+ case hal::V1_3::ErrorStatus::GENERAL_FAILURE:
+ case hal::V1_3::ErrorStatus::OUTPUT_INSUFFICIENT_SIZE:
+ case hal::V1_3::ErrorStatus::INVALID_ARGUMENT:
+ case hal::V1_3::ErrorStatus::MISSED_DEADLINE_TRANSIENT:
+ case hal::V1_3::ErrorStatus::MISSED_DEADLINE_PERSISTENT:
+ case hal::V1_3::ErrorStatus::RESOURCE_EXHAUSTED_TRANSIENT:
+ case hal::V1_3::ErrorStatus::RESOURCE_EXHAUSTED_PERSISTENT:
+ return static_cast<ErrorStatus>(status);
+ }
+ return NN_ERROR() << "Invalid ErrorStatus " << underlyingType(status);
+}
+
+Result<std::vector<BufferRole>> convert(
+ const hardware::hidl_vec<hal::V1_3::BufferRole>& bufferRoles) {
+ return convertVec(bufferRoles);
+}
+
+} // namespace android::nn
+
+namespace android::hardware::neuralnetworks::V1_3::utils {
+namespace {
+
+using utils::convert;
+
+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& dataLocation) {
+ return V1_0::utils::convert(dataLocation);
+}
+
+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);
+}
+
+nn::Result<V1_0::RequestArgument> convert(const nn::Request::Argument& argument) {
+ return V1_0::utils::convert(argument);
+}
+
+nn::Result<V1_2::Operand::ExtraParams> convert(const nn::Operand::ExtraParams& extraParams) {
+ return V1_2::utils::convert(extraParams);
+}
+
+nn::Result<V1_2::Model::ExtensionNameAndPrefix> convert(
+ const nn::Model::ExtensionNameAndPrefix& extensionNameAndPrefix) {
+ return V1_2::utils::convert(extensionNameAndPrefix);
+}
+
+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<Request::MemoryPool> makeMemoryPool(const nn::Memory& memory) {
+ Request::MemoryPool ret;
+ ret.hidlMemory(NN_TRY(convert(memory)));
+ return ret;
+}
+
+nn::Result<Request::MemoryPool> makeMemoryPool(const nn::Request::MemoryDomainToken& token) {
+ Request::MemoryPool ret;
+ ret.token(underlyingType(token));
+ return ret;
+}
+
+nn::Result<Request::MemoryPool> makeMemoryPool(
+ const std::shared_ptr<const nn::IBuffer>& /*buffer*/) {
+ return NN_ERROR() << "Unable to make memory pool from IBuffer";
+}
+
+} // 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<Priority> convert(const nn::Priority& priority) {
+ return static_cast<Priority>(priority);
+}
+
+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)),
+ .ifPerformance = NN_TRY(convert(capabilities.ifPerformance)),
+ .whilePerformance = NN_TRY(convert(capabilities.whilePerformance)),
+ };
+}
+
+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<OperandLifeTime> convert(const nn::Operand::LifeTime& operandLifeTime) {
+ if (operandLifeTime == nn::Operand::LifeTime::POINTER) {
+ return NN_ERROR() << "Model cannot be converted because it contains pointer-based memory";
+ }
+ return static_cast<OperandLifeTime>(operandLifeTime);
+}
+
+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<Model> convert(const nn::Model& model) {
+ if (!hal::utils::hasNoPointerData(model)) {
+ return NN_ERROR() << "Model cannot be converted because it contains pointer-based memory";
+ }
+
+ return Model{
+ .main = NN_TRY(convert(model.main)),
+ .referenced = NN_TRY(convert(model.referenced)),
+ .operandValues = NN_TRY(convert(model.operandValues)),
+ .pools = NN_TRY(convert(model.pools)),
+ .relaxComputationFloat32toFloat16 = model.relaxComputationFloat32toFloat16,
+ .extensionNameToPrefix = NN_TRY(convert(model.extensionNameToPrefix)),
+ };
+}
+
+nn::Result<Subgraph> convert(const nn::Model::Subgraph& subgraph) {
+ auto operands = NN_TRY(convert(subgraph.operands));
+
+ // Update number of consumers.
+ const auto numberOfConsumers =
+ hal::utils::countNumberOfConsumers(operands.size(), subgraph.operations);
+ CHECK(operands.size() == numberOfConsumers.size());
+ for (size_t i = 0; i < operands.size(); ++i) {
+ operands[i].numberOfConsumers = numberOfConsumers[i];
+ }
+
+ return Subgraph{
+ .operands = std::move(operands),
+ .operations = NN_TRY(convert(subgraph.operations)),
+ .inputIndexes = subgraph.inputIndexes,
+ .outputIndexes = subgraph.outputIndexes,
+ };
+}
+
+nn::Result<BufferDesc> convert(const nn::BufferDesc& bufferDesc) {
+ return BufferDesc{.dimensions = bufferDesc.dimensions};
+}
+
+nn::Result<BufferRole> convert(const nn::BufferRole& bufferRole) {
+ return BufferRole{
+ .modelIndex = bufferRole.modelIndex,
+ .ioIndex = bufferRole.ioIndex,
+ .frequency = bufferRole.frequency,
+ };
+}
+
+nn::Result<Request> convert(const nn::Request& request) {
+ if (!hal::utils::hasNoPointerData(request)) {
+ return NN_ERROR() << "Request cannot be converted because it contains pointer-based memory";
+ }
+
+ return Request{
+ .inputs = NN_TRY(convert(request.inputs)),
+ .outputs = NN_TRY(convert(request.outputs)),
+ .pools = NN_TRY(convert(request.pools)),
+ };
+}
+
+nn::Result<Request::MemoryPool> convert(const nn::Request::MemoryPool& memoryPool) {
+ return std::visit([](const auto& o) { return makeMemoryPool(o); }, memoryPool);
+}
+
+nn::Result<OptionalTimePoint> convert(const nn::OptionalTimePoint& optionalTimePoint) {
+ OptionalTimePoint ret;
+ if (optionalTimePoint.has_value()) {
+ const auto count = optionalTimePoint.value().time_since_epoch().count();
+ if (count < 0) {
+ return NN_ERROR() << "Unable to convert OptionalTimePoint because time since epoch "
+ "count is negative";
+ }
+ ret.nanosecondsSinceEpoch(count);
+ }
+ return ret;
+}
+
+nn::Result<OptionalTimeoutDuration> convert(
+ const nn::OptionalTimeoutDuration& optionalTimeoutDuration) {
+ OptionalTimeoutDuration ret;
+ if (optionalTimeoutDuration.has_value()) {
+ const auto count = optionalTimeoutDuration.value().count();
+ if (count < 0) {
+ return NN_ERROR()
+ << "Unable to convert OptionalTimeoutDuration because count is negative";
+ }
+ ret.nanoseconds(count);
+ }
+ return ret;
+}
+
+nn::Result<ErrorStatus> convert(const nn::ErrorStatus& errorStatus) {
+ switch (errorStatus) {
+ case nn::ErrorStatus::NONE:
+ case nn::ErrorStatus::DEVICE_UNAVAILABLE:
+ case nn::ErrorStatus::GENERAL_FAILURE:
+ case nn::ErrorStatus::OUTPUT_INSUFFICIENT_SIZE:
+ case nn::ErrorStatus::INVALID_ARGUMENT:
+ case nn::ErrorStatus::MISSED_DEADLINE_TRANSIENT:
+ case nn::ErrorStatus::MISSED_DEADLINE_PERSISTENT:
+ case nn::ErrorStatus::RESOURCE_EXHAUSTED_TRANSIENT:
+ case nn::ErrorStatus::RESOURCE_EXHAUSTED_PERSISTENT:
+ return static_cast<ErrorStatus>(errorStatus);
+ default:
+ return ErrorStatus::GENERAL_FAILURE;
+ }
+}
+
+nn::Result<hidl_vec<BufferRole>> convert(const std::vector<nn::BufferRole>& bufferRoles) {
+ return convertVec(bufferRoles);
+}
+
+} // namespace android::hardware::neuralnetworks::V1_3::utils
diff --git a/neuralnetworks/utils/OWNERS b/neuralnetworks/utils/OWNERS
new file mode 100644
index 0000000..e4feee3
--- /dev/null
+++ b/neuralnetworks/utils/OWNERS
@@ -0,0 +1,11 @@
+# Neuralnetworks team
+butlermichael@google.com
+dgross@google.com
+galarragas@google.com
+jeanluc@google.com
+levp@google.com
+miaowang@google.com
+pszczepaniak@google.com
+slavash@google.com
+vddang@google.com
+xusongw@google.com
diff --git a/neuralnetworks/utils/common/Android.bp b/neuralnetworks/utils/common/Android.bp
new file mode 100644
index 0000000..b61dc97
--- /dev/null
+++ b/neuralnetworks/utils/common/Android.bp
@@ -0,0 +1,29 @@
+//
+// 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.
+//
+
+cc_library_static {
+ name: "neuralnetworks_utils_hal_common",
+ defaults: ["neuralnetworks_utils_defaults"],
+ srcs: ["src/*"],
+ local_include_dirs: ["include/nnapi/hal"],
+ export_include_dirs: ["include"],
+ static_libs: [
+ "neuralnetworks_types",
+ ],
+ shared_libs: [
+ "libhidlbase",
+ ],
+}
diff --git a/neuralnetworks/utils/common/include/nnapi/hal/CommonUtils.h b/neuralnetworks/utils/common/include/nnapi/hal/CommonUtils.h
new file mode 100644
index 0000000..8c01368
--- /dev/null
+++ b/neuralnetworks/utils/common/include/nnapi/hal/CommonUtils.h
@@ -0,0 +1,59 @@
+/*
+ * 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.
+ */
+
+#ifndef ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_COMMON_COMMON_UTILS_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_COMMON_COMMON_UTILS_H
+
+#include <nnapi/Result.h>
+#include <nnapi/Types.h>
+#include <vector>
+
+// Shorthand
+namespace android::hardware::neuralnetworks {
+namespace hal = ::android::hardware::neuralnetworks;
+} // namespace android::hardware::neuralnetworks
+
+// Shorthand
+namespace android::nn {
+namespace hal = ::android::hardware::neuralnetworks;
+}
+
+namespace android::hardware::neuralnetworks::utils {
+
+nn::Capabilities::OperandPerformanceTable makeQuantized8PerformanceConsistentWithP(
+ const nn::Capabilities::PerformanceInfo& float32Performance,
+ const nn::Capabilities::PerformanceInfo& quantized8Performance);
+
+// Indicates if the object contains no pointer-based data that could be relocated to shared memory.
+bool hasNoPointerData(const nn::Model& model);
+bool hasNoPointerData(const nn::Request& request);
+
+// Relocate pointer-based data to shared memory.
+nn::Result<nn::Model> flushDataFromPointerToShared(const nn::Model& model);
+nn::Result<nn::Request> flushDataFromPointerToShared(const nn::Request& request);
+
+// Undoes `flushDataFromPointerToShared` on a Request object. More specifically,
+// `unflushDataFromSharedToPointer` copies the output shared memory data from the transformed
+// Request object back to the output pointer-based memory in the original Request object.
+nn::Result<void> unflushDataFromSharedToPointer(const nn::Request& request,
+ const nn::Request& requestInShared);
+
+std::vector<uint32_t> countNumberOfConsumers(size_t numberOfOperands,
+ const std::vector<nn::Operation>& operations);
+
+} // namespace android::hardware::neuralnetworks::utils
+
+#endif // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_COMMON_COMMON_UTILS_H
diff --git a/neuralnetworks/utils/common/src/CommonUtils.cpp b/neuralnetworks/utils/common/src/CommonUtils.cpp
new file mode 100644
index 0000000..667189b
--- /dev/null
+++ b/neuralnetworks/utils/common/src/CommonUtils.cpp
@@ -0,0 +1,224 @@
+/*
+ * 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 "CommonUtils.h"
+
+#include <android-base/logging.h>
+#include <nnapi/Result.h>
+#include <nnapi/SharedMemory.h>
+#include <nnapi/TypeUtils.h>
+#include <nnapi/Types.h>
+#include <nnapi/Validation.h>
+
+#include <algorithm>
+#include <any>
+#include <optional>
+#include <variant>
+#include <vector>
+
+namespace android::hardware::neuralnetworks::utils {
+namespace {
+
+bool hasNoPointerData(const nn::Operand& operand);
+bool hasNoPointerData(const nn::Model::Subgraph& subgraph);
+bool hasNoPointerData(const nn::Request::Argument& argument);
+
+template <typename Type>
+bool hasNoPointerData(const std::vector<Type>& objects) {
+ return std::all_of(objects.begin(), objects.end(),
+ [](const auto& object) { return hasNoPointerData(object); });
+}
+
+bool hasNoPointerData(const nn::DataLocation& location) {
+ return std::visit([](auto ptr) { return ptr == nullptr; }, location.pointer);
+}
+
+bool hasNoPointerData(const nn::Operand& operand) {
+ return hasNoPointerData(operand.location);
+}
+
+bool hasNoPointerData(const nn::Model::Subgraph& subgraph) {
+ return hasNoPointerData(subgraph.operands);
+}
+
+bool hasNoPointerData(const nn::Request::Argument& argument) {
+ return hasNoPointerData(argument.location);
+}
+
+void copyPointersToSharedMemory(nn::Operand* operand, nn::ConstantMemoryBuilder* memoryBuilder) {
+ CHECK(operand != nullptr);
+ CHECK(memoryBuilder != nullptr);
+
+ if (operand->lifetime != nn::Operand::LifeTime::POINTER) {
+ return;
+ }
+
+ const void* data = std::visit([](auto ptr) { return static_cast<const void*>(ptr); },
+ operand->location.pointer);
+ CHECK(data != nullptr);
+ operand->lifetime = nn::Operand::LifeTime::CONSTANT_REFERENCE;
+ operand->location = memoryBuilder->append(data, operand->location.length);
+}
+
+void copyPointersToSharedMemory(nn::Model::Subgraph* subgraph,
+ nn::ConstantMemoryBuilder* memoryBuilder) {
+ CHECK(subgraph != nullptr);
+ std::for_each(subgraph->operands.begin(), subgraph->operands.end(),
+ [memoryBuilder](auto& operand) {
+ copyPointersToSharedMemory(&operand, memoryBuilder);
+ });
+}
+
+} // anonymous namespace
+
+nn::Capabilities::OperandPerformanceTable makeQuantized8PerformanceConsistentWithP(
+ const nn::Capabilities::PerformanceInfo& float32Performance,
+ const nn::Capabilities::PerformanceInfo& quantized8Performance) {
+ // In Android P, most data types are treated as having the same performance as
+ // TENSOR_QUANT8_ASYMM. This collection must be in sorted order.
+ std::vector<nn::Capabilities::OperandPerformance> operandPerformances = {
+ {.type = nn::OperandType::FLOAT32, .info = float32Performance},
+ {.type = nn::OperandType::INT32, .info = quantized8Performance},
+ {.type = nn::OperandType::UINT32, .info = quantized8Performance},
+ {.type = nn::OperandType::TENSOR_FLOAT32, .info = float32Performance},
+ {.type = nn::OperandType::TENSOR_INT32, .info = quantized8Performance},
+ {.type = nn::OperandType::TENSOR_QUANT8_ASYMM, .info = quantized8Performance},
+ {.type = nn::OperandType::OEM, .info = quantized8Performance},
+ {.type = nn::OperandType::TENSOR_OEM_BYTE, .info = quantized8Performance},
+ };
+ return nn::Capabilities::OperandPerformanceTable::create(std::move(operandPerformances))
+ .value();
+}
+
+bool hasNoPointerData(const nn::Model& model) {
+ return hasNoPointerData(model.main) && hasNoPointerData(model.referenced);
+}
+
+bool hasNoPointerData(const nn::Request& request) {
+ return hasNoPointerData(request.inputs) && hasNoPointerData(request.outputs);
+}
+
+nn::Result<nn::Model> flushDataFromPointerToShared(const nn::Model& model) {
+ auto modelInShared = model;
+
+ nn::ConstantMemoryBuilder memoryBuilder(modelInShared.pools.size());
+ copyPointersToSharedMemory(&modelInShared.main, &memoryBuilder);
+ std::for_each(modelInShared.referenced.begin(), modelInShared.referenced.end(),
+ [&memoryBuilder](auto& subgraph) {
+ copyPointersToSharedMemory(&subgraph, &memoryBuilder);
+ });
+
+ if (!memoryBuilder.empty()) {
+ auto memory = NN_TRY(memoryBuilder.finish());
+ modelInShared.pools.push_back(std::move(memory));
+ }
+
+ return modelInShared;
+}
+
+nn::Result<nn::Request> flushDataFromPointerToShared(const nn::Request& request) {
+ auto requestInShared = request;
+
+ // Change input pointers to shared memory.
+ nn::ConstantMemoryBuilder inputBuilder(requestInShared.pools.size());
+ for (auto& input : requestInShared.inputs) {
+ const auto& location = input.location;
+ if (input.lifetime != nn::Request::Argument::LifeTime::POINTER) {
+ continue;
+ }
+
+ input.lifetime = nn::Request::Argument::LifeTime::POOL;
+ const void* data = std::visit([](auto ptr) { return static_cast<const void*>(ptr); },
+ location.pointer);
+ CHECK(data != nullptr);
+ input.location = inputBuilder.append(data, location.length);
+ }
+
+ // Allocate input memory.
+ if (!inputBuilder.empty()) {
+ auto memory = NN_TRY(inputBuilder.finish());
+ requestInShared.pools.push_back(std::move(memory));
+ }
+
+ // Change output pointers to shared memory.
+ nn::MutableMemoryBuilder outputBuilder(requestInShared.pools.size());
+ for (auto& output : requestInShared.outputs) {
+ const auto& location = output.location;
+ if (output.lifetime != nn::Request::Argument::LifeTime::POINTER) {
+ continue;
+ }
+
+ output.lifetime = nn::Request::Argument::LifeTime::POOL;
+ output.location = outputBuilder.append(location.length);
+ }
+
+ // Allocate output memory.
+ if (!outputBuilder.empty()) {
+ auto memory = NN_TRY(outputBuilder.finish());
+ requestInShared.pools.push_back(std::move(memory));
+ }
+
+ return requestInShared;
+}
+
+nn::Result<void> unflushDataFromSharedToPointer(const nn::Request& request,
+ const nn::Request& requestInShared) {
+ if (requestInShared.pools.empty() ||
+ !std::holds_alternative<nn::Memory>(requestInShared.pools.back())) {
+ return {};
+ }
+
+ // Map the memory.
+ const auto& outputMemory = std::get<nn::Memory>(requestInShared.pools.back());
+ const auto [pointer, size, context] = NN_TRY(map(outputMemory));
+ const uint8_t* constantPointer =
+ std::visit([](const auto& o) { return static_cast<const uint8_t*>(o); }, pointer);
+
+ // Flush each output pointer.
+ CHECK_EQ(request.outputs.size(), requestInShared.outputs.size());
+ for (size_t i = 0; i < request.outputs.size(); ++i) {
+ const auto& location = request.outputs[i].location;
+ const auto& locationInShared = requestInShared.outputs[i].location;
+ if (!std::holds_alternative<void*>(location.pointer)) {
+ continue;
+ }
+
+ // Get output pointer and size.
+ void* data = std::get<void*>(location.pointer);
+ CHECK(data != nullptr);
+ const size_t length = location.length;
+
+ // Get output pool location.
+ CHECK(requestInShared.outputs[i].lifetime == nn::Request::Argument::LifeTime::POOL);
+ const size_t index = locationInShared.poolIndex;
+ const size_t offset = locationInShared.offset;
+ const size_t outputPoolIndex = requestInShared.pools.size() - 1;
+ CHECK(locationInShared.length == length);
+ CHECK(index == outputPoolIndex);
+
+ // Flush memory.
+ std::memcpy(data, constantPointer + offset, length);
+ }
+
+ return {};
+}
+
+std::vector<uint32_t> countNumberOfConsumers(size_t numberOfOperands,
+ const std::vector<nn::Operation>& operations) {
+ return nn::countNumberOfConsumers(numberOfOperands, operations);
+}
+
+} // namespace android::hardware::neuralnetworks::utils
