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
Merged-In: I0732e658c1f4ed40cd122f1ca8581fb40b056757
(cherry picked from commit a685c3dbf4afb35d0a80488155ce2bde30c9d6e9)
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