Implement NNAPI canonical interfaces
This CL implements the canonical IDevice, IPreparedModel, and IBuffer
interfaces for the 1.0, 1.1, 1.2, and 1.3 NN HIDL HAL interfaces.
Further, it introduces "Resilient" adapter interfaces to automatically
retrieve a handle to a recovered interface object after it has died and
rebooted.
This CL also updates the conversion code from returning nn::Result to
nn::GeneralResult, which includes a ErrorStatus code in the case of an
error.
Finally, this CL introduces a new static library
neuralnetworks_utils_hal_service which consists of a single function
::android::nn::hal::getDevices which can be used by the NNAPI runtime to
retrieve the HIDL services without knowing the underlying HIDL types.
Bug: 160668438
Test: mma
Test: NeuralNetworksTest_static
Change-Id: Iec6ae739df196b4034ffb35ea76781fd541ffec3
Merged-In: Iec6ae739df196b4034ffb35ea76781fd541ffec3
(cherry picked from commit 3670c385c4b12aef975ab67e5d2b0f5fe79134c2)
diff --git a/neuralnetworks/1.1/utils/src/Conversions.cpp b/neuralnetworks/1.1/utils/src/Conversions.cpp
index 7fee16b..ffe0752 100644
--- a/neuralnetworks/1.1/utils/src/Conversions.cpp
+++ b/neuralnetworks/1.1/utils/src/Conversions.cpp
@@ -42,7 +42,7 @@
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) {
+GeneralResult<std::vector<convertOutput<Type>>> convert(const hidl_vec<Type>& arguments) {
std::vector<convertOutput<Type>> canonical;
canonical.reserve(arguments.size());
for (const auto& argument : arguments) {
@@ -53,11 +53,11 @@
} // anonymous namespace
-Result<OperationType> convert(const hal::V1_1::OperationType& operationType) {
+GeneralResult<OperationType> convert(const hal::V1_1::OperationType& operationType) {
return static_cast<OperationType>(operationType);
}
-Result<Capabilities> convert(const hal::V1_1::Capabilities& capabilities) {
+GeneralResult<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 =
@@ -73,7 +73,7 @@
};
}
-Result<Operation> convert(const hal::V1_1::Operation& operation) {
+GeneralResult<Operation> convert(const hal::V1_1::Operation& operation) {
return Operation{
.type = NN_TRY(convert(operation.type)),
.inputs = operation.inputs,
@@ -81,7 +81,7 @@
};
}
-Result<Model> convert(const hal::V1_1::Model& model) {
+GeneralResult<Model> convert(const hal::V1_1::Model& model) {
auto operations = NN_TRY(convert(model.operations));
// Verify number of consumers.
@@ -90,9 +90,9 @@
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;
+ return NN_ERROR(nn::ErrorStatus::GENERAL_FAILURE)
+ << "Invalid numberOfConsumers for operand " << i << ", expected "
+ << numberOfConsumers[i] << " but found " << model.operands[i].numberOfConsumers;
}
}
@@ -111,7 +111,8 @@
};
}
-Result<ExecutionPreference> convert(const hal::V1_1::ExecutionPreference& executionPreference) {
+GeneralResult<ExecutionPreference> convert(
+ const hal::V1_1::ExecutionPreference& executionPreference) {
return static_cast<ExecutionPreference>(executionPreference);
}
@@ -122,20 +123,20 @@
using utils::convert;
-nn::Result<V1_0::PerformanceInfo> convert(
+nn::GeneralResult<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) {
+nn::GeneralResult<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) {
+nn::GeneralResult<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) {
+nn::GeneralResult<hidl_memory> convert(const nn::Memory& memory) {
return V1_0::utils::convert(memory);
}
@@ -143,7 +144,7 @@
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) {
+nn::GeneralResult<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]));
@@ -153,11 +154,11 @@
} // anonymous namespace
-nn::Result<OperationType> convert(const nn::OperationType& operationType) {
+nn::GeneralResult<OperationType> convert(const nn::OperationType& operationType) {
return static_cast<OperationType>(operationType);
}
-nn::Result<Capabilities> convert(const nn::Capabilities& capabilities) {
+nn::GeneralResult<Capabilities> convert(const nn::Capabilities& capabilities) {
return Capabilities{
.float32Performance = NN_TRY(convert(
capabilities.operandPerformance.lookup(nn::OperandType::TENSOR_FLOAT32))),
@@ -168,7 +169,7 @@
};
}
-nn::Result<Operation> convert(const nn::Operation& operation) {
+nn::GeneralResult<Operation> convert(const nn::Operation& operation) {
return Operation{
.type = NN_TRY(convert(operation.type)),
.inputs = operation.inputs,
@@ -176,9 +177,10 @@
};
}
-nn::Result<Model> convert(const nn::Model& model) {
+nn::GeneralResult<Model> convert(const nn::Model& model) {
if (!hal::utils::hasNoPointerData(model)) {
- return NN_ERROR() << "Mdoel cannot be converted because it contains pointer-based memory";
+ return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT)
+ << "Mdoel cannot be converted because it contains pointer-based memory";
}
auto operands = NN_TRY(convert(model.main.operands));
@@ -202,7 +204,7 @@
};
}
-nn::Result<ExecutionPreference> convert(const nn::ExecutionPreference& executionPreference) {
+nn::GeneralResult<ExecutionPreference> convert(const nn::ExecutionPreference& executionPreference) {
return static_cast<ExecutionPreference>(executionPreference);
}
diff --git a/neuralnetworks/1.1/utils/src/Device.cpp b/neuralnetworks/1.1/utils/src/Device.cpp
new file mode 100644
index 0000000..03b0d6e
--- /dev/null
+++ b/neuralnetworks/1.1/utils/src/Device.cpp
@@ -0,0 +1,202 @@
+/*
+ * 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 "Device.h"
+
+#include "Conversions.h"
+#include "Utils.h"
+
+#include <android/hardware/neuralnetworks/1.0/types.h>
+#include <android/hardware/neuralnetworks/1.1/IDevice.h>
+#include <android/hardware/neuralnetworks/1.1/types.h>
+#include <nnapi/IBuffer.h>
+#include <nnapi/IDevice.h>
+#include <nnapi/IPreparedModel.h>
+#include <nnapi/OperandTypes.h>
+#include <nnapi/Result.h>
+#include <nnapi/Types.h>
+#include <nnapi/hal/1.0/Callbacks.h>
+#include <nnapi/hal/CommonUtils.h>
+#include <nnapi/hal/HandleError.h>
+#include <nnapi/hal/ProtectCallback.h>
+
+#include <functional>
+#include <memory>
+#include <optional>
+#include <string>
+#include <vector>
+
+namespace android::hardware::neuralnetworks::V1_1::utils {
+namespace {
+
+nn::GeneralResult<nn::Capabilities> initCapabilities(V1_1::IDevice* device) {
+ CHECK(device != nullptr);
+
+ nn::GeneralResult<nn::Capabilities> result = NN_ERROR(nn::ErrorStatus::GENERAL_FAILURE)
+ << "uninitialized";
+ const auto cb = [&result](V1_0::ErrorStatus status, const Capabilities& capabilities) {
+ if (status != V1_0::ErrorStatus::NONE) {
+ const auto canonical =
+ validatedConvertToCanonical(status).value_or(nn::ErrorStatus::GENERAL_FAILURE);
+ result = NN_ERROR(canonical) << "getCapabilities_1_1 failed with " << toString(status);
+ } else {
+ result = validatedConvertToCanonical(capabilities);
+ }
+ };
+
+ const auto ret = device->getCapabilities_1_1(cb);
+ NN_TRY(hal::utils::handleTransportError(ret));
+
+ return result;
+}
+
+} // namespace
+
+nn::GeneralResult<std::shared_ptr<const Device>> Device::create(std::string name,
+ sp<V1_1::IDevice> device) {
+ if (name.empty()) {
+ return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT)
+ << "V1_1::utils::Device::create must have non-empty name";
+ }
+ if (device == nullptr) {
+ return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT)
+ << "V1_1::utils::Device::create must have non-null device";
+ }
+
+ auto capabilities = NN_TRY(initCapabilities(device.get()));
+
+ auto deathHandler = NN_TRY(hal::utils::DeathHandler::create(device));
+ return std::make_shared<const Device>(PrivateConstructorTag{}, std::move(name),
+ std::move(capabilities), std::move(device),
+ std::move(deathHandler));
+}
+
+Device::Device(PrivateConstructorTag /*tag*/, std::string name, nn::Capabilities capabilities,
+ sp<V1_1::IDevice> device, hal::utils::DeathHandler deathHandler)
+ : kName(std::move(name)),
+ kCapabilities(std::move(capabilities)),
+ kDevice(std::move(device)),
+ kDeathHandler(std::move(deathHandler)) {}
+
+const std::string& Device::getName() const {
+ return kName;
+}
+
+const std::string& Device::getVersionString() const {
+ return kVersionString;
+}
+
+nn::Version Device::getFeatureLevel() const {
+ return nn::Version::ANDROID_P;
+}
+
+nn::DeviceType Device::getType() const {
+ return nn::DeviceType::UNKNOWN;
+}
+
+const std::vector<nn::Extension>& Device::getSupportedExtensions() const {
+ return kExtensions;
+}
+
+const nn::Capabilities& Device::getCapabilities() const {
+ return kCapabilities;
+}
+
+std::pair<uint32_t, uint32_t> Device::getNumberOfCacheFilesNeeded() const {
+ return std::make_pair(/*numModelCache=*/0, /*numDataCache=*/0);
+}
+
+nn::GeneralResult<void> Device::wait() const {
+ const auto ret = kDevice->ping();
+ return hal::utils::handleTransportError(ret);
+}
+
+nn::GeneralResult<std::vector<bool>> Device::getSupportedOperations(const nn::Model& model) const {
+ // Ensure that model is ready for IPC.
+ std::optional<nn::Model> maybeModelInShared;
+ const nn::Model& modelInShared =
+ NN_TRY(hal::utils::flushDataFromPointerToShared(&model, &maybeModelInShared));
+
+ const auto hidlModel = NN_TRY(convert(modelInShared));
+
+ nn::GeneralResult<std::vector<bool>> result = NN_ERROR(nn::ErrorStatus::GENERAL_FAILURE)
+ << "uninitialized";
+ auto cb = [&result, &model](V1_0::ErrorStatus status,
+ const hidl_vec<bool>& supportedOperations) {
+ if (status != V1_0::ErrorStatus::NONE) {
+ const auto canonical =
+ validatedConvertToCanonical(status).value_or(nn::ErrorStatus::GENERAL_FAILURE);
+ result = NN_ERROR(canonical)
+ << "getSupportedOperations_1_1 failed with " << toString(status);
+ } else if (supportedOperations.size() != model.main.operations.size()) {
+ result = NN_ERROR(nn::ErrorStatus::GENERAL_FAILURE)
+ << "getSupportedOperations_1_1 returned vector of size "
+ << supportedOperations.size() << " but expected "
+ << model.main.operations.size();
+ } else {
+ result = supportedOperations;
+ }
+ };
+
+ const auto ret = kDevice->getSupportedOperations_1_1(hidlModel, cb);
+ NN_TRY(hal::utils::handleTransportError(ret));
+
+ return result;
+}
+
+nn::GeneralResult<nn::SharedPreparedModel> Device::prepareModel(
+ const nn::Model& model, nn::ExecutionPreference preference, nn::Priority /*priority*/,
+ nn::OptionalTimePoint /*deadline*/, const std::vector<nn::NativeHandle>& /*modelCache*/,
+ const std::vector<nn::NativeHandle>& /*dataCache*/, const nn::CacheToken& /*token*/) const {
+ // Ensure that model is ready for IPC.
+ std::optional<nn::Model> maybeModelInShared;
+ const nn::Model& modelInShared =
+ NN_TRY(hal::utils::flushDataFromPointerToShared(&model, &maybeModelInShared));
+
+ const auto hidlModel = NN_TRY(convert(modelInShared));
+ const auto hidlPreference = NN_TRY(convert(preference));
+
+ const auto cb = sp<V1_0::utils::PreparedModelCallback>::make();
+ const auto scoped = kDeathHandler.protectCallback(cb.get());
+
+ const auto ret = kDevice->prepareModel_1_1(hidlModel, hidlPreference, cb);
+ const auto status = NN_TRY(hal::utils::handleTransportError(ret));
+ if (status != V1_0::ErrorStatus::NONE) {
+ const auto canonical =
+ validatedConvertToCanonical(status).value_or(nn::ErrorStatus::GENERAL_FAILURE);
+ return NN_ERROR(canonical) << "prepareModel failed with " << toString(status);
+ }
+
+ return cb->get();
+}
+
+nn::GeneralResult<nn::SharedPreparedModel> Device::prepareModelFromCache(
+ nn::OptionalTimePoint /*deadline*/, const std::vector<nn::NativeHandle>& /*modelCache*/,
+ const std::vector<nn::NativeHandle>& /*dataCache*/, const nn::CacheToken& /*token*/) const {
+ return NN_ERROR(nn::ErrorStatus::GENERAL_FAILURE)
+ << "IDevice::prepareModelFromCache not supported on 1.1 HAL service";
+}
+
+nn::GeneralResult<nn::SharedBuffer> Device::allocate(
+ const nn::BufferDesc& /*desc*/,
+ const std::vector<nn::SharedPreparedModel>& /*preparedModels*/,
+ const std::vector<nn::BufferRole>& /*inputRoles*/,
+ const std::vector<nn::BufferRole>& /*outputRoles*/) const {
+ return NN_ERROR(nn::ErrorStatus::GENERAL_FAILURE)
+ << "IDevice::allocate not supported on 1.1 HAL service";
+}
+
+} // namespace android::hardware::neuralnetworks::V1_1::utils
diff --git a/neuralnetworks/1.1/utils/src/Service.cpp b/neuralnetworks/1.1/utils/src/Service.cpp
new file mode 100644
index 0000000..e2d3240
--- /dev/null
+++ b/neuralnetworks/1.1/utils/src/Service.cpp
@@ -0,0 +1,41 @@
+/*
+ * 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 "Service.h"
+
+#include <nnapi/IDevice.h>
+#include <nnapi/Result.h>
+#include <nnapi/Types.h>
+#include <nnapi/hal/ResilientDevice.h>
+#include <string>
+#include "Device.h"
+
+namespace android::hardware::neuralnetworks::V1_1::utils {
+
+nn::GeneralResult<nn::SharedDevice> getDevice(const std::string& name) {
+ hal::utils::ResilientDevice::Factory makeDevice =
+ [name](bool blocking) -> nn::GeneralResult<nn::SharedDevice> {
+ auto service = blocking ? IDevice::getService(name) : IDevice::tryGetService(name);
+ if (service == nullptr) {
+ return NN_ERROR() << (blocking ? "getService" : "tryGetService") << " returned nullptr";
+ }
+ return Device::create(name, std::move(service));
+ };
+
+ return hal::utils::ResilientDevice::create(std::move(makeDevice));
+}
+
+} // namespace android::hardware::neuralnetworks::V1_1::utils