Create NNAPI adapter interface
This CL creates Device, PreparedModel, and Buffer classes which adapt a
canonical interface to a NN 1.3 HIDL interface.
Bug: 160668438
Test: mma
Change-Id: Ifee27858efd280307a299b3fe2e1a4bd9240e9de
Merged-In: Ifee27858efd280307a299b3fe2e1a4bd9240e9de
(cherry picked from commit 323dd0c2dc3ca771a5c66a8fe5fd41819e841a1c)
diff --git a/neuralnetworks/utils/adapter/Android.bp b/neuralnetworks/utils/adapter/Android.bp
new file mode 100644
index 0000000..e8dc3e7
--- /dev/null
+++ b/neuralnetworks/utils/adapter/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_adapter",
+ defaults: ["neuralnetworks_utils_defaults"],
+ srcs: ["src/*"],
+ local_include_dirs: ["include/nnapi/hal"],
+ export_include_dirs: ["include"],
+ static_libs: [
+ "neuralnetworks_types",
+ "neuralnetworks_utils_hal_1_0",
+ "neuralnetworks_utils_hal_1_1",
+ "neuralnetworks_utils_hal_1_2",
+ "neuralnetworks_utils_hal_1_3",
+ ],
+ shared_libs: [
+ "android.hardware.neuralnetworks@1.0",
+ "android.hardware.neuralnetworks@1.1",
+ "android.hardware.neuralnetworks@1.2",
+ "android.hardware.neuralnetworks@1.3",
+ "libfmq",
+ ],
+}
diff --git a/neuralnetworks/utils/adapter/include/nnapi/hal/Adapter.h b/neuralnetworks/utils/adapter/include/nnapi/hal/Adapter.h
new file mode 100644
index 0000000..da00a09
--- /dev/null
+++ b/neuralnetworks/utils/adapter/include/nnapi/hal/Adapter.h
@@ -0,0 +1,72 @@
+/*
+ * 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_ADAPTER_ADAPTER_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_ADAPTER_ADAPTER_H
+
+#include <android/hardware/neuralnetworks/1.3/IDevice.h>
+#include <nnapi/IDevice.h>
+#include <nnapi/Types.h>
+#include <sys/types.h>
+#include <functional>
+#include <memory>
+
+// See hardware/interfaces/neuralnetworks/utils/README.md for more information on HIDL interface
+// lifetimes across processes and for protecting asynchronous calls across HIDL.
+
+namespace android::hardware::neuralnetworks::adapter {
+
+/**
+ * A self-contained unit of work to be executed.
+ */
+using Task = std::function<void()>;
+
+/**
+ * A type-erased executor which executes a task asynchronously.
+ *
+ * This executor is also provided with an Application ID (Android User ID) and an optional deadline
+ * for when the caller expects is the upper bound for the amount of time to complete the task.
+ */
+using Executor = std::function<void(Task, uid_t, nn::OptionalTimePoint)>;
+
+/**
+ * Adapt an NNAPI canonical interface object to a HIDL NN HAL interface object.
+ *
+ * The IPreparedModel object created from IDevice::prepareModel or IDevice::preparedModelFromCache
+ * must return "const nn::Model*" from IPreparedModel::getUnderlyingResource().
+ *
+ * @param device NNAPI canonical IDevice interface object to be adapted.
+ * @param executor Type-erased executor to handle executing tasks asynchronously.
+ * @return HIDL NN HAL IDevice interface object.
+ */
+sp<V1_3::IDevice> adapt(nn::SharedDevice device, Executor executor);
+
+/**
+ * Adapt an NNAPI canonical interface object to a HIDL NN HAL interface object.
+ *
+ * The IPreparedModel object created from IDevice::prepareModel or IDevice::preparedModelFromCache
+ * must return "const nn::Model*" from IPreparedModel::getUnderlyingResource().
+ *
+ * This function uses a default executor, which will execute tasks from a detached thread.
+ *
+ * @param device NNAPI canonical IDevice interface object to be adapted.
+ * @return HIDL NN HAL IDevice interface object.
+ */
+sp<V1_3::IDevice> adapt(nn::SharedDevice device);
+
+} // namespace android::hardware::neuralnetworks::adapter
+
+#endif // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_ADAPTER_ADAPTER_H
diff --git a/neuralnetworks/utils/adapter/include/nnapi/hal/Buffer.h b/neuralnetworks/utils/adapter/include/nnapi/hal/Buffer.h
new file mode 100644
index 0000000..e53c7d4
--- /dev/null
+++ b/neuralnetworks/utils/adapter/include/nnapi/hal/Buffer.h
@@ -0,0 +1,46 @@
+/*
+ * 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_ADAPTER_BUFFER_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_ADAPTER_BUFFER_H
+
+#include <android/hardware/neuralnetworks/1.3/IBuffer.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <nnapi/IBuffer.h>
+#include <nnapi/Types.h>
+#include <memory>
+
+// See hardware/interfaces/neuralnetworks/utils/README.md for more information on HIDL interface
+// lifetimes across processes and for protecting asynchronous calls across HIDL.
+
+namespace android::hardware::neuralnetworks::adapter {
+
+// Class that adapts nn::IBuffer to V1_3::IBuffer.
+class Buffer final : public V1_3::IBuffer {
+ public:
+ explicit Buffer(nn::SharedBuffer buffer);
+
+ Return<V1_3::ErrorStatus> copyTo(const hidl_memory& dst) override;
+ Return<V1_3::ErrorStatus> copyFrom(const hidl_memory& src,
+ const hidl_vec<uint32_t>& dimensions) override;
+
+ private:
+ const nn::SharedBuffer kBuffer;
+};
+
+} // namespace android::hardware::neuralnetworks::adapter
+
+#endif // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_ADAPTER_BUFFER_H
diff --git a/neuralnetworks/utils/adapter/include/nnapi/hal/Device.h b/neuralnetworks/utils/adapter/include/nnapi/hal/Device.h
new file mode 100644
index 0000000..148d0a0
--- /dev/null
+++ b/neuralnetworks/utils/adapter/include/nnapi/hal/Device.h
@@ -0,0 +1,96 @@
+/*
+ * 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_ADAPTER_DEVICE_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_ADAPTER_DEVICE_H
+
+#include "nnapi/hal/Adapter.h"
+
+#include <android/hardware/neuralnetworks/1.0/IPreparedModelCallback.h>
+#include <android/hardware/neuralnetworks/1.0/types.h>
+#include <android/hardware/neuralnetworks/1.1/types.h>
+#include <android/hardware/neuralnetworks/1.2/IPreparedModelCallback.h>
+#include <android/hardware/neuralnetworks/1.2/types.h>
+#include <android/hardware/neuralnetworks/1.3/IDevice.h>
+#include <android/hardware/neuralnetworks/1.3/IPreparedModelCallback.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <nnapi/IDevice.h>
+#include <nnapi/Types.h>
+#include <memory>
+
+// See hardware/interfaces/neuralnetworks/utils/README.md for more information on HIDL interface
+// lifetimes across processes and for protecting asynchronous calls across HIDL.
+
+namespace android::hardware::neuralnetworks::adapter {
+
+using CacheToken = hidl_array<uint8_t, nn::kByteSizeOfCacheToken>;
+
+// Class that adapts nn::IDevice to V1_3::IDevice.
+class Device final : public V1_3::IDevice {
+ public:
+ Device(nn::SharedDevice device, Executor executor);
+
+ Return<void> getCapabilities(getCapabilities_cb cb) override;
+ Return<void> getCapabilities_1_1(getCapabilities_1_1_cb cb) override;
+ Return<void> getCapabilities_1_2(getCapabilities_1_2_cb cb) override;
+ Return<void> getCapabilities_1_3(getCapabilities_1_3_cb cb) override;
+ Return<void> getVersionString(getVersionString_cb cb) override;
+ Return<void> getType(getType_cb cb) override;
+ Return<void> getSupportedExtensions(getSupportedExtensions_cb) override;
+ Return<void> getSupportedOperations(const V1_0::Model& model,
+ getSupportedOperations_cb cb) override;
+ Return<void> getSupportedOperations_1_1(const V1_1::Model& model,
+ getSupportedOperations_1_1_cb cb) override;
+ Return<void> getSupportedOperations_1_2(const V1_2::Model& model,
+ getSupportedOperations_1_2_cb cb) override;
+ Return<void> getSupportedOperations_1_3(const V1_3::Model& model,
+ getSupportedOperations_1_3_cb cb) override;
+ Return<void> getNumberOfCacheFilesNeeded(getNumberOfCacheFilesNeeded_cb cb) override;
+ Return<V1_0::ErrorStatus> prepareModel(
+ const V1_0::Model& model, const sp<V1_0::IPreparedModelCallback>& callback) override;
+ Return<V1_0::ErrorStatus> prepareModel_1_1(
+ const V1_1::Model& model, V1_1::ExecutionPreference preference,
+ const sp<V1_0::IPreparedModelCallback>& callback) override;
+ Return<V1_0::ErrorStatus> prepareModel_1_2(
+ const V1_2::Model& model, V1_1::ExecutionPreference preference,
+ const hidl_vec<hidl_handle>& modelCache, const hidl_vec<hidl_handle>& dataCache,
+ const CacheToken& token, const sp<V1_2::IPreparedModelCallback>& callback) override;
+ Return<V1_3::ErrorStatus> prepareModel_1_3(
+ const V1_3::Model& model, V1_1::ExecutionPreference preference, V1_3::Priority priority,
+ const V1_3::OptionalTimePoint& deadline, const hidl_vec<hidl_handle>& modelCache,
+ const hidl_vec<hidl_handle>& dataCache, const CacheToken& token,
+ const sp<V1_3::IPreparedModelCallback>& callback) override;
+ Return<V1_0::ErrorStatus> prepareModelFromCache(
+ const hidl_vec<hidl_handle>& modelCache, const hidl_vec<hidl_handle>& dataCache,
+ const CacheToken& token, const sp<V1_2::IPreparedModelCallback>& callback) override;
+ Return<V1_3::ErrorStatus> prepareModelFromCache_1_3(
+ const V1_3::OptionalTimePoint& deadline, const hidl_vec<hidl_handle>& modelCache,
+ const hidl_vec<hidl_handle>& dataCache, const CacheToken& token,
+ const sp<V1_3::IPreparedModelCallback>& callback) override;
+ Return<V1_0::DeviceStatus> getStatus() override;
+ Return<void> allocate(const V1_3::BufferDesc& desc,
+ const hidl_vec<sp<V1_3::IPreparedModel>>& preparedModels,
+ const hidl_vec<V1_3::BufferRole>& inputRoles,
+ const hidl_vec<V1_3::BufferRole>& outputRoles, allocate_cb cb) override;
+
+ private:
+ const nn::SharedDevice kDevice;
+ const Executor kExecutor;
+};
+
+} // namespace android::hardware::neuralnetworks::adapter
+
+#endif // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_ADAPTER_DEVICE_H
diff --git a/neuralnetworks/utils/adapter/include/nnapi/hal/PreparedModel.h b/neuralnetworks/utils/adapter/include/nnapi/hal/PreparedModel.h
new file mode 100644
index 0000000..65763b8
--- /dev/null
+++ b/neuralnetworks/utils/adapter/include/nnapi/hal/PreparedModel.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_UTILS_ADAPTER_PREPARED_MODEL_H
+#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_ADAPTER_PREPARED_MODEL_H
+
+#include "nnapi/hal/Adapter.h"
+
+#include <android/hardware/neuralnetworks/1.0/IExecutionCallback.h>
+#include <android/hardware/neuralnetworks/1.0/types.h>
+#include <android/hardware/neuralnetworks/1.2/IBurstCallback.h>
+#include <android/hardware/neuralnetworks/1.2/IExecutionCallback.h>
+#include <android/hardware/neuralnetworks/1.2/types.h>
+#include <android/hardware/neuralnetworks/1.3/IExecutionCallback.h>
+#include <android/hardware/neuralnetworks/1.3/IPreparedModel.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <nnapi/IPreparedModel.h>
+#include <nnapi/Types.h>
+#include <memory>
+
+// See hardware/interfaces/neuralnetworks/utils/README.md for more information on HIDL interface
+// lifetimes across processes and for protecting asynchronous calls across HIDL.
+
+namespace android::hardware::neuralnetworks::adapter {
+
+// Class that adapts nn::IPreparedModel to V1_3::IPreparedModel.
+class PreparedModel final : public V1_3::IPreparedModel {
+ public:
+ PreparedModel(nn::SharedPreparedModel preparedModel, Executor executor, uid_t userId);
+
+ Return<V1_0::ErrorStatus> execute(const V1_0::Request& request,
+ const sp<V1_0::IExecutionCallback>& callback) override;
+ Return<V1_0::ErrorStatus> execute_1_2(const V1_0::Request& request, V1_2::MeasureTiming measure,
+ const sp<V1_2::IExecutionCallback>& callback) override;
+ Return<V1_3::ErrorStatus> execute_1_3(const V1_3::Request& request, V1_2::MeasureTiming measure,
+ const V1_3::OptionalTimePoint& deadline,
+ const V1_3::OptionalTimeoutDuration& loopTimeoutDuration,
+ const sp<V1_3::IExecutionCallback>& callback) override;
+ Return<void> executeSynchronously(const V1_0::Request& request, V1_2::MeasureTiming measure,
+ executeSynchronously_cb cb) override;
+ Return<void> executeSynchronously_1_3(const V1_3::Request& request, V1_2::MeasureTiming measure,
+ const V1_3::OptionalTimePoint& deadline,
+ const V1_3::OptionalTimeoutDuration& loopTimeoutDuration,
+ executeSynchronously_1_3_cb cb) override;
+ Return<void> configureExecutionBurst(
+ const sp<V1_2::IBurstCallback>& callback,
+ const MQDescriptorSync<V1_2::FmqRequestDatum>& requestChannel,
+ const MQDescriptorSync<V1_2::FmqResultDatum>& resultChannel,
+ configureExecutionBurst_cb cb) override;
+ Return<void> executeFenced(const V1_3::Request& request, const hidl_vec<hidl_handle>& waitFor,
+ V1_2::MeasureTiming measure, const V1_3::OptionalTimePoint& deadline,
+ const V1_3::OptionalTimeoutDuration& loopTimeoutDuration,
+ const V1_3::OptionalTimeoutDuration& duration,
+ executeFenced_cb callback) override;
+
+ nn::SharedPreparedModel getUnderlyingPreparedModel() const;
+
+ private:
+ const nn::SharedPreparedModel kPreparedModel;
+ const Executor kExecutor;
+ const uid_t kUserId;
+};
+
+} // namespace android::hardware::neuralnetworks::adapter
+
+#endif // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_UTILS_ADAPTER_PREPARED_MODEL_H
diff --git a/neuralnetworks/utils/adapter/src/Adapter.cpp b/neuralnetworks/utils/adapter/src/Adapter.cpp
new file mode 100644
index 0000000..d6f53f0
--- /dev/null
+++ b/neuralnetworks/utils/adapter/src/Adapter.cpp
@@ -0,0 +1,46 @@
+/*
+ * 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 "Adapter.h"
+
+#include "Device.h"
+
+#include <android/hardware/neuralnetworks/1.3/IDevice.h>
+#include <nnapi/IDevice.h>
+#include <nnapi/Types.h>
+#include <sys/types.h>
+
+#include <functional>
+#include <memory>
+#include <thread>
+
+// See hardware/interfaces/neuralnetworks/utils/README.md for more information on HIDL interface
+// lifetimes across processes and for protecting asynchronous calls across HIDL.
+
+namespace android::hardware::neuralnetworks::adapter {
+
+sp<V1_3::IDevice> adapt(nn::SharedDevice device, Executor executor) {
+ return sp<Device>::make(std::move(device), std::move(executor));
+}
+
+sp<V1_3::IDevice> adapt(nn::SharedDevice device) {
+ Executor defaultExecutor = [](Task task, uid_t /*uid*/, nn::OptionalTimePoint /*deadline*/) {
+ std::thread(std::move(task)).detach();
+ };
+ return adapt(std::move(device), std::move(defaultExecutor));
+}
+
+} // namespace android::hardware::neuralnetworks::adapter
diff --git a/neuralnetworks/utils/adapter/src/Buffer.cpp b/neuralnetworks/utils/adapter/src/Buffer.cpp
new file mode 100644
index 0000000..3a04bf6
--- /dev/null
+++ b/neuralnetworks/utils/adapter/src/Buffer.cpp
@@ -0,0 +1,83 @@
+/*
+ * 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 "Buffer.h"
+
+#include <android-base/logging.h>
+#include <android/hardware/neuralnetworks/1.3/IBuffer.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <nnapi/IBuffer.h>
+#include <nnapi/TypeUtils.h>
+#include <nnapi/Types.h>
+#include <nnapi/hal/1.3/Utils.h>
+#include <memory>
+
+// See hardware/interfaces/neuralnetworks/utils/README.md for more information on HIDL interface
+// lifetimes across processes and for protecting asynchronous calls across HIDL.
+
+namespace android::hardware::neuralnetworks::adapter {
+namespace {
+
+template <typename Type>
+auto convertInput(const Type& object) -> decltype(nn::convert(std::declval<Type>())) {
+ auto result = nn::convert(object);
+ if (!result.has_value()) {
+ result.error().code = nn::ErrorStatus::INVALID_ARGUMENT;
+ }
+ return result;
+}
+
+nn::GeneralResult<void> copyTo(const nn::SharedBuffer& buffer, const hidl_memory& dst) {
+ const auto memory = NN_TRY(convertInput(dst));
+ NN_TRY(buffer->copyTo(memory));
+ return {};
+}
+
+nn::GeneralResult<void> copyFrom(const nn::SharedBuffer& buffer, const hidl_memory& src,
+ const hidl_vec<uint32_t>& dimensions) {
+ const auto memory = NN_TRY(convertInput(src));
+ NN_TRY(buffer->copyFrom(memory, dimensions));
+ return {};
+}
+
+} // namespace
+
+Buffer::Buffer(nn::SharedBuffer buffer) : kBuffer(std::move(buffer)) {
+ CHECK(kBuffer != nullptr);
+}
+
+Return<V1_3::ErrorStatus> Buffer::copyTo(const hidl_memory& dst) {
+ auto result = adapter::copyTo(kBuffer, dst);
+ if (!result.has_value()) {
+ const auto [message, code] = std::move(result).error();
+ LOG(ERROR) << "adapter::Buffer::copyTo failed with " << code << ": " << message;
+ return V1_3::utils::convert(code).value();
+ }
+ return V1_3::ErrorStatus::NONE;
+}
+
+Return<V1_3::ErrorStatus> Buffer::copyFrom(const hidl_memory& src,
+ const hidl_vec<uint32_t>& dimensions) {
+ auto result = adapter::copyFrom(kBuffer, src, dimensions);
+ if (!result.has_value()) {
+ const auto [message, code] = std::move(result).error();
+ LOG(ERROR) << "adapter::Buffer::copyFrom failed with " << code << ": " << message;
+ return V1_3::utils::convert(code).value();
+ }
+ return V1_3::ErrorStatus::NONE;
+}
+
+} // namespace android::hardware::neuralnetworks::adapter
diff --git a/neuralnetworks/utils/adapter/src/Device.cpp b/neuralnetworks/utils/adapter/src/Device.cpp
new file mode 100644
index 0000000..96142c3
--- /dev/null
+++ b/neuralnetworks/utils/adapter/src/Device.cpp
@@ -0,0 +1,556 @@
+/*
+ * 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 "Buffer.h"
+#include "PreparedModel.h"
+
+#include <android-base/logging.h>
+#include <android/hardware/neuralnetworks/1.0/IPreparedModelCallback.h>
+#include <android/hardware/neuralnetworks/1.0/types.h>
+#include <android/hardware/neuralnetworks/1.1/types.h>
+#include <android/hardware/neuralnetworks/1.2/IPreparedModelCallback.h>
+#include <android/hardware/neuralnetworks/1.2/types.h>
+#include <android/hardware/neuralnetworks/1.3/IDevice.h>
+#include <android/hardware/neuralnetworks/1.3/IPreparedModelCallback.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <hwbinder/IPCThreadState.h>
+#include <nnapi/IBuffer.h>
+#include <nnapi/IDevice.h>
+#include <nnapi/IPreparedModel.h>
+#include <nnapi/Result.h>
+#include <nnapi/TypeUtils.h>
+#include <nnapi/Types.h>
+#include <nnapi/hal/1.0/Conversions.h>
+#include <nnapi/hal/1.0/Utils.h>
+#include <nnapi/hal/1.1/Conversions.h>
+#include <nnapi/hal/1.1/Utils.h>
+#include <nnapi/hal/1.2/Conversions.h>
+#include <nnapi/hal/1.2/Utils.h>
+#include <nnapi/hal/1.3/Conversions.h>
+#include <nnapi/hal/1.3/Utils.h>
+#include <sys/types.h>
+
+#include <memory>
+
+// See hardware/interfaces/neuralnetworks/utils/README.md for more information on HIDL interface
+// lifetimes across processes and for protecting asynchronous calls across HIDL.
+
+namespace android::hardware::neuralnetworks::adapter {
+namespace {
+
+template <typename Type>
+auto convertInput(const Type& object) -> decltype(nn::convert(std::declval<Type>())) {
+ auto result = nn::convert(object);
+ if (!result.has_value()) {
+ result.error().code = nn::ErrorStatus::INVALID_ARGUMENT;
+ }
+ return result;
+}
+
+using PrepareModelResult = nn::GeneralResult<nn::SharedPreparedModel>;
+
+sp<PreparedModel> adaptPreparedModel(nn::SharedPreparedModel preparedModel, Executor executor,
+ uid_t userId) {
+ if (preparedModel == nullptr) {
+ return nullptr;
+ }
+ return sp<PreparedModel>::make(std::move(preparedModel), std::move(executor), userId);
+}
+
+void notify(V1_0::IPreparedModelCallback* callback, nn::ErrorStatus status,
+ const sp<PreparedModel>& hidlPreparedModel) {
+ if (callback != nullptr) {
+ const auto hidlStatus = V1_0::utils::convert(status).value();
+ const auto ret = callback->notify(hidlStatus, hidlPreparedModel);
+ if (!ret.isOk()) {
+ LOG(ERROR) << "V1_0::IPreparedModelCallback::notify failed with " << ret.description();
+ }
+ }
+}
+
+void notify(V1_2::IPreparedModelCallback* callback, nn::ErrorStatus status,
+ const sp<PreparedModel>& hidlPreparedModel) {
+ if (callback != nullptr) {
+ const auto hidlStatus = V1_2::utils::convert(status).value();
+ const auto ret = callback->notify_1_2(hidlStatus, hidlPreparedModel);
+ if (!ret.isOk()) {
+ LOG(ERROR) << "V1_2::IPreparedModelCallback::notify_1_2 failed with "
+ << ret.description();
+ }
+ }
+}
+
+void notify(V1_3::IPreparedModelCallback* callback, nn::ErrorStatus status,
+ const sp<PreparedModel>& hidlPreparedModel) {
+ if (callback != nullptr) {
+ const auto hidlStatus = V1_3::utils::convert(status).value();
+ const auto ret = callback->notify_1_3(hidlStatus, hidlPreparedModel);
+ if (!ret.isOk()) {
+ LOG(ERROR) << "V1_3::IPreparedModelCallback::notify_1_3 failed with "
+ << ret.description();
+ }
+ }
+}
+
+template <typename CallbackType>
+void notify(CallbackType* callback, PrepareModelResult result, Executor executor, uid_t userId) {
+ if (!result.has_value()) {
+ const auto [message, status] = std::move(result).error();
+ LOG(ERROR) << message;
+ notify(callback, status, nullptr);
+ } else {
+ auto preparedModel = std::move(result).value();
+ auto hidlPreparedModel =
+ adaptPreparedModel(std::move(preparedModel), std::move(executor), userId);
+ notify(callback, nn::ErrorStatus::NONE, std::move(hidlPreparedModel));
+ }
+}
+
+template <typename ModelType>
+nn::GeneralResult<hidl_vec<bool>> getSupportedOperations(const nn::SharedDevice& device,
+ const ModelType& model) {
+ const auto nnModel = NN_TRY(convertInput(model));
+ return NN_TRY(device->getSupportedOperations(nnModel));
+}
+
+nn::GeneralResult<void> prepareModel(const nn::SharedDevice& device, const Executor& executor,
+ const V1_0::Model& model,
+ const sp<V1_0::IPreparedModelCallback>& callback) {
+ if (callback.get() == nullptr) {
+ return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback";
+ }
+
+ auto nnModel = NN_TRY(convertInput(model));
+
+ const uid_t userId = hardware::IPCThreadState::self()->getCallingUid();
+ Task task = [device, nnModel = std::move(nnModel), userId, executor, callback] {
+ auto result = device->prepareModel(nnModel, nn::ExecutionPreference::DEFAULT,
+ nn::Priority::DEFAULT, {}, {}, {}, {});
+ notify(callback.get(), std::move(result), executor, userId);
+ };
+ executor(std::move(task), userId, {});
+
+ return {};
+}
+
+nn::GeneralResult<void> prepareModel_1_1(const nn::SharedDevice& device, const Executor& executor,
+ const V1_1::Model& model,
+ V1_1::ExecutionPreference preference,
+ const sp<V1_0::IPreparedModelCallback>& callback) {
+ if (callback.get() == nullptr) {
+ return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback";
+ }
+
+ auto nnModel = NN_TRY(convertInput(model));
+ const auto nnPreference = NN_TRY(convertInput(preference));
+
+ const uid_t userId = hardware::IPCThreadState::self()->getCallingUid();
+ Task task = [device, nnModel = std::move(nnModel), nnPreference, userId, executor, callback] {
+ auto result =
+ device->prepareModel(nnModel, nnPreference, nn::Priority::DEFAULT, {}, {}, {}, {});
+ notify(callback.get(), std::move(result), executor, userId);
+ };
+ executor(std::move(task), userId, {});
+
+ return {};
+}
+
+nn::GeneralResult<void> prepareModel_1_2(const nn::SharedDevice& device, const Executor& executor,
+ const V1_2::Model& model,
+ V1_1::ExecutionPreference preference,
+ const hidl_vec<hidl_handle>& modelCache,
+ const hidl_vec<hidl_handle>& dataCache,
+ const CacheToken& token,
+ const sp<V1_2::IPreparedModelCallback>& callback) {
+ if (callback.get() == nullptr) {
+ return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback";
+ }
+
+ auto nnModel = NN_TRY(convertInput(model));
+ const auto nnPreference = NN_TRY(convertInput(preference));
+ auto nnModelCache = NN_TRY(convertInput(modelCache));
+ auto nnDataCache = NN_TRY(convertInput(dataCache));
+ const auto nnToken = nn::CacheToken(token);
+
+ const uid_t userId = hardware::IPCThreadState::self()->getCallingUid();
+ Task task = [device, nnModel = std::move(nnModel), nnPreference,
+ nnModelCache = std::move(nnModelCache), nnDataCache = std::move(nnDataCache),
+ nnToken, userId, executor, callback] {
+ auto result = device->prepareModel(nnModel, nnPreference, nn::Priority::DEFAULT, {},
+ nnModelCache, nnDataCache, nnToken);
+ notify(callback.get(), std::move(result), executor, userId);
+ };
+ executor(std::move(task), userId, {});
+
+ return {};
+}
+
+nn::GeneralResult<void> prepareModel_1_3(
+ const nn::SharedDevice& device, const Executor& executor, const V1_3::Model& model,
+ V1_1::ExecutionPreference preference, V1_3::Priority priority,
+ const V1_3::OptionalTimePoint& deadline, const hidl_vec<hidl_handle>& modelCache,
+ const hidl_vec<hidl_handle>& dataCache, const CacheToken& token,
+ const sp<V1_3::IPreparedModelCallback>& callback) {
+ if (callback.get() == nullptr) {
+ return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback";
+ }
+
+ auto nnModel = NN_TRY(convertInput(model));
+ const auto nnPreference = NN_TRY(convertInput(preference));
+ const auto nnPriority = NN_TRY(convertInput(priority));
+ const auto nnDeadline = NN_TRY(convertInput(deadline));
+ auto nnModelCache = NN_TRY(convertInput(modelCache));
+ auto nnDataCache = NN_TRY(convertInput(dataCache));
+ const auto nnToken = nn::CacheToken(token);
+
+ const uid_t userId = hardware::IPCThreadState::self()->getCallingUid();
+ Task task = [device, nnModel = std::move(nnModel), nnPreference, nnPriority, nnDeadline,
+ nnModelCache = std::move(nnModelCache), nnDataCache = std::move(nnDataCache),
+ nnToken, userId, executor, callback] {
+ auto result = device->prepareModel(nnModel, nnPreference, nnPriority, nnDeadline,
+ nnModelCache, nnDataCache, nnToken);
+ notify(callback.get(), std::move(result), executor, userId);
+ };
+ executor(std::move(task), userId, nnDeadline);
+
+ return {};
+}
+
+nn::GeneralResult<void> prepareModelFromCache(const nn::SharedDevice& device,
+ const Executor& executor,
+ const hidl_vec<hidl_handle>& modelCache,
+ const hidl_vec<hidl_handle>& dataCache,
+ const CacheToken& token,
+ const sp<V1_2::IPreparedModelCallback>& callback) {
+ if (callback.get() == nullptr) {
+ return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback";
+ }
+
+ auto nnModelCache = NN_TRY(convertInput(modelCache));
+ auto nnDataCache = NN_TRY(convertInput(dataCache));
+ const auto nnToken = nn::CacheToken(token);
+
+ const uid_t userId = hardware::IPCThreadState::self()->getCallingUid();
+ Task task = [device, nnModelCache = std::move(nnModelCache),
+ nnDataCache = std::move(nnDataCache), nnToken, userId, executor, callback] {
+ auto result = device->prepareModelFromCache({}, nnModelCache, nnDataCache, nnToken);
+ notify(callback.get(), std::move(result), executor, userId);
+ };
+ executor(std::move(task), userId, {});
+
+ return {};
+}
+
+nn::GeneralResult<void> prepareModelFromCache_1_3(
+ const nn::SharedDevice& device, const Executor& executor,
+ const V1_3::OptionalTimePoint& deadline, const hidl_vec<hidl_handle>& modelCache,
+ const hidl_vec<hidl_handle>& dataCache, const CacheToken& token,
+ const sp<V1_3::IPreparedModelCallback>& callback) {
+ if (callback.get() == nullptr) {
+ return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback";
+ }
+
+ const auto nnDeadline = NN_TRY(convertInput(deadline));
+ auto nnModelCache = NN_TRY(convertInput(modelCache));
+ auto nnDataCache = NN_TRY(convertInput(dataCache));
+ const auto nnToken = nn::CacheToken(token);
+
+ const uid_t userId = hardware::IPCThreadState::self()->getCallingUid();
+ auto task = [device, nnDeadline, nnModelCache = std::move(nnModelCache),
+ nnDataCache = std::move(nnDataCache), nnToken, userId, executor, callback] {
+ auto result = device->prepareModelFromCache(nnDeadline, nnModelCache, nnDataCache, nnToken);
+ notify(callback.get(), std::move(result), executor, userId);
+ };
+ executor(std::move(task), userId, nnDeadline);
+
+ return {};
+}
+
+nn::GeneralResult<nn::SharedPreparedModel> downcast(const sp<V1_3::IPreparedModel>& preparedModel) {
+ if (preparedModel == nullptr) {
+ return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "preparedModel is nullptr";
+ }
+ if (preparedModel->isRemote()) {
+ return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Cannot convert remote models";
+ }
+
+ // This static_cast is safe because adapter::PreparedModel is the only class that implements
+ // the IPreparedModel interface in the adapter service code.
+ const auto* casted = static_cast<const PreparedModel*>(preparedModel.get());
+ return casted->getUnderlyingPreparedModel();
+}
+
+nn::GeneralResult<std::vector<nn::SharedPreparedModel>> downcastAll(
+ const hidl_vec<sp<V1_3::IPreparedModel>>& preparedModels) {
+ std::vector<nn::SharedPreparedModel> canonical;
+ canonical.reserve(preparedModels.size());
+ for (const auto& preparedModel : preparedModels) {
+ canonical.push_back(NN_TRY(downcast(preparedModel)));
+ }
+ return canonical;
+}
+
+nn::GeneralResult<std::pair<sp<V1_3::IBuffer>, uint32_t>> allocate(
+ const nn::SharedDevice& device, const V1_3::BufferDesc& desc,
+ const hidl_vec<sp<V1_3::IPreparedModel>>& preparedModels,
+ const hidl_vec<V1_3::BufferRole>& inputRoles,
+ const hidl_vec<V1_3::BufferRole>& outputRoles) {
+ auto nnDesc = NN_TRY(convertInput(desc));
+ auto nnPreparedModels = NN_TRY(downcastAll(preparedModels));
+ auto nnInputRoles = NN_TRY(convertInput(inputRoles));
+ auto nnOutputRoles = NN_TRY(convertInput(outputRoles));
+
+ auto buffer = NN_TRY(device->allocate(nnDesc, nnPreparedModels, nnInputRoles, nnOutputRoles));
+
+ const nn::Request::MemoryDomainToken token = buffer->getToken();
+ auto hidlBuffer = sp<Buffer>::make(std::move(buffer));
+ return std::make_pair(std::move(hidlBuffer), static_cast<uint32_t>(token));
+}
+
+} // namespace
+
+Device::Device(nn::SharedDevice device, Executor executor)
+ : kDevice(std::move(device)), kExecutor(std::move(executor)) {
+ CHECK(kDevice != nullptr);
+ CHECK(kExecutor != nullptr);
+}
+
+Return<void> Device::getCapabilities(getCapabilities_cb cb) {
+ const auto capabilities = V1_0::utils::convert(kDevice->getCapabilities()).value();
+ cb(V1_0::ErrorStatus::NONE, capabilities);
+ return Void();
+}
+
+Return<void> Device::getCapabilities_1_1(getCapabilities_1_1_cb cb) {
+ const auto capabilities = V1_1::utils::convert(kDevice->getCapabilities()).value();
+ cb(V1_0::ErrorStatus::NONE, capabilities);
+ return Void();
+}
+
+Return<void> Device::getCapabilities_1_2(getCapabilities_1_2_cb cb) {
+ const auto capabilities = V1_2::utils::convert(kDevice->getCapabilities()).value();
+ cb(V1_0::ErrorStatus::NONE, capabilities);
+ return Void();
+}
+
+Return<void> Device::getCapabilities_1_3(getCapabilities_1_3_cb cb) {
+ const auto capabilities = V1_3::utils::convert(kDevice->getCapabilities()).value();
+ cb(V1_3::ErrorStatus::NONE, capabilities);
+ return Void();
+}
+
+Return<void> Device::getVersionString(getVersionString_cb cb) {
+ cb(V1_0::ErrorStatus::NONE, kDevice->getVersionString());
+ return Void();
+}
+
+Return<void> Device::getType(getType_cb cb) {
+ const auto maybeDeviceType = V1_2::utils::convert(kDevice->getType());
+ if (!maybeDeviceType.has_value()) {
+ const auto& [message, code] = maybeDeviceType.error();
+ LOG(ERROR) << "adapter::Device::getType failed with " << code << ": " << message;
+ cb(V1_2::utils::convert(code).value(), {});
+ } else {
+ cb(V1_0::ErrorStatus::NONE, maybeDeviceType.value());
+ }
+ return Void();
+}
+
+Return<void> Device::getSupportedExtensions(getSupportedExtensions_cb cb) {
+ const auto maybeSupportedExtensions = V1_2::utils::convert(kDevice->getSupportedExtensions());
+ if (!maybeSupportedExtensions.has_value()) {
+ const auto& [message, code] = maybeSupportedExtensions.error();
+ LOG(ERROR) << "adapter::Device::getSupportedExtensions failed with " << code << ": "
+ << message;
+ cb(V1_2::utils::convert(code).value(), {});
+ } else {
+ cb(V1_0::ErrorStatus::NONE, maybeSupportedExtensions.value());
+ }
+ return Void();
+}
+
+Return<void> Device::getSupportedOperations(const V1_0::Model& model,
+ getSupportedOperations_cb cb) {
+ const auto result = adapter::getSupportedOperations(kDevice, model);
+ if (!result.has_value()) {
+ const auto& [message, code] = result.error();
+ LOG(ERROR) << "adapter::Device::getSupportedOperations_1_0 failed with " << code << ": "
+ << message;
+ cb(V1_0::utils::convert(code).value(), {});
+ } else {
+ cb(V1_0::ErrorStatus::NONE, result.value());
+ }
+ return Void();
+}
+
+Return<void> Device::getSupportedOperations_1_1(const V1_1::Model& model,
+ getSupportedOperations_1_1_cb cb) {
+ const auto result = adapter::getSupportedOperations(kDevice, model);
+ if (!result.has_value()) {
+ const auto& [message, code] = result.error();
+ LOG(ERROR) << "adapter::Device::getSupportedOperations_1_1 failed with " << code << ": "
+ << message;
+ cb(V1_1::utils::convert(code).value(), {});
+ } else {
+ cb(V1_0::ErrorStatus::NONE, result.value());
+ }
+ return Void();
+}
+
+Return<void> Device::getSupportedOperations_1_2(const V1_2::Model& model,
+ getSupportedOperations_1_2_cb cb) {
+ const auto result = adapter::getSupportedOperations(kDevice, model);
+ if (!result.has_value()) {
+ const auto& [message, code] = result.error();
+ LOG(ERROR) << "adapter::Device::getSupportedOperations_1_2 failed with " << code << ": "
+ << message;
+ cb(V1_2::utils::convert(code).value(), {});
+ } else {
+ cb(V1_0::ErrorStatus::NONE, result.value());
+ }
+ return Void();
+}
+
+Return<void> Device::getSupportedOperations_1_3(const V1_3::Model& model,
+ getSupportedOperations_1_3_cb cb) {
+ const auto result = adapter::getSupportedOperations(kDevice, model);
+ if (!result.has_value()) {
+ const auto& [message, code] = result.error();
+ LOG(ERROR) << "adapter::Device::getSupportedOperations_1_3 failed with " << code << ": "
+ << message;
+ cb(V1_3::utils::convert(code).value(), {});
+ } else {
+ cb(V1_3::ErrorStatus::NONE, result.value());
+ }
+ return Void();
+}
+
+Return<void> Device::getNumberOfCacheFilesNeeded(getNumberOfCacheFilesNeeded_cb cb) {
+ const auto [numModelCache, numDataCache] = kDevice->getNumberOfCacheFilesNeeded();
+ cb(V1_0::ErrorStatus::NONE, numModelCache, numDataCache);
+ return Void();
+}
+
+Return<V1_0::ErrorStatus> Device::prepareModel(const V1_0::Model& model,
+ const sp<V1_0::IPreparedModelCallback>& callback) {
+ auto result = adapter::prepareModel(kDevice, kExecutor, model, callback);
+ if (!result.has_value()) {
+ auto [message, code] = std::move(result).error();
+ LOG(ERROR) << "adapter::Device::prepareModel failed with " << code << ": " << message;
+ notify(callback.get(), code, nullptr);
+ return V1_0::utils::convert(code).value();
+ }
+ return V1_0::ErrorStatus::NONE;
+}
+
+Return<V1_0::ErrorStatus> Device::prepareModel_1_1(
+ const V1_1::Model& model, V1_1::ExecutionPreference preference,
+ const sp<V1_0::IPreparedModelCallback>& callback) {
+ auto result = adapter::prepareModel_1_1(kDevice, kExecutor, model, preference, callback);
+ if (!result.has_value()) {
+ auto [message, code] = std::move(result).error();
+ LOG(ERROR) << "adapter::Device::prepareModel_1_1 failed with " << code << ": " << message;
+ notify(callback.get(), code, nullptr);
+ return V1_1::utils::convert(code).value();
+ }
+ return V1_0::ErrorStatus::NONE;
+}
+
+Return<V1_0::ErrorStatus> Device::prepareModel_1_2(
+ const V1_2::Model& model, V1_1::ExecutionPreference preference,
+ const hidl_vec<hidl_handle>& modelCache, const hidl_vec<hidl_handle>& dataCache,
+ const CacheToken& token, const sp<V1_2::IPreparedModelCallback>& callback) {
+ auto result = adapter::prepareModel_1_2(kDevice, kExecutor, model, preference, modelCache,
+ dataCache, token, callback);
+ if (!result.has_value()) {
+ auto [message, code] = std::move(result).error();
+ LOG(ERROR) << "adapter::Device::prepareModel_1_2 failed with " << code << ": " << message;
+ notify(callback.get(), code, nullptr);
+ return V1_2::utils::convert(code).value();
+ }
+ return V1_0::ErrorStatus::NONE;
+}
+
+Return<V1_3::ErrorStatus> Device::prepareModel_1_3(
+ const V1_3::Model& model, V1_1::ExecutionPreference preference, V1_3::Priority priority,
+ const V1_3::OptionalTimePoint& deadline, const hidl_vec<hidl_handle>& modelCache,
+ const hidl_vec<hidl_handle>& dataCache, const CacheToken& token,
+ const sp<V1_3::IPreparedModelCallback>& callback) {
+ auto result = adapter::prepareModel_1_3(kDevice, kExecutor, model, preference, priority,
+ deadline, modelCache, dataCache, token, callback);
+ if (!result.has_value()) {
+ auto [message, code] = std::move(result).error();
+ LOG(ERROR) << "adapter::Device::prepareModel_1_3 failed with " << code << ": " << message;
+ notify(callback.get(), code, nullptr);
+ return V1_3::utils::convert(code).value();
+ }
+ return V1_3::ErrorStatus::NONE;
+}
+
+Return<V1_0::ErrorStatus> Device::prepareModelFromCache(
+ const hidl_vec<hidl_handle>& modelCache, const hidl_vec<hidl_handle>& dataCache,
+ const CacheToken& token, const sp<V1_2::IPreparedModelCallback>& callback) {
+ auto result = adapter::prepareModelFromCache(kDevice, kExecutor, modelCache, dataCache, token,
+ callback);
+ if (!result.has_value()) {
+ auto [message, code] = std::move(result).error();
+ LOG(ERROR) << "adapter::Device::prepareModelFromCache failed with " << code << ": "
+ << message;
+ notify(callback.get(), code, nullptr);
+ return V1_2::utils::convert(code).value();
+ }
+ return V1_0::ErrorStatus::NONE;
+}
+
+Return<V1_3::ErrorStatus> Device::prepareModelFromCache_1_3(
+ const V1_3::OptionalTimePoint& deadline, const hidl_vec<hidl_handle>& modelCache,
+ const hidl_vec<hidl_handle>& dataCache, const CacheToken& token,
+ const sp<V1_3::IPreparedModelCallback>& callback) {
+ auto result = adapter::prepareModelFromCache_1_3(kDevice, kExecutor, deadline, modelCache,
+ dataCache, token, callback);
+ if (!result.has_value()) {
+ auto [message, code] = std::move(result).error();
+ LOG(ERROR) << "adapter::Device::prepareModelFromCache_1_3 failed with " << code << ": "
+ << message;
+ notify(callback.get(), code, nullptr);
+ return V1_3::utils::convert(code).value();
+ }
+ return V1_3::ErrorStatus::NONE;
+}
+
+Return<V1_0::DeviceStatus> Device::getStatus() {
+ return V1_0::DeviceStatus::AVAILABLE;
+}
+
+Return<void> Device::allocate(const V1_3::BufferDesc& desc,
+ const hidl_vec<sp<V1_3::IPreparedModel>>& preparedModels,
+ const hidl_vec<V1_3::BufferRole>& inputRoles,
+ const hidl_vec<V1_3::BufferRole>& outputRoles, allocate_cb cb) {
+ auto result = adapter::allocate(kDevice, desc, preparedModels, inputRoles, outputRoles);
+ if (!result.has_value()) {
+ const auto [message, code] = std::move(result).error();
+ LOG(ERROR) << "adapter::Device::allocate failed with " << code << ": " << message;
+ cb(V1_3::utils::convert(code).value(), nullptr, /*token=*/0);
+ return Void();
+ }
+ auto [buffer, token] = std::move(result).value();
+ cb(V1_3::ErrorStatus::NONE, buffer, token);
+ return Void();
+}
+
+} // namespace android::hardware::neuralnetworks::adapter
diff --git a/neuralnetworks/utils/adapter/src/PreparedModel.cpp b/neuralnetworks/utils/adapter/src/PreparedModel.cpp
new file mode 100644
index 0000000..8968c2c
--- /dev/null
+++ b/neuralnetworks/utils/adapter/src/PreparedModel.cpp
@@ -0,0 +1,417 @@
+/*
+ * 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 "PreparedModel.h"
+
+#include <ExecutionBurstServer.h>
+#include <android-base/logging.h>
+#include <android/hardware/neuralnetworks/1.0/IExecutionCallback.h>
+#include <android/hardware/neuralnetworks/1.0/types.h>
+#include <android/hardware/neuralnetworks/1.2/IBurstCallback.h>
+#include <android/hardware/neuralnetworks/1.2/IExecutionCallback.h>
+#include <android/hardware/neuralnetworks/1.2/types.h>
+#include <android/hardware/neuralnetworks/1.3/IExecutionCallback.h>
+#include <android/hardware/neuralnetworks/1.3/IFencedExecutionCallback.h>
+#include <android/hardware/neuralnetworks/1.3/IPreparedModel.h>
+#include <android/hardware/neuralnetworks/1.3/types.h>
+#include <hwbinder/IPCThreadState.h>
+#include <nnapi/IPreparedModel.h>
+#include <nnapi/TypeUtils.h>
+#include <nnapi/Types.h>
+#include <nnapi/Validation.h>
+#include <nnapi/hal/1.0/Utils.h>
+#include <nnapi/hal/1.2/Utils.h>
+#include <nnapi/hal/1.3/Conversions.h>
+#include <nnapi/hal/1.3/Utils.h>
+#include <nnapi/hal/HandleError.h>
+#include <sys/types.h>
+
+#include <memory>
+#include <thread>
+
+// See hardware/interfaces/neuralnetworks/utils/README.md for more information on HIDL interface
+// lifetimes across processes and for protecting asynchronous calls across HIDL.
+
+namespace android::hardware::neuralnetworks::adapter {
+namespace {
+
+template <typename Type>
+auto convertInput(const Type& object) -> decltype(nn::convert(std::declval<Type>())) {
+ auto result = nn::convert(object);
+ if (!result.has_value()) {
+ result.error().code = nn::ErrorStatus::INVALID_ARGUMENT;
+ }
+ return result;
+}
+
+class FencedExecutionCallback final : public V1_3::IFencedExecutionCallback {
+ public:
+ explicit FencedExecutionCallback(const nn::ExecuteFencedInfoCallback& callback)
+ : kCallback(callback) {
+ CHECK(callback != nullptr);
+ }
+
+ Return<void> getExecutionInfo(getExecutionInfo_cb cb) override {
+ const auto result = kCallback();
+ if (!result.has_value()) {
+ const auto& [message, code] = result.error();
+ const auto status =
+ V1_3::utils::convert(code).value_or(V1_3::ErrorStatus::GENERAL_FAILURE);
+ LOG(ERROR) << message;
+ cb(status, V1_2::utils::kNoTiming, V1_2::utils::kNoTiming);
+ return Void();
+ }
+ const auto [timingLaunched, timingFenced] = result.value();
+ const auto hidlTimingLaunched = V1_3::utils::convert(timingLaunched).value();
+ const auto hidlTimingFenced = V1_3::utils::convert(timingFenced).value();
+ cb(V1_3::ErrorStatus::NONE, hidlTimingLaunched, hidlTimingFenced);
+ return Void();
+ }
+
+ private:
+ const nn::ExecuteFencedInfoCallback kCallback;
+};
+
+using ExecutionResult = nn::ExecutionResult<std::pair<std::vector<nn::OutputShape>, nn::Timing>>;
+
+void notify(V1_0::IExecutionCallback* callback, nn::ErrorStatus status,
+ const std::vector<nn::OutputShape>& /*outputShapes*/, const nn::Timing& /*timing*/) {
+ if (callback != nullptr) {
+ const auto hidlStatus = V1_0::utils::convert(status).value();
+ const auto ret = callback->notify(hidlStatus);
+ if (!ret.isOk()) {
+ LOG(ERROR) << "V1_0::IExecutionCallback::notify failed with " << ret.description();
+ }
+ }
+}
+
+void notify(V1_2::IExecutionCallback* callback, nn::ErrorStatus status,
+ const std::vector<nn::OutputShape>& outputShapes, const nn::Timing& timing) {
+ if (callback != nullptr) {
+ const auto hidlStatus = V1_2::utils::convert(status).value();
+ const auto hidlOutputShapes = V1_2::utils::convert(outputShapes).value();
+ const auto hidlTiming = V1_2::utils::convert(timing).value();
+ const auto ret = callback->notify_1_2(hidlStatus, hidlOutputShapes, hidlTiming);
+ if (!ret.isOk()) {
+ LOG(ERROR) << "V1_2::IExecutionCallback::notify_1_2 failed with " << ret.description();
+ }
+ }
+}
+
+void notify(V1_3::IExecutionCallback* callback, nn::ErrorStatus status,
+ const std::vector<nn::OutputShape>& outputShapes, const nn::Timing& timing) {
+ if (callback != nullptr) {
+ const auto hidlStatus = V1_3::utils::convert(status).value();
+ const auto hidlOutputShapes = V1_3::utils::convert(outputShapes).value();
+ const auto hidlTiming = V1_3::utils::convert(timing).value();
+ const auto ret = callback->notify_1_3(hidlStatus, hidlOutputShapes, hidlTiming);
+ if (!ret.isOk()) {
+ LOG(ERROR) << "V1_3::IExecutionCallback::notify_1_3 failed with " << ret.description();
+ }
+ }
+}
+
+template <typename CallbackType>
+void notify(CallbackType* callback, ExecutionResult result) {
+ if (!result.has_value()) {
+ const auto [message, status, outputShapes] = std::move(result).error();
+ LOG(ERROR) << message;
+ notify(callback, status, outputShapes, {});
+ } else {
+ const auto [outputShapes, timing] = std::move(result).value();
+ notify(callback, nn::ErrorStatus::NONE, outputShapes, timing);
+ }
+}
+
+nn::GeneralResult<void> execute(const nn::SharedPreparedModel& preparedModel, uid_t userId,
+ const Executor& executor, const V1_0::Request& request,
+ const sp<V1_0::IExecutionCallback>& callback) {
+ if (callback.get() == nullptr) {
+ return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback";
+ }
+
+ auto nnRequest = NN_TRY(convertInput(request));
+
+ const std::any resource = preparedModel->getUnderlyingResource();
+ if (const auto* model = std::any_cast<const nn::Model*>(&resource)) {
+ CHECK(*model != nullptr);
+ NN_TRY(utils::makeGeneralFailure(nn::validateRequestForModel(nnRequest, **model),
+ nn::ErrorStatus::INVALID_ARGUMENT));
+ }
+
+ Task task = [preparedModel, nnRequest = std::move(nnRequest), callback] {
+ auto result = preparedModel->execute(nnRequest, nn::MeasureTiming::NO, {}, {});
+ notify(callback.get(), std::move(result));
+ };
+ executor(std::move(task), userId, {});
+
+ return {};
+}
+
+nn::GeneralResult<void> execute_1_2(const nn::SharedPreparedModel& preparedModel, uid_t userId,
+ const Executor& executor, const V1_0::Request& request,
+ V1_2::MeasureTiming measure,
+ const sp<V1_2::IExecutionCallback>& callback) {
+ if (callback.get() == nullptr) {
+ return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback";
+ }
+
+ auto nnRequest = NN_TRY(convertInput(request));
+ const auto nnMeasure = NN_TRY(convertInput(measure));
+
+ const std::any resource = preparedModel->getUnderlyingResource();
+ if (const auto* model = std::any_cast<const nn::Model*>(&resource)) {
+ CHECK(*model != nullptr);
+ NN_TRY(utils::makeGeneralFailure(nn::validateRequestForModel(nnRequest, **model),
+ nn::ErrorStatus::INVALID_ARGUMENT));
+ }
+
+ Task task = [preparedModel, nnRequest = std::move(nnRequest), nnMeasure, callback] {
+ auto result = preparedModel->execute(nnRequest, nnMeasure, {}, {});
+ notify(callback.get(), std::move(result));
+ };
+ executor(std::move(task), userId, {});
+
+ return {};
+}
+
+nn::GeneralResult<void> execute_1_3(const nn::SharedPreparedModel& preparedModel, uid_t userId,
+ const Executor& executor, const V1_3::Request& request,
+ V1_2::MeasureTiming measure,
+ const V1_3::OptionalTimePoint& deadline,
+ const V1_3::OptionalTimeoutDuration& loopTimeoutDuration,
+ const sp<V1_3::IExecutionCallback>& callback) {
+ if (callback.get() == nullptr) {
+ return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback";
+ }
+
+ auto nnRequest = NN_TRY(convertInput(request));
+ const auto nnMeasure = NN_TRY(convertInput(measure));
+ const auto nnDeadline = NN_TRY(convertInput(deadline));
+ const auto nnLoopTimeoutDuration = NN_TRY(convertInput(loopTimeoutDuration));
+
+ const std::any resource = preparedModel->getUnderlyingResource();
+ if (const auto* model = std::any_cast<const nn::Model*>(&resource)) {
+ CHECK(*model != nullptr);
+ NN_TRY(utils::makeGeneralFailure(nn::validateRequestForModel(nnRequest, **model),
+ nn::ErrorStatus::INVALID_ARGUMENT));
+ }
+
+ Task task = [preparedModel, nnRequest = std::move(nnRequest), nnMeasure, nnDeadline,
+ nnLoopTimeoutDuration, callback] {
+ auto result =
+ preparedModel->execute(nnRequest, nnMeasure, nnDeadline, nnLoopTimeoutDuration);
+ notify(callback.get(), std::move(result));
+ };
+ executor(std::move(task), userId, nnDeadline);
+
+ return {};
+}
+
+nn::ExecutionResult<std::pair<hidl_vec<V1_2::OutputShape>, V1_2::Timing>> executeSynchronously(
+ const nn::SharedPreparedModel& preparedModel, const V1_0::Request& request,
+ V1_2::MeasureTiming measure) {
+ const auto nnRequest = NN_TRY(utils::makeExecutionFailure(convertInput(request)));
+ const auto nnMeasure = NN_TRY(utils::makeExecutionFailure(convertInput(measure)));
+
+ const auto [outputShapes, timing] =
+ NN_TRY(preparedModel->execute(nnRequest, nnMeasure, {}, {}));
+
+ auto hidlOutputShapes = NN_TRY(utils::makeExecutionFailure(V1_2::utils::convert(outputShapes)));
+ const auto hidlTiming = NN_TRY(utils::makeExecutionFailure(V1_2::utils::convert(timing)));
+ return std::make_pair(std::move(hidlOutputShapes), hidlTiming);
+}
+
+nn::ExecutionResult<std::pair<hidl_vec<V1_2::OutputShape>, V1_2::Timing>> executeSynchronously_1_3(
+ const nn::SharedPreparedModel& preparedModel, const V1_3::Request& request,
+ V1_2::MeasureTiming measure, const V1_3::OptionalTimePoint& deadline,
+ const V1_3::OptionalTimeoutDuration& loopTimeoutDuration) {
+ const auto nnRequest = NN_TRY(utils::makeExecutionFailure(convertInput(request)));
+ const auto nnMeasure = NN_TRY(utils::makeExecutionFailure(convertInput(measure)));
+ const auto nnDeadline = NN_TRY(utils::makeExecutionFailure(convertInput(deadline)));
+ const auto nnLoopTimeoutDuration =
+ NN_TRY(utils::makeExecutionFailure(convertInput(loopTimeoutDuration)));
+
+ const auto [outputShapes, timing] =
+ NN_TRY(preparedModel->execute(nnRequest, nnMeasure, nnDeadline, nnLoopTimeoutDuration));
+
+ auto hidlOutputShapes = NN_TRY(utils::makeExecutionFailure(V1_3::utils::convert(outputShapes)));
+ const auto hidlTiming = NN_TRY(utils::makeExecutionFailure(V1_3::utils::convert(timing)));
+ return std::make_pair(std::move(hidlOutputShapes), hidlTiming);
+}
+
+nn::GeneralResult<std::vector<nn::SyncFence>> convertSyncFences(
+ const hidl_vec<hidl_handle>& handles) {
+ std::vector<nn::SyncFence> syncFences;
+ syncFences.reserve(handles.size());
+ for (const auto& handle : handles) {
+ auto nativeHandle = NN_TRY(convertInput(handle));
+ auto syncFence = NN_TRY(utils::makeGeneralFailure(
+ nn::SyncFence::create(std::move(nativeHandle)), nn::ErrorStatus::INVALID_ARGUMENT));
+ syncFences.push_back(std::move(syncFence));
+ }
+ return syncFences;
+}
+
+nn::GeneralResult<std::pair<hidl_handle, sp<V1_3::IFencedExecutionCallback>>> executeFenced(
+ const nn::SharedPreparedModel& preparedModel, const V1_3::Request& request,
+ const hidl_vec<hidl_handle>& waitFor, V1_2::MeasureTiming measure,
+ const V1_3::OptionalTimePoint& deadline,
+ const V1_3::OptionalTimeoutDuration& loopTimeoutDuration,
+ const V1_3::OptionalTimeoutDuration& duration) {
+ const auto nnRequest = NN_TRY(convertInput(request));
+ const auto nnWaitFor = NN_TRY(convertSyncFences(waitFor));
+ const auto nnMeasure = NN_TRY(convertInput(measure));
+ const auto nnDeadline = NN_TRY(convertInput(deadline));
+ const auto nnLoopTimeoutDuration = NN_TRY(convertInput(loopTimeoutDuration));
+ const auto nnDuration = NN_TRY(convertInput(duration));
+
+ auto [syncFence, executeFencedCallback] = NN_TRY(preparedModel->executeFenced(
+ nnRequest, nnWaitFor, nnMeasure, nnDeadline, nnLoopTimeoutDuration, nnDuration));
+
+ auto hidlSyncFence = NN_TRY(V1_3::utils::convert(syncFence.getSharedHandle()));
+ auto hidlExecuteFencedCallback = sp<FencedExecutionCallback>::make(executeFencedCallback);
+ return std::make_pair(std::move(hidlSyncFence), std::move(hidlExecuteFencedCallback));
+}
+
+} // namespace
+
+PreparedModel::PreparedModel(nn::SharedPreparedModel preparedModel, Executor executor, uid_t userId)
+ : kPreparedModel(std::move(preparedModel)), kExecutor(std::move(executor)), kUserId(userId) {
+ CHECK(kPreparedModel != nullptr);
+ CHECK(kExecutor != nullptr);
+}
+
+nn::SharedPreparedModel PreparedModel::getUnderlyingPreparedModel() const {
+ return kPreparedModel;
+}
+
+Return<V1_0::ErrorStatus> PreparedModel::execute(const V1_0::Request& request,
+ const sp<V1_0::IExecutionCallback>& callback) {
+ auto result = adapter::execute(kPreparedModel, kUserId, kExecutor, request, callback);
+ if (!result.has_value()) {
+ auto [message, code] = std::move(result).error();
+ LOG(ERROR) << "adapter::PreparedModel::execute failed with " << code << ": " << message;
+ notify(callback.get(), code, {}, {});
+ return V1_0::utils::convert(code).value();
+ }
+ return V1_0::ErrorStatus::NONE;
+}
+
+Return<V1_0::ErrorStatus> PreparedModel::execute_1_2(const V1_0::Request& request,
+ V1_2::MeasureTiming measure,
+ const sp<V1_2::IExecutionCallback>& callback) {
+ auto result =
+ adapter::execute_1_2(kPreparedModel, kUserId, kExecutor, request, measure, callback);
+ if (!result.has_value()) {
+ auto [message, code] = std::move(result).error();
+ LOG(ERROR) << "adapter::PreparedModel::execute_1_2 failed with " << code << ": " << message;
+ notify(callback.get(), code, {}, {});
+ return V1_2::utils::convert(code).value();
+ }
+ return V1_0::ErrorStatus::NONE;
+}
+
+Return<V1_3::ErrorStatus> PreparedModel::execute_1_3(
+ const V1_3::Request& request, V1_2::MeasureTiming measure,
+ const V1_3::OptionalTimePoint& deadline,
+ const V1_3::OptionalTimeoutDuration& loopTimeoutDuration,
+ const sp<V1_3::IExecutionCallback>& callback) {
+ auto result = adapter::execute_1_3(kPreparedModel, kUserId, kExecutor, request, measure,
+ deadline, loopTimeoutDuration, callback);
+ if (!result.has_value()) {
+ auto [message, code] = std::move(result).error();
+ LOG(ERROR) << "adapter::PreparedModel::execute_1_3 failed with " << code << ": " << message;
+ notify(callback.get(), code, {}, {});
+ return V1_3::utils::convert(code).value();
+ }
+ return V1_3::ErrorStatus::NONE;
+}
+
+Return<void> PreparedModel::executeSynchronously(const V1_0::Request& request,
+ V1_2::MeasureTiming measure,
+ executeSynchronously_cb cb) {
+ auto result = adapter::executeSynchronously(kPreparedModel, request, measure);
+ if (!result.has_value()) {
+ auto [message, code, outputShapes] = std::move(result).error();
+ LOG(ERROR) << "adapter::PreparedModel::executeSynchronously failed with " << code << ": "
+ << message;
+ cb(V1_2::utils::convert(code).value(), V1_2::utils::convert(outputShapes).value(),
+ V1_2::utils::kNoTiming);
+ return Void();
+ }
+ auto [outputShapes, timing] = std::move(result).value();
+ cb(V1_0::ErrorStatus::NONE, outputShapes, timing);
+ return Void();
+}
+
+Return<void> PreparedModel::executeSynchronously_1_3(
+ const V1_3::Request& request, V1_2::MeasureTiming measure,
+ const V1_3::OptionalTimePoint& deadline,
+ const V1_3::OptionalTimeoutDuration& loopTimeoutDuration, executeSynchronously_1_3_cb cb) {
+ auto result = adapter::executeSynchronously_1_3(kPreparedModel, request, measure, deadline,
+ loopTimeoutDuration);
+ if (!result.has_value()) {
+ auto [message, code, outputShapes] = std::move(result).error();
+ LOG(ERROR) << "adapter::PreparedModel::executeSynchronously_1_3 failed with " << code
+ << ": " << message;
+ cb(V1_3::utils::convert(code).value(), V1_3::utils::convert(outputShapes).value(),
+ V1_2::utils::kNoTiming);
+ return Void();
+ }
+ auto [outputShapes, timing] = std::move(result).value();
+ cb(V1_3::ErrorStatus::NONE, outputShapes, timing);
+ return Void();
+}
+
+Return<void> PreparedModel::configureExecutionBurst(
+ const sp<V1_2::IBurstCallback>& callback,
+ const MQDescriptorSync<V1_2::FmqRequestDatum>& requestChannel,
+ const MQDescriptorSync<V1_2::FmqResultDatum>& resultChannel,
+ configureExecutionBurst_cb cb) {
+ const sp<V1_2::IBurstContext> burst = nn::ExecutionBurstServer::create(
+ callback, requestChannel, resultChannel, this, std::chrono::microseconds{0});
+
+ if (burst == nullptr) {
+ cb(V1_0::ErrorStatus::GENERAL_FAILURE, {});
+ } else {
+ cb(V1_0::ErrorStatus::NONE, burst);
+ }
+ return Void();
+}
+
+Return<void> PreparedModel::executeFenced(const V1_3::Request& request,
+ const hidl_vec<hidl_handle>& waitFor,
+ V1_2::MeasureTiming measure,
+ const V1_3::OptionalTimePoint& deadline,
+ const V1_3::OptionalTimeoutDuration& loopTimeoutDuration,
+ const V1_3::OptionalTimeoutDuration& duration,
+ executeFenced_cb callback) {
+ auto result = adapter::executeFenced(kPreparedModel, request, waitFor, measure, deadline,
+ loopTimeoutDuration, duration);
+ if (!result.has_value()) {
+ auto [message, code] = std::move(result).error();
+ LOG(ERROR) << "adapter::PreparedModel::executeFenced failed with " << code << ": "
+ << message;
+ callback(V1_3::utils::convert(code).value(), {}, nullptr);
+ return Void();
+ }
+ auto [syncFence, executeFencedCallback] = std::move(result).value();
+ callback(V1_3::ErrorStatus::NONE, syncFence, executeFencedCallback);
+ return Void();
+}
+
+} // namespace android::hardware::neuralnetworks::adapter