NNAPI: Add execution preference to prepareModel (HAL)

A model can be prepared in different ways to optimize for different
use-cases. This CL propagates the execution preference across the HAL so
that the NN service can better fit the users needs.

Bug: 77864669
Test: mma
Test: NeuralNetworksTest_static
Test: VtsHalNeuralnetworksV1_1TargetTest
Merged-In: Ib928d510d462f36b6a87d5e81010513db7829fa8
Change-Id: Ib928d510d462f36b6a87d5e81010513db7829fa8
(cherry picked from commit 2504c2fe4f)

neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp

@@ -242,8 +242,8 @@ void Execute(const sp<V1_1::IDevice>& device, std::function<V1_1::Model(void)> c
     // launch prepare model
     sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
     ASSERT_NE(nullptr, preparedModelCallback.get());
-    Return<ErrorStatus> prepareLaunchStatus =
-        device->prepareModel_1_1(model, preparedModelCallback);
+    Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_1(
+        model, ExecutionPreference::FAST_SINGLE_ANSWER, preparedModelCallback);
     ASSERT_TRUE(prepareLaunchStatus.isOk());
     ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
 

neuralnetworks/1.1/IDevice.hal

@@ -102,6 +102,8 @@ interface IDevice extends @1.0::IDevice {
      * Multiple threads can call prepareModel on the same model concurrently.
      *
      * @param model The model to be prepared for execution.
+     * @param preference Indicates the intended execution behavior of a prepared
+     *                   model.
      * @param callback A callback object used to return the error status of
      *                 preparing the model for execution and the prepared model
      *                 if successful, nullptr otherwise. The callback object's
@@ -115,6 +117,7 @@ interface IDevice extends @1.0::IDevice {
      *                - INVALID_ARGUMENT if one of the input arguments is
      *                  invalid
      */
-    prepareModel_1_1(Model model, IPreparedModelCallback callback)
+    prepareModel_1_1(Model model, ExecutionPreference preference,
+                     IPreparedModelCallback callback)
           generates (ErrorStatus status);
 };

neuralnetworks/1.1/types.hal

@@ -382,3 +382,24 @@ struct Model {
      */
     bool relaxComputationFloat32toFloat16;
 };
+
+/**
+ * Execution preferences.
+ */
+enum ExecutionPreference : int32_t {
+    /**
+     * Prefer executing in a way that minimizes battery drain.
+     * This is desirable for compilations that will be executed often.
+     */
+    LOW_POWER = 0,
+    /**
+     * Prefer returning a single answer as fast as possible, even if this causes
+     * more power consumption.
+     */
+    FAST_SINGLE_ANSWER = 1,
+    /**
+     * Prefer maximizing the throughput of successive frames, for example when
+     * processing successive frames coming from the camera.
+     */
+    SUSTAINED_SPEED = 2,
+};

neuralnetworks/1.1/vts/functional/ValidateModel.cpp

@@ -50,13 +50,13 @@ static void validateGetSupportedOperations(const sp<IDevice>& device, const std:
 }
 
 static void validatePrepareModel(const sp<IDevice>& device, const std::string& message,
-                                 const V1_1::Model& model) {
+                                 const V1_1::Model& model, ExecutionPreference preference) {
     SCOPED_TRACE(message + " [prepareModel_1_1]");
 
     sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
     ASSERT_NE(nullptr, preparedModelCallback.get());
     Return<ErrorStatus> prepareLaunchStatus =
-        device->prepareModel_1_1(model, preparedModelCallback);
+        device->prepareModel_1_1(model, preference, preparedModelCallback);
     ASSERT_TRUE(prepareLaunchStatus.isOk());
     ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(prepareLaunchStatus));
 
@@ -67,15 +67,24 @@ static void validatePrepareModel(const sp<IDevice>& device, const std::string& m
     ASSERT_EQ(nullptr, preparedModel.get());
 }
 
+static bool validExecutionPreference(ExecutionPreference preference) {
+    return preference == ExecutionPreference::LOW_POWER ||
+           preference == ExecutionPreference::FAST_SINGLE_ANSWER ||
+           preference == ExecutionPreference::SUSTAINED_SPEED;
+}
+
 // Primary validation function. This function will take a valid model, apply a
 // mutation to it to invalidate the model, then pass it to interface calls that
 // use the model. Note that the model here is passed by value, and any mutation
 // to the model does not leave this function.
 static void validate(const sp<IDevice>& device, const std::string& message, V1_1::Model model,
-                     const std::function<void(Model*)>& mutation) {
+                     const std::function<void(Model*)>& mutation,
+                     ExecutionPreference preference = ExecutionPreference::FAST_SINGLE_ANSWER) {
     mutation(&model);
-    validateGetSupportedOperations(device, message, model);
-    validatePrepareModel(device, message, model);
+    if (validExecutionPreference(preference)) {
+        validateGetSupportedOperations(device, message, model);
+    }
+    validatePrepareModel(device, message, model, preference);
 }
 
 // Delete element from hidl_vec. hidl_vec doesn't support a "remove" operation,
@@ -486,6 +495,22 @@ static void addOperationOutputTest(const sp<IDevice>& device, const V1_1::Model&
     }
 }
 
+///////////////////////// VALIDATE EXECUTION PREFERENCE /////////////////////////
+
+static const int32_t invalidExecutionPreferences[] = {
+    static_cast<int32_t>(ExecutionPreference::LOW_POWER) - 1,        // lower bound
+    static_cast<int32_t>(ExecutionPreference::SUSTAINED_SPEED) + 1,  // upper bound
+};
+
+static void mutateExecutionPreferenceTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (int32_t preference : invalidExecutionPreferences) {
+        const std::string message =
+            "mutateExecutionPreferenceTest: preference " + std::to_string(preference);
+        validate(device, message, model, [](Model*) {},
+                 static_cast<ExecutionPreference>(preference));
+    }
+}
+
 ////////////////////////// ENTRY POINT //////////////////////////////
 
 void ValidationTest::validateModel(const V1_1::Model& model) {
@@ -503,6 +528,7 @@ void ValidationTest::validateModel(const V1_1::Model& model) {
     removeOperationOutputTest(device, model);
     addOperationInputTest(device, model);
     addOperationOutputTest(device, model);
+    mutateExecutionPreferenceTest(device, model);
 }
 
 }  // namespace functional

neuralnetworks/1.1/vts/functional/ValidateRequest.cpp

@@ -60,8 +60,8 @@ static void createPreparedModel(const sp<IDevice>& device, const V1_1::Model& mo
     // launch prepare model
     sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
     ASSERT_NE(nullptr, preparedModelCallback.get());
-    Return<ErrorStatus> prepareLaunchStatus =
-        device->prepareModel_1_1(model, preparedModelCallback);
+    Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_1(
+        model, ExecutionPreference::FAST_SINGLE_ANSWER, preparedModelCallback);
     ASSERT_TRUE(prepareLaunchStatus.isOk());
     ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));