Introduce reusable execution to canonical interface -- HAL.

This CL modifies the canonical interface for reusable executions:
- Add new interface: IExecution with compute and computeFenced methods
- Add new method IPreparedModel::createExecution

In NNAPI runtime, the new interface IExecution is used to
memoize request-specific execution resources (e.g. converted HAL
request). The expected usage is that, IPreparedModel::createExecution
will be invoked in the first computation of a reusable NDK ANNExecution
object, and IExecution::compute* will be invoked repeatedly.

The IPreparedModel::execute* methods are preserved to avoid redundant
object creation and memoization overhead for a single-time
(non-reusable) execution.

For a vendor implementing the canonical interfaces, only the
IPreparedModel::execute* methods will be called because there is
currently no reusable execution at HAL interface. A DefaultExecution
implementation is provided to reduce the work needed on the vendor side.

Bug: 184073769
Test: NNT_static
Test: neuralnetworks_utils_hal_1_0_test
Test: neuralnetworks_utils_hal_1_1_test
Test: neuralnetworks_utils_hal_1_2_test
Test: neuralnetworks_utils_hal_1_3_test
Test: neuralnetworks_utils_hal_common_test
Test: neuralnetworks_utils_hal_aidl_test
Change-Id: I91790bb5ccf5ae648687fe603f88ffda2c9fd2b2

1 files changed, 30 insertions, 0 deletions

diff --git a/neuralnetworks/utils/common/src/ResilientPreparedModel.cpp b/neuralnetworks/utils/common/src/ResilientPreparedModel.cpp
index 5dd5f99f5f..1ae19bc6ca 100644
--- a/neuralnetworks/utils/common/src/ResilientPreparedModel.cpp
+++ b/neuralnetworks/utils/common/src/ResilientPreparedModel.cpp
@@ -17,7 +17,9 @@
 #include "ResilientPreparedModel.h"
 
 #include "InvalidBurst.h"
+#include "InvalidExecution.h"
 #include "ResilientBurst.h"
+#include "ResilientExecution.h"
 
 #include <android-base/logging.h>
 #include <android-base/thread_annotations.h>
@@ -127,6 +129,21 @@ ResilientPreparedModel::executeFenced(const nn::Request& request,
     return protect(*this, fn);
 }
 
+nn::GeneralResult<nn::SharedExecution> ResilientPreparedModel::createReusableExecution(
+        const nn::Request& request, nn::MeasureTiming measure,
+        const nn::OptionalDuration& loopTimeoutDuration) const {
+#if 0
+    auto self = shared_from_this();
+    ResilientExecution::Factory makeExecution =
+            [preparedModel = std::move(self), request, measure, loopTimeoutDuration] {
+        return preparedModel->createReusableExecutionInternal(request, measure, loopTimeoutDuration);
+    };
+    return ResilientExecution::create(std::move(makeExecution));
+#else
+    return createReusableExecutionInternal(request, measure, loopTimeoutDuration);
+#endif
+}
+
 nn::GeneralResult<nn::SharedBurst> ResilientPreparedModel::configureExecutionBurst() const {
 #if 0
     auto self = shared_from_this();
@@ -140,6 +157,19 @@ nn::GeneralResult<nn::SharedBurst> ResilientPreparedModel::configureExecutionBur
 #endif
 }
 
+nn::GeneralResult<nn::SharedExecution> ResilientPreparedModel::createReusableExecutionInternal(
+        const nn::Request& request, nn::MeasureTiming measure,
+        const nn::OptionalDuration& loopTimeoutDuration) const {
+    if (!isValidInternal()) {
+        return std::make_shared<const InvalidExecution>();
+    }
+    const auto fn = [&request, measure,
+                     &loopTimeoutDuration](const nn::IPreparedModel& preparedModel) {
+        return preparedModel.createReusableExecution(request, measure, loopTimeoutDuration);
+    };
+    return protect(*this, fn);
+}
+
 std::any ResilientPreparedModel::getUnderlyingResource() const {
     return getPreparedModel()->getUnderlyingResource();
 }