Add @1.2::IPreparedModel::executeSynchronously() and corresponding VTS tests.
Bug: 119274127
Test: all of the following, with the appropriate android.hardware.neuralnetworks@1.${X}::IDevice/sample-all
VtsHalNeuralnetworksV1_0TargetTest
VtsHalNeuralnetworksV1_0TargetTest
VtsHalNeuralnetworksV1_1CompatV1_0TargetTest
VtsHalNeuralnetworksV1_1CompatV1_0TargetTest
VtsHalNeuralnetworksV1_1TargetTest
VtsHalNeuralnetworksV1_1TargetTest
VtsHalNeuralnetworksV1_2CompatV1_0TargetTest
VtsHalNeuralnetworksV1_2CompatV1_0TargetTest
VtsHalNeuralnetworksV1_2CompatV1_1TargetTest
VtsHalNeuralnetworksV1_2CompatV1_1TargetTest
VtsHalNeuralnetworksV1_2TargetTest
VtsHalNeuralnetworksV1_2TargetTest
Change-Id: Iedfa485b4008d9cec3b81ff4c0ce3ebc0b83c823
(cherry picked from commit 49e41678f5781230b9f7bf02cc4886fee9891b71)
diff --git a/neuralnetworks/1.2/IPreparedModel.hal b/neuralnetworks/1.2/IPreparedModel.hal
index 5590487..4e91c67 100644
--- a/neuralnetworks/1.2/IPreparedModel.hal
+++ b/neuralnetworks/1.2/IPreparedModel.hal
@@ -51,8 +51,9 @@
* and complete successfully (ErrorStatus::NONE). There must be
* no failure unless the device itself is in a bad state.
*
- * Multiple threads can call the execute_1_2 function on the same IPreparedModel
- * object concurrently with different requests.
+ * Any number of calls to the execute, execute_1_2, and executeSynchronously
+ * functions, in any combination, may be made concurrently, even on the same
+ * IPreparedModel object.
*
* @param request The input and output information on which the prepared
* model is to be executed.
@@ -71,4 +72,39 @@
*/
execute_1_2(Request request, IExecutionCallback callback)
generates (ErrorStatus status);
+
+ /**
+ * Performs a synchronous execution on a prepared model.
+ *
+ * The execution is performed synchronously with respect to the caller.
+ * executeSynchronously must verify the inputs to the function are
+ * correct. If there is an error, executeSynchronously must immediately
+ * return with the appropriate ErrorStatus value. If the inputs to the
+ * function are valid and there is no error, executeSynchronously must
+ * perform the execution, and must not return until the execution is
+ * complete.
+ *
+ * If the prepared model was prepared from a model wherein all tensor
+ * operands have fully specified dimensions, and the inputs to the function
+ * are valid, then the execution should complete successfully
+ * (ErrorStatus::NONE). There must be no failure unless the device itself is
+ * in a bad state.
+ *
+ * Any number of calls to the execute, execute_1_2, and executeSynchronously
+ * functions, in any combination, may be made concurrently, even on the same
+ * IPreparedModel object.
+ *
+ * @param request The input and output information on which the prepared
+ * model is to be executed.
+ * @return status Error status of the execution, must be:
+ * - NONE if execution is performed successfully
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ * - OUTPUT_INSUFFICIENT_SIZE if provided output buffer is
+ * not large enough to store the resultant values
+ * - INVALID_ARGUMENT if one of the input arguments is
+ * invalid
+ */
+ executeSynchronously(Request request)
+ generates (ErrorStatus status);
};
diff --git a/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp b/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp
index e2722aa..d80fbcf 100644
--- a/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp
+++ b/neuralnetworks/1.2/vts/functional/ValidateRequest.cpp
@@ -97,18 +97,29 @@
static void validate(const sp<IPreparedModel>& preparedModel, const std::string& message,
Request request, const std::function<void(Request*)>& mutation) {
mutation(&request);
- SCOPED_TRACE(message + " [execute]");
- sp<ExecutionCallback> executionCallback = new ExecutionCallback();
- ASSERT_NE(nullptr, executionCallback.get());
- Return<ErrorStatus> executeLaunchStatus =
- preparedModel->execute_1_2(request, executionCallback);
- ASSERT_TRUE(executeLaunchStatus.isOk());
- ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeLaunchStatus));
+ {
+ SCOPED_TRACE(message + " [execute_1_2]");
- executionCallback->wait();
- ErrorStatus executionReturnStatus = executionCallback->getStatus();
- ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, executionReturnStatus);
+ sp<ExecutionCallback> executionCallback = new ExecutionCallback();
+ ASSERT_NE(nullptr, executionCallback.get());
+ Return<ErrorStatus> executeLaunchStatus =
+ preparedModel->execute_1_2(request, executionCallback);
+ ASSERT_TRUE(executeLaunchStatus.isOk());
+ ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeLaunchStatus));
+
+ executionCallback->wait();
+ ErrorStatus executionReturnStatus = executionCallback->getStatus();
+ ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, executionReturnStatus);
+ }
+
+ {
+ SCOPED_TRACE(message + " [executeSynchronously]");
+
+ Return<ErrorStatus> executeStatus = preparedModel->executeSynchronously(request);
+ ASSERT_TRUE(executeStatus.isOk());
+ ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(executeStatus));
+ }
}
// Delete element from hidl_vec. hidl_vec doesn't support a "remove" operation,