Cleanup how transport errors are handled in NN utils
Prior to this change, whenever the NN utility code encountered a HIDL
transport error, the error message would display the file and line
number of the "handleTransportError" function itself. This change
introduces a new macro "HANDLE_TRANSPORT_FAILURE" that handles the
transport error in a similar way but now the error message displays
the file and line number of where the macro is called.
Bug: N/A
Test: mma
Change-Id: I35b34f8f5be52b7fcff0fbb58a37ab2b8c7dd8bb
Merged-In: I35b34f8f5be52b7fcff0fbb58a37ab2b8c7dd8bb
(cherry picked from commit 61f508e018cff89c93a7e08b3dbde1eb6a2155df)
diff --git a/neuralnetworks/1.3/utils/src/Buffer.cpp b/neuralnetworks/1.3/utils/src/Buffer.cpp
index a880031..ffdeccd 100644
--- a/neuralnetworks/1.3/utils/src/Buffer.cpp
+++ b/neuralnetworks/1.3/utils/src/Buffer.cpp
@@ -64,7 +64,7 @@
const auto hidlDst = NN_TRY(convert(dst));
const auto ret = kBuffer->copyTo(hidlDst);
- const auto status = NN_TRY(hal::utils::handleTransportError(ret));
+ const auto status = HANDLE_TRANSPORT_FAILURE(ret);
if (status != ErrorStatus::NONE) {
const auto canonical = nn::convert(status).value_or(nn::ErrorStatus::GENERAL_FAILURE);
return NN_ERROR(canonical) << "IBuffer::copyTo failed with " << toString(status);
@@ -79,7 +79,7 @@
const auto hidlDimensions = hidl_vec<uint32_t>(dimensions);
const auto ret = kBuffer->copyFrom(hidlSrc, hidlDimensions);
- const auto status = NN_TRY(hal::utils::handleTransportError(ret));
+ const auto status = HANDLE_TRANSPORT_FAILURE(ret);
if (status != ErrorStatus::NONE) {
const auto canonical = nn::convert(status).value_or(nn::ErrorStatus::GENERAL_FAILURE);
return NN_ERROR(canonical) << "IBuffer::copyFrom failed with " << toString(status);
diff --git a/neuralnetworks/1.3/utils/src/Device.cpp b/neuralnetworks/1.3/utils/src/Device.cpp
index 7a7e251..82837ba 100644
--- a/neuralnetworks/1.3/utils/src/Device.cpp
+++ b/neuralnetworks/1.3/utils/src/Device.cpp
@@ -86,7 +86,7 @@
};
const auto ret = device->getCapabilities_1_3(cb);
- NN_TRY(hal::utils::handleTransportError(ret));
+ HANDLE_TRANSPORT_FAILURE(ret);
return result;
}
@@ -162,7 +162,8 @@
nn::GeneralResult<void> Device::wait() const {
const auto ret = kDevice->ping();
- return hal::utils::handleTransportError(ret);
+ HANDLE_TRANSPORT_FAILURE(ret);
+ return {};
}
nn::GeneralResult<std::vector<bool>> Device::getSupportedOperations(const nn::Model& model) const {
@@ -191,7 +192,7 @@
};
const auto ret = kDevice->getSupportedOperations_1_3(hidlModel, cb);
- NN_TRY(hal::utils::handleTransportError(ret));
+ HANDLE_TRANSPORT_FAILURE(ret);
return result;
}
@@ -219,7 +220,7 @@
const auto ret =
kDevice->prepareModel_1_3(hidlModel, hidlPreference, hidlPriority, hidlDeadline,
hidlModelCache, hidlDataCache, hidlToken, cb);
- const auto status = NN_TRY(hal::utils::handleTransportError(ret));
+ const auto status = HANDLE_TRANSPORT_FAILURE(ret);
if (status != ErrorStatus::NONE) {
const auto canonical = nn::convert(status).value_or(nn::ErrorStatus::GENERAL_FAILURE);
return NN_ERROR(canonical) << "prepareModel_1_3 failed with " << toString(status);
@@ -241,7 +242,7 @@
const auto ret = kDevice->prepareModelFromCache_1_3(hidlDeadline, hidlModelCache, hidlDataCache,
hidlToken, cb);
- const auto status = NN_TRY(hal::utils::handleTransportError(ret));
+ const auto status = HANDLE_TRANSPORT_FAILURE(ret);
if (status != ErrorStatus::NONE) {
const auto canonical = nn::convert(status).value_or(nn::ErrorStatus::GENERAL_FAILURE);
return NN_ERROR(canonical) << "prepareModelFromCache_1_3 failed with " << toString(status);
@@ -277,7 +278,7 @@
const auto ret =
kDevice->allocate(hidlDesc, hidlPreparedModels, hidlInputRoles, hidlOutputRoles, cb);
- NN_TRY(hal::utils::handleTransportError(ret));
+ HANDLE_TRANSPORT_FAILURE(ret);
return result;
}
diff --git a/neuralnetworks/1.3/utils/src/PreparedModel.cpp b/neuralnetworks/1.3/utils/src/PreparedModel.cpp
index 5d82110..49b9b0b 100644
--- a/neuralnetworks/1.3/utils/src/PreparedModel.cpp
+++ b/neuralnetworks/1.3/utils/src/PreparedModel.cpp
@@ -89,7 +89,7 @@
};
const auto ret = callback->getExecutionInfo(cb);
- NN_TRY(hal::utils::handleTransportError(ret));
+ HANDLE_TRANSPORT_FAILURE(ret);
return result;
};
@@ -133,7 +133,7 @@
const auto ret = kPreparedModel->executeSynchronously_1_3(request, measure, deadline,
loopTimeoutDuration, cb);
- NN_TRY(hal::utils::makeExecutionFailure(hal::utils::handleTransportError(ret)));
+ HANDLE_TRANSPORT_FAILURE(ret);
return result;
}
@@ -147,8 +147,7 @@
const auto ret =
kPreparedModel->execute_1_3(request, measure, deadline, loopTimeoutDuration, cb);
- const auto status =
- NN_TRY(hal::utils::makeExecutionFailure(hal::utils::handleTransportError(ret)));
+ const auto status = HANDLE_TRANSPORT_FAILURE(ret);
if (status != ErrorStatus::NONE) {
const auto canonical = nn::convert(status).value_or(nn::ErrorStatus::GENERAL_FAILURE);
return NN_ERROR(canonical) << "executeAsynchronously failed with " << toString(status);
@@ -230,7 +229,7 @@
const auto ret = kPreparedModel->executeFenced(hidlRequest, hidlWaitFor, hidlMeasure,
hidlDeadline, hidlLoopTimeoutDuration,
hidlTimeoutDurationAfterFence, cb);
- NN_TRY(hal::utils::handleTransportError(ret));
+ HANDLE_TRANSPORT_FAILURE(ret);
auto [syncFence, callback] = NN_TRY(std::move(result));
// If executeFenced required the request memory to be moved into shared memory, block here until