Fix some Thread Sanitizer warnings

In ConnectionAcknowledgedCommandHandler use std::atomic to manage access to 'enabled' flag.

In StridedSliceAsyncEndToEndTest use separate output buffers for each inference.

Neither of these fixes were likely to be causing real bugs in practice but it's helpful
for Thread Sanitizer to run cleanly so that they don't hide other real bugs.

Signed-off-by: Matthew Bentham <matthew.bentham@arm.com>
Change-Id: I2fcc19bd0afcea3494d9081226754b906bd2bd44

1 files changed, 38 insertions, 21 deletions

diff --git a/src/backends/backendsCommon/test/StridedSliceAsyncEndToEndTest.hpp b/src/backends/backendsCommon/test/StridedSliceAsyncEndToEndTest.hpp
index e29782f890..c6bfc5d92e 100644
--- a/src/backends/backendsCommon/test/StridedSliceAsyncEndToEndTest.hpp
+++ b/src/backends/backendsCommon/test/StridedSliceAsyncEndToEndTest.hpp
@@ -41,7 +41,6 @@ void AsyncThreadedEndToEndTestImpl(INetworkPtr network,
     // Optimize the Network
     IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime->GetDeviceSpec());
 
-
     // Creates AsyncNetwork
     NetworkId networkId = 0;
     std::string errorMessage;
@@ -128,6 +127,9 @@ void AsyncEndToEndTestImpl(INetworkPtr network,
                            float tolerance = 0.000001f,
                            size_t numThreads = 1)
 {
+    ARMNN_ASSERT(numThreads >= 1);
+    const unsigned int numberOfInferences = numThreads == 1 ? 1 : 1000;
+
     // Create Runtime in which test will run
     IRuntime::CreationOptions options;
     IRuntimePtr               runtime(IRuntime::Create(options));
@@ -154,26 +156,37 @@ void AsyncEndToEndTestImpl(INetworkPtr network,
                                 ConstTensor(inputTensorInfo, it.second.data())});
     }
 
-    OutputTensors outputTensors;
-    outputTensors.reserve(expectedOutputData.size());
-    std::map<int, std::vector<TOutput>> outputStorage;
-    for (auto&& it : expectedOutputData)
+    std::vector<OutputTensors> outputTensorsVec;
+    std::vector<std::map<int, std::vector<TOutput>>> outputStorageVec;
+
+    outputTensorsVec.reserve(numberOfInferences);
+    outputStorageVec.reserve(numberOfInferences);
+    for (unsigned int i = 0; i < numberOfInferences; ++i)
     {
-        std::vector<TOutput> out(it.second.size());
-        outputStorage.emplace(it.first, out);
-        outputTensors.push_back({it.first,
-                                 Tensor(runtime->GetOutputTensorInfo(networkId, it.first),
-                                        outputStorage.at(it.first).data())});
+        OutputTensors outputTensors;
+        outputStorageVec.emplace_back(std::map<int, std::vector<TOutput>>());
+
+        outputTensors.reserve(expectedOutputData.size());
+        for (auto&& it : expectedOutputData)
+        {
+            std::vector<TOutput> out(it.second.size());
+            outputStorageVec[i].emplace(it.first, out);
+            outputTensors.push_back({it.first,
+                                     Tensor(runtime->GetOutputTensorInfo(networkId, it.first),
+                                            outputStorageVec[i].at(it.first).data())});
+        }
+
+        outputTensorsVec.push_back(outputTensors);
     }
 
-    if (numThreads <= 1)
+    if (numThreads == 1)
     {
         // Create WorkingMemHandle for this async network
         std::unique_ptr<IWorkingMemHandle> workingMemHandle = runtime->CreateWorkingMemHandle(networkId);
         IWorkingMemHandle& workingMemHandleRef = *workingMemHandle.get();
 
         // Run the async network
-        runtime->Execute(workingMemHandleRef, inputTensors, outputTensors);
+        runtime->Execute(workingMemHandleRef, inputTensors, outputTensorsVec[0]);
     }
     else
     {
@@ -188,18 +201,18 @@ void AsyncEndToEndTestImpl(INetworkPtr network,
         AsyncCallbackManager callbackManager;
 
         // For the asyncronous execution, we are adding a pool of working memory handles (1 per thread) in the
-        // LoadedNetwork with a each scheduled inference having a spefic priority
-        for (size_t i = 0; i < 1000; ++i)
+        // LoadedNetwork with each scheduled inference having a random priority
+        for (size_t i = 0; i < numberOfInferences; ++i)
         {
             threadpool.Schedule(networkId,
                                 inputTensors,
-                                outputTensors,
+                                outputTensorsVec[i],
                                 static_cast<QosExecPriority>(rand()%3),
                                 callbackManager.GetNewCallback());
         }
 
         // Wait until the execution signals a notify
-        for (size_t i = 0; i < 1000; ++i)
+        for (size_t i = 0; i < numberOfInferences; ++i)
         {
             auto cb = callbackManager.GetNotifiedCallback();
 
@@ -208,13 +221,17 @@ void AsyncEndToEndTestImpl(INetworkPtr network,
         }
     }
 
-    for (auto&& it : expectedOutputData)
+    for (auto&& outputStorage : outputStorageVec)
     {
-        std::vector<TOutput> out = outputStorage.at(it.first);
-
-        for (unsigned int i = 0; i < out.size(); ++i)
+        for (auto&& it : expectedOutputData)
         {
-            CHECK(Compare<ArmnnOType>(it.second[i], out[i], tolerance) == true);
+            std::vector<TOutput> out = outputStorage.at(it.first);
+
+            for (unsigned int i = 0; i < out.size(); ++i)
+            {
+                //CHECK(Compare<ArmnnOType>(it.second[i], out[i], tolerance) == true);
+                CHECK(it.second[i] == doctest::Approx(out[i]).epsilon(tolerance));
+            }
         }
     }
 }