Test full error returned when custom batcher init fails

qa/L0_batch_custom/batch_custom_test.py

@@ -122,8 +122,21 @@ def check_response(self,
 
     def check_status(self, model_name, batch_exec, request_cnt, infer_cnt,
                      exec_count):
-        stats = self.triton_client_.get_inference_statistics(model_name, "1")
-        self.assertEqual(len(stats.model_stats), 1, "expect 1 model stats")
+        # There is a time window between when responses are returned and statistics are updated.
+        # To prevent intermittent test failure during that window, wait up to 10 seconds for the
+        # inference statistics to be ready.
+        num_tries = 10
+        for i in range(num_tries):
+            stats = self.triton_client_.get_inference_statistics(
+                model_name, "1")
+            self.assertEqual(len(stats.model_stats), 1, "expect 1 model stats")
+            actual_exec_cnt = stats.model_stats[0].execution_count
+            if actual_exec_cnt == exec_count:
+                break
+            print("WARNING: expect {} executions, got {} (attempt {})".format(
+                exec_count, actual_exec_cnt, i))
+            time.sleep(1)
+
         self.assertEqual(stats.model_stats[0].name, model_name,
                          "expect model stats for model {}".format(model_name))
         self.assertEqual(

qa/L0_batch_custom/test.sh

@@ -102,7 +102,7 @@ cp -r backend/examples/batching_strategies/single_batching/build/libtriton_singl
 # Run a test to validate the single batching strategy example.
 # Then, run tests to validate the volume batching example being passed in via the backend dir, model dir, version dir, and model config.
 BACKEND_DIR="/opt/tritonserver/backends/onnxruntime"
-MODEL_DIR="models/$MODEL_NAME/"
+MODEL_DIR="models/$MODEL_NAME"
 VERSION_DIR="$MODEL_DIR/1/"
 
 test_types=('single_batching_backend' 'backend_directory' 'model_directory' 'version_directory' 'model_config')
@@ -151,10 +151,41 @@ for i in "${!test_setups[@]}"; do
     wait $SERVER_PID
 done
 
+# Test ModelBatchInitialize failure
+FILE_PATH="backend/examples/batching_strategies/volume_batching/src/volume_batching.cc"
+OLD_STRING="\/\/ Batcher will point to an unsigned integer representing the maximum"
+NEW_STRING="return TRITONSERVER_ErrorNew(TRITONSERVER_ERROR_NOT_FOUND,\"Failure test case\");"
+
+sed -i "s/${OLD_STRING}/${NEW_STRING}/g" ${FILE_PATH}
+
+(cd backend/examples/batching_strategies/volume_batching &&
+ cd build &&
+ cmake -DCMAKE_INSTALL_PREFIX:PATH=`pwd`/install \
+       -DTRITON_CORE_REPO_TAG=$TRITON_CORE_REPO_TAG .. &&
+ make -j4 install)
+
+cp -r backend/examples/batching_strategies/volume_batching/build/libtriton_volumebatching.so models/${MODEL_NAME}/libtriton_volumebatching.so
+
+SERVER_LOG=${SERVER_LOG_BASE}_batching_init_failure
+
+run_server
+if [ "$SERVER_PID" != "0" ]; then
+    cat $SERVER_LOG
+    echo -e "\n***\n*** ModelBatchInit Error Test: unexpected successful server start $SERVER\n***"
+    kill_server
+    RET=1
+else
+    if [ `grep -c "Failure test case" $SERVER_LOG` -lt 1 ] || [ `grep -c "Not found" $SERVER_LOG` -lt 1 ]; then
+        cat $SERVER_LOG
+        echo -e "\n***\n*** ModelBatchInit Error Test: failed to find \"Failure test case\" message and/or \"Not found\" error type"
+        RET=1
+    fi
+fi
+
+
 if [ $RET -eq 0 ]; then
     echo -e "\n***\n*** Test Passed\n***"
 else
-    cat $CLIENT_LOG
     echo -e "\n***\n*** Test FAILED\n***"
 fi
 

qa/L0_optional_input/optional_input_test.py

@@ -124,8 +124,21 @@ def check_response(self, thresholds, provided_inputs=("INPUT0", "INPUT1")):
             self.add_deferred_exception(ex)
 
     def check_status(self, model_name, batch_exec, request_cnt, infer_cnt):
-        stats = self.triton_client_.get_inference_statistics(model_name, "1")
-        self.assertEqual(len(stats.model_stats), 1, "expect 1 model stats")
+        # There is a time window between when responses are returned and statistics are updated.
+        # To prevent intermittent test failure during that window, wait up to 10 seconds for the
+        # inference statistics to be ready.
+        num_tries = 10
+        for i in range(num_tries):
+            stats = self.triton_client_.get_inference_statistics(
+                model_name, "1")
+            self.assertEqual(len(stats.model_stats), 1, "expect 1 model stats")
+            actual_exec_cnt = stats.model_stats[0].execution_count
+            if actual_exec_cnt == exec_cnt:
+                break
+            print("WARNING: expect {} executions, got {} (attempt {})".format(
+                exec_cnt, actual_exec_cnt, i))
+            time.sleep(1)
+
         self.assertEqual(stats.model_stats[0].name, model_name,
                          "expect model stats for model {}".format(model_name))
         self.assertEqual(

qa/L0_trt_shape_tensors/trt_shape_tensor_test.py

@@ -130,8 +130,21 @@ def check_setup(self, model_name):
                          _max_queue_delay_ms * 1000)  # 10 secs
 
     def check_status(self, model_name, batch_exec, exec_cnt, infer_cnt):
-        stats = self.triton_client_.get_inference_statistics(model_name, "1")
-        self.assertEqual(len(stats.model_stats), 1, "expect 1 model stats")
+        # There is a time window between when responses are returned and statistics are updated.
+        # To prevent intermittent test failure during that window, wait up to 10 seconds for the
+        # inference statistics to be ready.
+        num_tries = 10
+        for i in range(num_tries):
+            stats = self.triton_client_.get_inference_statistics(
+                model_name, "1")
+            self.assertEqual(len(stats.model_stats), 1, "expect 1 model stats")
+            actual_exec_cnt = stats.model_stats[0].execution_count
+            if actual_exec_cnt == exec_cnt:
+                break
+            print("WARNING: expect {} executions, got {} (attempt {})".format(
+                exec_cnt, actual_exec_cnt, i))
+            time.sleep(1)
+
         self.assertEqual(stats.model_stats[0].name, model_name,
                          "expect model stats for model {}".format(model_name))
         self.assertEqual(