Merge branch 'master' into Script_run_refactor_and_LLAPI

Author: alonre24

src/background_workers.c

@@ -391,19 +391,17 @@ void *RedisAI_Run_ThreadMain(void *arg) {
 
                 // Run is over, now iterate over the run info structs in the batch
                 // and see if any error was generated
-                int dagError = 0;
+                bool first_dag_error = false;
                 for (long long i = 0; i < array_len(batch_rinfo); i++) {
                     RedisAI_RunInfo *rinfo = batch_rinfo[i];
-                    // We lock on the DAG because error could be set from
-                    // other threads operating on the same DAG (TODO: use atomic)
-                    dagError = __atomic_load_n(rinfo->dagError, __ATOMIC_RELAXED);
-
                     // We record that there was an error for later on
-                    run_error = dagError;
-
+                    run_error = __atomic_load_n(rinfo->dagError, __ATOMIC_RELAXED);
+                    if (i == 0 && run_error == 1) {
+                        first_dag_error = true;
+                    }
                     // If there was an error and the reference count for the dag
                     // has gone to zero and the client is still around, we unblock
-                    if (dagError) {
+                    if (run_error) {
                         RedisAI_RunInfo *orig = rinfo->orig_copy;
                         long long dagRefCount = RAI_DagRunInfoFreeShallowCopy(rinfo);
                         if (dagRefCount == 0) {
@@ -415,12 +413,17 @@ void *RedisAI_Run_ThreadMain(void *arg) {
                         __atomic_add_fetch(rinfo->dagCompleteOpCount, 1, __ATOMIC_RELAXED);
                     }
                 }
+                if (first_dag_error) {
+                    run_queue_len = queueLength(run_queue_info->run_queue);
+                    continue;
+                }
             }
 
             // We initialize variables where we'll store the fact hat, after the current
             // run, all ops for the device or all ops in the dag could be complete. This
             // way we can avoid placing the op back on the queue if there's nothing left
             // to do.
+            RedisModule_Assert(run_error == 0);
             int device_complete_after_run = RedisAI_DagDeviceComplete(batch_rinfo[0]);
             int dag_complete_after_run = RedisAI_DagComplete(batch_rinfo[0]);
 

tests/flow/tests_onnx.py

@@ -63,21 +63,21 @@ def test_onnx_modelrun_mnist(env):
     except Exception as e:
         exception = e
         env.assertEqual(type(exception), redis.exceptions.ResponseError)
-        env.assertEqual("No graph was found in the protobuf.", exception.__str__())
+        env.assertEqual("No graph was found in the protobuf.", str(exception))
 
     try:
         con.execute_command('AI.MODELSET', 'm_1{1}', 'ONNX', 'BLOB', model_pb)
     except Exception as e:
         exception = e
         env.assertEqual(type(exception), redis.exceptions.ResponseError)
-        env.assertEqual("Invalid DEVICE", exception.__str__())
+        env.assertEqual("Invalid DEVICE", str(exception))
 
     try:
         con.execute_command('AI.MODELSET', 'm_2{1}', model_pb)
     except Exception as e:
         exception = e
         env.assertEqual(type(exception), redis.exceptions.ResponseError)
-        env.assertEqual("wrong number of arguments for 'AI.MODELSET' command", exception.__str__())
+        env.assertEqual("wrong number of arguments for 'AI.MODELSET' command", str(exception))
 
     con.execute_command('AI.TENSORSET', 'a{1}', 'FLOAT', 1, 1, 28, 28, 'BLOB', sample_raw)
 
@@ -86,56 +86,64 @@ def test_onnx_modelrun_mnist(env):
     except Exception as e:
         exception = e
         env.assertEqual(type(exception), redis.exceptions.ResponseError)
-        env.assertEqual("model key is empty", exception.__str__())
+        env.assertEqual("model key is empty", str(exception))
 
     try:
         con.execute_command('AI.MODELRUN', 'm_2{1}', 'INPUTS', 'a{1}', 'b{1}', 'c{1}')
     except Exception as e:
         exception = e
         env.assertEqual(type(exception), redis.exceptions.ResponseError)
-        env.assertEqual("model key is empty", exception.__str__())
+        env.assertEqual("model key is empty", str(exception))
 
     try:
         con.execute_command('AI.MODELRUN', 'm_3{1}', 'a{1}', 'b{1}', 'c{1}')
     except Exception as e:
         exception = e
         env.assertEqual(type(exception), redis.exceptions.ResponseError)
-        env.assertEqual("model key is empty", exception.__str__())
+        env.assertEqual("model key is empty", str(exception))
 
     try:
         con.execute_command('AI.MODELRUN', 'm_1{1}', 'OUTPUTS', 'c{1}')
     except Exception as e:
         exception = e
         env.assertEqual(type(exception), redis.exceptions.ResponseError)
-        env.assertEqual("model key is empty", exception.__str__())
+        env.assertEqual("model key is empty", str(exception))
 
     try:
         con.execute_command('AI.MODELRUN', 'm{1}', 'OUTPUTS', 'c{1}')
     except Exception as e:
         exception = e
         env.assertEqual(type(exception), redis.exceptions.ResponseError)
-        env.assertEqual("INPUTS not specified", exception.__str__())
+        env.assertEqual("INPUTS not specified", str(exception))
 
     try:
         con.execute_command('AI.MODELRUN', 'm{1}', 'INPUTS', 'a{1}', 'b{1}')
     except Exception as e:
         exception = e
         env.assertEqual(type(exception), redis.exceptions.ResponseError)
-        env.assertEqual("tensor key is empty", exception.__str__())
+        env.assertEqual("tensor key is empty", str(exception))
 
     try:
         con.execute_command('AI.MODELRUN', 'm_1{1}', 'INPUTS', 'OUTPUTS')
     except Exception as e:
         exception = e
         env.assertEqual(type(exception), redis.exceptions.ResponseError)
-        env.assertEqual("model key is empty", exception.__str__())
+        env.assertEqual("model key is empty", str(exception))
 
     try:
         con.execute_command('AI.MODELRUN', 'm_1{1}', 'INPUTS', 'a{1}', 'OUTPUTS', 'b{1}')
     except Exception as e:
         exception = e
         env.assertEqual(type(exception), redis.exceptions.ResponseError)
-        env.assertEqual("model key is empty", exception.__str__())
+        env.assertEqual("model key is empty", str(exception))
+
+    # This error is caught after the model is sent to the backend, not in parsing like before.
+    try:
+        con.execute_command('AI.MODELRUN', 'm{1}', 'INPUTS', 'a{1}', 'a{1}', 'OUTPUTS', 'b{1}')
+    except Exception as e:
+        exception = e
+        env.assertEqual(type(exception), redis.exceptions.ResponseError)
+        env.assertEqual('Expected 1 inputs but got 2', str(exception))
 
     con.execute_command('AI.MODELRUN', 'm{1}', 'INPUTS', 'a{1}', 'OUTPUTS', 'b{1}')
 

tests/flow/tests_tensorflow.py

@@ -638,12 +638,12 @@ def run(name=model_name, output_name='output{1}'):
                             'INPUTS', 'input{1}', 'OUTPUTS', output_name)
 
     # Running thrice since minbatchsize = 2
-    p1 = mp.Process(target=run)
-    p1.start()
-    p2 = mp.Process(target=run)
-    p2.start()
-    p3 = mp.Process(target=run)
-    p3.start()
+    # The third process will hang until termintation or until a new process will execute the model with the same properties.
+    processes = []
+    for i in range(3):
+        p = mp.Process(target=run)
+        p.start()
+        processes.append(p)
 
     time.sleep(3)
 
@@ -655,9 +655,10 @@ def run(name=model_name, output_name='output{1}'):
 
     p1b = mp.Process(target=run, args=(another_model_name, 'final1{1}'))
     p1b.start()
-
     run(another_model_name, 'final2{1}')
 
+    p1b.join()
+
     _, dtype, _, shape, _, data = con.execute_command('AI.TENSORGET', 'final1{1}', 'META', 'BLOB')
     dtype_map = {b'FLOAT': np.float32}
     tensor = np.frombuffer(data, dtype=dtype_map[dtype]).reshape(shape)
@@ -667,7 +668,8 @@ def run(name=model_name, output_name='output{1}'):
 
     env.assertEqual(label, 'giant_panda')
 
-    p3.terminate()
+    for p in processes:
+        p.terminate()
 
 
 @skip_if_no_TF