Add AI.CONFIG GET sub-command

docs/commands.md

@@ -1144,7 +1144,7 @@ The **AI.CONFIG** command sets the value of configuration directives at run-time
 
 **Redis API**
 ```
-AI.CONFIG <BACKENDSPATH <path>> | <LOADBACKEND <backend> <path>> | <MODEL_CHUNK_SIZE <chunk_size>>
+AI.CONFIG <BACKENDSPATH <path>> | <LOADBACKEND <backend> <path>> | <MODEL_CHUNK_SIZE <chunk_size>> | <GET <BACKENDSPATH | MODEL_CHUNK_SIZE>> 
 ```
 
 _Arguments_
@@ -1156,6 +1156,7 @@ _Arguments_
     * **TORCH**: The PyTorch backend
     * **ONNX**: ONNXRuntime backend
 * **MODEL_CHUNK_SIZE**: Sets the size of chunks (in bytes) in which model payloads are split for serialization, replication and `MODELGET`. Default is `511 * 1024 * 1024`.
+* **GET**: Retrieve the current value of the `BACKENDSPATH / MODEL_CHUNK_SIZE` configurations. Note that additional information about the module's runtime configuration can be retrieved as part of Redis' info report via `INFO MODULES` command.  
 
 _Return_
 
@@ -1190,3 +1191,10 @@ This sets model chunk size to one megabyte (not recommended):
 redis> AI.CONFIG MODEL_CHUNK_SIZE 1048576
 OK
 ```
+
+This returns the current model chunk size configuration:
+
+```
+redis> AI.CONFIG GET MODEL_CHUNK_SIZE
+1048576
+```

src/redisai.c

@@ -939,7 +939,20 @@ int RedisAI_Config_RedisCommand(RedisModuleCtx *ctx, RedisModuleString **argv, i
             return RedisModule_ReplyWithError(ctx, "ERR MODEL_CHUNK_SIZE: missing chunk size");
         }
     }
-
+    if (!strcasecmp(subcommand, "GET")) {
+        if (argc > 2) {
+            const char *config = RedisModule_StringPtrLen(argv[2], NULL);
+            if (!strcasecmp(config, "BACKENDSPATH")) {
+                return RedisModule_ReplyWithCString(ctx, Config_GetBackendsPath());
+            } else if (!strcasecmp(config, "MODEL_CHUNK_SIZE")) {
+                return RedisModule_ReplyWithLongLong(ctx, Config_GetModelChunkSize());
+            } else {
+                return RedisModule_ReplyWithNull(ctx);
+            }
+        } else {
+            return RedisModule_WrongArity(ctx);
+        }
+    }
     return RedisModule_ReplyWithError(ctx, "ERR unsupported subcommand");
 }
 

tests/flow/tests_commands.py

@@ -536,3 +536,79 @@ def run_model_execute_from_llapi():
     info = info_to_dict(con.execute_command('AI.INFO', 'm{1}'))
     env.assertGreaterEqual(info['calls'], 0)
     env.assertGreaterEqual(num_parallel_clients, info['calls'])
+
+
+def test_ai_config(env):
+    if not TEST_PT:
+        env.debugPrint("skipping {} since TEST_PT=0".format(sys._getframe().f_code.co_name), force=True)
+        return
+
+    conns = env.getOSSMasterNodesConnectionList()
+    if env.isCluster():
+        env.assertEqual(len(conns), env.shardsCount)
+
+    model = load_file_content('pt-minimal.pt')
+
+    for con in conns:
+        # Get the default configs.
+        res = con.execute_command('AI.CONFIG', 'GET', 'BACKENDSPATH')
+        env.assertEqual(res, None)
+        res = con.execute_command('AI.CONFIG', 'GET', 'MODEL_CHUNK_SIZE')
+        env.assertEqual(res, 511*1024*1024)
+
+        # Change the default backends path and load backend.
+        path = f'{ROOT}/install-{DEVICE.lower()}'
+        con.execute_command('AI.CONFIG', 'BACKENDSPATH', path)
+        res = con.execute_command('AI.CONFIG', 'GET', 'BACKENDSPATH')
+        env.assertEqual(res, path.encode())
+        be_info = get_info_section(con, "backends_info")
+        env.assertEqual(len(be_info), 0)  # no backends are loaded.
+        check_error_message(env, con, 'error loading backend', 'AI.CONFIG', 'LOADBACKEND', 'TORCH', ".")
+
+        res = con.execute_command('AI.CONFIG', 'LOADBACKEND', 'TORCH', "backends/redisai_torch/redisai_torch.so")
+        env.assertEqual(res, b'OK')
+        be_info = get_info_section(con, "backends_info")
+        env.assertEqual(len(be_info), 1)  # one backend is loaded now - torch.
+
+    # Set the same model twice on some shard - with and without chunks, and assert equality.
+    con = get_connection(env, '{1}')
+    chunk_size = len(model) // 3
+    model_chunks = [model[i:i + chunk_size] for i in range(0, len(model), chunk_size)]
+    con.execute_command('AI.MODELSTORE', 'm1{1}', 'TORCH', DEVICE, 'BLOB', model)
+    con.execute_command('AI.MODELSTORE', 'm2{1}', 'TORCH', DEVICE, 'BLOB', *model_chunks)
+    model1 = con.execute_command('AI.MODELGET', 'm1{1}', 'BLOB')
+    model2 = con.execute_command('AI.MODELGET', 'm2{1}', 'BLOB')
+    env.assertEqual(model1, model2)
+
+    for con in conns:
+        # Change the default model_chunk_size.
+        ret = con.execute_command('AI.CONFIG', 'MODEL_CHUNK_SIZE', chunk_size)
+        env.assertEqual(ret, b'OK')
+        res = con.execute_command('AI.CONFIG', 'GET', 'MODEL_CHUNK_SIZE')
+        env.assertEqual(res, chunk_size)
+
+    # Verify that AI.MODELGET returns the model's blob in chunks, with or without the META arg.
+    con = get_connection(env, '{1}')
+    model2 = con.execute_command('AI.MODELGET', 'm1{1}', 'BLOB')
+    env.assertEqual(len(model2), len(model_chunks))
+    env.assertTrue(all([el1 == el2 for el1, el2 in zip(model2, model_chunks)]))
+
+    model3 = con.execute_command('AI.MODELGET', 'm1{1}', 'META', 'BLOB')[-1]  # Extract the BLOB list from the result
+    env.assertEqual(len(model3), len(model_chunks))
+    env.assertTrue(all([el1 == el2 for el1, el2 in zip(model3, model_chunks)]))
+
+
+def test_ai_config_errors(env):
+    con = get_connection(env, '{1}')
+
+    check_error_message(env, con, "wrong number of arguments for 'AI.CONFIG' command", 'AI.CONFIG')
+    check_error_message(env, con, 'unsupported subcommand', 'AI.CONFIG', "bad_subcommand")
+    check_error_message(env, con, "wrong number of arguments for 'AI.CONFIG' command", 'AI.CONFIG', 'LOADBACKEND')
+    check_error_message(env, con, 'unsupported backend', 'AI.CONFIG', 'LOADBACKEND', 'bad_backend', "backends/redisai_torch/redisai_torch.so")
+    check_error_message(env, con, "wrong number of arguments for 'AI.CONFIG' command", 'AI.CONFIG', 'LOADBACKEND', "TORCH")
+
+    check_error_message(env, con, 'BACKENDSPATH: missing path argument', 'AI.CONFIG', 'BACKENDSPATH')
+    check_error_message(env, con, 'MODEL_CHUNK_SIZE: missing chunk size', 'AI.CONFIG', 'MODEL_CHUNK_SIZE')
+
+    check_error_message(env, con, "wrong number of arguments for 'AI.CONFIG' command", 'AI.CONFIG', 'GET')
+    env.assertEqual(con.execute_command('AI.CONFIG', 'GET', 'bad_config'), None)

tests/flow/tests_pytorch.py

@@ -9,37 +9,6 @@
 '''
 
 
-def test_pytorch_chunked_modelstore(env):
-    if not TEST_PT:
-        env.debugPrint("skipping {} since TEST_PT=0".format(sys._getframe().f_code.co_name), force=True)
-        return
-
-    con = get_connection(env, '{1}')
-    model = load_file_content('pt-minimal.pt')
-
-    chunk_size = len(model) // 3
-
-    model_chunks = [model[i:i + chunk_size] for i in range(0, len(model), chunk_size)]
-
-    ret = con.execute_command('AI.MODELSTORE', 'm1{1}', 'TORCH', DEVICE, 'BLOB', model)
-    ret = con.execute_command('AI.MODELSTORE', 'm2{1}', 'TORCH', DEVICE, 'BLOB', *model_chunks)
-
-    model1 = con.execute_command('AI.MODELGET', 'm1{1}', 'BLOB')
-    model2 = con.execute_command('AI.MODELGET', 'm2{1}', 'BLOB')
-
-    env.assertEqual(model1, model2)
-
-    ret = con.execute_command('AI.CONFIG', 'MODEL_CHUNK_SIZE', chunk_size)
-
-    model2 = con.execute_command('AI.MODELGET', 'm2{1}', 'BLOB')
-    env.assertEqual(len(model2), len(model_chunks))
-    env.assertTrue(all([el1 == el2 for el1, el2 in zip(model2, model_chunks)]))
-
-    model3 = con.execute_command('AI.MODELGET', 'm2{1}', 'META', 'BLOB')[-1]  # Extract the BLOB list from the result
-    env.assertEqual(len(model3), len(model_chunks))
-    env.assertTrue(all([el1 == el2 for el1, el2 in zip(model3, model_chunks)]))
-
-
 def test_pytorch_modelrun(env):
     if not TEST_PT:
         env.debugPrint("skipping {} since TEST_PT=0".format(sys._getframe().f_code.co_name), force=True)