fix pipeline check error (#455)

* fix pipeline check error

* update

modelscope/pipeline_inputs.py

@@ -222,10 +222,13 @@ TASK_INPUTS = {
     },
 
     # ============ nlp tasks ===================
-    Tasks.chat: {
-        'text': InputType.TEXT,
-        'history': InputType.LIST,
-    },
+    Tasks.chat: [
+        InputType.TEXT,
+        {
+            'text': InputType.TEXT,
+            'history': InputType.LIST,
+        }
+    ],
     Tasks.text_classification: [
         InputType.TEXT,
         (InputType.TEXT, InputType.TEXT),
@@ -234,7 +237,13 @@ TASK_INPUTS = {
             'text2': InputType.TEXT
         },
     ],
-    Tasks.sentence_similarity: (InputType.TEXT, InputType.TEXT),
+    Tasks.sentence_similarity: [
+        (InputType.TEXT, InputType.TEXT),
+        {
+            'source_text': InputType.TEXT,
+            'target_text': InputType.TEXT,
+        },
+    ],
     Tasks.nli: (InputType.TEXT, InputType.TEXT),
     Tasks.sentiment_classification:
     InputType.TEXT,
@@ -275,10 +284,6 @@ TASK_INPUTS = {
     },
     Tasks.fill_mask:
     InputType.TEXT,
-    Tasks.task_oriented_conversation: {
-        'user_input': InputType.TEXT,
-        'history': InputType.DICT,
-    },
     Tasks.table_question_answering: {
         'question': InputType.TEXT,
         'history_sql': InputType.DICT,

modelscope/pipelines/base.py

@@ -346,12 +346,13 @@ class Pipeline(ABC):
             if isinstance(input_type, str):
                 check_input_type(input_type, input)
             elif isinstance(input_type, tuple):
+                assert isinstance(input, tuple), 'input should be a tuple'
                 for t, input_ele in zip(input_type, input):
                     check_input_type(t, input_ele)
             elif isinstance(input_type, dict):
                 for k in input_type.keys():
                     # allow single input for multi-modal models
-                    if k in input:
+                    if isinstance(input, dict) and k in input:
                         check_input_type(input_type[k], input[k])
             else:
                 raise ValueError(f'invalid input_type definition {input_type}')
@@ -373,7 +374,7 @@ class Pipeline(ABC):
         input = input.keys() if isinstance(input,
                                            (dict, ModelOutputBase)) else input
         for k in output_keys:
-            if k not in input:
+            if isinstance(k, (dict, ModelOutputBase)) and k not in input:
                 missing_keys.append(k)
         if len(missing_keys) > 0:
             raise ValueError(f'expected output keys are {output_keys}, '

tests/pipelines/test_base.py

@@ -141,6 +141,39 @@ class CustomPipelineTest(unittest.TestCase):
             self.assertEqual(out['url'], img_url + 'dummy_end')
             self.assertEqual(out['img'].shape, (1, 640, 640, 3))
 
+    def test_chat_task(self):
+        dummy_module = 'dummy_module'
+
+        @PIPELINES.register_module(
+            group_key=Tasks.chat, module_name=dummy_module)
+        class CustomChat(Pipeline):
+
+            def __init__(self,
+                         config_file: str = None,
+                         model=None,
+                         preprocessor=None,
+                         **kwargs):
+
+                def f(x):
+                    return x
+
+                preprocessor = f
+                super().__init__(config_file, model, preprocessor, **kwargs)
+
+            def forward(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+                """ Provide default implementation using self.model and user can reimplement it
+                """
+                return inputs
+
+            def postprocess(self, out, **kwargs):
+                return {'response': 'xxx', 'history': []}
+
+        pipe = pipeline(
+            task=Tasks.chat, pipeline_name=dummy_module, model=self.model_dir)
+        pipe('text')
+        inputs = {'text': 'aaa', 'history': [('dfd', 'fds')]}
+        pipe(inputs)
+
     def test_custom(self):
         dummy_task = 'dummy-task'