fixed unit tests

runtimes/huggingface/mlserver_huggingface/codecs/base.py

@@ -172,8 +172,8 @@ def encode_request(cls, payload: Dict[str, Any], **kwargs) -> InferenceRequest:
     @classmethod
     def decode_request(cls, request: InferenceRequest) -> Dict[str, Any]:
         """
-        Decode Inference requst into dictionary
-        extra Inference kwargs can be kept in 'InferenceRequest.parameters.extra'
+        Decode Inference request into dictionary
+        extra Inference kwargs are extracted from 'InferenceRequest.parameters.extra'
         """
         values = {}
         field_codecs = cls._find_decode_codecs(request)
@@ -193,7 +193,8 @@ def decode_request(cls, request: InferenceRequest) -> Dict[str, Any]:
                     values.update(extra)
                 else:
                     logging.warn(
-                        "Extra inference kwargs should be kept in a dictionary."
+                        f"Extra parameters is provided with value '{extra}' and type '{type(extra)}' \n"
+                        "Extra parameters cannot be parsed, expected a dictionary."
                     )
         return values
 

runtimes/huggingface/tests/test_codecs.py

@@ -1,5 +1,5 @@
 import pytest
-
+import logging
 from mlserver.types import (
     InferenceRequest,
     InferenceResponse,
@@ -53,6 +53,62 @@ def test_decode_request(inference_request, expected):
     assert payload == expected
 
 
+@pytest.mark.parametrize(
+    "inference_request, expected_payload, expected_log_msg",
+    [
+        (
+            InferenceRequest(
+                parameters=Parameters(content_type="str", extra="foo3"),
+                inputs=[
+                    RequestInput(
+                        name="foo",
+                        datatype="BYTES",
+                        data=["bar1", "bar2"],
+                        shape=[2, 1],
+                    ),
+                    RequestInput(
+                        name="foo2", datatype="BYTES", data=["var1"], shape=[1, 1]
+                    ),
+                ],
+            ),
+            {"foo": ["bar1", "bar2"]},
+            logging.warn(
+                f"Extra parameters is provided with value: 'foo3' and type '<class 'str'> \n"
+                "Extra parameters cannot be parsed, expected a dictionary."
+            ),
+        ),
+        (
+            InferenceRequest(
+                parameters=Parameters(content_type="str", extra=123),
+                inputs=[
+                    RequestInput(
+                        name="foo",
+                        datatype="BYTES",
+                        data=["bar1", "bar2"],
+                        shape=[2, 1],
+                    ),
+                    RequestInput(
+                        name="foo2", datatype="BYTES", data=["var1"], shape=[1, 1]
+                    ),
+                ],
+            ),
+            {"foo": ["bar1", "bar2"]},
+            logging.warn(
+                f"Extra parameters is provided with value '123' and type '<class 'int'> \n"
+                "Extra parameters cannot be parsed, expected a dictionary."
+            ),
+        ),
+    ],
+)
+def test_decode_request_with_invalid_parameter_extra(
+    inference_request, expected_payload, expected_log_msg, caplog
+):
+    caplog.set_level(logging.WARN)
+    payload = HuggingfaceRequestCodec.decode_request(inference_request)
+    assert payload == expected_payload
+    assert expected_log_msg in caplog.text
+
+
 @pytest.mark.parametrize(
     "payload, use_bytes, expected",
     [

runtimes/huggingface/tests/test_common.py

@@ -16,6 +16,7 @@
 from mlserver_huggingface.common import load_pipeline_from_settings
 from mlserver.types import InferenceRequest, RequestInput
 from mlserver.types.dataplane import Parameters
+from mlserver_huggingface.codecs.base import MultiInputRequestCodec
 
 
 @pytest.mark.parametrize(
@@ -252,7 +253,9 @@ async def test_pipeline_uses_inference_kwargs(
     tokenizer = runtime._model.tokenizer
 
     prediction = await runtime.predict(payload)
-    generated_text = json.loads(prediction.outputs[0].data[0])["generated_text"]
+    generated_text = MultiInputRequestCodec.decode_response(prediction)["output"][0][
+        "generated_text"
+    ]
     assert isinstance(generated_text, str)
     tokenized_generated_text = tokenizer.tokenize(generated_text)
     num_predicted_tokens = len(tokenized_generated_text)