feat: LLM - Added stop_sequences parameter to streaming methods and…

… `CodeChatModel`

PiperOrigin-RevId: 562915062

tests/unit/aiplatform/test_language_models.py

@@ -1304,6 +1304,7 @@ def test_text_generation_model_predict_streaming(self):
                 temperature=0.0,
                 top_p=1.0,
                 top_k=5,
+                stop_sequences=["# %%"],
             ):
                 assert len(response.text) > 10
 
@@ -1969,6 +1970,7 @@ def test_chat_model_send_message_streaming(self):
                 ),
             ],
             temperature=0.0,
+            stop_sequences=["\n"],
         )
 
         # Using list instead of a generator so that it can be reused.
@@ -1983,6 +1985,7 @@ def test_chat_model_send_message_streaming(self):
         message_max_output_tokens = 200
         message_top_k = 2
         message_top_p = 0.2
+        message_stop_sequences = ["# %%"]
 
         with mock.patch.object(
             target=prediction_service_client.PredictionServiceClient,
@@ -1998,6 +2001,7 @@ def test_chat_model_send_message_streaming(self):
                     temperature=message_temperature,
                     top_k=message_top_k,
                     top_p=message_top_p,
+                    stop_sequences=message_stop_sequences,
                 )
             ):
                 assert len(response.text) > 10
@@ -2036,6 +2040,7 @@ def test_code_chat(self):
         code_chat = model.start_chat(
             max_output_tokens=128,
             temperature=0.2,
+            stop_sequences=["\n"],
         )
 
         gca_predict_response1 = gca_prediction_service.PredictResponse()
@@ -2075,12 +2080,15 @@ def test_code_chat(self):
         # Validating the parameters
         chat_temperature = 0.1
         chat_max_output_tokens = 100
+        chat_stop_sequences = ["\n"]
         message_temperature = 0.2
         message_max_output_tokens = 200
+        message_stop_sequences = ["# %%"]
 
         code_chat2 = model.start_chat(
             temperature=chat_temperature,
             max_output_tokens=chat_max_output_tokens,
+            stop_sequences=chat_stop_sequences,
         )
 
         gca_predict_response3 = gca_prediction_service.PredictResponse()
@@ -2097,15 +2105,18 @@ def test_code_chat(self):
             prediction_parameters = mock_predict.call_args[1]["parameters"]
             assert prediction_parameters["temperature"] == chat_temperature
             assert prediction_parameters["maxDecodeSteps"] == chat_max_output_tokens
+            assert prediction_parameters["stopSequences"] == chat_stop_sequences
 
             code_chat2.send_message(
                 "Please help write a function to calculate the min of two numbers",
                 temperature=message_temperature,
                 max_output_tokens=message_max_output_tokens,
+                stop_sequences=message_stop_sequences,
             )
             prediction_parameters = mock_predict.call_args[1]["parameters"]
             assert prediction_parameters["temperature"] == message_temperature
             assert prediction_parameters["maxDecodeSteps"] == message_max_output_tokens
+            assert prediction_parameters["stopSequences"] == message_stop_sequences
 
     def test_code_chat_model_send_message_streaming(self):
         """Tests the chat generation model."""
@@ -2122,7 +2133,7 @@ def test_code_chat_model_send_message_streaming(self):
         ):
             model = language_models.CodeChatModel.from_pretrained("codechat-bison@001")
 
-        chat = model.start_chat(temperature=0.0)
+        chat = model.start_chat(temperature=0.0, stop_sequences=["\n"])
 
         # Using list instead of a generator so that it can be reused.
         response_generator = [

vertexai/language_models/_language_models.py

@@ -734,6 +734,7 @@ def predict_streaming(
         temperature: Optional[float] = None,
         top_k: Optional[int] = None,
         top_p: Optional[float] = None,
+        stop_sequences: Optional[List[str]] = None,
     ) -> Iterator[TextGenerationResponse]:
         """Gets a streaming model response for a single prompt.
 
@@ -745,6 +746,7 @@ def predict_streaming(
             temperature: Controls the randomness of predictions. Range: [0, 1]. Default: 0.
             top_k: The number of highest probability vocabulary tokens to keep for top-k-filtering. Range: [1, 40]. Default: 40.
             top_p: The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Range: [0, 1]. Default: 0.95.
+            stop_sequences: Customized stop sequences to stop the decoding process.
 
         Yields:
             A stream of `TextGenerationResponse` objects that contain partial
@@ -771,6 +773,9 @@ def predict_streaming(
         if top_k:
             prediction_parameters["topK"] = top_k
 
+        if stop_sequences:
+            prediction_parameters["stopSequences"] = stop_sequences
+
         for prediction_dict in _streaming_prediction.predict_stream_of_dicts_from_single_dict(
             prediction_service_client=prediction_service_client,
             endpoint_name=self._endpoint_name,
@@ -1299,12 +1304,14 @@ def start_chat(
         max_output_tokens: Optional[int] = None,
         temperature: Optional[float] = None,
         message_history: Optional[List[ChatMessage]] = None,
+        stop_sequences: Optional[List[str]] = None,
     ) -> "CodeChatSession":
         """Starts a chat session with the code chat model.
 
         Args:
             max_output_tokens: Max length of the output text in tokens. Range: [1, 1000].
             temperature: Controls the randomness of predictions. Range: [0, 1].
+            stop_sequences: Customized stop sequences to stop the decoding process.
 
         Returns:
             A `ChatSession` object.
@@ -1314,6 +1321,7 @@ def start_chat(
             max_output_tokens=max_output_tokens,
             temperature=temperature,
             message_history=message_history,
+            stop_sequences=stop_sequences,
         )
 
 
@@ -1541,6 +1549,7 @@ def send_message_streaming(
         temperature: Optional[float] = None,
         top_k: Optional[int] = None,
         top_p: Optional[float] = None,
+        stop_sequences: Optional[List[str]] = None,
     ) -> Iterator[TextGenerationResponse]:
         """Sends message to the language model and gets a streamed response.
 
@@ -1556,6 +1565,8 @@ def send_message_streaming(
                 Uses the value specified when calling `ChatModel.start_chat` by default.
             top_p: The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Range: [0, 1]. Default: 0.95.
                 Uses the value specified when calling `ChatModel.start_chat` by default.
+            stop_sequences: Customized stop sequences to stop the decoding process.
+                Uses the value specified when calling `ChatModel.start_chat` by default.
 
         Yields:
             A stream of `TextGenerationResponse` objects that contain partial
@@ -1567,6 +1578,7 @@ def send_message_streaming(
             temperature=temperature,
             top_k=top_k,
             top_p=top_p,
+            stop_sequences=stop_sequences,
         )
 
         prediction_service_client = self._model._endpoint._prediction_client
@@ -1644,12 +1656,14 @@ def __init__(
         max_output_tokens: Optional[int] = None,
         temperature: Optional[float] = None,
         message_history: Optional[List[ChatMessage]] = None,
+        stop_sequences: Optional[List[str]] = None,
     ):
         super().__init__(
             model=model,
             max_output_tokens=max_output_tokens,
             temperature=temperature,
             message_history=message_history,
+            stop_sequences=stop_sequences,
         )
 
     def send_message(
@@ -1658,6 +1672,7 @@ def send_message(
         *,
         max_output_tokens: Optional[int] = None,
         temperature: Optional[float] = None,
+        stop_sequences: Optional[List[str]] = None,
     ) -> "TextGenerationResponse":
         """Sends message to the code chat model and gets a response.
 
@@ -1667,6 +1682,7 @@ def send_message(
                 Uses the value specified when calling `CodeChatModel.start_chat` by default.
             temperature: Controls the randomness of predictions. Range: [0, 1].
                  Uses the value specified when calling `CodeChatModel.start_chat` by default.
+            stop_sequences: Customized stop sequences to stop the decoding process.
 
         Returns:
             A `TextGenerationResponse` object that contains the text produced by the model.
@@ -1675,6 +1691,7 @@ def send_message(
             message=message,
             max_output_tokens=max_output_tokens,
             temperature=temperature,
+            stop_sequences=stop_sequences,
         )
 
     def send_message_streaming(
@@ -1683,6 +1700,7 @@ def send_message_streaming(
         *,
         max_output_tokens: Optional[int] = None,
         temperature: Optional[float] = None,
+        stop_sequences: Optional[List[str]] = None,
     ) -> Iterator[TextGenerationResponse]:
         """Sends message to the language model and gets a streamed response.
 
@@ -1694,6 +1712,8 @@ def send_message_streaming(
                 Uses the value specified when calling `ChatModel.start_chat` by default.
             temperature: Controls the randomness of predictions. Range: [0, 1]. Default: 0.
                 Uses the value specified when calling `ChatModel.start_chat` by default.
+            stop_sequences: Customized stop sequences to stop the decoding process.
+                Uses the value specified when calling `ChatModel.start_chat` by default.
 
         Returns:
             A stream of `TextGenerationResponse` objects that contain partial
@@ -1703,6 +1723,7 @@ def send_message_streaming(
             message=message,
             max_output_tokens=max_output_tokens,
             temperature=temperature,
+            stop_sequences=stop_sequences,
         )
 
 
@@ -1811,6 +1832,7 @@ def predict_streaming(
         *,
         max_output_tokens: Optional[int] = None,
         temperature: Optional[float] = None,
+        stop_sequences: Optional[List[str]] = None,
     ) -> Iterator[TextGenerationResponse]:
         """Predicts the code based on previous code.
 
@@ -1821,6 +1843,7 @@ def predict_streaming(
             suffix: Code after the current point.
             max_output_tokens: Max length of the output text in tokens. Range: [1, 1000].
             temperature: Controls the randomness of predictions. Range: [0, 1].
+            stop_sequences: Customized stop sequences to stop the decoding process.
 
         Yields:
             A stream of `TextGenerationResponse` objects that contain partial
@@ -1831,6 +1854,7 @@ def predict_streaming(
             suffix=suffix,
             max_output_tokens=max_output_tokens,
             temperature=temperature,
+            stop_sequences=stop_sequences,
         )
 
         prediction_service_client = self._endpoint._prediction_client