feat: LLM - Added support for async prediction methods

PiperOrigin-RevId: 566381589

tests/system/aiplatform/test_language_models.py

@@ -17,6 +17,9 @@
 
 # pylint: disable=protected-access, g-multiple-import
 
+import pytest
+
+
 from google.cloud import aiplatform
 from google.cloud.aiplatform.compat.types import (
     job_state as gca_job_state,
@@ -54,6 +57,22 @@ def test_text_generation(self):
             stop_sequences=["# %%"],
         ).text
 
+    @pytest.mark.asyncio
+    async def test_text_generation_model_predict_async(self):
+        aiplatform.init(project=e2e_base._PROJECT, location=e2e_base._LOCATION)
+
+        model = TextGenerationModel.from_pretrained("google/text-bison@001")
+
+        response = await model.predict_async(
+            "What is the best recipe for banana bread? Recipe:",
+            max_output_tokens=128,
+            temperature=0.0,
+            top_p=1.0,
+            top_k=5,
+            stop_sequences=["# %%"],
+        )
+        assert response.text
+
     def test_text_generation_streaming(self):
         aiplatform.init(project=e2e_base._PROJECT, location=e2e_base._LOCATION)
 
@@ -107,6 +126,46 @@ def test_chat_on_chat_model(self):
         assert chat.message_history[2].content == message2
         assert chat.message_history[3].author == chat.MODEL_AUTHOR
 
+    @pytest.mark.asyncio
+    async def test_chat_model_async(self):
+        aiplatform.init(project=e2e_base._PROJECT, location=e2e_base._LOCATION)
+
+        chat_model = ChatModel.from_pretrained("google/chat-bison@001")
+        chat = chat_model.start_chat(
+            context="My name is Ned. You are my personal assistant. My favorite movies are Lord of the Rings and Hobbit.",
+            examples=[
+                InputOutputTextPair(
+                    input_text="Who do you work for?",
+                    output_text="I work for Ned.",
+                ),
+                InputOutputTextPair(
+                    input_text="What do I like?",
+                    output_text="Ned likes watching movies.",
+                ),
+            ],
+            temperature=0.0,
+            stop_sequences=["# %%"],
+        )
+
+        message1 = "Are my favorite movies based on a book series?"
+        response1 = await chat.send_message_async(message1)
+        assert response1.text
+        assert len(chat.message_history) == 2
+        assert chat.message_history[0].author == chat.USER_AUTHOR
+        assert chat.message_history[0].content == message1
+        assert chat.message_history[1].author == chat.MODEL_AUTHOR
+
+        message2 = "When were these books published?"
+        response2 = await chat.send_message_async(
+            message2,
+            temperature=0.1,
+        )
+        assert response2.text
+        assert len(chat.message_history) == 4
+        assert chat.message_history[2].author == chat.USER_AUTHOR
+        assert chat.message_history[2].content == message2
+        assert chat.message_history[3].author == chat.MODEL_AUTHOR
+
     def test_chat_model_send_message_streaming(self):
         aiplatform.init(project=e2e_base._PROJECT, location=e2e_base._LOCATION)
 
@@ -161,6 +220,23 @@ def test_text_embedding(self):
         assert embeddings[1].statistics.token_count > 1000
         assert embeddings[1].statistics.truncated
 
+    @pytest.mark.asyncio
+    async def test_text_embedding_async(self):
+        aiplatform.init(project=e2e_base._PROJECT, location=e2e_base._LOCATION)
+
+        model = TextEmbeddingModel.from_pretrained("google/textembedding-gecko@001")
+        # One short text, one llong text (to check truncation)
+        texts = ["What is life?", "What is life?" * 1000]
+        embeddings = await model.get_embeddings_async(texts)
+        assert len(embeddings) == 2
+        assert len(embeddings[0].values) == 768
+        assert embeddings[0].statistics.token_count > 0
+        assert not embeddings[0].statistics.truncated
+
+        assert len(embeddings[1].values) == 768
+        assert embeddings[1].statistics.token_count > 1000
+        assert embeddings[1].statistics.truncated
+
     def test_tuning(self, shared_state):
         """Test tuning, listing and loading models."""
         aiplatform.init(project=e2e_base._PROJECT, location=e2e_base._LOCATION)

tests/unit/aiplatform/test_language_models.py

@@ -40,7 +40,10 @@
     model_service_client,
     pipeline_service_client,
 )
-from google.cloud.aiplatform.compat.services import prediction_service_client
+from google.cloud.aiplatform.compat.services import (
+    prediction_service_client,
+    prediction_service_async_client,
+)
 from google.cloud.aiplatform.compat.types import (
     artifact as gca_artifact,
     prediction_service as gca_prediction_service,
@@ -1273,6 +1276,49 @@ def test_text_generation_ga(self):
         assert "topP" not in prediction_parameters
         assert "topK" not in prediction_parameters
 
+    @pytest.mark.asyncio
+    async def test_text_generation_async(self):
+        """Tests the text generation model."""
+        aiplatform.init(
+            project=_TEST_PROJECT,
+            location=_TEST_LOCATION,
+        )
+        with mock.patch.object(
+            target=model_garden_service_client.ModelGardenServiceClient,
+            attribute="get_publisher_model",
+            return_value=gca_publisher_model.PublisherModel(
+                _TEXT_BISON_PUBLISHER_MODEL_DICT
+            ),
+        ):
+            model = language_models.TextGenerationModel.from_pretrained(
+                "text-bison@001"
+            )
+
+        gca_predict_response = gca_prediction_service.PredictResponse()
+        gca_predict_response.predictions.append(_TEST_TEXT_GENERATION_PREDICTION)
+
+        with mock.patch.object(
+            target=prediction_service_async_client.PredictionServiceAsyncClient,
+            attribute="predict",
+            return_value=gca_predict_response,
+        ) as mock_predict:
+            response = await model.predict_async(
+                "What is the best recipe for banana bread? Recipe:",
+                max_output_tokens=128,
+                temperature=0.0,
+                top_p=1.0,
+                top_k=5,
+                stop_sequences=["\n"],
+            )
+
+        prediction_parameters = mock_predict.call_args[1]["parameters"]
+        assert prediction_parameters["maxDecodeSteps"] == 128
+        assert prediction_parameters["temperature"] == 0.0
+        assert prediction_parameters["topP"] == 1.0
+        assert prediction_parameters["topK"] == 5
+        assert prediction_parameters["stopSequences"] == ["\n"]
+        assert response.text == _TEST_TEXT_GENERATION_PREDICTION["content"]
+
     def test_text_generation_model_predict_streaming(self):
         """Tests the TextGenerationModel.predict_streaming method."""
         with mock.patch.object(