feat: LLM - Released the LLM SDK to GA

This commit also refactors the machinery around the mapping between schemas and interface classes.
The central table is not needed now. Each interface class knows its own instance schema.
This architecture makes it much easier to experiment with different interface classes since `MyClass.from_pretrained` now returns an instance of `MyClass` (if the model's instance schema matches).
This change makes it trivial to have multiple versions of an interface class (e.g. GA, preview etc). It was much harder with a centralized table that could only hold a single interface class for each instance schema.

PiperOrigin-RevId: 538100676

tests/unit/aiplatform/test_language_models.py

@@ -53,7 +53,10 @@
     model as gca_model,
 )
 
-from vertexai.preview import language_models
+from vertexai.preview import (
+    language_models as preview_language_models,
+)
+from vertexai import language_models
 from google.cloud.aiplatform_v1 import Execution as GapicExecution
 from google.cloud.aiplatform.compat.types import (
     encryption_spec as gca_encryption_spec,
@@ -456,7 +459,7 @@ def get_endpoint_mock():
 @pytest.fixture
 def mock_get_tuned_model(get_endpoint_mock):
     with mock.patch.object(
-        language_models.TextGenerationModel, "get_tuned_model"
+        preview_language_models.TextGenerationModel, "get_tuned_model"
     ) as mock_text_generation_model:
         mock_text_generation_model._model_id = (
             test_constants.ModelConstants._TEST_MODEL_RESOURCE_NAME
@@ -519,6 +522,50 @@ def teardown_method(self):
         initializer.global_pool.shutdown(wait=True)
 
     def test_text_generation(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
+            ),
+        ) as mock_get_publisher_model:
+            model = preview_language_models.TextGenerationModel.from_pretrained(
+                "text-bison@001"
+            )
+
+        mock_get_publisher_model.assert_called_once_with(
+            name="publishers/google/models/text-bison@001", retry=base._DEFAULT_RETRY
+        )
+
+        assert (
+            model._model_resource_name
+            == f"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/publishers/google/models/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_client.PredictionServiceClient,
+            attribute="predict",
+            return_value=gca_predict_response,
+        ):
+            response = model.predict(
+                "What is the best recipe for banana bread? Recipe:",
+                max_output_tokens=128,
+                temperature=0,
+                top_p=1,
+                top_k=5,
+            )
+
+        assert response.text == _TEST_TEXT_GENERATION_PREDICTION["content"]
+
+    def test_text_generation_ga(self):
         """Tests the text generation model."""
         aiplatform.init(
             project=_TEST_PROJECT,
@@ -596,7 +643,7 @@ def test_tune_model(
                 _TEXT_BISON_PUBLISHER_MODEL_DICT
             ),
         ):
-            model = language_models.TextGenerationModel.from_pretrained(
+            model = preview_language_models.TextGenerationModel.from_pretrained(
                 "text-bison@001"
             )
 
@@ -631,7 +678,7 @@ def test_get_tuned_model(
                 _TEXT_BISON_PUBLISHER_MODEL_DICT
             ),
         ):
-            tuned_model = language_models.TextGenerationModel.get_tuned_model(
+            tuned_model = preview_language_models.TextGenerationModel.get_tuned_model(
                 test_constants.ModelConstants._TEST_MODEL_RESOURCE_NAME
             )
 
@@ -651,7 +698,7 @@ def get_tuned_model_raises_if_not_called_with_mg_model(self):
         )
 
         with pytest.raises(ValueError):
-            language_models.TextGenerationModel.get_tuned_model(
+            preview_language_models.TextGenerationModel.get_tuned_model(
                 test_constants.ModelConstants._TEST_MODEL_RESOURCE_NAME
             )
 
@@ -668,7 +715,7 @@ def test_chat(self):
                 _CHAT_BISON_PUBLISHER_MODEL_DICT
             ),
         ) as mock_get_publisher_model:
-            model = language_models.ChatModel.from_pretrained("chat-bison@001")
+            model = preview_language_models.ChatModel.from_pretrained("chat-bison@001")
 
         mock_get_publisher_model.assert_called_once_with(
             name="publishers/google/models/chat-bison@001", retry=base._DEFAULT_RETRY
@@ -681,11 +728,11 @@ def test_chat(self):
             My favorite movies are Lord of the Rings and Hobbit.
             """,
             examples=[
-                language_models.InputOutputTextPair(
+                preview_language_models.InputOutputTextPair(
                     input_text="Who do you work for?",
                     output_text="I work for Ned.",
                 ),
-                language_models.InputOutputTextPair(
+                preview_language_models.InputOutputTextPair(
                     input_text="What do I like?",
                     output_text="Ned likes watching movies.",
                 ),
@@ -786,7 +833,7 @@ def test_code_chat(self):
                 _CODECHAT_BISON_PUBLISHER_MODEL_DICT
             ),
         ) as mock_get_publisher_model:
-            model = language_models.CodeChatModel.from_pretrained(
+            model = preview_language_models.CodeChatModel.from_pretrained(
                 "google/codechat-bison@001"
             )
 
@@ -882,7 +929,7 @@ def test_code_generation(self):
                 _CODE_GENERATION_BISON_PUBLISHER_MODEL_DICT
             ),
         ) as mock_get_publisher_model:
-            model = language_models.CodeGenerationModel.from_pretrained(
+            model = preview_language_models.CodeGenerationModel.from_pretrained(
                 "google/code-bison@001"
             )
 
@@ -909,9 +956,11 @@ def test_code_generation(self):
         # Validating the parameters
         predict_temperature = 0.1
         predict_max_output_tokens = 100
-        default_temperature = language_models.CodeGenerationModel._DEFAULT_TEMPERATURE
+        default_temperature = (
+            preview_language_models.CodeGenerationModel._DEFAULT_TEMPERATURE
+        )
         default_max_output_tokens = (
-            language_models.CodeGenerationModel._DEFAULT_MAX_OUTPUT_TOKENS
+            preview_language_models.CodeGenerationModel._DEFAULT_MAX_OUTPUT_TOKENS
         )
 
         with mock.patch.object(
@@ -948,7 +997,7 @@ def test_code_completion(self):
                 _CODE_COMPLETION_BISON_PUBLISHER_MODEL_DICT
             ),
         ) as mock_get_publisher_model:
-            model = language_models.CodeGenerationModel.from_pretrained(
+            model = preview_language_models.CodeGenerationModel.from_pretrained(
                 "google/code-gecko@001"
             )
 
@@ -975,9 +1024,11 @@ def test_code_completion(self):
         # Validating the parameters
         predict_temperature = 0.1
         predict_max_output_tokens = 100
-        default_temperature = language_models.CodeGenerationModel._DEFAULT_TEMPERATURE
+        default_temperature = (
+            preview_language_models.CodeGenerationModel._DEFAULT_TEMPERATURE
+        )
         default_max_output_tokens = (
-            language_models.CodeGenerationModel._DEFAULT_MAX_OUTPUT_TOKENS
+            preview_language_models.CodeGenerationModel._DEFAULT_MAX_OUTPUT_TOKENS
         )
 
         with mock.patch.object(
@@ -1002,6 +1053,43 @@ def test_code_completion(self):
             assert prediction_parameters["maxOutputTokens"] == default_max_output_tokens
 
     def test_text_embedding(self):
+        """Tests the text embedding 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_EMBEDDING_GECKO_PUBLISHER_MODEL_DICT
+            ),
+        ) as mock_get_publisher_model:
+            model = preview_language_models.TextEmbeddingModel.from_pretrained(
+                "textembedding-gecko@001"
+            )
+
+        mock_get_publisher_model.assert_called_once_with(
+            name="publishers/google/models/textembedding-gecko@001",
+            retry=base._DEFAULT_RETRY,
+        )
+
+        gca_predict_response = gca_prediction_service.PredictResponse()
+        gca_predict_response.predictions.append(_TEST_TEXT_EMBEDDING_PREDICTION)
+
+        with mock.patch.object(
+            target=prediction_service_client.PredictionServiceClient,
+            attribute="predict",
+            return_value=gca_predict_response,
+        ):
+            embeddings = model.get_embeddings(["What is life?"])
+            assert embeddings
+            for embedding in embeddings:
+                vector = embedding.values
+                assert len(vector) == _TEXT_EMBEDDING_VECTOR_LENGTH
+                assert vector == _TEST_TEXT_EMBEDDING_PREDICTION["embeddings"]["values"]
+
+    def test_text_embedding_ga(self):
         """Tests the text embedding model."""
         aiplatform.init(
             project=_TEST_PROJECT,

tests/unit/aiplatform/test_model_garden_models.py

@@ -18,7 +18,6 @@
 import pytest
 from importlib import reload
 from unittest import mock
-from typing import Dict, Type
 
 from google.cloud import aiplatform
 from google.cloud.aiplatform import base
@@ -53,14 +52,7 @@ class TestModelGardenModels:
     """Unit tests for the _ModelGardenModel base class."""
 
     class FakeModelGardenModel(_model_garden_models._ModelGardenModel):
-        @staticmethod
-        def _get_public_preview_class_map() -> Dict[
-            str, Type[_model_garden_models._ModelGardenModel]
-        ]:
-            test_map = {
-                "gs://google-cloud-aiplatform/schema/predict/instance/text_generation_1.0.0.yaml": TestModelGardenModels.FakeModelGardenModel
-            }
-            return test_map
+        _INSTANCE_SCHEMA_URI = "gs://google-cloud-aiplatform/schema/predict/instance/text_generation_1.0.0.yaml"
 
     def setup_method(self):
         reload(initializer)

vertexai/_model_garden/_model_garden_models.py

@@ -107,7 +107,9 @@ def _get_model_info(
     )
 
     if not interface_class:
-        raise ValueError(f"Unknown model {publisher_model_res.name}")
+        raise ValueError(
+            f"Unknown model {publisher_model_res.name}; {schema_to_class_map}"
+        )
 
     return _ModelInfo(
         endpoint_name=endpoint_name,
@@ -120,18 +122,8 @@ def _get_model_info(
 class _ModelGardenModel:
     """Base class for shared methods and properties across Model Garden models."""
 
-    @staticmethod
-    @abc.abstractmethod
-    def _get_public_preview_class_map() -> Dict[str, Type["_ModelGardenModel"]]:
-        """Returns a Dict mapping schema URI to model class.
-
-        Subclasses should implement this method. Example mapping:
-
-            {
-                "gs://google-cloud-aiplatform/schema/predict/instance/text_generation_1.0.0.yaml": TextGenerationModel
-            }
-        """
-        pass
+    # Subclasses override this attribute to specify their instance schema
+    _INSTANCE_SCHEMA_URI: Optional[str] = None
 
     def __init__(self, model_id: str, endpoint_name: Optional[str] = None):
         """Creates a _ModelGardenModel.
@@ -168,8 +160,13 @@ def from_pretrained(cls, model_name: str) -> "_ModelGardenModel":
             ValueError: If model does not support this class.
         """
 
+        if not cls._INSTANCE_SCHEMA_URI:
+            raise ValueError(
+                f"Class {cls} is not a correct model interface class since it does not have an instance schema URI."
+            )
+
         model_info = _get_model_info(
-            model_id=model_name, schema_to_class_map=cls._get_public_preview_class_map()
+            model_id=model_name, schema_to_class_map={cls._INSTANCE_SCHEMA_URI: cls}
         )
 
         if not issubclass(model_info.interface_class, cls):

vertexai/language_models/__init__.py

@@ -12,5 +12,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+"""Classes for working with language models."""
 
-from vertexai.language_models import _language_models
+from vertexai.language_models._language_models import (
+    InputOutputTextPair,
+    TextEmbedding,
+    TextEmbeddingModel,
+    TextGenerationModel,
+    TextGenerationResponse,
+)
+
+__all__ = [
+    "InputOutputTextPair",
+    "TextEmbedding",
+    "TextEmbeddingModel",
+    "TextGenerationModel",
+    "TextGenerationResponse",
+]