fix: LLM - Fixed batch prediction on tuned models

PiperOrigin-RevId: 560910428

tests/unit/aiplatform/test_language_models.py

@@ -45,7 +45,7 @@
     artifact as gca_artifact,
     prediction_service as gca_prediction_service,
     context as gca_context,
-    endpoint as gca_endpoint,
+    endpoint_v1 as gca_endpoint,
     pipeline_job as gca_pipeline_job,
     pipeline_state as gca_pipeline_state,
     deployed_model_ref_v1,
@@ -1030,6 +1030,11 @@ def get_endpoint_mock():
         get_endpoint_mock.return_value = gca_endpoint.Endpoint(
             display_name="test-display-name",
             name=test_constants.EndpointConstants._TEST_ENDPOINT_NAME,
+            deployed_models=[
+                gca_endpoint.DeployedModel(
+                    model=test_constants.ModelConstants._TEST_MODEL_RESOURCE_NAME
+                ),
+            ],
         )
         yield get_endpoint_mock
 
@@ -2420,7 +2425,10 @@ def test_text_embedding_ga(self):
                 assert len(vector) == _TEXT_EMBEDDING_VECTOR_LENGTH
                 assert vector == _TEST_TEXT_EMBEDDING_PREDICTION["embeddings"]["values"]
 
-    def test_batch_prediction(self):
+    def test_batch_prediction(
+        self,
+        get_endpoint_mock,
+    ):
         """Tests batch prediction."""
         aiplatform.init(
             project=_TEST_PROJECT,
@@ -2447,7 +2455,29 @@ def test_batch_prediction(self):
                 model_parameters={"temperature": 0.1},
             )
             mock_create.assert_called_once_with(
-                model_name="publishers/google/models/text-bison@001",
+                model_name=f"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/publishers/google/models/text-bison@001",
+                job_display_name=None,
+                gcs_source="gs://test-bucket/test_table.jsonl",
+                gcs_destination_prefix="gs://test-bucket/results/",
+                model_parameters={"temperature": 0.1},
+            )
+
+        # Testing tuned model batch prediction
+        tuned_model = language_models.TextGenerationModel(
+            model_id=model._model_id,
+            endpoint_name=test_constants.EndpointConstants._TEST_ENDPOINT_NAME,
+        )
+        with mock.patch.object(
+            target=aiplatform.BatchPredictionJob,
+            attribute="create",
+        ) as mock_create:
+            tuned_model.batch_predict(
+                dataset="gs://test-bucket/test_table.jsonl",
+                destination_uri_prefix="gs://test-bucket/results/",
+                model_parameters={"temperature": 0.1},
+            )
+            mock_create.assert_called_once_with(
+                model_name=test_constants.ModelConstants._TEST_MODEL_RESOURCE_NAME,
                 job_display_name=None,
                 gcs_source="gs://test-bucket/test_table.jsonl",
                 gcs_destination_prefix="gs://test-bucket/results/",
@@ -2481,7 +2511,7 @@ def test_batch_prediction_for_text_embedding(self):
                 model_parameters={},
             )
             mock_create.assert_called_once_with(
-                model_name="publishers/google/models/textembedding-gecko@001",
+                model_name=f"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/publishers/google/models/textembedding-gecko@001",
                 job_display_name=None,
                 gcs_source="gs://test-bucket/test_table.jsonl",
                 gcs_destination_prefix="gs://test-bucket/results/",

vertexai/language_models/_language_models.py

@@ -839,11 +839,6 @@ def batch_predict(
             raise ValueError(f"Unsupported destination_uri: {destination_uri_prefix}")
 
         model_name = self._model_resource_name
-        # TODO(b/284512065): Batch prediction service does not support
-        # fully qualified publisher model names yet
-        publishers_index = model_name.index("/publishers/")
-        if publishers_index > 0:
-            model_name = model_name[publishers_index + 1 :]
 
         job = aiplatform.BatchPredictionJob.create(
             model_name=model_name,