feat: GenAI - Tuning - Added support for tuned model rebasing. Added rebase_tuned_model to vertexai.preview.tuning.sft.

PiperOrigin-RevId: 688795387

Author: Ark-kun

vertexai/preview/tuning/sft.py

@@ -20,8 +20,12 @@
     train,
     SupervisedTuningJob,
 )
+from vertexai.tuning._tuning import (
+    rebase_tuned_model,
+)
 
 __all__ = [
+    "rebase_tuned_model",
     "train",
     "SupervisedTuningJob",
 ]

vertexai/tuning/_tuning.py

@@ -257,3 +257,85 @@ def _dashboard_url(self) -> str:
         job = list(fields.values())[0]
         url = f"https://console.cloud.google.com/vertex-ai/generative/language/locations/{location}/tuning/tuningJob/{job}?project={project}"
         return url
+
+
+def rebase_tuned_model(
+    tuned_model_ref: str,
+    *,
+    # TODO(b/372291558): Add support for overriding tuning job config
+    # tuning_job_config: Optional["TuningJob"] = None,
+    artifact_destination: Optional[str] = None,
+    deploy_to_same_endpoint: Optional[bool] = False,
+):
+    """Re-runs fine tuning on top of a new foundational model.
+
+    Takes a legacy Tuned GenAI model Reference and creates a TuningJob based
+    on a new model.
+
+    Args:
+        tuned_model_ref: Required. TunedModel reference to retrieve
+            the legacy model information.
+        tuning_job_config: The TuningJob to be updated. Users
+            can use this TuningJob field to overwrite tuning
+            configs.
+        artifact_destination: The Google Cloud Storage location to write the artifacts.
+        deploy_to_same_endpoint:
+            Optional. By default, bison to gemini
+            migration will always create new model/endpoint,
+            but for gemini-1.0 to gemini-1.5 migration, we
+            default deploy to the same endpoint. See details
+            in this Section.
+
+    Returns:
+        The new TuningJob.
+    """
+    parent = aiplatform_initializer.global_config.common_location_path(
+        project=aiplatform_initializer.global_config.project,
+        location=aiplatform_initializer.global_config.location,
+    )
+
+    if "/tuningJobs/" in tuned_model_ref:
+        gapic_tuned_model_ref = gca_types.TunedModelRef(
+            tuning_job=tuned_model_ref,
+        )
+    elif "/pipelineJobs/" in tuned_model_ref:
+        gapic_tuned_model_ref = gca_types.TunedModelRef(
+            pipeline_job=tuned_model_ref,
+        )
+    elif "/models/" in tuned_model_ref:
+        gapic_tuned_model_ref = gca_types.TunedModelRef(
+            tuned_model=tuned_model_ref,
+        )
+    else:
+        raise ValueError(f"Unsupported tuned_model_ref: {tuned_model_ref}.")
+
+    # gapic_tuning_job_config = tuning_job._gca_resource if tuning_job else None
+    gapic_tuning_job_config = None
+
+    gapic_artifact_destination = (
+        gca_types.GcsDestination(output_uri_prefix=artifact_destination)
+        if artifact_destination
+        else None
+    )
+
+    api_client: gen_ai_tuning_service_v1beta1.GenAiTuningServiceClient = (
+        TuningJob._instantiate_client(
+            location=aiplatform_initializer.global_config.location,
+            credentials=aiplatform_initializer.global_config.credentials,
+        )
+    )
+    rebase_operation = api_client.rebase_tuned_model(
+        gca_types.RebaseTunedModelRequest(
+            parent=parent,
+            tuned_model_ref=gapic_tuned_model_ref,
+            tuning_job=gapic_tuning_job_config,
+            artifact_destination=gapic_artifact_destination,
+            deploy_to_same_endpoint=deploy_to_same_endpoint,
+        )
+    )
+    _LOGGER.log_create_with_lro(TuningJob, lro=rebase_operation)
+    gapic_rebase_tuning_job: gca_types.TuningJob = rebase_operation.result()
+    rebase_tuning_job = TuningJob._construct_sdk_resource_from_gapic(
+        gapic_resource=gapic_rebase_tuning_job,
+    )
+    return rebase_tuning_job