feat(components): Implement new component to preprocess and validate …

…inputs for rlhf

PiperOrigin-RevId: 617285265

components/google-cloud/google_cloud_pipeline_components/_implementation/llm/rlhf_preprocessor.py

@@ -0,0 +1,60 @@
+# Copyright 2024 The Kubeflow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Component that preprocesses inputs for Reinforcement Learning from Human Feedback (RLHF)."""
+
+import os
+
+from google_cloud_pipeline_components import _placeholders
+from google_cloud_pipeline_components import utils as gcpc_utils
+from google_cloud_pipeline_components._implementation.llm import utils
+from kfp import dsl
+
+
+@dsl.container_component
+def rlhf_preprocessor(
+    gcp_resources: dsl.OutputPath(str),  # pytype: disable=invalid-annotation
+    has_tensorboard_id: dsl.OutputPath(bool),  # pytype: disable=invalid-annotation
+    has_inference_dataset: dsl.OutputPath(bool),  # pytype: disable=invalid-annotation
+    evaluation_dataset: str = '',
+    tensorboard_resource_id: str = '',
+    image_uri: str = utils.get_default_image_uri('refined_cpu', ''),
+) -> dsl.ContainerSpec:  # pylint: disable=g-doc-args
+  """Preprocess RLHF pipeline inputs.
+
+  Args:
+    evaluation_dataset: Path to evaluation data.
+    tensorboard_resource_id: TensorBoard resource id.
+
+  Returns:
+    gcp_resources: GCP resources that can be used to track the custom job.
+    has_tensorboard_id: Whether a tensorboard id is provided.
+    has_inference_dataset: Whether inference data are provided.
+  """
+  return gcpc_utils.build_serverless_customjob_container_spec(
+      project=_placeholders.PROJECT_ID_PLACEHOLDER,
+      location=_placeholders.LOCATION_PLACEHOLDER,
+      custom_job_payload=utils.build_payload(
+          display_name='rlhf_preprocessor',
+          machine_type='n1-standard-4',
+          image_uri=image_uri,
+          args=[
+              '--app_name=rlhf_preprocessor',
+              f'--evaluation_dataset={evaluation_dataset}',
+              f'--tensorboard_resource_id={tensorboard_resource_id}',
+              f'--has_tensorboard_id_path={has_tensorboard_id}',
+              f'--has_inference_dataset_path={has_inference_dataset}',
+          ],
+      ),
+      gcp_resources=gcp_resources,
+  )