feat(components): Add Feature Attribution components to _implementation/model_evaluation. Add LLM Eval text generation and text classification pipelines to preview namespace init file

PiperOrigin-RevId: 557226606

components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/__init__.py

@@ -19,25 +19,31 @@ from google_cloud_pipeline_components._implementation.model_evaluation.data_samp
 from google_cloud_pipeline_components._implementation.model_evaluation.dataset_preprocessor.component import dataset_preprocessor_error_analysis as EvaluationDatasetPreprocessorOp
 from google_cloud_pipeline_components._implementation.model_evaluation.error_analysis_annotation.component import error_analysis_annotation as ErrorAnalysisAnnotationOp
 from google_cloud_pipeline_components._implementation.model_evaluation.evaluated_annotation.component import evaluated_annotation as EvaluatedAnnotationOp
+from google_cloud_pipeline_components._implementation.model_evaluation.feature_attribution.feature_attribution_component import feature_attribution as ModelEvaluationFeatureAttributionOp
+from google_cloud_pipeline_components._implementation.model_evaluation.feature_attribution.feature_attribution_graph_component import feature_attribution_graph_component as FeatureAttributionGraphComponentOp
 from google_cloud_pipeline_components._implementation.model_evaluation.feature_extractor.component import feature_extractor_error_analysis as FeatureExtractorOp
 from google_cloud_pipeline_components._implementation.model_evaluation.import_evaluated_annotation.component import evaluated_annotation_import as ModelImportEvaluatedAnnotationOp
 from google_cloud_pipeline_components._implementation.model_evaluation.import_evaluation.component import model_evaluation_import as ModelImportEvaluationOp
 from google_cloud_pipeline_components._implementation.model_evaluation.llm_classification_postprocessor.component import llm_classification_predictions_postprocessor_graph_component as LLMEvaluationClassificationPredictionsPostprocessorOp
 from google_cloud_pipeline_components._implementation.model_evaluation.llm_evaluation.component import model_evaluation_text_generation as LLMEvaluationTextGenerationOp
 from google_cloud_pipeline_components._implementation.model_evaluation.llm_safety_bias.component import llm_safety_bias_metrics as LLMSafetyBiasMetricsOp
+from google_cloud_pipeline_components._implementation.model_evaluation.llm_safety_bias.evaluation_llm_safety_bias_pipeline import evaluation_llm_safety_bias_pipeline
 from google_cloud_pipeline_components._implementation.model_evaluation.target_field_data_remover.component import target_field_data_remover as TargetFieldDataRemoverOp
 
 
 __all__ = [
+    'evaluation_llm_safety_bias_pipeline',
     'EvaluationDataSamplerOp',
     'EvaluationDatasetPreprocessorOp',
     'ErrorAnalysisAnnotationOp',
     'EvaluatedAnnotationOp',
+    'FeatureAttributionGraphComponentOp',
     'FeatureExtractorOp',
     'LLMEvaluationClassificationPredictionsPostprocessorOp',
     'LLMEvaluationTextGenerationOp',
+    'LLMSafetyBiasMetricsOp',
+    'ModelEvaluationFeatureAttributionOp',
     'ModelImportEvaluatedAnnotationOp',
     'ModelImportEvaluationOp',
-    'LLMSafetyBiasMetricsOp',
     'TargetFieldDataRemoverOp',
 ]

components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/feature_attribution/__init__.py

@@ -0,0 +1,14 @@
+# Copyright 2023 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.
+"""Google Cloud Pipeline Evaluation Feature Extractor Component."""

components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/feature_attribution/feature_attribution_component.py

@@ -0,0 +1,179 @@
+# Copyright 2023 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.
+
+from google_cloud_pipeline_components import _placeholders
+from google_cloud_pipeline_components._implementation.model_evaluation import version
+from google_cloud_pipeline_components.types.artifact_types import BQTable
+from kfp.dsl import Artifact
+from kfp.dsl import ConcatPlaceholder
+from kfp.dsl import container_component
+from kfp.dsl import ContainerSpec
+from kfp.dsl import IfPresentPlaceholder
+from kfp.dsl import Input
+from kfp.dsl import Metrics
+from kfp.dsl import Output
+from kfp.dsl import OutputPath
+from kfp.dsl import PIPELINE_JOB_ID_PLACEHOLDER
+from kfp.dsl import PIPELINE_ROOT_PLACEHOLDER
+from kfp.dsl import PIPELINE_TASK_ID_PLACEHOLDER
+
+
+@container_component
+def feature_attribution(
+    gcp_resources: OutputPath(str),
+    feature_attributions: Output[Metrics],
+    problem_type: str,
+    location: str = 'us-central1',
+    predictions_format: str = 'jsonl',
+    predictions_gcs_source: Input[Artifact] = None,
+    predictions_bigquery_source: Input[BQTable] = None,
+    dataflow_service_account: str = '',
+    dataflow_disk_size_gb: int = 50,
+    dataflow_machine_type: str = 'n1-standard-4',
+    dataflow_workers_num: int = 1,
+    dataflow_max_workers_num: int = 5,
+    dataflow_subnetwork: str = '',
+    dataflow_use_public_ips: bool = True,
+    encryption_spec_key_name: str = '',
+    force_runner_mode: str = '',
+    project: str = _placeholders.PROJECT_ID_PLACEHOLDER,
+):
+  # fmt: off
+  """Compute feature attribution on a trained model's batch explanation
+  results.
+
+  Creates a dataflow job with Apache Beam and TFMA to compute feature
+  attributions. Will compute feature attribution for every target label if
+  possible, typically possible for AutoML Classification models.
+
+  Args:
+      location: Location running feature attribution. If not
+        set, defaulted to `us-central1`.
+      problem_type: Problem type of the pipeline: one of `classification`,
+      `regression` and `forecasting`.
+      predictions_format: The file format for the batch
+        prediction results. `jsonl`, `csv`, and `bigquery` are the allowed
+        formats, from Vertex Batch Prediction. If not set, defaulted to `jsonl`.
+      predictions_gcs_source: An artifact with its
+        URI pointing toward a GCS directory with prediction or explanation files
+        to be used for this evaluation. For prediction results, the files should
+        be named "prediction.results-*" or "predictions_". For explanation
+        results, the files should be named "explanation.results-*".
+      predictions_bigquery_source: BigQuery table
+        with prediction or explanation data to be used for this evaluation. For
+        prediction results, the table column should be named "predicted_*".
+      dataflow_service_account: Service account to run the
+        dataflow job. If not set, dataflow will use the default worker service
+        account. For more details, see
+        https://cloud.google.com/dataflow/docs/concepts/security-and-permissions#default_worker_service_account
+      dataflow_disk_size_gb: The disk size (in GB) of the machine
+        executing the evaluation run. If not set, defaulted to `50`.
+      dataflow_machine_type: The machine type executing the
+        evaluation run. If not set, defaulted to `n1-standard-4`.
+      dataflow_workers_num: The number of workers executing the
+        evaluation run. If not set, defaulted to `10`.
+      dataflow_max_workers_num: The max number of workers
+        executing the evaluation run. If not set, defaulted to `25`.
+      dataflow_subnetwork: Dataflow's fully qualified subnetwork
+        name, when empty the default subnetwork will be used. More details:
+        https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications
+      dataflow_use_public_ips: Specifies whether Dataflow
+        workers use public IP addresses.
+      encryption_spec_key_name: Customer-managed encryption key
+        for the Dataflow job. If this is set, then all resources created by the
+        Dataflow job will be encrypted with the provided encryption key.
+      force_runner_mode: Flag to choose Beam runner. Valid options are `DirectRunner`
+        and `Dataflow`.
+      project: Project to run feature attribution container. Defaults to the project in which the PipelineJob is run.
+
+  Returns:
+      gcs_output_directory: JsonArray of the downsampled dataset GCS
+        output.
+      bigquery_output_table: String of the downsampled dataset BigQuery
+        output.
+      gcp_resources: Serialized gcp_resources proto tracking the dataflow
+        job. For more details, see
+        https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/proto/README.md.
+  """
+  # fmt: on
+  return ContainerSpec(
+      image=version.EVAL_IMAGE_TAG,
+      command=[
+          'python3',
+          '/main.py',
+      ],
+      args=[
+          '--task',
+          'explanation',
+          '--setup_file',
+          '/setup.py',
+          '--project_id',
+          project,
+          '--location',
+          location,
+          '--problem_type',
+          problem_type,
+          '--root_dir',
+          f'{PIPELINE_ROOT_PLACEHOLDER}/{PIPELINE_JOB_ID_PLACEHOLDER}-{PIPELINE_TASK_ID_PLACEHOLDER}',
+          '--batch_prediction_format',
+          predictions_format,
+          IfPresentPlaceholder(
+              input_name='predictions_gcs_source',
+              then=[
+                  '--batch_prediction_gcs_source',
+                  predictions_gcs_source.uri,
+              ],
+          ),
+          IfPresentPlaceholder(
+              input_name='predictions_bigquery_source',
+              then=[
+                  '--batch_prediction_bigquery_source',
+                  ConcatPlaceholder([
+                      'bq://',
+                      predictions_bigquery_source.metadata['projectId'],
+                      '.',
+                      predictions_bigquery_source.metadata['datasetId'],
+                      '.',
+                      predictions_bigquery_source.metadata['tableId'],
+                  ]),
+              ],
+          ),
+          '--dataflow_job_prefix',
+          f'evaluation-feautre-attribution-{PIPELINE_JOB_ID_PLACEHOLDER}-{PIPELINE_TASK_ID_PLACEHOLDER}',
+          '--dataflow_service_account',
+          dataflow_service_account,
+          '--dataflow_disk_size',
+          dataflow_disk_size_gb,
+          '--dataflow_machine_type',
+          dataflow_machine_type,
+          '--dataflow_workers_num',
+          dataflow_workers_num,
+          '--dataflow_max_workers_num',
+          dataflow_max_workers_num,
+          '--dataflow_subnetwork',
+          dataflow_subnetwork,
+          '--dataflow_use_public_ips',
+          dataflow_use_public_ips,
+          '--kms_key_name',
+          encryption_spec_key_name,
+          '--force_runner_mode',
+          force_runner_mode,
+          '--gcs_output_path',
+          feature_attributions.path,
+          '--gcp_resources',
+          gcp_resources,
+          '--executor_input',
+          '{{$}}',
+      ],
+  )

components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/feature_attribution/feature_attribution_graph_component.py

@@ -0,0 +1,247 @@
+# Copyright 2023 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.
+"""Graph Component for feature attribution evaluation."""
+
+from typing import List, NamedTuple
+
+from google_cloud_pipeline_components import _placeholders
+from google_cloud_pipeline_components._implementation.model_evaluation.data_sampler.component import evaluation_data_sampler as EvaluationDataSamplerOp
+from google_cloud_pipeline_components._implementation.model_evaluation.feature_attribution.feature_attribution_component import feature_attribution as ModelEvaluationFeatureAttributionOp
+from google_cloud_pipeline_components.types.artifact_types import VertexModel
+from google_cloud_pipeline_components.v1.batch_predict_job import ModelBatchPredictOp
+import kfp
+
+
+@kfp.dsl.pipeline(name='feature-attribution-graph-component')
+def feature_attribution_graph_component(  # pylint: disable=dangerous-default-value
+    location: str,
+    prediction_type: str,
+    vertex_model: VertexModel,
+    batch_predict_instances_format: str,
+    batch_predict_gcs_destination_output_uri: str,
+    batch_predict_gcs_source_uris: List[str] = [],  # pylint: disable=g-bare-generic
+    batch_predict_bigquery_source_uri: str = '',
+    batch_predict_predictions_format: str = 'jsonl',
+    batch_predict_bigquery_destination_output_uri: str = '',
+    batch_predict_machine_type: str = 'n1-standard-16',
+    batch_predict_starting_replica_count: int = 5,
+    batch_predict_max_replica_count: int = 10,
+    batch_predict_explanation_metadata: dict = {},  # pylint: disable=g-bare-generic
+    batch_predict_explanation_parameters: dict = {},  # pylint: disable=g-bare-generic
+    batch_predict_explanation_data_sample_size: int = 10000,
+    batch_predict_accelerator_type: str = '',
+    batch_predict_accelerator_count: int = 0,
+    dataflow_machine_type: str = 'n1-standard-4',
+    dataflow_max_num_workers: int = 5,
+    dataflow_disk_size_gb: int = 50,
+    dataflow_service_account: str = '',
+    dataflow_subnetwork: str = '',
+    dataflow_use_public_ips: bool = True,
+    encryption_spec_key_name: str = '',
+    force_runner_mode: str = '',
+    project: str = _placeholders.PROJECT_ID_PLACEHOLDER,
+) -> NamedTuple('outputs', feature_attributions=kfp.dsl.Metrics):
+  """A pipeline to compute feature attributions by sampling data for batch explanations.
+
+  This pipeline guarantees support for AutoML Tabular models that contain a
+  valid explanation_spec.
+
+  Args:
+    location: The GCP region that runs the pipeline components.
+    prediction_type: The type of prediction the model is to produce.
+      "classification", "regression", or "forecasting".
+    vertex_model: The Vertex model artifact used for batch explanation.
+    batch_predict_instances_format: The format in which instances are given,
+      must be one of the Model's supportedInputStorageFormats. For more details
+      about this input config, see
+      https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.
+    batch_predict_gcs_destination_output_uri: The Google Cloud Storage location
+      of the directory where the output is to be written to. In the given
+      directory a new directory is created. Its name is
+      ``prediction-<model-display-name>-<job-create-time>``, where timestamp is
+      in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files
+      ``predictions_0001.<extension>``, ``predictions_0002.<extension>``, ...,
+      ``predictions_N.<extension>`` are created where ``<extension>`` depends on
+      chosen ``predictions_format``, and N may equal 0001 and depends on the
+      total number of successfully predicted instances. If the Model has both
+      ``instance`` and ``prediction`` schemata defined then each such file
+      contains predictions as per the ``predictions_format``. If prediction for
+      any instance failed (partially or completely), then an additional
+      ``errors_0001.<extension>``, ``errors_0002.<extension>``,...,
+      ``errors_N.<extension>`` files are created (N depends on total number of
+      failed predictions). These files contain the failed instances, as per
+      their schema, followed by an additional ``error`` field which as value has
+      ``google.rpc.Status`` containing only ``code`` and ``message`` fields. For
+      more details about this output config, see
+      https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#OutputConfig.
+    batch_predict_gcs_source_uris: Google Cloud Storage URI(-s) to your
+      instances to run batch prediction on. May contain wildcards. For more
+      information on wildcards, see
+      https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames. For
+        more details about this input config, see
+      https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.
+    batch_predict_bigquery_source_uri: Google BigQuery URI to your instances to
+      run batch prediction on. May contain wildcards. For more details about
+      this input config, see
+      https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.
+    batch_predict_predictions_format: The format in which Vertex AI gives the
+      predictions. Must be one of the Model's supportedOutputStorageFormats. For
+      more details about this output config, see
+      https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#OutputConfig.
+    batch_predict_bigquery_destination_output_uri: The BigQuery project location
+      where the output is to be written to. In the given project a new dataset
+      is created with name ``prediction_<model-display-name>_<job-create-time>``
+      where is made BigQuery-dataset-name compatible (for example, most special
+      characters become underscores), and timestamp is in
+      YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two
+      tables will be created, ``predictions``, and ``errors``. If the Model has
+      both ``instance`` and ``prediction`` schemata defined then the tables have
+      columns as follows: The ``predictions`` table contains instances for which
+      the prediction succeeded, it has columns as per a concatenation of the
+      Model's instance and prediction schemata. The ``errors`` table contains
+      rows for which the prediction has failed, it has instance columns, as per
+      the instance schema, followed by a single "errors" column, which as values
+      has ````google.rpc.Status`` <Status>``__ represented as a STRUCT, and
+      containing only ``code`` and ``message``.  For more details about this
+      output config, see
+      https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#OutputConfig.
+    batch_predict_machine_type: The type of machine for running batch prediction
+      on dedicated resources. If the Model supports DEDICATED_RESOURCES this
+      config may be provided (and the job will use these resources). If the
+      Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
+      For more details about the BatchDedicatedResources, see
+      https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#BatchDedicatedResources.
+        For more details about the machine spec, see
+      https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec
+    batch_predict_starting_replica_count: The number of machine replicas used at
+      the start of the batch operation. If not set, Vertex AI decides starting
+      number, not greater than ``max_replica_count``. Only used if
+      ``machine_type`` is set.
+    batch_predict_max_replica_count: The maximum number of machine replicas the
+      batch operation may be scaled to. Only used if ``machine_type`` is set.
+    batch_predict_explanation_metadata: Explanation metadata configuration for
+      this BatchPredictionJob. Can be specified only if ``generate_explanation``
+      is set to ``True``. This value overrides the value of
+      ``Model.explanation_metadata``. All fields of ``explanation_metadata`` are
+      optional in the request. If a field of the ``explanation_metadata`` object
+      is not populated, the corresponding field of the
+      ``Model.explanation_metadata`` object is inherited. For more details, see
+      https://cloud.google.com/vertex-ai/docs/reference/rest/v1/ExplanationSpec#explanationmetadata.
+    batch_predict_explanation_parameters: Parameters to configure explaining for
+      Model's predictions. Can be specified only if ``generate_explanation`` is
+      set to ``True``. This value overrides the value of
+      ``Model.explanation_parameters``. All fields of ``explanation_parameters``
+      are optional in the request. If a field of the ``explanation_parameters``
+      object is not populated, the corresponding field of the
+      ``Model.explanation_parameters`` object is inherited. For more details,
+      see
+      https://cloud.google.com/vertex-ai/docs/reference/rest/v1/ExplanationSpec#ExplanationParameters.
+    batch_predict_explanation_data_sample_size: Desired size to downsample the
+      input dataset that will then be used for batch explanation.
+    batch_predict_accelerator_type: The type of accelerator(s) that may be
+      attached to the machine as per ``batch_predict_accelerator_count``. Only
+      used if ``batch_predict_machine_type`` is set. For more details about the
+      machine spec, see
+      https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec
+    batch_predict_accelerator_count: The number of accelerators to attach to the
+      ``batch_predict_machine_type``. Only used if
+      ``batch_predict_machine_type`` is set.
+    dataflow_machine_type: The Dataflow machine type for evaluation components.
+    dataflow_max_num_workers: The max number of Dataflow workers for evaluation
+      components.
+    dataflow_disk_size_gb: Dataflow worker's disk size in GB for evaluation
+      components.
+    dataflow_service_account: Custom service account to run Dataflow jobs.
+    dataflow_subnetwork: Dataflow's fully qualified subnetwork name, when empty
+      the default subnetwork will be used. Example:
+      https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications
+    dataflow_use_public_ips: Specifies whether Dataflow workers use public IP
+      addresses.
+    encryption_spec_key_name:  Customer-managed encryption key options. If set,
+      resources created by this pipeline will be encrypted with the provided
+      encryption key. Has the form:
+      ``projects/my-project/locations/my-location/keyRings/my-kr/cryptoKeys/my-key``.
+      The key needs to be in the same region as where the compute resource is
+      created.
+    force_runner_mode: Indicate the runner mode to use forcely. Valid options
+      are ``Dataflow`` and ``DirectRunner``.
+    project: The GCP project that runs the pipeline components. Defaults to the
+      project in which the PipelineJob is run.
+
+  Returns:
+    A system.Metrics artifact with feature attributions.
+  """
+  outputs = NamedTuple('outputs', feature_attributions=kfp.dsl.Metrics)
+
+  # Sample the input dataset for a quicker batch explanation.
+  data_sampler_task = EvaluationDataSamplerOp(
+      project=project,
+      location=location,
+      gcs_source_uris=batch_predict_gcs_source_uris,
+      bigquery_source_uri=batch_predict_bigquery_source_uri,
+      instances_format=batch_predict_instances_format,
+      sample_size=batch_predict_explanation_data_sample_size,
+      force_runner_mode=force_runner_mode,
+  )
+
+  # Run batch explain.
+  batch_explain_task = ModelBatchPredictOp(
+      project=project,
+      location=location,
+      model=vertex_model,
+      job_display_name='model-registry-batch-explain-evaluation-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}',
+      gcs_source_uris=data_sampler_task.outputs['gcs_output_directory'],
+      bigquery_source_input_uri=data_sampler_task.outputs[
+          'bigquery_output_table'
+      ],
+      instances_format=batch_predict_instances_format,
+      predictions_format=batch_predict_predictions_format,
+      gcs_destination_output_uri_prefix=batch_predict_gcs_destination_output_uri,
+      bigquery_destination_output_uri=batch_predict_bigquery_destination_output_uri,
+      generate_explanation=True,
+      explanation_parameters=batch_predict_explanation_parameters,
+      explanation_metadata=batch_predict_explanation_metadata,
+      machine_type=batch_predict_machine_type,
+      starting_replica_count=batch_predict_starting_replica_count,
+      max_replica_count=batch_predict_max_replica_count,
+      encryption_spec_key_name=encryption_spec_key_name,
+      accelerator_type=batch_predict_accelerator_type,
+      accelerator_count=batch_predict_accelerator_count,
+  )
+
+  # Generate feature attributions from explanations.
+  feature_attribution_task = ModelEvaluationFeatureAttributionOp(
+      project=project,
+      location=location,
+      problem_type=prediction_type,
+      predictions_format=batch_predict_predictions_format,
+      predictions_gcs_source=batch_explain_task.outputs['gcs_output_directory'],
+      predictions_bigquery_source=batch_explain_task.outputs[
+          'bigquery_output_table'
+      ],
+      dataflow_machine_type=dataflow_machine_type,
+      dataflow_max_workers_num=dataflow_max_num_workers,
+      dataflow_disk_size_gb=dataflow_disk_size_gb,
+      dataflow_service_account=dataflow_service_account,
+      dataflow_subnetwork=dataflow_subnetwork,
+      dataflow_use_public_ips=dataflow_use_public_ips,
+      encryption_spec_key_name=encryption_spec_key_name,
+      force_runner_mode=force_runner_mode,
+  )
+
+  return outputs(
+      feature_attributions=feature_attribution_task.outputs[
+          'feature_attributions'
+      ]
+  )

components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/llm_safety_bias/evaluation_llm_safety_bias_pipeline.py

@@ -17,7 +17,7 @@ from typing import NamedTuple
 
 from google_cloud_pipeline_components import _image
 from google_cloud_pipeline_components import _placeholders
-from google_cloud_pipeline_components._implementation.model_evaluation import LLMSafetyBiasMetricsOp
+from google_cloud_pipeline_components._implementation.model_evaluation.llm_safety_bias.component import llm_safety_bias_metrics as LLMSafetyBiasMetricsOp
 from google_cloud_pipeline_components.types.artifact_types import VertexBatchPredictionJob
 from kfp import dsl
 from kfp.dsl import Artifact

components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/__init__.py

@@ -14,11 +14,15 @@
 """Model evaluation preview components."""
 
 from google_cloud_pipeline_components.preview.model_evaluation.data_bias_component import detect_data_bias as DetectDataBiasOp
+from google_cloud_pipeline_components.preview.model_evaluation.evaluation_llm_classification_pipeline import evaluation_llm_classification_pipeline
+from google_cloud_pipeline_components.preview.model_evaluation.evaluation_llm_text_generation_pipeline import evaluation_llm_text_generation_pipeline
 from google_cloud_pipeline_components.preview.model_evaluation.feature_attribution_component import feature_attribution as ModelEvaluationFeatureAttributionOp
 from google_cloud_pipeline_components.preview.model_evaluation.feature_attribution_graph_component import feature_attribution_graph_component as FeatureAttributionGraphComponentOp
 from google_cloud_pipeline_components.preview.model_evaluation.model_bias_component import detect_model_bias as DetectModelBiasOp
 
 __all__ = [
+    'evaluation_llm_classification_pipeline',
+    'evaluation_llm_text_generation_pipeline',
     'ModelEvaluationFeatureAttributionOp',
     'FeatureAttributionGraphComponentOp',
     'DetectModelBiasOp',

components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/feature_attribution_graph_component.py

@@ -16,7 +16,7 @@ from typing import List, NamedTuple
 
 from google_cloud_pipeline_components import _placeholders
 from google_cloud_pipeline_components._implementation.model_evaluation import EvaluationDataSamplerOp
-from google_cloud_pipeline_components.preview.model_evaluation import ModelEvaluationFeatureAttributionOp
+from google_cloud_pipeline_components.preview.model_evaluation.feature_attribution_component import feature_attribution as ModelEvaluationFeatureAttributionOp
 from google_cloud_pipeline_components.types.artifact_types import VertexModel
 from google_cloud_pipeline_components.v1.batch_predict_job import ModelBatchPredictOp
 import kfp

components/google-cloud/google_cloud_pipeline_components/v1/model_evaluation/evaluation_automl_tabular_feature_attribution_pipeline.py

@@ -16,8 +16,8 @@ from typing import Any, Dict, List, NamedTuple
 
 from google_cloud_pipeline_components import _placeholders
 from google_cloud_pipeline_components._implementation.model import GetVertexModelOp
+from google_cloud_pipeline_components._implementation.model_evaluation import FeatureAttributionGraphComponentOp
 from google_cloud_pipeline_components._implementation.model_evaluation import ModelImportEvaluationOp
-from google_cloud_pipeline_components.preview.model_evaluation import FeatureAttributionGraphComponentOp
 from google_cloud_pipeline_components.types.artifact_types import ClassificationMetrics
 from google_cloud_pipeline_components.types.artifact_types import RegressionMetrics
 from google_cloud_pipeline_components.v1.batch_predict_job import ModelBatchPredictOp

components/google-cloud/google_cloud_pipeline_components/v1/model_evaluation/evaluation_feature_attribution_pipeline.py

@@ -16,9 +16,9 @@ from typing import Any, Dict, List, NamedTuple
 
 from google_cloud_pipeline_components import _placeholders
 from google_cloud_pipeline_components._implementation.model import GetVertexModelOp
+from google_cloud_pipeline_components._implementation.model_evaluation import FeatureAttributionGraphComponentOp
 from google_cloud_pipeline_components._implementation.model_evaluation import ModelImportEvaluationOp
 from google_cloud_pipeline_components._implementation.model_evaluation import TargetFieldDataRemoverOp
-from google_cloud_pipeline_components.preview.model_evaluation import FeatureAttributionGraphComponentOp
 from google_cloud_pipeline_components.types.artifact_types import ClassificationMetrics
 from google_cloud_pipeline_components.types.artifact_types import RegressionMetrics
 from google_cloud_pipeline_components.v1.batch_predict_job import ModelBatchPredictOp