feat(components): Set display names for SFT, RLHF and LLM inference pipelines

PiperOrigin-RevId: 572897105

components/google-cloud/RELEASE.md

@@ -2,6 +2,7 @@
 * Upload tensorboard metrics from `preview.llm.rlhf_pipeline` if a `tensorboard_resource_id` is provided at runtime.
 * Support `incremental_train_base_model`, `parent_model`, `is_default_version`, `model_version_aliases`, `model_version_description` in `AutoMLImageTrainingJobRunOp`.
 * Add `preview.automl.vision` and `DataConverterJobOp`.
+* Set display names for `preview.llm` pipelines.
 
 ## Release 2.4.1
 * Disable caching for LLM pipeline tasks that store temporary artifacts.

components/google-cloud/google_cloud_pipeline_components/preview/llm/infer/component.py

@@ -64,14 +64,14 @@ def infer_pipeline(
   machine_spec = function_based.resolve_machine_spec(
       location=location,
       use_test_spec=env.get_use_test_machine_spec(),
-  )
+  ).set_display_name('Resolve Machine Spec')
   reference_model_metadata = function_based.resolve_reference_model_metadata(
       large_model_reference=large_model_reference
-  ).set_display_name('BaseModelMetadataResolver')
+  ).set_display_name('Resolve Model Metadata')
 
   prompt_dataset_image_uri = function_based.resolve_private_image_uri(
       image_name='text_importer',
-  ).set_display_name('PromptDatasetImageUriResolver')
+  ).set_display_name('Resolve Prompt Dataset Image URI')
   prompt_dataset_importer = (
       private_text_importer.PrivateTextImporter(
           project=project,
@@ -86,7 +86,7 @@ def infer_pipeline(
           image_uri=prompt_dataset_image_uri.output,
           instruction=instruction,
       )
-      .set_display_name('PromptDatasetImporter')
+      .set_display_name('Import Prompt Dataset')
       .set_caching_options(False)
   )
 
@@ -94,7 +94,7 @@ def infer_pipeline(
       image_name='infer',
       accelerator_type=machine_spec.outputs['accelerator_type'],
       accelerator_count=machine_spec.outputs['accelerator_count'],
-  ).set_display_name('BulkInferrerImageUriResolver')
+  ).set_display_name('Resolve Bulk Inferrer Image URI')
   bulk_inference = bulk_inferrer.BulkInferrer(
       project=project,
       location=location,

components/google-cloud/google_cloud_pipeline_components/preview/llm/rlhf/component.py

@@ -93,15 +93,15 @@ def rlhf_pipeline(
   upload_location = 'us-central1'
   machine_spec = function_based.resolve_machine_spec(
       location=location, use_test_spec=env.get_use_test_machine_spec()
-  )
+  ).set_display_name('Resolve Machine Spec')
 
   reference_model_metadata = function_based.resolve_reference_model_metadata(
       large_model_reference=large_model_reference,
-  ).set_display_name('BaseModelMetadataResolver')
+  ).set_display_name('Resolve Model Metadata')
 
   prompt_dataset_image_uri = function_based.resolve_private_image_uri(
       image_name='text_importer'
-  ).set_display_name('PromptDatasetImageUriResolver')
+  ).set_display_name('Resolve Prompt Dataset Image URI')
   prompt_dataset_importer = (
       private_text_importer.PrivateTextImporter(
           project=project,
@@ -117,13 +117,13 @@ def rlhf_pipeline(
           image_uri=prompt_dataset_image_uri.output,
           instruction=instruction,
       )
-      .set_display_name('PromptDatasetImporter')
+      .set_display_name('Import Prompt Dataset')
       .set_caching_options(False)
   )
 
   preference_dataset_image_uri = function_based.resolve_private_image_uri(
       image_name='text_comparison_importer'
-  ).set_display_name('PreferenceDatasetImageUriResolver')
+  ).set_display_name('Resolve Preference Dataset Image URI')
   comma_separated_candidates_field_names = (
       function_based.convert_to_delimited_string(items=candidate_columns)
   )
@@ -142,7 +142,7 @@ def rlhf_pipeline(
           image_uri=preference_dataset_image_uri.output,
           instruction=instruction,
       )
-      .set_display_name('PreferenceDatasetImporter')
+      .set_display_name('Import Preference Dataset')
       .set_caching_options(False)
   )
 
@@ -150,7 +150,7 @@ def rlhf_pipeline(
       image_name='reward_model',
       accelerator_type=machine_spec.outputs['accelerator_type'],
       accelerator_count=machine_spec.outputs['accelerator_count'],
-  ).set_display_name('RewardModelImageUriResolver')
+  ).set_display_name('Resolve Reward Model Image URI')
   reward_model = (
       reward_model_trainer.RewardModelTrainer(
           project=project,
@@ -175,13 +175,13 @@ def rlhf_pipeline(
           learning_rate_multiplier=reward_model_learning_rate_multiplier,
           lora_dim=reward_model_lora_dim,
       )
-      .set_display_name('RewardModelTrainer')
+      .set_display_name('Reward Model Trainer')
       .set_caching_options(False)
   )
 
   has_tensorboard_id = function_based.value_exists(
       value=tensorboard_resource_id
-  )
+  ).set_display_name('Resolve Tensorboard Resource ID')
   with kfp.dsl.Condition(  # pytype: disable=wrong-arg-types
       has_tensorboard_id.output == True,  # pylint: disable=singleton-comparison, g-explicit-bool-comparison
       name='Upload Reward Model Tensorboard Metrics',
@@ -194,13 +194,13 @@ def rlhf_pipeline(
             f'{kfp.dsl.PIPELINE_JOB_ID_PLACEHOLDER}-'
             f'{kfp.dsl.PIPELINE_TASK_ID_PLACEHOLDER}'
         ),
-    )
+    ).set_display_name('Reward Model Tensorboard Metrics Uploader')
 
   rl_image_uri = function_based.resolve_private_image_uri(
       image_name='reinforcer',
       accelerator_type=machine_spec.outputs['accelerator_type'],
       accelerator_count=machine_spec.outputs['accelerator_count'],
-  ).set_display_name('ReinforcerImageUriResolver')
+  ).set_display_name('Resolve Reinforcer Image URI')
   rl_model = (
       reinforcer.Reinforcer(
           project=project,
@@ -246,9 +246,11 @@ def rlhf_pipeline(
             f'{kfp.dsl.PIPELINE_JOB_ID_PLACEHOLDER}-'
             f'{kfp.dsl.PIPELINE_TASK_ID_PLACEHOLDER}'
         ),
-    )
+    ).set_display_name('Reinforcement Learning Tensorboard Metrics Uploader')
 
-  should_perform_inference = function_based.value_exists(value=eval_dataset)
+  should_perform_inference = function_based.value_exists(
+      value=eval_dataset
+  ).set_display_name('Resolve Inference Dataset')
   with kfp.dsl.Condition(
       should_perform_inference.output == True, name='Perform Inference'  # pylint: disable=singleton-comparison
   ):
@@ -266,22 +268,23 @@ def rlhf_pipeline(
   adapter_artifact = kfp.dsl.importer(
       artifact_uri=rl_model.outputs['output_adapter_path'],
       artifact_class=kfp.dsl.Artifact,
-  )
+  ).set_display_name('Import Tuned Adapter')
   regional_endpoint = function_based.resolve_regional_endpoint(
       upload_location=upload_location
-  )
+  ).set_display_name('Resolve Regional Endpoint')
   display_name = function_based.resolve_model_display_name(
       large_model_reference=reference_model_metadata.outputs[
           'large_model_reference'
       ],
       model_display_name=model_display_name,
-  )
+  ).set_display_name('Resolve Model Display Name')
   upload_model = function_based.resolve_upload_model(
       large_model_reference=reference_model_metadata.outputs[
           'large_model_reference'
       ]
-  )
+  ).set_display_name('Resolve Upload Model')
-  upload_task = upload_llm_model.upload_llm_model(
+  upload_task = (
+      upload_llm_model.upload_llm_model(
           project=_placeholders.PROJECT_ID_PLACEHOLDER,
           location=upload_location,
           regional_endpoint=regional_endpoint.output,
@@ -289,16 +292,19 @@ def rlhf_pipeline(
           model_display_name=display_name.output,
           model_reference_name='text-bison@001',
           upload_model=upload_model.output,
-  ).set_env_variable(
+      )
+      .set_env_variable(
           name='VERTEX_AI_PIPELINES_RUN_LABELS',
           value=json.dumps({'tune-type': 'rlhf'}),
       )
+      .set_display_name('Upload Model')
+  )
   deploy_model = function_based.resolve_deploy_model(
       deploy_model=deploy_model,
       large_model_reference=reference_model_metadata.outputs[
           'large_model_reference'
       ],
-  )
+  ).set_display_name('Resolve Deploy Model')
   deploy_task = deploy_llm_model.create_endpoint_and_deploy_model(
       project=_placeholders.PROJECT_ID_PLACEHOLDER,
       location=upload_location,
@@ -306,7 +312,7 @@ def rlhf_pipeline(
       display_name=display_name.output,
       regional_endpoint=regional_endpoint.output,
       deploy_model=deploy_model.output,
-  )
+  ).set_display_name('Deploy Model')
 
   return PipelineOutput(
       model_resource_name=upload_task.outputs['model_resource_name'],