change: Add warning for ignored job args in pipeline steps

src/sagemaker/workflow/steps.py

@@ -14,6 +14,7 @@
 from __future__ import absolute_import
 
 import abc
+import logging
 import warnings
 
 from enum import Enum
@@ -54,6 +55,14 @@
 if TYPE_CHECKING:
     from sagemaker.workflow.step_collections import StepCollection
 
+logger = logging.getLogger(__name__)
+
+JOB_ARG_IGNORE_WARN_MSG_TEMPLATE = (
+    "The job specific arguments (%s) supplied to the step will be ignored, "
+    "because `step_args` is presented. These job specific arguments should be supplied "
+    "in %s to generate the `step_args`."
+)
+
 
 class StepTypeEnum(Enum, metaclass=DefaultEnumMeta):
     """Enum of `Step` types."""
@@ -424,6 +433,9 @@ def __init__(
                 error_message="The step_args of TrainingStep must be obtained from estimator.fit().",
             )
 
+            if inputs:
+                logger.warning(JOB_ARG_IGNORE_WARN_MSG_TEMPLATE, "`inputs`", "estimator.fit()")
+
         self.step_args = step_args
         self.estimator = estimator
         self.inputs = inputs
@@ -680,6 +692,11 @@ def __init__(
                 "from transformer.transform().",
             )
 
+            if inputs:
+                logger.warning(
+                    JOB_ARG_IGNORE_WARN_MSG_TEMPLATE, "`inputs`", "transformer.transform()"
+                )
+
         self.step_args = step_args
         self.transformer = transformer
         self.inputs = inputs
@@ -817,6 +834,13 @@ def __init__(
                 error_message="The step_args of ProcessingStep must be obtained from processor.run().",
             )
 
+            if job_arguments or inputs or outputs or code or kms_key:
+                logger.warning(
+                    JOB_ARG_IGNORE_WARN_MSG_TEMPLATE,
+                    "`job_arguments`, `inputs`, `outputs`, `code` and `kms_key`",
+                    "processor.run()",
+                )
+
         self.step_args = step_args
         self.processor = processor
         self.inputs = inputs
@@ -997,6 +1021,9 @@ def __init__(
                 error_message="The step_args of TuningStep must be obtained from tuner.fit().",
             )
 
+            if inputs:
+                logger.warning(JOB_ARG_IGNORE_WARN_MSG_TEMPLATE, "`inputs`", "tuner.fit()")
+
         self.step_args = step_args
         self.tuner = tuner
         self.inputs = inputs

src/sagemaker/workflow/utilities.py

@@ -363,8 +363,8 @@ def override_pipeline_parameter_var(func):
     We should remove this decorator after the grace period.
     """
     warning_msg_template = (
-        "The input argument %s of function (%s) is a pipeline variable (%s), which is not allowed. "
-        "The default_value of this Parameter object will be used to override it. "
+        "The input argument %s of function (%s) is a pipeline variable (%s), which will not work. "
+        "Thus the default_value of this Parameter object will be used to override it. "
         "Please make sure the default_value is valid."
     )
 

tests/unit/sagemaker/workflow/test_processing_step.py

@@ -1115,3 +1115,44 @@ def test_processor_with_role_as_pipeline_parameter(
 
     step_def = json.loads(pipeline.definition())["Steps"][0]
     assert step_def["Arguments"]["RoleArn"] == {"Get": f"Parameters.{_PARAM_ROLE_NAME}"}
+
+
+def test_processing_step_with_extra_job_args(pipeline_session, processing_input):
+    processor = Processor(
+        image_uri=IMAGE_URI,
+        role=ROLE,
+        instance_count=1,
+        instance_type=INSTANCE_TYPE,
+        sagemaker_session=pipeline_session,
+    )
+
+    step_args = processor.run(inputs=processing_input)
+
+    ignored_code = f"s3://{BUCKET}/my-code-to-be-ignored"
+    ignored_kms_key = "ignored_kms_key"
+    step = ProcessingStep(
+        name="MyProcessingStep",
+        step_args=step_args,
+        description="ProcessingStep description",
+        display_name="MyProcessingStep",
+        code=ignored_code,
+        kms_key=ignored_kms_key,
+    )
+
+    pipeline = Pipeline(
+        name="MyPipeline",
+        steps=[step],
+        sagemaker_session=pipeline_session,
+    )
+    step_args = get_step_args_helper(step_args, "Processing")
+    pipeline_def = pipeline.definition()
+    step_def = json.loads(pipeline_def)["Steps"][0]
+    assert step_def == {
+        "Name": "MyProcessingStep",
+        "Description": "ProcessingStep description",
+        "DisplayName": "MyProcessingStep",
+        "Type": "Processing",
+        "Arguments": step_args,
+    }
+    assert ignored_code not in pipeline_def
+    assert ignored_kms_key not in pipeline_def

tests/unit/sagemaker/workflow/test_training_step.py

@@ -801,3 +801,41 @@ def test_insert_wrong_step_args_into_training_step(inputs, pipeline_session):
         )
 
     assert "The step_args of TrainingStep must be obtained from estimator.fit()" in str(error.value)
+
+
+def test_training_step_estimator_with_extra_job_args(pipeline_session, training_input):
+    estimator = Estimator(
+        role=ROLE,
+        instance_count=1,
+        instance_type=INSTANCE_TYPE,
+        sagemaker_session=pipeline_session,
+        image_uri=IMAGE_URI,
+    )
+
+    step_args = estimator.fit(inputs=training_input)
+
+    ignored_train_input = f"s3://{BUCKET}/my-training-input-to-be-ignored"
+    step = TrainingStep(
+        name="MyTrainingStep",
+        step_args=step_args,
+        description="TrainingStep description",
+        display_name="MyTrainingStep",
+        inputs=ignored_train_input,
+    )
+
+    pipeline = Pipeline(
+        name="MyPipeline",
+        steps=[step],
+        sagemaker_session=pipeline_session,
+    )
+    step_args = get_step_args_helper(step_args, "Training")
+    pipeline_def = pipeline.definition()
+    step_def = json.loads(pipeline_def)["Steps"][0]
+    assert step_def == {
+        "Name": "MyTrainingStep",
+        "Description": "TrainingStep description",
+        "DisplayName": "MyTrainingStep",
+        "Type": "Training",
+        "Arguments": step_args,
+    }
+    assert ignored_train_input not in pipeline_def

tests/unit/sagemaker/workflow/test_transform_step.py

@@ -287,3 +287,47 @@ def test_insert_wrong_step_args_into_transform_step(inputs, pipeline_session):
     assert "The step_args of TransformStep must be obtained from transformer.transform()" in str(
         error.value
     )
+
+
+def test_transform_step_with_extra_job_args(pipeline_session):
+    transformer = Transformer(
+        model_name="my_model",
+        instance_type="ml.m5.xlarge",
+        instance_count=1,
+        output_path="s3://my-bucket/my-output-path",
+        sagemaker_session=pipeline_session,
+    )
+    transform_inputs = TransformInput(data="s3://my-bucket/my-data")
+
+    step_args = transformer.transform(
+        data=transform_inputs.data,
+        data_type=transform_inputs.data_type,
+        content_type=transform_inputs.content_type,
+        compression_type=transform_inputs.compression_type,
+        split_type=transform_inputs.split_type,
+        input_filter=transform_inputs.input_filter,
+        output_filter=transform_inputs.output_filter,
+        join_source=transform_inputs.join_source,
+        model_client_config=transform_inputs.model_client_config,
+    )
+
+    ignored_data_path = "s3://my-bucket/my-data-to-be-ignored"
+    step = TransformStep(
+        name="MyTransformStep", step_args=step_args, inputs=TransformInput(data=ignored_data_path)
+    )
+
+    pipeline = Pipeline(
+        name="MyPipeline",
+        steps=[step],
+        sagemaker_session=pipeline_session,
+    )
+
+    step_args = get_step_args_helper(step_args, "Transform")
+    pipeline_def = pipeline.definition()
+    step_def = json.loads(pipeline_def)["Steps"][0]
+    assert step_def == {
+        "Name": "MyTransformStep",
+        "Type": "Transform",
+        "Arguments": step_args,
+    }
+    assert ignored_data_path not in pipeline_def

tests/unit/sagemaker/workflow/test_tuning_step.py

@@ -301,3 +301,70 @@ def test_insert_wrong_step_args_into_tuning_step(inputs, pipeline_session):
         )
 
     assert "The step_args of TuningStep must be obtained from tuner.fit()" in str(error.value)
+
+
+def test_tuning_step_with_extra_job_args(pipeline_session, entry_point):
+    pytorch_estimator = PyTorch(
+        entry_point=entry_point,
+        role=ROLE,
+        framework_version="1.5.0",
+        py_version="py3",
+        instance_count=1,
+        instance_type="ml.m5.xlarge",
+        sagemaker_session=pipeline_session,
+        enable_sagemaker_metrics=True,
+        max_retry_attempts=3,
+    )
+
+    hyperparameter_ranges = {
+        "batch-size": IntegerParameter(64, 128),
+    }
+
+    tuner = HyperparameterTuner(
+        estimator=pytorch_estimator,
+        objective_metric_name="test:acc",
+        objective_type="Maximize",
+        hyperparameter_ranges=hyperparameter_ranges,
+        metric_definitions=[{"Name": "test:acc", "Regex": "Overall test accuracy: (.*?);"}],
+        max_jobs=2,
+        max_parallel_jobs=2,
+    )
+
+    step_args = tuner.fit(inputs=TrainingInput(s3_data="s3://my-bucket/my-training-input"))
+
+    ignored_input = "s3://my-bucket/my-input-to-be-ignored"
+    step = TuningStep(
+        name="MyTuningStep",
+        step_args=step_args,
+        inputs=ignored_input,
+    )
+
+    pipeline = Pipeline(
+        name="MyPipeline",
+        steps=[step],
+        sagemaker_session=pipeline_session,
+    )
+
+    step_args = get_step_args_helper(step_args, "HyperParameterTuning")
+    pipeline_def = pipeline.definition()
+    step_def = json.loads(pipeline_def)["Steps"][0]
+
+    # delete sagemaker_job_name b/c of timestamp collision
+    del step_args["TrainingJobDefinition"]["StaticHyperParameters"]["sagemaker_job_name"]
+    del step_def["Arguments"]["TrainingJobDefinition"]["StaticHyperParameters"][
+        "sagemaker_job_name"
+    ]
+
+    # delete S3 path assertions for now because job name is included with timestamp. These will be re-enabled after
+    # caching improvements phase 2.
+    del step_args["TrainingJobDefinition"]["StaticHyperParameters"]["sagemaker_submit_directory"]
+    del step_def["Arguments"]["TrainingJobDefinition"]["StaticHyperParameters"][
+        "sagemaker_submit_directory"
+    ]
+
+    assert step_def == {
+        "Name": "MyTuningStep",
+        "Type": "Tuning",
+        "Arguments": step_args,
+    }
+    assert ignored_input not in pipeline_def