fix: Support PipelineVariable ModelQualityCheckConfig attributes

Author: qidewenwhen

src/sagemaker/workflow/quality_check_step.py

@@ -13,6 +13,7 @@
 """The step definitions for workflow."""
 from __future__ import absolute_import
 
+import logging
 from abc import ABC
 from typing import List, Union, Optional
 import os
@@ -24,7 +25,8 @@
 from sagemaker.processing import ProcessingOutput, ProcessingJob, Processor, ProcessingInput
 from sagemaker.workflow import is_pipeline_variable
 
-from sagemaker.workflow.entities import RequestType, PipelineVariable
+from sagemaker.workflow.entities import RequestType, PipelineVariable, PrimitiveType
+from sagemaker.workflow.parameters import Parameter, ParameterString
 from sagemaker.workflow.properties import (
     Properties,
 )
@@ -47,6 +49,9 @@
 _DATA_QUALITY_TYPE = "DATA_QUALITY"
 
 
+logger = logging.getLogger(__name__)
+
+
 @attr.s
 class QualityCheckConfig(ABC):
     """Quality Check Config.
@@ -407,25 +412,19 @@ def _generate_baseline_processor(
                 post_processor_script_container_path=post_processor_script_container_path,
             )
         else:
-            inference_attribute = (
-                str(quality_check_cfg.inference_attribute)
-                if quality_check_cfg.inference_attribute is not None
-                else None
+            inference_attribute = _format_env_variable_value(
+                var_value=quality_check_cfg.inference_attribute, var_name="inference_attribute"
             )
-            probability_attribute = (
-                str(quality_check_cfg.probability_attribute)
-                if quality_check_cfg.probability_attribute is not None
-                else None
+            probability_attribute = _format_env_variable_value(
+                var_value=quality_check_cfg.probability_attribute, var_name="probability_attribute"
             )
-            ground_truth_attribute = (
-                str(quality_check_cfg.ground_truth_attribute)
-                if quality_check_cfg.ground_truth_attribute is not None
-                else None
+            ground_truth_attribute = _format_env_variable_value(
+                var_value=quality_check_cfg.ground_truth_attribute,
+                var_name="ground_truth_attribute",
             )
-            probability_threshold_attr = (
-                str(quality_check_cfg.probability_threshold_attribute)
-                if quality_check_cfg.probability_threshold_attribute is not None
-                else None
+            probability_threshold_attr = _format_env_variable_value(
+                var_value=quality_check_cfg.probability_threshold_attribute,
+                var_name="probability_threshold_attr",
             )
             normalized_env = ModelMonitor._generate_env_map(
                 env=self._model_monitor.env,
@@ -458,3 +457,22 @@ def _generate_baseline_processor(
             tags=self._model_monitor.tags,
             network_config=self._model_monitor.network_config,
         )
+
+
+def _format_env_variable_value(var_value: Union[PrimitiveType, PipelineVariable], var_name: str):
+    """Helper function to format the variable values passed to env var
+
+    Args:
+        var_value (PrimitiveType or PipelineVariable): The value of the variable.
+        var_name (str): The name of the variable.
+    """
+    if var_value is None:
+        return None
+
+    if is_pipeline_variable(var_value):
+        if isinstance(var_value, Parameter) and not isinstance(var_value, ParameterString):
+            raise ValueError(f"{var_name} cannot be Parameter types other than ParameterString.")
+        logger.warning("%s's runtime value must be the string type.", var_name)
+        return var_value
+
+    return str(var_value)

tests/unit/sagemaker/workflow/test_quality_check_step.py

@@ -16,7 +16,8 @@
 import pytest
 
 from sagemaker.model_monitor import DatasetFormat
-from sagemaker.workflow.parameters import ParameterString
+from sagemaker.workflow.execution_variables import ExecutionVariable
+from sagemaker.workflow.parameters import ParameterString, ParameterInteger
 from sagemaker.workflow.pipeline import Pipeline
 from sagemaker.workflow.pipeline import PipelineDefinitionConfig
 from sagemaker.workflow.quality_check_step import (
@@ -178,8 +179,6 @@
             "dataset_source": "/opt/ml/processing/input/baseline_dataset_input",
             "analysis_type": "MODEL_QUALITY",
             "problem_type": "BinaryClassification",
-            "probability_attribute": "0",
-            "probability_threshold_attribute": "0.5",
         },
         "StoppingCondition": {"MaxRuntimeInSeconds": 1800},
     },
@@ -269,23 +268,54 @@ def test_data_quality_check_step(
     assert step_definition == _expected_data_quality_dsl
 
 
+@pytest.mark.parametrize(
+    "quality_cfg_attr_value, expected_value_in_dsl",
+    [
+        (0, "0"),
+        ("attr", "attr"),
+        (None, None),
+        (ParameterString(name="ParamStringEnvVar"), {"Get": "Parameters.ParamStringEnvVar"}),
+        (ExecutionVariable("PipelineArn"), {"Get": "Execution.PipelineArn"}),
+        (ParameterInteger(name="ParamIntEnvVar"), "Error"),
+    ],
+)
 def test_model_quality_check_step(
     sagemaker_session,
     check_job_config,
     model_package_group_name,
     supplied_baseline_statistics_uri,
     supplied_baseline_constraints_uri,
+    quality_cfg_attr_value,
+    expected_value_in_dsl,
 ):
     model_quality_check_config = ModelQualityCheckConfig(
         baseline_dataset="baseline_dataset_s3_url",
         dataset_format=DatasetFormat.csv(header=True),
         problem_type="BinaryClassification",
-        probability_attribute=0,  # the integer should be converted to str by SDK
-        ground_truth_attribute=None,
-        probability_threshold_attribute=0.5,  # the float should be converted to str by SDK
+        inference_attribute=quality_cfg_attr_value,
+        probability_attribute=quality_cfg_attr_value,
+        ground_truth_attribute=quality_cfg_attr_value,
+        probability_threshold_attribute=quality_cfg_attr_value,
         post_analytics_processor_script="s3://my_bucket/data_quality/postprocessor.py",
         output_s3_uri="",
     )
+
+    if expected_value_in_dsl == "Error":
+        with pytest.raises(ValueError) as err:
+            QualityCheckStep(
+                name="ModelQualityCheckStep",
+                register_new_baseline=False,
+                skip_check=False,
+                fail_on_violation=True,
+                quality_check_config=model_quality_check_config,
+                check_job_config=check_job_config,
+                model_package_group_name=model_package_group_name,
+                supplied_baseline_statistics=supplied_baseline_statistics_uri,
+                supplied_baseline_constraints=supplied_baseline_constraints_uri,
+            )
+        assert "cannot be Parameter types other than ParameterString" in str(err)
+        return
+
     model_quality_check_step = QualityCheckStep(
         name="ModelQualityCheckStep",
         register_new_baseline=False,
@@ -297,6 +327,7 @@ def test_model_quality_check_step(
         supplied_baseline_statistics=supplied_baseline_statistics_uri,
         supplied_baseline_constraints=supplied_baseline_constraints_uri,
     )
+
     pipeline = Pipeline(
         name="MyPipeline",
         parameters=[
@@ -310,6 +341,16 @@ def test_model_quality_check_step(
 
     step_definition = _get_step_definition_for_test(pipeline)
 
+    step_def_env = step_definition["Arguments"]["Environment"]
+    for var in [
+        "inference_attribute",
+        "probability_attribute",
+        "ground_truth_attribute",
+        "probability_threshold_attribute",
+    ]:
+        env_var_dsl = step_def_env.pop(var, None)
+        assert env_var_dsl == expected_value_in_dsl
+
     assert step_definition == _expected_model_quality_dsl