feat: Support selective pipeline execution between function step and regular step

src/sagemaker/workflow/function_step.py

@@ -33,12 +33,11 @@
     PipelineVariable,
 )
 
-from sagemaker.workflow.execution_variables import ExecutionVariables
 from sagemaker.workflow.properties import Properties
 from sagemaker.workflow.retry import RetryPolicy
 from sagemaker.workflow.steps import Step, ConfigurableRetryStep, StepTypeEnum
 from sagemaker.workflow.step_collections import StepCollection
-from sagemaker.workflow.step_outputs import StepOutput
+from sagemaker.workflow.step_outputs import StepOutput, get_step
 from sagemaker.workflow.utilities import trim_request_dict, load_step_compilation_context
 
 from sagemaker.s3_utils import s3_path_join
@@ -277,14 +276,12 @@ def _to_json_get(self) -> JsonGet:
         """Expression structure for workflow service calls using JsonGet resolution."""
         from sagemaker.remote_function.core.stored_function import (
             JSON_SERIALIZED_RESULT_KEY,
-            RESULTS_FOLDER,
             JSON_RESULTS_FILE,
         )
 
         if not self._step.name:
             raise ValueError("Step name is not defined.")
 
-        s3_root_uri = self._step._job_settings.s3_root_uri
         # Resolve json path --
         #   Deserializer will be able to resolve a JsonGet using path "Return[1]" to
         #   access value 10 from following serialized JSON:
@@ -308,13 +305,9 @@ def _to_json_get(self) -> JsonGet:
 
         return JsonGet(
             s3_uri=Join(
-                "/",
-                [
-                    s3_root_uri,
-                    ExecutionVariables.PIPELINE_NAME,
-                    ExecutionVariables.PIPELINE_EXECUTION_ID,
-                    self._step.name,
-                    RESULTS_FOLDER,
+                on="/",
+                values=[
+                    get_step(self)._properties.OutputDataConfig.S3OutputPath,
                     JSON_RESULTS_FILE,
                 ],
             ),

src/sagemaker/workflow/functions.py

@@ -21,7 +21,7 @@
 from sagemaker.workflow.entities import PipelineVariable
 from sagemaker.workflow.execution_variables import ExecutionVariable
 from sagemaker.workflow.parameters import Parameter
-from sagemaker.workflow.properties import PropertyFile
+from sagemaker.workflow.properties import PropertyFile, Properties
 
 if TYPE_CHECKING:
     from sagemaker.workflow.steps import Step
@@ -172,9 +172,9 @@ def _validate_json_get_s3_uri(self):
         for join_arg in s3_uri.values:
             if not is_pipeline_variable(join_arg):
                 continue
-            if not isinstance(join_arg, (Parameter, ExecutionVariable)):
+            if not isinstance(join_arg, (Parameter, ExecutionVariable, Properties)):
                 raise ValueError(
                     f"Invalid JsonGet function {self.expr}. "
-                    f"The Join values in JsonGet's s3_uri can only be a primitive object "
-                    f"or Parameter or ExecutionVariable."
+                    f"The Join values in JsonGet's s3_uri can only be a primitive object, "
+                    f"Parameter, ExecutionVariable or Properties."
                 )

src/sagemaker/workflow/pipeline.py

@@ -41,7 +41,6 @@
     RESOURCE_NOT_FOUND_EXCEPTION,
     EXECUTION_TIME_PIPELINE_PARAMETER_FORMAT,
 )
-from sagemaker.workflow.function_step import DelayedReturn
 from sagemaker.workflow.lambda_step import LambdaOutput, LambdaStep
 from sagemaker.workflow.entities import (
     Expression,
@@ -725,10 +724,7 @@ def _interpolate(
         pipeline_name (str): The name of the pipeline to be interpolated.
     """
     if isinstance(obj, (Expression, Parameter, Properties, StepOutput)):
-        updated_obj = _replace_pipeline_name_in_json_get_s3_uri(
-            obj=obj, pipeline_name=pipeline_name
-        )
-        return updated_obj.expr
+        return obj.expr
 
     if isinstance(obj, CallbackOutput):
         step_name = callback_output_to_step_map[obj.output_name]
@@ -760,27 +756,6 @@ def _interpolate(
     return new
 
 
-# TODO: we should remove this once the ExecutionVariables.PIPELINE_NAME is fixed in backend
-def _replace_pipeline_name_in_json_get_s3_uri(obj: Union[RequestType, Any], pipeline_name: str):
-    """Replace the ExecutionVariables.PIPELINE_NAME in DelayedReturn's JsonGet s3_uri
-
-    with the pipeline_name, because ExecutionVariables.PIPELINE_NAME
-    is parsed as all lower-cased str in backend.
-    """
-    if not isinstance(obj, DelayedReturn):
-        return obj
-
-    json_get = obj._to_json_get()
-
-    if not json_get.s3_uri:
-        return obj
-    # the s3 uri has to be a Join, which has been validated in JsonGet init
-    for i in range(len(json_get.s3_uri.values)):
-        if json_get.s3_uri.values[i] == ExecutionVariables.PIPELINE_NAME:
-            json_get.s3_uri.values[i] = pipeline_name
-    return json_get
-
-
 def _map_callback_outputs(steps: List[Step]):
     """Iterate over the provided steps, building a map of callback output parameters to step names.
 

tests/integ/sagemaker/workflow/helpers.py

@@ -33,7 +33,7 @@ def create_and_execute_pipeline(
     region_name,
     role,
     no_of_steps,
-    last_step_name,
+    last_step_name_prefix,
     execution_parameters,
     step_status,
     step_result_type=None,
@@ -66,7 +66,7 @@ def create_and_execute_pipeline(
         len(execution_steps) == no_of_steps
     ), f"Expected {no_of_steps}, instead found {len(execution_steps)}"
 
-    assert last_step_name in execution_steps[0]["StepName"]
+    assert last_step_name_prefix in execution_steps[0]["StepName"]
     assert execution_steps[0]["StepStatus"] == step_status
     if step_result_type:
         result = execution.result(execution_steps[0]["StepName"])

tests/integ/sagemaker/workflow/test_selective_execution.py

@@ -16,6 +16,7 @@
 
 import pytest
 
+from sagemaker.processing import ProcessingInput
 from tests.integ import DATA_DIR
 from sagemaker.sklearn import SKLearnProcessor
 from sagemaker.workflow.step_outputs import get_step
@@ -84,7 +85,7 @@ def sum(a, b):
             region_name=region_name,
             role=role,
             no_of_steps=2,
-            last_step_name="sum",
+            last_step_name_prefix="sum",
             execution_parameters=dict(),
             step_status="Succeeded",
             step_result_type=int,
@@ -97,7 +98,7 @@ def sum(a, b):
             region_name=region_name,
             role=role,
             no_of_steps=2,
-            last_step_name="sum",
+            last_step_name_prefix="sum",
             execution_parameters=dict(),
             step_status="Succeeded",
             step_result_type=int,
@@ -115,7 +116,7 @@ def sum(a, b):
             pass
 
 
-def test_selective_execution_of_regular_step_depended_by_function_step(
+def test_selective_execution_of_regular_step_referenced_by_function_step(
     sagemaker_session,
     role,
     pipeline_name,
@@ -168,7 +169,7 @@ def func_2(arg):
             region_name=region_name,
             role=role,
             no_of_steps=2,
-            last_step_name="func",
+            last_step_name_prefix="func",
             execution_parameters=dict(),
             step_status="Succeeded",
             step_result_type=str,
@@ -182,7 +183,7 @@ def func_2(arg):
             region_name=region_name,
             role=role,
             no_of_steps=2,
-            last_step_name="func",
+            last_step_name_prefix="func",
             execution_parameters=dict(),
             step_status="Succeeded",
             step_result_type=str,
@@ -199,3 +200,102 @@ def func_2(arg):
             pipeline.delete()
         except Exception:
             pass
+
+
+def test_selective_execution_of_function_step_referenced_by_regular_step(
+    pipeline_session,
+    role,
+    pipeline_name,
+    region_name,
+    dummy_container_without_error,
+    sklearn_latest_version,
+):
+    # Test Selective Pipeline Execution on function step -> [select: regular step]
+    os.environ["AWS_DEFAULT_REGION"] = region_name
+    processing_job_instance_counts = 2
+
+    @step(
+        name="step1",
+        role=role,
+        image_uri=dummy_container_without_error,
+        instance_type=INSTANCE_TYPE,
+        keep_alive_period_in_seconds=60,
+    )
+    def func(var: int):
+        return 1, var
+
+    step_output = func(processing_job_instance_counts)
+
+    script_path = os.path.join(DATA_DIR, "dummy_script.py")
+    input_file_path = os.path.join(DATA_DIR, "dummy_input.txt")
+    inputs = [
+        ProcessingInput(source=input_file_path, destination="/opt/ml/processing/inputs/"),
+    ]
+
+    sklearn_processor = SKLearnProcessor(
+        framework_version=sklearn_latest_version,
+        role=role,
+        instance_type=INSTANCE_TYPE,
+        instance_count=step_output[1],
+        command=["python3"],
+        sagemaker_session=pipeline_session,
+        base_job_name="test-sklearn",
+    )
+
+    step_args = sklearn_processor.run(
+        inputs=inputs,
+        code=script_path,
+    )
+    process_step = ProcessingStep(
+        name="MyProcessStep",
+        step_args=step_args,
+    )
+
+    pipeline = Pipeline(
+        name=pipeline_name,
+        steps=[process_step],
+        sagemaker_session=pipeline_session,
+    )
+
+    try:
+        execution, _ = create_and_execute_pipeline(
+            pipeline=pipeline,
+            pipeline_name=pipeline_name,
+            region_name=region_name,
+            role=role,
+            no_of_steps=2,
+            last_step_name_prefix=process_step.name,
+            execution_parameters=dict(),
+            step_status="Succeeded",
+            wait_duration=1000,  # seconds
+        )
+
+        _, execution_steps2 = create_and_execute_pipeline(
+            pipeline=pipeline,
+            pipeline_name=pipeline_name,
+            region_name=region_name,
+            role=role,
+            no_of_steps=2,
+            last_step_name_prefix=process_step.name,
+            execution_parameters=dict(),
+            step_status="Succeeded",
+            wait_duration=1000,  # seconds
+            selective_execution_config=SelectiveExecutionConfig(
+                source_pipeline_execution_arn=execution.arn,
+                selected_steps=[process_step.name],
+            ),
+        )
+
+        execution_proc_job = pipeline_session.describe_processing_job(
+            execution_steps2[0]["Metadata"]["ProcessingJob"]["Arn"].split("/")[-1]
+        )
+        assert (
+            execution_proc_job["ProcessingResources"]["ClusterConfig"]["InstanceCount"]
+            == processing_job_instance_counts
+        )
+
+    finally:
+        try:
+            pipeline.delete()
+        except Exception:
+            pass