fix: Correctly interpolate Callback output parameters

src/sagemaker/workflow/callback_step.py

@@ -58,19 +58,20 @@ def to_request(self) -> RequestType:
             "OutputType": self.output_type.value,
         }
 
-    @property
-    def expr(self) -> Dict[str, str]:
-        """The 'Get' expression dict for a `Parameter`."""
-        return CallbackOutput._expr(self.output_name)
+    def expr(self, step_name) -> Dict[str, str]:
+        """The 'Get' expression dict for a `CallbackOutput`."""
+        return CallbackOutput._expr(self.output_name, step_name)
 
     @classmethod
-    def _expr(cls, name):
+    def _expr(cls, name, step_name):
         """An internal classmethod for the 'Get' expression dict for a `CallbackOutput`.
 
         Args:
             name (str): The name of the callback output.
+            step_name (str): The name of the step the callback step associated
+                with this output belongs to.
         """
-        return {"Get": f"Steps.{name}.OutputParameters['{name}']"}
+        return {"Get": f"Steps.{step_name}.OutputParameters['{name}']"}
 
 
 class CallbackStep(Step):

src/sagemaker/workflow/pipeline.py

@@ -24,7 +24,7 @@
 
 from sagemaker._studio import _append_project_tags
 from sagemaker.session import Session
-from sagemaker.workflow.callback_step import CallbackOutput
+from sagemaker.workflow.callback_step import CallbackOutput, CallbackStep
 from sagemaker.workflow.entities import (
     Entity,
     Expression,
@@ -240,9 +240,12 @@ def definition(self) -> str:
         """Converts a request structure to string representation for workflow service calls."""
         request_dict = self.to_request()
         request_dict["PipelineExperimentConfig"] = interpolate(
-            request_dict["PipelineExperimentConfig"]
+            request_dict["PipelineExperimentConfig"], {}
+        )
+        callback_output_to_step_map = _map_callback_outputs(self.steps)
+        request_dict["Steps"] = interpolate(
+            request_dict["Steps"], callback_output_to_step_map=callback_output_to_step_map
         )
-        request_dict["Steps"] = interpolate(request_dict["Steps"])
 
         return json.dumps(request_dict)
 
@@ -263,38 +266,62 @@ def format_start_parameters(parameters: Dict[str, Any]) -> List[Dict[str, Any]]:
     return [{"Name": name, "Value": str(value)} for name, value in parameters.items()]
 
 
-def interpolate(request_obj: RequestType) -> RequestType:
+def interpolate(
+    request_obj: RequestType, callback_output_to_step_map: Dict[str, str]
+) -> RequestType:
     """Replaces Parameter values in a list of nested Dict[str, Any] with their workflow expression.
 
     Args:
         request_obj (RequestType): The request dict.
+        callback_output_to_step_map (Dict[str, str]): A dict of output name -> step name.
 
     Returns:
         RequestType: The request dict with Parameter values replaced by their expression.
     """
     request_obj_copy = deepcopy(request_obj)
-    return _interpolate(request_obj_copy)
+    return _interpolate(request_obj_copy, callback_output_to_step_map=callback_output_to_step_map)
 
 
-def _interpolate(obj: Union[RequestType, Any]):
+def _interpolate(obj: Union[RequestType, Any], callback_output_to_step_map: Dict[str, str]):
     """Walks the nested request dict, replacing Parameter type values with workflow expressions.
 
     Args:
         obj (Union[RequestType, Any]): The request dict.
+        callback_output_to_step_map (Dict[str, str]): A dict of output name -> step name.
     """
-    if isinstance(obj, (Expression, Parameter, Properties, CallbackOutput)):
+    if isinstance(obj, (Expression, Parameter, Properties)):
         return obj.expr
+    if isinstance(obj, CallbackOutput):
+        step_name = callback_output_to_step_map[obj.output_name]
+        return obj.expr(step_name)
     if isinstance(obj, dict):
         new = obj.__class__()
         for key, value in obj.items():
-            new[key] = interpolate(value)
+            new[key] = interpolate(value, callback_output_to_step_map)
     elif isinstance(obj, (list, set, tuple)):
-        new = obj.__class__(interpolate(value) for value in obj)
+        new = obj.__class__(interpolate(value, callback_output_to_step_map) for value in obj)
     else:
         return obj
     return new
 
 
+def _map_callback_outputs(steps: List[Step]):
+    """Iterate over the provided steps, building a map of callback output parameters to step names.
+
+    Args:
+        step (List[Step]): The steps list.
+    """
+
+    callback_output_map = {}
+    for step in steps:
+        if isinstance(step, CallbackStep):
+            if step.outputs:
+                for output in step.outputs:
+                    callback_output_map[output.output_name] = step.name
+
+    return callback_output_map
+
+
 def update_args(args: Dict[str, Any], **kwargs):
     """Updates the request arguments dict with a value, if populated.
 

tests/integ/test_workflow.py

@@ -739,6 +739,47 @@ def test_one_step_callback_pipeline(sagemaker_session, role, pipeline_name, regi
             pass
 
 
+def test_two_step_callback_pipeline_with_output_reference(
+    sagemaker_session, role, pipeline_name, region_name
+):
+    instance_count = ParameterInteger(name="InstanceCount", default_value=2)
+
+    outputParam1 = CallbackOutput(output_name="output1", output_type=CallbackOutputTypeEnum.String)
+    step_callback1 = CallbackStep(
+        name="callback-step1",
+        sqs_queue_url="https://sqs.us-east-2.amazonaws.com/123456789012/MyQueue",
+        inputs={"arg1": "foo"},
+        outputs=[outputParam1],
+    )
+
+    step_callback2 = CallbackStep(
+        name="callback-step2",
+        sqs_queue_url="https://sqs.us-east-2.amazonaws.com/123456789012/MyQueue",
+        inputs={"arg1": outputParam1},
+        outputs=[],
+    )
+
+    pipeline = Pipeline(
+        name=pipeline_name,
+        parameters=[instance_count],
+        steps=[step_callback1, step_callback2],
+        sagemaker_session=sagemaker_session,
+    )
+
+    try:
+        response = pipeline.create(role)
+        create_arn = response["PipelineArn"]
+        assert re.match(
+            fr"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}",
+            create_arn,
+        )
+    finally:
+        try:
+            pipeline.delete()
+        except Exception:
+            pass
+
+
 def test_conditional_pytorch_training_model_registration(
     sagemaker_session,
     role,

tests/unit/sagemaker/workflow/test_callback_step.py

@@ -88,7 +88,7 @@ def test_pipeline_interpolates_callback_outputs():
         name="MyCallbackStep2",
         depends_on=["TestStep"],
         sqs_queue_url="https://sqs.us-east-2.amazonaws.com/123456789012/MyQueue",
-        inputs={"arg1": cb_step1.properties.Outputs["output1"]},
+        inputs={"arg1": outputParam1},
         outputs=[outputParam2],
     )