feat: Support selective pipeline execution for function step

src/sagemaker/remote_function/core/pipeline_variables.py

@@ -19,6 +19,7 @@
 
 from sagemaker.s3 import s3_path_join
 from sagemaker.remote_function.core.serialization import deserialize_obj_from_s3
+from sagemaker.workflow.step_outputs import get_step
 
 
 @dataclass
@@ -92,7 +93,7 @@ class _S3BaseUriIdentifier:
 class _DelayedReturn:
     """Delayed return from a function."""
 
-    uri: List[Union[str, _Parameter, _ExecutionVariable]]
+    uri: Union[_Properties, List[Union[str, _Parameter, _ExecutionVariable]]]
     reference_path: Tuple = field(default_factory=tuple)
 
 
@@ -164,6 +165,7 @@ def __init__(
         self,
         delayed_returns: List[_DelayedReturn],
         hmac_key: str,
+        properties_resolver: _PropertiesResolver,
         parameter_resolver: _ParameterResolver,
         execution_variable_resolver: _ExecutionVariableResolver,
         s3_base_uri: str,
@@ -174,6 +176,7 @@ def __init__(
         Args:
             delayed_returns: list of delayed returns to resolve.
             hmac_key: key used to encrypt serialized and deserialized function and arguments.
+            properties_resolver: resolver used to resolve step properties.
             parameter_resolver: resolver used to pipeline parameters.
             execution_variable_resolver: resolver used to resolve execution variables.
             s3_base_uri (str): the s3 base uri of the function step that
@@ -184,6 +187,7 @@ def __init__(
         self._s3_base_uri = s3_base_uri
         self._parameter_resolver = parameter_resolver
         self._execution_variable_resolver = execution_variable_resolver
+        self._properties_resolver = properties_resolver
         # different delayed returns can have the same uri, so we need to dedupe
         uris = {
             self._resolve_delayed_return_uri(delayed_return) for delayed_return in delayed_returns
@@ -214,7 +218,10 @@ def resolve(self, delayed_return: _DelayedReturn) -> Any:
 
     def _resolve_delayed_return_uri(self, delayed_return: _DelayedReturn):
         """Resolve the s3 uri of the delayed return."""
+        if isinstance(delayed_return.uri, _Properties):
+            return self._properties_resolver.resolve(delayed_return.uri)
 
+        # Keep the following old resolution logics to keep backward compatible
         uri = []
         for component in delayed_return.uri:
             if isinstance(component, _Parameter):
@@ -274,6 +281,7 @@ def resolve_pipeline_variables(
     delayed_return_resolver = _DelayedReturnResolver(
         delayed_returns=delayed_returns,
         hmac_key=hmac_key,
+        properties_resolver=properties_resolver,
         parameter_resolver=parameter_resolver,
         execution_variable_resolver=execution_variable_resolver,
         s3_base_uri=s3_base_uri,
@@ -325,27 +333,12 @@ def convert_pipeline_variables_to_pickleable(func_args: Tuple, func_kwargs: Dict
 
     from sagemaker.workflow.entities import PipelineVariable
 
-    from sagemaker.workflow.execution_variables import ExecutionVariables
-
     from sagemaker.workflow.function_step import DelayedReturn
 
-    # Notes:
-    # 1. The s3_base_uri = s3_root_uri + pipeline_name, but the two may be unknown
-    # when defining function steps. After step-level arg serialization,
-    # it's hard to update the s3_base_uri in pipeline compile time.
-    # Thus set a placeholder: _S3BaseUriIdentifier, and let the runtime job to resolve it.
-    # 2. For saying s3_root_uri is unknown, it's because when defining function steps,
-    # the pipeline's sagemaker_session is not passed in, but the default s3_root_uri
-    # should be retrieved from the pipeline's sagemaker_session.
     def convert(arg):
         if isinstance(arg, DelayedReturn):
             return _DelayedReturn(
-                uri=[
-                    _S3BaseUriIdentifier(),
-                    ExecutionVariables.PIPELINE_EXECUTION_ID._pickleable,
-                    arg._step.name,
-                    "results",
-                ],
+                uri=get_step(arg)._properties.OutputDataConfig.S3OutputPath._pickleable,
                 reference_path=arg._reference_path,
             )
 

src/sagemaker/remote_function/job.py

@@ -60,6 +60,7 @@
 from sagemaker import vpc_utils
 from sagemaker.remote_function.core.stored_function import StoredFunction, _SerializedData
 from sagemaker.remote_function.core.pipeline_variables import Context
+
 from sagemaker.remote_function.runtime_environment.runtime_environment_manager import (
     RuntimeEnvironmentManager,
     _DependencySettings,
@@ -72,6 +73,8 @@
     copy_workdir,
     resolve_custom_file_filter_from_config_file,
 )
+from sagemaker.workflow.function_step import DelayedReturn
+from sagemaker.workflow.step_outputs import get_step
 
 if TYPE_CHECKING:
     from sagemaker.workflow.entities import PipelineVariable
@@ -701,6 +704,7 @@ def compile(
         """Build the artifacts and generate the training job request."""
         from sagemaker.workflow.properties import Properties
         from sagemaker.workflow.parameters import Parameter
+        from sagemaker.workflow.functions import Join
         from sagemaker.workflow.execution_variables import ExecutionVariables, ExecutionVariable
         from sagemaker.workflow.utilities import load_step_compilation_context
 
@@ -760,7 +764,19 @@ def compile(
             job_settings=job_settings, s3_base_uri=s3_base_uri
         )
 
-        output_config = {"S3OutputPath": s3_base_uri}
+        if step_compilation_context:
+            s3_output_path = Join(
+                on="/",
+                values=[
+                    s3_base_uri,
+                    ExecutionVariables.PIPELINE_EXECUTION_ID,
+                    step_compilation_context.step_name,
+                    "results",
+                ],
+            )
+            output_config = {"S3OutputPath": s3_output_path}
+        else:
+            output_config = {"S3OutputPath": s3_base_uri}
         if job_settings.s3_kms_key is not None:
             output_config["KmsKeyId"] = job_settings.s3_kms_key
         request_dict["OutputDataConfig"] = output_config
@@ -804,6 +820,11 @@ def compile(
                 if isinstance(arg, (Parameter, ExecutionVariable, Properties)):
                     container_args.extend([arg.expr["Get"], arg.to_string()])
 
+                if isinstance(arg, DelayedReturn):
+                    # The uri is a Properties object
+                    uri = get_step(arg)._properties.OutputDataConfig.S3OutputPath
+                    container_args.extend([uri.expr["Get"], uri.to_string()])
+
         if run_info is not None:
             container_args.extend(["--run_in_context", json.dumps(dataclasses.asdict(run_info))])
         elif _RunContext.get_current_run() is not None:

src/sagemaker/remote_function/runtime_environment/runtime_environment_manager.py

@@ -252,7 +252,7 @@ def _is_file_exists(self, dependencies):
 
     def _install_requirements_txt(self, local_path, python_executable):
         """Install requirements.txt file"""
-        cmd = f"{python_executable} -m pip install -r {local_path}"
+        cmd = f"{python_executable} -m pip install -r {local_path} -U"
         logger.info("Running command: '%s' in the dir: '%s' ", cmd, os.getcwd())
         _run_shell_cmd(cmd)
         logger.info("Command %s ran successfully", cmd)
@@ -268,7 +268,7 @@ def _create_conda_env(self, env_name, local_path):
     def _install_req_txt_in_conda_env(self, env_name, local_path):
         """Install requirements.txt in the given conda environment"""
 
-        cmd = f"{self._get_conda_exe()} run -n {env_name} pip install -r {local_path}"
+        cmd = f"{self._get_conda_exe()} run -n {env_name} pip install -r {local_path} -U"
         logger.info("Activating conda env and installing requirements: %s", cmd)
         _run_shell_cmd(cmd)
         logger.info("Requirements installed successfully in conda env %s", env_name)

src/sagemaker/workflow/function_step.py

@@ -34,6 +34,7 @@
 )
 
 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
@@ -101,6 +102,12 @@ def __init__(
 
         self.__job_settings = None
 
+        # It's for internal usage to retrieve execution id from the properties.
+        # However, we won't expose the properties of function step to customers.
+        self._properties = Properties(
+            step_name=name, step=self, shape_name="DescribeTrainingJobResponse"
+        )
+
         (
             self._converted_func_args,
             self._converted_func_kwargs,

src/sagemaker/workflow/pipeline.py

@@ -1039,11 +1039,19 @@ def get_function_step_result(
         raise ValueError(_ERROR_MSG_OF_WRONG_STEP_TYPE)
     s3_output_path = describe_training_job_response["OutputDataConfig"]["S3OutputPath"]
 
+    s3_uri_suffix = s3_path_join(execution_id, step_name, RESULTS_FOLDER)
+    if s3_output_path.endswith(s3_uri_suffix) or s3_output_path[0:-1].endswith(s3_uri_suffix):
+        s3_uri = s3_output_path
+    else:
+        # This is the obsoleted version of s3_output_path
+        # Keeping it for backward compatible
+        s3_uri = s3_path_join(s3_output_path, s3_uri_suffix)
+
     job_status = describe_training_job_response["TrainingJobStatus"]
     if job_status == "Completed":
         return deserialize_obj_from_s3(
             sagemaker_session=sagemaker_session,
-            s3_uri=s3_path_join(s3_output_path, execution_id, step_name, RESULTS_FOLDER),
+            s3_uri=s3_uri,
             hmac_key=describe_training_job_response["Environment"]["REMOTE_FUNCTION_SECRET_KEY"],
         )
 

tests/integ/sagemaker/workflow/helpers.py

@@ -39,18 +39,24 @@ def create_and_execute_pipeline(
     step_result_type=None,
     step_result_value=None,
     wait_duration=400,  # seconds
+    selective_execution_config=None,
 ):
-    response = pipeline.create(role)
-
-    create_arn = response["PipelineArn"]
-    assert re.match(
-        rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}",
-        create_arn,
+    create_arn = None
+    if not selective_execution_config:
+        response = pipeline.create(role)
+        create_arn = response["PipelineArn"]
+        assert re.match(
+            rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}",
+            create_arn,
+        )
+
+    execution = pipeline.start(
+        parameters=execution_parameters, selective_execution_config=selective_execution_config
     )
 
-    execution = pipeline.start(parameters=execution_parameters)
-    response = execution.describe()
-    assert response["PipelineArn"] == create_arn
+    if create_arn:
+        response = execution.describe()
+        assert response["PipelineArn"] == create_arn
 
     wait_pipeline_execution(execution=execution, delay=20, max_attempts=int(wait_duration / 20))
 
@@ -71,6 +77,16 @@ def create_and_execute_pipeline(
     if step_result_value:
         result = execution.result(execution_steps[0]["StepName"])
         assert result == step_result_value, f"Expected {step_result_value}, instead found {result}"
+
+    if selective_execution_config:
+        for exe_step in execution_steps:
+            if exe_step["StepName"] in selective_execution_config.selected_steps:
+                continue
+            assert (
+                exe_step["SelectiveExecutionResult"]["SourcePipelineExecutionArn"]
+                == selective_execution_config.source_pipeline_execution_arn
+            )
+
     return execution, execution_steps