change: turn off PipelineVariable inheritance from python primitives

Author: Dewen Qi

src/sagemaker/estimator.py

@@ -483,6 +483,7 @@ def __init__(
         if (
             not self.sagemaker_session.local_mode
             and output_path
+            and not is_pipeline_variable(output_path)
             and output_path.startswith("file://")
         ):
             raise RuntimeError("file:// output paths are only supported in Local Mode")

src/sagemaker/fw_utils.py

@@ -428,6 +428,10 @@ def warn_if_parameter_server_with_multi_gpu(training_instance_type, distribution
     """
     if training_instance_type == "local" or distribution is None:
         return
+    if is_pipeline_variable(training_instance_type):
+        # The training_instance_type is not available in compile time.
+        # Rather, it's given in Pipeline execution time
+        return
 
     is_multi_gpu_instance = (
         training_instance_type == "local_gpu"
@@ -485,6 +489,10 @@ def validate_smdistributed(
     if "smdistributed" not in distribution:
         # Distribution strategy other than smdistributed is selected
         return
+    if is_pipeline_variable(instance_type):
+        # The instance_type is not available in compile time.
+        # Rather, it's given in Pipeline execution time
+        return
 
     # distribution contains smdistributed
     smdistributed = distribution["smdistributed"]

src/sagemaker/workflow/__init__.py

@@ -12,14 +12,8 @@
 # language governing permissions and limitations under the License.
 """Defines Types etc. used in workflow."""
 from __future__ import absolute_import
-from typing import Union
 
 from sagemaker.workflow.entities import Expression
-from sagemaker.workflow.execution_variables import ExecutionVariable
-from sagemaker.workflow.parameters import Parameter
-from sagemaker.workflow.properties import Properties
-
-PipelineNonPrimitiveInputTypes = Union[ExecutionVariable, Expression, Parameter, Properties]
 
 
 def is_pipeline_variable(var: object) -> bool:

src/sagemaker/workflow/clarify_check_step.py

@@ -37,8 +37,8 @@
 from sagemaker.model_monitor.model_monitoring import _MODEL_MONITOR_S3_PATH
 from sagemaker.processing import ProcessingInput, ProcessingOutput, ProcessingJob
 from sagemaker.utils import name_from_base
-from sagemaker.workflow import PipelineNonPrimitiveInputTypes, is_pipeline_variable
-from sagemaker.workflow.entities import RequestType
+from sagemaker.workflow import is_pipeline_variable
+from sagemaker.workflow.entities import RequestType, PipelineVariable
 from sagemaker.workflow.properties import Properties
 from sagemaker.workflow.step_collections import StepCollection
 from sagemaker.workflow.steps import Step, StepTypeEnum, CacheConfig
@@ -59,7 +59,7 @@ class ClarifyCheckConfig(ABC):
         data_config (DataConfig): Config of the input/output data.
         kms_key (str): The ARN of the KMS key that is used to encrypt the
             user code file (default: None).
-            This field CANNOT be any of PipelineNonPrimitiveInputTypes.
+            This field CANNOT be any type of the `PipelineVariable`.
         monitoring_analysis_config_uri: (str): The uri of monitoring analysis config.
             This field does not take input.
             It will be generated once uploading the created analysis config file.
@@ -86,7 +86,7 @@ class DataBiasCheckConfig(ClarifyCheckConfig):
             "`KS <https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-data-bias-metric-kolmogorov-smirnov.html>`_",
             "`CDDL <https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-data-bias-metric-cddl.html>`_"].
             Defaults to computing all.
-            This field CANNOT be any of PipelineNonPrimitiveInputTypes.
+            This field CANNOT be any type of the `PipelineVariable`.
     """  # noqa E501
 
     data_bias_config: BiasConfig = attr.ib()
@@ -115,7 +115,7 @@ class ModelBiasCheckConfig(ClarifyCheckConfig):
             ", "`TE <https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-te.html>`_",
             "`FT <https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-ft.html>`_"].
             Defaults to computing all.
-            This field CANNOT be any of PipelineNonPrimitiveInputTypes.
+            This field CANNOT be any type of the `PipelineVariable`.
     """
 
     data_bias_config: BiasConfig = attr.ib()
@@ -136,7 +136,7 @@ class ModelExplainabilityCheckConfig(ClarifyCheckConfig):
             in the model output for the predicted scores to be explained (default: None).
             This is not required if the model output is a single score. Alternatively,
             an instance of ModelPredictedLabelConfig can be provided
-            but this field CANNOT be any of PipelineNonPrimitiveInputTypes.
+            but this field CANNOT be any type of the `PipelineVariable`.
     """
 
     model_config: ModelConfig = attr.ib()
@@ -152,10 +152,10 @@ def __init__(
         name: str,
         clarify_check_config: ClarifyCheckConfig,
         check_job_config: CheckJobConfig,
-        skip_check: Union[bool, PipelineNonPrimitiveInputTypes] = False,
-        register_new_baseline: Union[bool, PipelineNonPrimitiveInputTypes] = False,
-        model_package_group_name: Union[str, PipelineNonPrimitiveInputTypes] = None,
-        supplied_baseline_constraints: Union[str, PipelineNonPrimitiveInputTypes] = None,
+        skip_check: Union[bool, PipelineVariable] = False,
+        register_new_baseline: Union[bool, PipelineVariable] = False,
+        model_package_group_name: Union[str, PipelineVariable] = None,
+        supplied_baseline_constraints: Union[str, PipelineVariable] = None,
         display_name: str = None,
         description: str = None,
         cache_config: CacheConfig = None,
@@ -167,14 +167,14 @@ def __init__(
             name (str): The name of the ClarifyCheckStep step.
             clarify_check_config (ClarifyCheckConfig): A ClarifyCheckConfig instance.
             check_job_config (CheckJobConfig): A CheckJobConfig instance.
-            skip_check (bool or PipelineNonPrimitiveInputTypes): Whether the check
+            skip_check (bool or PipelineVariable): Whether the check
                 should be skipped (default: False).
-            register_new_baseline (bool or PipelineNonPrimitiveInputTypes): Whether
+            register_new_baseline (bool or PipelineVariable): Whether
                 the new baseline should be registered (default: False).
-            model_package_group_name (str or PipelineNonPrimitiveInputTypes): The name of a
+            model_package_group_name (str or PipelineVariable): The name of a
                 registered model package group, among which the baseline will be fetched
                 from the latest approved model (default: None).
-            supplied_baseline_constraints (str or PipelineNonPrimitiveInputTypes): The S3 path
+            supplied_baseline_constraints (str or PipelineVariable): The S3 path
                 to the supplied constraints object representing the constraints JSON file
                 which will be used for drift to check (default: None).
             display_name (str): The display name of the ClarifyCheckStep step (default: None).

src/sagemaker/workflow/entities.py

@@ -16,7 +16,7 @@
 import abc
 
 from enum import EnumMeta
-from typing import Any, Dict, List, Union, Optional
+from typing import Any, Dict, List, Union
 
 PrimitiveType = Union[str, int, bool, float, None]
 RequestType = Union[Dict[str, Any], List[Dict[str, Any]]]
@@ -102,39 +102,3 @@ def to_string(self):
     @abc.abstractmethod
     def expr(self) -> RequestType:
         """Get the expression structure for workflow service calls."""
-
-    def startswith(
-        self,
-        prefix: Union[str, tuple],  # pylint: disable=unused-argument
-        start: Optional[int] = None,  # pylint: disable=unused-argument
-        end: Optional[int] = None,  # pylint: disable=unused-argument
-    ) -> bool:
-        """Simulate the Python string's built-in method: startswith
-
-        Args:
-            prefix (str, tuple): The (tuple of) string to be checked.
-            start (int): To set the start index of the matching boundary (default: None).
-            end (int): To set the end index of the matching boundary (default: None).
-
-        Return:
-            bool: Always return False as Pipeline variables are parsed during execution runtime
-        """
-        return False
-
-    def endswith(
-        self,
-        suffix: Union[str, tuple],  # pylint: disable=unused-argument
-        start: Optional[int] = None,  # pylint: disable=unused-argument
-        end: Optional[int] = None,  # pylint: disable=unused-argument
-    ) -> bool:
-        """Simulate the Python string's built-in method: endswith
-
-        Args:
-            suffix (str, tuple): The (tuple of) string to be checked.
-            start (int): To set the start index of the matching boundary (default: None).
-            end (int): To set the end index of the matching boundary (default: None).
-
-        Return:
-            bool: Always return False as Pipeline variables are parsed during execution runtime
-        """
-        return False

src/sagemaker/workflow/fail_step.py

@@ -15,9 +15,9 @@
 
 from typing import List, Union, Optional
 
-from sagemaker.workflow import PipelineNonPrimitiveInputTypes
 from sagemaker.workflow.entities import (
     RequestType,
+    PipelineVariable,
 )
 from sagemaker.workflow.step_collections import StepCollection
 from sagemaker.workflow.steps import Step, StepTypeEnum
@@ -29,7 +29,7 @@ class FailStep(Step):
     def __init__(
         self,
         name: str,
-        error_message: Union[str, PipelineNonPrimitiveInputTypes] = None,
+        error_message: Union[str, PipelineVariable] = None,
         display_name: str = None,
         description: str = None,
         depends_on: Optional[List[Union[str, Step, StepCollection]]] = None,
@@ -39,7 +39,7 @@ def __init__(
         Args:
             name (str): The name of the `FailStep`. A name is required and must be
                 unique within a pipeline.
-            error_message (str or PipelineNonPrimitiveInputTypes):
+            error_message (str or PipelineVariable):
                 An error message defined by the user.
                 Once the `FailStep` is reached, the execution fails and the
                 error message is set as the failure reason (default: None).

src/sagemaker/workflow/parameters.py

@@ -99,29 +99,6 @@ def _expr(cls, name):
         """
         return {"Get": f"Parameters.{name}"}
 
-    @classmethod
-    def _implicit_value(cls, value, python_type, args, kwargs):
-        """Determine the implicit value from the arguments.
-
-        The implicit value of the instance should be the default_value if present.
-
-        Args:
-            value: The default implicit value.
-            python_type: The Python type the implicit value should be.
-            args: The list of positional arguments.
-            kwargs: The dict of keyword arguments.
-
-        Returns:
-            The implicit value that should be used.
-        """
-        if len(args) == 2:
-            value = args[1] or value
-        elif kwargs:
-            value = kwargs.get("default_value", value)
-        cls._check_default_value_type(value, python_type)
-
-        return value
-
     @classmethod
     def _check_default_value_type(cls, value, python_type):
         """Check whether the default value is compatible with the parameter type.
@@ -143,14 +120,9 @@ def _check_default_value_type(cls, value, python_type):
 ParameterBoolean = partial(Parameter, parameter_type=ParameterTypeEnum.BOOLEAN)
 
 
-class ParameterString(Parameter, str):
+class ParameterString(Parameter):
     """String parameter for pipelines."""
 
-    def __new__(cls, *args, **kwargs):  # pylint: disable=unused-argument
-        """Subclass str"""
-        val = cls._implicit_value("", str, args, kwargs)
-        return str.__new__(cls, val)
-
     def __init__(self, name: str, default_value: str = None, enum_values: List[str] = None):
         """Create a pipeline string parameter.
 
@@ -186,14 +158,9 @@ def to_request(self) -> RequestType:
         return request_dict
 
 
-class ParameterInteger(Parameter, int):
+class ParameterInteger(Parameter):
     """Integer parameter for pipelines."""
 
-    def __new__(cls, *args, **kwargs):
-        """Subclass int"""
-        val = cls._implicit_value(0, int, args, kwargs)
-        return int.__new__(cls, val)
-
     def __init__(self, name: str, default_value: int = None):
         """Create a pipeline integer parameter.
 
@@ -209,14 +176,9 @@ def __init__(self, name: str, default_value: int = None):
         )
 
 
-class ParameterFloat(Parameter, float):
+class ParameterFloat(Parameter):
     """Float parameter for pipelines."""
 
-    def __new__(cls, *args, **kwargs):
-        """Subclass float"""
-        val = cls._implicit_value(0.0, float, args, kwargs)
-        return float.__new__(cls, val)
-
     def __init__(self, name: str, default_value: float = None):
         """Create a pipeline float parameter.