handle instrumentation of delegators

instrumentation/opentelemetry-instrumentation-sklearn/src/opentelemetry/instrumentation/sklearn/__init__.py

@@ -68,6 +68,7 @@
 from sklearn.base import BaseEstimator
 from sklearn.pipeline import FeatureUnion, Pipeline
 from sklearn.tree import BaseDecisionTree
+from sklearn.utils.metaestimators import _IffHasAttrDescriptor
 
 from opentelemetry.instrumentation.instrumentor import BaseInstrumentor
 from opentelemetry.instrumentation.sklearn.version import __version__
@@ -104,6 +105,55 @@ def wrapper(*args, **kwargs):
     return wrapper
 
 
+def implement_spans_delegator(obj: _IffHasAttrDescriptor):
+    """Wrap the descriptor's fn with a span.
+
+    Args:
+        obj: An instance of _IffHasAttrDescriptor
+    """
+    # Don't instrument inherited delegators
+    if hasattr(obj, "_otel_original_fn"):
+        logger.debug("Already instrumented: %s", obj.fn.__qualname__)
+        return
+
+    def implement_spans_get(func: Callable):
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            with get_tracer(__name__, __version__).start_as_current_span(
+                name=func.__qualname__
+            ):
+                return func(*args, **kwargs)
+
+        return wrapper
+
+    logger.debug("Instrumenting: %s", obj.fn.__qualname__)
+
+    setattr(obj, "_otel_original_fn", getattr(obj, "fn"))
+    setattr(obj, "fn", implement_spans_get(obj.fn))
+
+
+def get_delegator(
+    estimator: Type[BaseEstimator], method_name: str
+) -> Union[_IffHasAttrDescriptor, None]:
+    """Get the delegator from a class method or None.
+
+    Args:
+        estimator (BaseEstimator): A class derived from ``sklearn``'s
+          ``BaseEstimator``.
+        method_name (str): The method name of the estimator on which to
+          check for delegation.
+
+    Returns:
+        The delegator, if one exists, otherwise None.
+    """
+    class_attr = getattr(estimator, method_name)
+    if getattr(class_attr, "__closure__", None) is not None:
+        for cell in class_attr.__closure__:
+            if isinstance(cell.cell_contents, _IffHasAttrDescriptor):
+                return cell.cell_contents
+    return None
+
+
 def get_base_estimators(packages: List[str]) -> Dict[str, Type[BaseEstimator]]:
     """Walk package hierarchies to get BaseEstimator-derived classes.
 
@@ -389,7 +439,7 @@ def _check_instrumented(
             method_name (str): The method name of the estimator on which to
               check for instrumentation.
         """
-        orig_method_name = "_original_" + method_name
+        orig_method_name = "_otel_original_" + method_name
         has_original = hasattr(estimator, orig_method_name)
         orig_class, orig_method = getattr(
             estimator, orig_method_name, (None, None)
@@ -419,11 +469,12 @@ def _uninstrument_class_method(
             method_name (str): The method name of the estimator on which to
               apply a span.
        """
-        orig_method_name = "_original_" + method_name
+        orig_method_name = "_otel_original_" + method_name
         if isclass(estimator):
             qualname = estimator.__qualname__
         else:
             qualname = estimator.__class__.__qualname__
+        delegator = get_delegator(estimator, method_name)
         if self._check_instrumented(estimator, method_name):
             logger.debug(
                 "Uninstrumenting: %s.%s", qualname, method_name,
@@ -433,6 +484,16 @@ def _uninstrument_class_method(
                 estimator, method_name, orig_method,
             )
             delattr(estimator, orig_method_name)
+        elif delegator is not None:
+            if not hasattr(delegator, "_otel_original_fn"):
+                logger.debug(
+                    "Already uninstrumented: %s.%s", qualname, method_name,
+                )
+                return
+            setattr(
+                delegator, "fn", getattr(delegator, "_otel_original_fn"),
+            )
+            delattr(delegator, "_otel_original_fn")
         else:
             logger.debug(
                 "Already uninstrumented: %s.%s", qualname, method_name,
@@ -452,7 +513,7 @@ def _uninstrument_instance_method(
             method_name (str): The method name of the estimator on which to
               apply a span.
        """
-        orig_method_name = "_original_" + method_name
+        orig_method_name = "_otel_original_" + method_name
         if isclass(estimator):
             qualname = estimator.__qualname__
         else:
@@ -496,37 +557,25 @@ def _instrument_class_method(
             )
             return
         class_attr = getattr(estimator, method_name)
+        delegator = get_delegator(estimator, method_name)
         if isinstance(class_attr, property):
             logger.debug(
                 "Not instrumenting found property: %s.%s",
                 estimator.__qualname__,
                 method_name,
             )
+        elif delegator is not None:
+            implement_spans_delegator(delegator)
         else:
             setattr(
-                estimator, "_original_" + method_name, (estimator, class_attr),
+                estimator,
+                "_otel_original_" + method_name,
+                (estimator, class_attr),
             )
             setattr(
                 estimator, method_name, self.spanner(class_attr, estimator),
             )
 
-    def _function_wrapper(self, function):
-        """Get the inner-most decorator of a function."""
-        if hasattr(function, "__wrapped__"):
-            if hasattr(function.__wrapped__, "__wrapped__"):
-                return self._function_wrapper(function.__wrapped__)
-            return function
-        return None
-
-    def _function_wrapper_wrapper(self, function):
-        """Get the second inner-most decorator of a function"""
-        if hasattr(function, "__wrapped__"):
-            if hasattr(function.__wrapped__, "__wrapped__"):
-                if hasattr(function.__wrapped__.__wrapped__, "__wrapped__"):
-                    return self._function_wrapper_wrapper(function.__wrapped__)
-                return function
-        return None
-
     def _unwrap_function(self, function):
         """Fetch the function underlying any decorators"""
         if hasattr(function, "__wrapped__"):
@@ -564,7 +613,9 @@ def _instrument_instance_method(
             )
         else:
             method = getattr(estimator, method_name)
-            setattr(estimator, "_original_" + method_name, (estimator, method))
+            setattr(
+                estimator, "_otel_original_" + method_name, (estimator, method)
+            )
             setattr(
                 estimator, method_name, self.spanner(method, estimator),
             )

instrumentation/opentelemetry-instrumentation-sklearn/tests/test_sklearn.py

@@ -5,13 +5,54 @@
     DEFAULT_METHODS,
     SklearnInstrumentor,
     get_base_estimators,
+    get_delegator,
 )
 from opentelemetry.test.test_base import TestBase
 from opentelemetry.trace import SpanKind
 
 from .fixtures import pipeline, random_input
 
 
+def assert_instrumented(base_estimators):
+    for _, estimator in base_estimators.items():
+        for method_name in DEFAULT_METHODS:
+            original_method_name = "_otel_original_" + method_name
+            if issubclass(estimator, tuple(DEFAULT_EXCLUDE_CLASSES)):
+                assert not hasattr(estimator, original_method_name)
+                continue
+            class_attr = getattr(estimator, method_name, None)
+            if isinstance(class_attr, property):
+                assert not hasattr(estimator, original_method_name)
+                continue
+            delegator = None
+            if hasattr(estimator, method_name):
+                delegator = get_delegator(estimator, method_name)
+            if delegator is not None:
+                assert hasattr(delegator, "_otel_original_fn")
+            elif hasattr(estimator, method_name):
+                assert hasattr(estimator, original_method_name)
+
+
+def assert_uninstrumented(base_estimators):
+    for _, estimator in base_estimators.items():
+        for method_name in DEFAULT_METHODS:
+            original_method_name = "_otel_original_" + method_name
+            if issubclass(estimator, tuple(DEFAULT_EXCLUDE_CLASSES)):
+                assert not hasattr(estimator, original_method_name)
+                continue
+            class_attr = getattr(estimator, method_name, None)
+            if isinstance(class_attr, property):
+                assert not hasattr(estimator, original_method_name)
+                continue
+            delegator = None
+            if hasattr(estimator, method_name):
+                delegator = get_delegator(estimator, method_name)
+            if delegator is not None:
+                assert not hasattr(delegator, "_otel_original_fn")
+            elif hasattr(estimator, method_name):
+                assert not hasattr(estimator, original_method_name)
+
+
 class TestSklearn(TestBase):
     def test_package_instrumentation(self):
         ski = SklearnInstrumentor()
@@ -21,42 +62,18 @@ def test_package_instrumentation(self):
         model = pipeline()
 
         ski.instrument()
-        # assert instrumented
-        for _, estimator in base_estimators.items():
-            for method_name in DEFAULT_METHODS:
-                if issubclass(estimator, tuple(DEFAULT_EXCLUDE_CLASSES)):
-                    assert not hasattr(estimator, "_original_" + method_name)
-                    continue
-                class_attr = getattr(estimator, method_name, None)
-                if isinstance(class_attr, property):
-                    assert not hasattr(estimator, "_original_" + method_name)
-                    continue
-                if hasattr(estimator, method_name):
-                    assert hasattr(estimator, "_original_" + method_name)
+        assert_instrumented(base_estimators)
 
         x_test = random_input()
 
         model.predict(x_test)
 
         spans = self.memory_exporter.get_finished_spans()
-        for span in spans:
-            print(span)
         self.assertEqual(len(spans), 8)
         self.memory_exporter.clear()
 
         ski.uninstrument()
-        # assert uninstrumented
-        for _, estimator in base_estimators.items():
-            for method_name in DEFAULT_METHODS:
-                if issubclass(estimator, tuple(DEFAULT_EXCLUDE_CLASSES)):
-                    assert not hasattr(estimator, "_original_" + method_name)
-                    continue
-                class_attr = getattr(estimator, method_name, None)
-                if isinstance(class_attr, property):
-                    assert not hasattr(estimator, "_original_" + method_name)
-                    continue
-                if hasattr(estimator, method_name):
-                    assert not hasattr(estimator, "_original_" + method_name)
+        assert_uninstrumented(base_estimators)
 
         model = pipeline()
         x_test = random_input()