make hash does not need to be part of basic memoizer

and is more reusable when it isn't

this isn't the only way to make a hash though. and
hashing isn't the only way to compare checkpoint
entries for equality.

parsl/dataflow/memoization.py

@@ -121,6 +121,42 @@ def id_for_memo_function(f: types.FunctionType, output_ref: bool = False) -> byt
     return pickle.dumps(["types.FunctionType", f.__name__, f.__module__])
 
 
+def make_hash(task: TaskRecord) -> str:
+    """Create a hash of the task inputs.
+
+    Args:
+        - task (dict) : Task dictionary from dfk.tasks
+
+    Returns:
+        - hash (str) : A unique hash string
+    """
+
+    t: List[bytes] = []
+
+    # if kwargs contains an outputs parameter, that parameter is removed
+    # and normalised differently - with output_ref set to True.
+    # kwargs listed in ignore_for_cache will also be removed
+
+    filtered_kw = task['kwargs'].copy()
+
+    ignore_list = task['ignore_for_cache']
+
+    logger.debug("Ignoring these kwargs for checkpointing: %s", ignore_list)
+    for k in ignore_list:
+        logger.debug("Ignoring kwarg %s", k)
+        del filtered_kw[k]
+
+    if 'outputs' in task['kwargs']:
+        outputs = task['kwargs']['outputs']
+        del filtered_kw['outputs']
+        t.append(id_for_memo(outputs, output_ref=True))
+
+    t.extend(map(id_for_memo, (filtered_kw, task['func'], task['args'])))
+
+    x = b''.join(t)
+    return hashlib.md5(x).hexdigest()
+
+
 class Memoizer:
     def start(self, *, dfk: DataFlowKernel, memoize: bool = True, checkpoint_files: Sequence[str], run_dir: str) -> None:
         raise NotImplementedError
@@ -200,41 +236,6 @@ def start(self, *, dfk: DataFlowKernel, memoize: bool = True, checkpoint_files:
             logger.info("App caching disabled for all apps")
             self.memo_lookup_table = {}
 
-    def make_hash(self, task: TaskRecord) -> str:
-        """Create a hash of the task inputs.
-
-        Args:
-            - task (dict) : Task dictionary from dfk.tasks
-
-        Returns:
-            - hash (str) : A unique hash string
-        """
-
-        t: List[bytes] = []
-
-        # if kwargs contains an outputs parameter, that parameter is removed
-        # and normalised differently - with output_ref set to True.
-        # kwargs listed in ignore_for_cache will also be removed
-
-        filtered_kw = task['kwargs'].copy()
-
-        ignore_list = task['ignore_for_cache']
-
-        logger.debug("Ignoring these kwargs for checkpointing: %s", ignore_list)
-        for k in ignore_list:
-            logger.debug("Ignoring kwarg %s", k)
-            del filtered_kw[k]
-
-        if 'outputs' in task['kwargs']:
-            outputs = task['kwargs']['outputs']
-            del filtered_kw['outputs']
-            t.append(id_for_memo(outputs, output_ref=True))
-
-        t.extend(map(id_for_memo, (filtered_kw, task['func'], task['args'])))
-
-        x = b''.join(t)
-        return hashlib.md5(x).hexdigest()
-
     def check_memo(self, task: TaskRecord) -> Optional[Future[Any]]:
         """Create a hash of the task and its inputs and check the lookup table for this hash.
 
@@ -256,7 +257,7 @@ def check_memo(self, task: TaskRecord) -> Optional[Future[Any]]:
             logger.debug("Task {} will not be memoized".format(task_id))
             return None
 
-        hashsum = self.make_hash(task)
+        hashsum = make_hash(task)
         logger.debug("Task {} has memoization hash {}".format(task_id, hashsum))
         result = None
         if hashsum in self.memo_lookup_table: