FEAT-#6990: Implement lazy execution for the Ray virtual partitions.

Author: AndreyPavlenko

modin/core/dataframe/pandas/partitioning/axis_partition.py

@@ -388,6 +388,7 @@ def deploy_axis_func(
         *partitions,
         lengths=None,
         manual_partition=False,
+        split_func=split_result_of_axis_func_pandas,
     ):
         """
         Deploy a function along a full axis.
@@ -413,13 +414,19 @@ def deploy_axis_func(
             The list of lengths to shuffle the object.
         manual_partition : bool, default: False
             If True, partition the result with `lengths`.
+        split_func : callable, optional
+            Split the result with the specified function.
 
         Returns
         -------
         list
             A list of pandas DataFrames.
         """
-        dataframe = pandas.concat(list(partitions), axis=axis, copy=False)
+        dataframe = (
+            partitions[0]
+            if len(partitions) == 1
+            else pandas.concat(partitions, axis=axis, copy=False)
+        )
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=FutureWarning)
             try:
@@ -451,7 +458,7 @@ def deploy_axis_func(
                 lengths = [len(part.columns) for part in partitions]
                 if sum(lengths) != len(result.columns):
                     lengths = None
-        return split_result_of_axis_func_pandas(axis, num_splits, result, lengths)
+        return split_func(axis, num_splits, result, lengths)
 
     @classmethod
     def deploy_func_between_two_axis_partitions(
@@ -464,6 +471,7 @@ def deploy_func_between_two_axis_partitions(
         len_of_left,
         other_shape,
         *partitions,
+        split_func=split_result_of_axis_func_pandas,
     ):
         """
         Deploy a function along a full axis between two data sets.
@@ -487,6 +495,8 @@ def deploy_func_between_two_axis_partitions(
             (other_shape[i-1], other_shape[i]) will indicate slice to restore i-1 axis partition.
         *partitions : iterable
             All partitions that make up the full axis (row or column) for both data sets.
+        split_func : callable, optional
+            Split the result with the specified function.
 
         Returns
         -------
@@ -510,7 +520,7 @@ def deploy_func_between_two_axis_partitions(
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=FutureWarning)
             result = func(lt_frame, rt_frame, *f_args, **f_kwargs)
-        return split_result_of_axis_func_pandas(axis, num_splits, result)
+        return split_func(axis, num_splits, result)
 
     @classmethod
     def drain(cls, df: pandas.DataFrame, call_queue: list):

modin/core/execution/ray/common/deferred_execution.py

@@ -72,12 +72,14 @@ class DeferredExecution:
         The execution input.
     func : callable or ObjectRefType
         A function to be executed.
-    args : list or tuple
+    args : list or tuple, optional
         Additional positional arguments to be passed in `func`.
-    kwargs : dict
+    kwargs : dict, optional
         Additional keyword arguments to be passed in `func`.
-    num_returns : int
+    num_returns : int, default: 1
         The number of the return values.
+    flat_data : bool
+        True means that the data is neither DeferredExecution nor list.
     flat_args : bool
         True means that there are no lists or DeferredExecution objects in `args`.
         In this case, no arguments processing is performed and `args` is passed
@@ -88,26 +90,29 @@ class DeferredExecution:
 
     def __init__(
         self,
-        data: Union[
-            ObjectRefType,
-            "DeferredExecution",
-            List[Union[ObjectRefType, "DeferredExecution"]],
-        ],
+        data: Any,
         func: Union[Callable, ObjectRefType],
-        args: Union[List[Any], Tuple[Any]],
-        kwargs: Dict[str, Any],
+        args: Union[List[Any], Tuple[Any]] = None,
+        kwargs: Dict[str, Any] = None,
         num_returns=1,
     ):
-        if isinstance(data, DeferredExecution):
-            data.subscribe()
+        self.flat_data = self._flat_args((data,))
         self.data = data
         self.func = func
-        self.args = args
-        self.kwargs = kwargs
         self.num_returns = num_returns
-        self.flat_args = self._flat_args(args)
-        self.flat_kwargs = self._flat_args(kwargs.values())
         self.subscribers = 0
+        if args is not None:
+            self.args = args
+            self.flat_args = self._flat_args(args)
+        else:
+            self.args = ()
+            self.flat_args = True
+        if kwargs is not None:
+            self.kwargs = kwargs
+            self.flat_kwargs = self._flat_args(kwargs.values())
+        else:
+            self.kwargs = {}
+            self.flat_kwargs = True
 
     @classmethod
     def _flat_args(cls, args: Iterable):
@@ -134,7 +139,7 @@ def _flat_args(cls, args: Iterable):
 
     def exec(
         self,
-    ) -> Tuple[ObjectRefOrListType, Union["MetaList", List], Union[int, List[int]]]:
+    ) -> Tuple[ObjectRefOrListType, "MetaList", Union[int, List[int]]]:
         """
         Execute this task, if required.
 
@@ -150,7 +155,7 @@ def exec(
             return self.data, self.meta, self.meta_offset
 
         if (
-            not isinstance(self.data, DeferredExecution)
+            self.flat_data
             and self.flat_args
             and self.flat_kwargs
             and self.num_returns == 1
@@ -166,14 +171,16 @@ def exec(
         # it back. After the execution, the result is saved and the counter has no effect.
         self.subscribers += 2
         consumers, output = self._deconstruct()
+        assert not any(isinstance(o, ListOrTuple) for o in output)
         # The last result is the MetaList, so adding +1 here.
         num_returns = sum(c.num_returns for c in consumers) + 1
         results = self._remote_exec_chain(num_returns, *output)
         meta = MetaList(results.pop())
         meta_offset = 0
         results = iter(results)
         for de in consumers:
-            if de.num_returns == 1:
+            num_returns = de.num_returns
+            if num_returns == 1:
                 de._set_result(next(results), meta, meta_offset)
                 meta_offset += 2
             else:
@@ -318,6 +325,7 @@ def _deconstruct_chain(
                 break
             elif not isinstance(data := de.data, DeferredExecution):
                 if isinstance(data, ListOrTuple):
+                    out_append(_Tag.LIST)
                     yield cls._deconstruct_list(
                         data, output, stack, result_consumers, out_append
                     )
@@ -394,7 +402,13 @@ def _deconstruct_list(
                 if out_pos := getattr(obj, "out_pos", None):
                     obj.unsubscribe()
                     if obj.has_result:
-                        out_append(obj.data)
+                        if isinstance(obj.data, ListOrTuple):
+                            out_append(_Tag.LIST)
+                            yield cls._deconstruct_list(
+                                obj.data, output, stack, result_consumers, out_append
+                            )
+                        else:
+                            out_append(obj.data)
                     else:
                         out_append(_Tag.REF)
                         out_append(out_pos)
@@ -432,13 +446,13 @@ def _remote_exec_chain(num_returns: int, *args: Tuple) -> List[Any]:
         list
             The execution results. The last element of this list is the ``MetaList``.
         """
-        # Prefer _remote_exec_single_chain(). It has fewer arguments and
-        # does not require the num_returns to be specified in options.
+        # Prefer _remote_exec_single_chain(). It does not require the num_returns
+        # to be specified in options.
         if num_returns == 2:
             return _remote_exec_single_chain.remote(*args)
         else:
             return _remote_exec_multi_chain.options(num_returns=num_returns).remote(
-                num_returns, *args
+                *args
             )
 
     def _set_result(
@@ -456,7 +470,7 @@ def _set_result(
         meta : MetaList
         meta_offset : int or list of int
         """
-        del self.func, self.args, self.kwargs, self.flat_args, self.flat_kwargs
+        del self.func, self.args, self.kwargs
         self.data = result
         self.meta = meta
         self.meta_offset = meta_offset
@@ -564,7 +578,7 @@ def exec_func(fn: Callable, obj: Any, args: Tuple, kwargs: Dict) -> Any:
             raise err
 
     @classmethod
-    def construct(cls, num_returns: int, args: Tuple):  # pragma: no cover
+    def construct(cls, args: Tuple):  # pragma: no cover
         """
         Construct and execute the specified chain.
 
@@ -574,7 +588,6 @@ def construct(cls, num_returns: int, args: Tuple):  # pragma: no cover
 
         Parameters
         ----------
-        num_returns : int
         args : tuple
 
         Yields
@@ -646,7 +659,7 @@ def construct_chain(
 
         while chain:
             fn = pop()
-            if fn == tg_e:
+            if fn is tg_e:
                 lst.append(obj)
                 break
 
@@ -676,10 +689,10 @@ def construct_chain(
 
             itr = iter([obj] if num_returns == 1 else obj)
             for _ in range(num_returns):
-                obj = next(itr)
-                meta.append(len(obj) if hasattr(obj, "__len__") else 0)
-                meta.append(len(obj.columns) if hasattr(obj, "columns") else 0)
-                yield obj
+                o = next(itr)
+                meta.append(len(o) if hasattr(o, "__len__") else 0)
+                meta.append(len(o.columns) if hasattr(o, "columns") else 0)
+                yield o
 
     @classmethod
     def construct_list(
@@ -793,20 +806,18 @@ def _remote_exec_single_chain(
     -------
     Generator
     """
-    return remote_executor.construct(num_returns=2, args=args)
+    return remote_executor.construct(args=args)
 
 
 @ray.remote
 def _remote_exec_multi_chain(
-    num_returns: int, *args: Tuple, remote_executor=_REMOTE_EXEC
+    *args: Tuple, remote_executor=_REMOTE_EXEC
 ) -> Generator:  # pragma: no cover
     """
     Execute the deconstructed chain with a multiple return values in a worker process.
 
     Parameters
     ----------
-    num_returns : int
-        The number of return values.
     *args : tuple
         A deconstructed chain to be executed.
     remote_executor : _RemoteExecutor, default: _REMOTE_EXEC
@@ -816,4 +827,4 @@ def _remote_exec_multi_chain(
     -------
     Generator
     """
-    return remote_executor.construct(num_returns, args)
+    return remote_executor.construct(args)

modin/core/execution/ray/implementations/pandas_on_ray/partitioning/partition.py

@@ -148,7 +148,7 @@ def add_to_apply_calls(
     def drain_call_queue(self):
         data = self._data_ref
         if not isinstance(data, DeferredExecution):
-            return data
+            return
 
         log = get_logger()
         self._is_debug(log) and log.debug(