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

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(