[pipeline]refactor ppschedule to support tensor list (#1050)

* [CLI] add CLI launcher

* Revert "[CLI] add CLI launcher"

This reverts commit df7e6506d4.

* refactor ppschedule to support tensor list

* polish

colossalai/communication/utils.py

@@ -9,14 +9,21 @@ from typing import Union, List, Tuple
 TensorShape = Union[torch.Size, List[int], Tuple[int]]
 
 
-def send_tensor_meta(tensor, need_meta=True, next_rank=None) -> bool:
-    """Sends tensor meta information before sending a specific tensor.
-    Since the recipient must know the shape of the tensor in p2p communications,
-    meta information of the tensor should be sent before communications. This function
-    synchronizes with :func:`recv_tensor_meta`.
+def send_meta_helper(obj, next_rank, tensor_kwargs):
+    send_shape = torch.tensor(obj.size(), **tensor_kwargs)
+    send_ndims = torch.tensor(len(obj.size()), **tensor_kwargs)
+    dist.send(send_ndims, next_rank)
+    dist.send(send_shape, next_rank)
+
+
+def send_obj_meta(obj, need_meta=True, next_rank=None) -> bool:
+    """Sends obj meta information before sending a specific obj.
+    Since the recipient must know the shape of the obj in p2p communications,
+    meta information of the obj should be sent before communications. This function
+    synchronizes with :func:`recv_obj_meta`.
 
     Args:
-        tensor (:class:`torch.Tensor`): Tensor to be sent.
+        obj (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): obj to be sent.
         need_meta (bool, optional): If False, meta information won't be sent.
         next_rank (int): The rank of the next member in pipeline parallel group.
 
@@ -28,42 +35,57 @@ def send_tensor_meta(tensor, need_meta=True, next_rank=None) -> bool:
             next_rank = gpc.get_next_global_rank(ParallelMode.PIPELINE)
 
         tensor_kwargs = {'dtype': torch.long, 'device': get_current_device()}
-
-        send_shape = torch.tensor(tensor.size(), **tensor_kwargs)
-        send_ndims = torch.tensor(len(tensor.size()), **tensor_kwargs)
-        dist.send(send_ndims, next_rank)
-        dist.send(send_shape, next_rank)
+        if isinstance(obj, torch.Tensor):
+            send_obj_nums = torch.tensor(1, **tensor_kwargs)
+            dist.send(send_obj_nums, next_rank)
+            send_meta_helper(obj, next_rank, tensor_kwargs)
+        else:
+            send_obj_nums = torch.tensor(len(obj), **tensor_kwargs)
+            dist.send(send_obj_nums, next_rank)
+            for tensor_to_send in obj:
+                send_meta_helper(tensor_to_send, next_rank, tensor_kwargs)
 
     return False
 
 
-def recv_tensor_meta(tensor_shape: TensorShape, prev_rank=None) -> torch.Size:
-    """Receives tensor meta information before receiving a specific tensor.
-    Since the recipient must know the shape of the tensor in p2p communications,
-    meta information of the tensor should be received before communications. This function
-    synchronizes with :func:`send_tensor_meta`.
+def recv_meta_helper(prev_rank, tensor_kwargs):
+    recv_ndims = torch.empty((), **tensor_kwargs)
+    dist.recv(recv_ndims, prev_rank)
+    recv_shape = torch.empty(recv_ndims, **tensor_kwargs)
+    dist.recv(recv_shape, prev_rank)
+    return recv_shape
+
+
+def recv_obj_meta(obj_shape, prev_rank=None) -> torch.Size:
+    """Receives obj meta information before receiving a specific obj.
+    Since the recipient must know the shape of the obj in p2p communications,
+    meta information of the obj should be received before communications. This function
+    synchronizes with :func:`send_obj_meta`.
 
     Args:
-        tensor_shape (:class:`torch.Size`): The shape of the tensor to be received.
-        prev_rank (int): The rank of the source of the tensor.
+        obj_shape (Union[:class:`torch.Size`, List[:class:`torch.Size`]]): The shape of the obj to be received.
+        prev_rank (int): The rank of the source of the obj.
 
     Returns:
-        :class:`torch.Size`: The shape of the tensor to be received.
+        Union[:class:`torch.Size`, List[:class:`torch.Size`]]: The shape of the obj to be received.
     """
-    if tensor_shape is None:
+    if obj_shape is None:
         if prev_rank is None:
             prev_rank = gpc.get_prev_global_rank(ParallelMode.PIPELINE)
 
         tensor_kwargs = {'dtype': torch.long, 'device': get_current_device()}
+        recv_obj_nums = torch.empty((), **tensor_kwargs)
+        dist.recv(recv_obj_nums, prev_rank)
+        if recv_obj_nums.item() == 1:
+            recv_shape = recv_meta_helper(prev_rank, tensor_kwargs)
+            obj_shape = torch.Size(recv_shape)
+        else:
+            obj_shape = []
+            for i in range(recv_obj_nums.item()):
+                recv_shape = recv_meta_helper(prev_rank, tensor_kwargs)
+                obj_shape.append(torch.Size(recv_shape))
 
-        recv_ndims = torch.empty((), **tensor_kwargs)
-        dist.recv(recv_ndims, prev_rank)
-        recv_shape = torch.empty(recv_ndims, **tensor_kwargs)
-        dist.recv(recv_shape, prev_rank)
-
-        tensor_shape = torch.Size(recv_shape)
-
-    return tensor_shape
+    return obj_shape
 
 
 def split_tensor_into_1d_equal_chunks(tensor: torch.Tensor, new_buffer=False) -> torch.Tensor: