revert zero tensors back (#829)

colossalai/zero/sharded_param/sharded_tensor.py

@@ -20,8 +20,8 @@ class ShardedTensor(StatefulTensor):
 
     @property
     def dtype(self) -> torch.dtype:
-        assert self.torch_tensor().dtype == self._origin_dtype
-        return self.torch_tensor().dtype
+        assert self._payload.dtype == self._origin_dtype
+        return self._payload.dtype
 
     @property
     def origin_numel(self) -> int:

colossalai/zero/sharded_param/tensorful_state.py

@@ -1,7 +1,6 @@
 from enum import Enum
 from typing import Optional
 import torch
-from colossalai.tensor import ColoTensor
 
 
 class TensorState(Enum):
@@ -12,7 +11,7 @@ class TensorState(Enum):
     COMPUTE = 4
 
 
-class StatefulTensor(ColoTensor):
+class StatefulTensor(object):
     """A Structure stores a Torch Tensor and labeled states. 
     Inspired from the paper:
     PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management
@@ -21,20 +20,15 @@ class StatefulTensor(ColoTensor):
     """
 
     def __init__(self, tensor: Optional[torch.Tensor], state: Optional[TensorState] = TensorState.HOLD) -> None:
-        if tensor is not None:
-            super().__init__(tensor.size(), dtype=tensor.dtype, requires_grad=tensor.requires_grad, \
-                pin_memory=tensor.pin_memory, torch_tensor=tensor)
-        else:
-            super().__init__(0)
-
         self._state = state
+        self._payload = tensor
         if self._state == TensorState.FREE:
-            assert self.torch_tensor().numel() == 0, f"payload has to None if state is {self._state}"
+            assert self._payload is None, f"payload has to None if state is {self._state}"
 
     def data_ptr(self):
-        if self.torch_tensor().numel() == 0:
+        if self._payload is None:
             return None
-        return self.torch_tensor().data_ptr()
+        return self._payload.data_ptr()
 
     @property
     def state(self) -> TensorState:
@@ -42,41 +36,42 @@ class StatefulTensor(ColoTensor):
 
     def set_null(self) -> None:
         self._state = TensorState.FREE
-        self.del_torch_tensor()
+        self._payload = None
 
     def is_null(self) -> bool:
         if self._state == TensorState.FREE:
-            assert self.torch_tensor().numel() == 0
+            assert self._payload is None
             return True
         return False
 
     def trans_state(self, state: TensorState) -> None:
         self._state = state
         if state == TensorState.FREE:
-            self.del_torch_tensor()
+            self._payload = None
 
     @property
     def payload(self) -> Optional[torch.Tensor]:
-        return self.torch_tensor()
+        return self._payload
 
     def copy_payload(self, tensor) -> None:
-        self.torch_tensor.view(-1).copy_(tensor.view(-1))
+        self._payload.view(-1).copy_(tensor.view(-1))
 
     def reset_payload(self, tensor) -> None:
-        self._torch_tensor = tensor
+        del self._payload
+        self._payload = tensor
         self.trans_state(TensorState.HOLD)
 
     @property
     def device(self) -> torch.device:
-        return self.torch_tensor().device
+        return self._payload.device
 
     @property
     def dtype(self) -> torch.dtype:
-        return self.torch_tensor().dtype
+        return self._payload.dtype
 
     @property
     def shape(self):
-        return self.torch_tensor().shape
+        return self._payload.shape
 
     def to(self, device: torch.device):
         raise RuntimeError("Use colo_model_tensor_move install of call .to() on ShardedTensor")