delete attribute seed of Initializer

mindspore/common/initializer.py

@@ -41,7 +41,6 @@ class Initializer:
         self._kwargs = kwargs
         self.shape = None
         self.dtype = None
-        self._seed = None
 
     def _initialize(self, *kwargs):
         raise NotImplementedError('Must be overridden!')
@@ -49,15 +48,6 @@ class Initializer:
     def __call__(self, arr):
         return self._initialize(arr)
 
-    @property
-    def seed(self):
-        return self._seed
-
-    @seed.setter
-    def seed(self, seed_):
-        """set the random seed."""
-        self._seed = seed_
-
     @property
     def shape(self):
         return self._shape
@@ -74,8 +64,15 @@ class Initializer:
     def dtype(self, dtype):
         self._dtype = dtype
 
-    def to_tensor(self):
-        """Get the tensor format data of this Initializer."""
+    def to_tensor(self, slice_index=None):
+        """
+        Get the tensor format data of this Initializer.
+
+        Args:
+            slice_index (int): Slice index of a parameter's slices.
+                Used when initialize a slice of a parameter, it guarantee that
+                devices use the same slice can generate the same tensor.
+        """
         arr = None
         try:
             arr = np.ndarray(self.shape)
@@ -83,10 +80,10 @@ class Initializer:
             msg = "Error shape={}".format(self.shape)
             logger.error(msg)
             raise ValueError(msg)
-        if self._seed is not None:
-            np.random.seed(self.seed)
+
+        if slice_index is not None:
+            np.random.seed(slice_index)
         self.__call__(arr)
-        self._seed = None
         return Tensor(arr, dtype=self.dtype)
 
 def _register(*aliases):

mindspore/common/parameter.py

@@ -22,7 +22,7 @@ from .initializer import initializer, Initializer
 from .tensor import Tensor, MetaTensor
 from .._checkparam import _check_str_by_regular
 from ..parallel._utils import _set_clone_info, _CloneInfo
-from ..parallel._tensor import _get_seed
+from ..parallel._tensor import _get_slice_index
 
 __all__ = ['Parameter', 'ParameterTuple']
 
@@ -250,9 +250,11 @@ class Parameter:
                 raise ValueError("The length of layout must be 3! layout is {}."
                                  .format(layout))
             self.init_mode.shape = layout[2]
-            self.init_mode.seed = int(_get_seed(layout[0], layout[1]))
+            slice_index = int(_get_slice_index(layout[0], layout[1]))
+            self.default_input = self.init_mode.to_tensor(slice_index)
+        else:
+            self.default_input = self.init_mode.to_tensor()
 
-        self.default_input = self.init_mode.to_tensor()
         self.init_mode = None
         if set_sliced:
             self.sliced = True

mindspore/parallel/_tensor.py

@@ -168,21 +168,21 @@ def _chunk_tensor_by_strategy(np_tensor, strategy):
         raise ValueError("The length of np_tensor does not match the length of strategy!")
     return _chunk_tensor(np_tensor, strategy, len(strategy))
 
-def _get_seed(dev_mat, tensor_map):
+def _get_slice_index(dev_mat, tensor_map):
     """
-    Get the random seed for current slice.
+    Get the slice index for current slice.
 
     Args:
         dev_mat (list): The device matrix of devices.
         tensor_map (list): The split strategy of tensor.
 
     Returns:
-        Integer, the local random seed for this device.
+        Integer, the slice index for slice on this device.
     """
     rank = get_rank()
     tensor_strategy = _get_tensor_strategy(dev_mat, tensor_map)
-    tensor_slice_seed = _get_tensor_slice_index(dev_mat, tensor_strategy, tensor_map, rank)
-    return tensor_slice_seed
+    tensor_slice_index = _get_tensor_slice_index(dev_mat, tensor_strategy, tensor_map, rank)
+    return tensor_slice_index
 
 def _load_tensor(tensor, dev_mat, tensor_map):
     """