modify_normal_seed

mindspore/common/initializer.py

@@ -19,7 +19,7 @@ import math
 from functools import reduce
 import numpy as np
 from scipy.stats import truncnorm
-
+from .seed import _get_graph_seed
 from . import dtype as mstype
 from .tensor import Tensor, MetaTensor
 from .._c_expression import random_normal
@@ -40,8 +40,19 @@ class Initializer:
     """
     def __init__(self, **kwargs):
         self._kwargs = kwargs
-        self.shape = None
-        self.dtype = None
+        self._seed = None
+
+    @property
+    def seed(self):
+        seed_ = self._seed if self._seed is not None else 1
+        _, seed = _get_graph_seed(seed_, "init")
+        return seed
+
+    @seed.setter
+    def seed(self, value):
+        if not isinstance(value, int):
+            raise TypeError("'value' must be int type.")
+        self._seed = value
 
     def _initialize(self, *kwargs):
         raise NotImplementedError('Must be overridden!')
@@ -353,7 +364,7 @@ class Normal(Initializer):
         self.sigma = sigma
 
     def _initialize(self, arr):
-        seed = np.random.get_state()[1][0]
+        seed = self.seed
         output_tensor = Tensor(np.zeros(arr.shape, dtype=np.float32))
         random_normal(0, self.sigma, arr.shape, seed, output_tensor)
         output_data = output_tensor.asnumpy()
@@ -434,8 +445,7 @@ def initializer(init, shape=None, dtype=mstype.float32):
     elif isinstance(init, numbers.Number):
         init = Constant(init)
     shape = shape if shape is not None else init.shape
-    dtype = init.dtype if init.dtype is not None else dtype
-    init_obj = MetaTensor(init, dtype, shape)
+    init_obj = MetaTensor(dtype, shape, init)
     return init_obj
 
 __all__ = [

mindspore/common/seed.py

@@ -30,7 +30,7 @@ def _reset_op_seed():
     """
     Reset op seeds in the kernel's dictionary.
     """
-    for kernel_name, op_seed in _KERNEL_SEED.items():
+    for (kernel_name, op_seed) in _KERNEL_SEED:
         _KERNEL_SEED[(kernel_name, op_seed)] = op_seed
 
 

mindspore/common/tensor.py

@@ -404,7 +404,7 @@ class MetaTensor(MetaTensor_):
     Returns:
         Array, an array after being initialized.
     """
-    def __init__(self, init, dtype, shape):
+    def __init__(self, dtype, shape, init=None):
         #check param
         self.init = init
         MetaTensor_.__init__(self, dtype, shape)
@@ -419,6 +419,9 @@ class MetaTensor(MetaTensor_):
                 using the same slice can generate the same tensor.
             shape (list[int]): Shape of the slice, it is used when initialize a slice of the parameter.
         """
+        if self.init is None:
+            raise TypeError("to_dense must be set MetaTensor.init, init can't be None")
+
         if shape is None:
             shape = self.shape
 
@@ -428,15 +431,28 @@ class MetaTensor(MetaTensor_):
             msg = "Error shape={}".format(shape)
             logger.error(msg)
             raise ValueError(msg)
-        from .seed import get_seed
-        global_seed = get_seed()
-        need_set_seed = ((slice_index is not None) and (global_seed is None))
-        seed_saved = np.random.get_state()[1][0]
-        if need_set_seed:
-            np.random.seed(slice_index)
-        self.init(arr)
-        if need_set_seed:
-            np.random.seed(seed_saved)
+        class seed_context:
+            '''set and restore seed'''
+            def __init__(self, init):
+                self.init = init
+                from .seed import get_seed
+                global_seed = get_seed()
+                self._np_seed = np.random.get_state()[1][0]
+                self.need_set_seed = ((slice_index is not None) and (global_seed is None))
+                self.seed = self.init.seed
+
+            def __enter__(self):
+                if self.need_set_seed:
+                    np.random.seed(slice_index)
+                    self.init.seed = slice_index
+
+            def __exit__(self, ptype, value, trace):
+                if self.need_set_seed:
+                    np.random.seed(self._np_seed)
+                    self.init.seed = self.seed
+
+        with seed_context(self.init):
+            self.init(arr)
         return Tensor(arr, dtype=self.dtype)
 
 

tests/st/ps/cmp_sparse_embedding/test_cmp_sparse_embedding.py

@@ -24,6 +24,7 @@ from mindspore import Tensor
 from mindspore.common import dtype as mstype
 from mindspore.nn import TrainOneStepCell, WithLossCell
 from mindspore.nn.optim import Adam
+from mindspore.common import set_seed
 from mindspore.ops import operations as P
 from mindspore.common.initializer import TruncatedNormal
 from mindspore.parallel._ps_context import _is_role_pserver, _is_role_worker
@@ -95,12 +96,12 @@ def do_sparse_embedding(ps=False):
 
 envs = os.environ
 if __name__ == "__main__":
-    np.random.seed(0)
+    set_seed(0)
     ps_loss = do_sparse_embedding(True)
 
     if _is_role_worker():
         context.reset_ps_context()
-        np.random.seed(0)
+        set_seed(0)
         no_ps_loss = do_sparse_embedding()
         context.set_ps_context(enable_ps=True)
 

tests/ut/python/parallel/test_using_seed_for_initializer.py

@@ -14,7 +14,7 @@
 
 import numpy as np
 from numpy import allclose
-
+from mindspore.common import set_seed
 import mindspore.common.initializer as init
 import mindspore.nn as nn
 from mindspore import Parameter
@@ -40,10 +40,10 @@ class ParameterNet(nn.Cell):
 
 
 def test_using_same_seed_for_initializer():
-    np.random.seed(0)
+    set_seed(0)
     net1 = ParameterNet()
     net1.init_parameters_data()
-    np.random.seed(0)
+    set_seed(0)
     net2 = ParameterNet()
     net2.init_parameters_data()
     for key in net1.parameters_dict():