change composite.random_ops.get_seed to internal interface

mindspore/common/seed.py

@@ -57,73 +57,73 @@ def set_seed(seed):
         TypeError: If seed isn't a int.
 
     Examples:
-        1. If global seed is not set, numpy.random and initializer will choose a random seed:
+        >>> # 1. If global seed is not set, numpy.random and initializer will choose a random seed:
         >>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A1
         >>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A2
         >>> w1 = Parameter(initializer("uniform", [2, 2], ms.float32), name="w1") # W1
         >>> w1 = Parameter(initializer("uniform", [2, 2], ms.float32), name="w1") # W2
-        Rerun the program will get diferent results:
+        >>> # Rerun the program will get diferent results:
         >>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A3
         >>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A4
         >>> w1 = Parameter(initializer("uniform", [2, 2], ms.float32), name="w1") # W3
         >>> w1 = Parameter(initializer("uniform", [2, 2], ms.float32), name="w1") # W4
-
-        2. If global seed is set, numpy.random and initializer will use it:
+        >>>
+        >>> 2. If global seed is set, numpy.random and initializer will use it:
         >>> set_seed(1234)
         >>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A1
         >>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A2
         >>> w1 = Parameter(initializer("uniform", [2, 2], ms.float32), name="w1") # W1
         >>> w1 = Parameter(initializer("uniform", [2, 2], ms.float32), name="w1") # W2
-        Rerun the program will get the same results:
+        >>> # Rerun the program will get the same results:
         >>> set_seed(1234)
         >>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A1
         >>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A2
         >>> w1 = Parameter(initializer("uniform", [2, 2], ms.float32), name="w1") # W1
         >>> w1 = Parameter(initializer("uniform", [2, 2], ms.float32), name="w1") # W2
-
-        3. If neither global seed nor op seed is set, mindspore.ops.composite.random_ops and
-        mindspore.nn.probability.distribution will choose a random seed:
+        >>>
+        >>> # 3. If neither global seed nor op seed is set, mindspore.ops.composite.random_ops and
+        >>> # mindspore.nn.probability.distribution will choose a random seed:
         >>> c1 = C.uniform((1, 4)) # C1
         >>> c2 = C.uniform((1, 4)) # C2
-        Rerun the program will get different results:
+        >>> Rerun the program will get different results:
         >>> c1 = C.uniform((1, 4)) # C3
         >>> c2 = C.uniform((1, 4)) # C4
-
-        4. If global seed is set, but op seed is not set, mindspore.ops.composite.random_ops and
-        mindspore.nn.probability.distribution will caculate a seed according to global seed and
-        default op seed. Each call will change the default op seed, thus each call get different
-        results.
+        >>>
+        >>> # 4. If global seed is set, but op seed is not set, mindspore.ops.composite.random_ops and
+        >>> # mindspore.nn.probability.distribution will caculate a seed according to global seed and
+        >>> # default op seed. Each call will change the default op seed, thus each call get different
+        >>> # results.
         >>> set_seed(1234)
         >>> c1 = C.uniform((1, 4)) # C1
         >>> c2 = C.uniform((1, 4)) # C2
-        Rerun the program will get the same results:
+        >>> # Rerun the program will get the same results:
         >>> set_seed(1234)
         >>> c1 = C.uniform((1, 4)) # C1
         >>> c2 = C.uniform((1, 4)) # C2
-
-        5. If both global seed and op seed are set, mindspore.ops.composite.random_ops and
-        mindspore.nn.probability.distribution will caculate a seed according to global seed and
-        op seed counter. Each call will change the op seed counter, thus each call get different
-        results.
+        >>>
+        >>> # 5. If both global seed and op seed are set, mindspore.ops.composite.random_ops and
+        >>> # mindspore.nn.probability.distribution will caculate a seed according to global seed and
+        >>> # op seed counter. Each call will change the op seed counter, thus each call get different
+        >>> # results.
         >>> set_seed(1234)
         >>> c1 = C.uniform((1, 4), seed=2) # C1
         >>> c2 = C.uniform((1, 4), seed=2) # C2
-        Rerun the program will get the same results:
+        >>> Rerun the program will get the same results:
         >>> set_seed(1234)
         >>> c1 = C.uniform((1, 4), seed=2) # C1
         >>> c2 = C.uniform((1, 4), seed=2) # C2
-
-        6. If op seed is set but global seed is not set, 0 will be used as global seed. Then
-        mindspore.ops.composite.random_ops and mindspore.nn.probability.distribution act as in
-        condition 5.
+        >>>
+        >>> # 6. If op seed is set but global seed is not set, 0 will be used as global seed. Then
+        >>> # mindspore.ops.composite.random_ops and mindspore.nn.probability.distribution act as in
+        >>> # condition 5.
         >>> c1 = C.uniform((1, 4), seed=2) # C1
         >>> c2 = C.uniform((1, 4), seed=2) # C2
-        Rerun the program will get the same results:
+        >>> #Rerun the program will get the same results:
         >>> c1 = C.uniform((1, 4), seed=2) # C1
         >>> c2 = C.uniform((1, 4), seed=2) # C2
-
-        7. Recall set_seed() in the program will reset numpy seed and op seed counter of
-        mindspore.ops.composite.random_ops and mindspore.nn.probability.distribution.
+        >>>
+        >>> # 7. Recall set_seed() in the program will reset numpy seed and op seed counter of
+        >>> # mindspore.ops.composite.random_ops and mindspore.nn.probability.distribution.
         >>> set_seed(1234)
         >>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A1
         >>> c1 = C.uniform((1, 4), seed=2) # C1

mindspore/ops/composite/random_ops.py

@@ -22,7 +22,7 @@ from ...common import dtype as mstype
 from ...common.seed import _get_graph_seed
 
 @constexpr
-def get_seed(op_seed, kernel_name):
+def _get_seed(op_seed, kernel_name):
     "Get the graph-level seed."
     return _get_graph_seed(op_seed, kernel_name)
 
@@ -57,7 +57,7 @@ def normal(shape, mean, stddev, seed=None):
     stddev_dtype = F.dtype(stddev)
     const_utils.check_valid_type(mean_dtype, mstype.int_type + (mstype.float16, mstype.float32), 'normal')
     const_utils.check_valid_type(stddev_dtype, mstype.int_type + (mstype.float16, mstype.float32), 'normal')
-    seed1, seed2 = get_seed(seed, "normal")
+    seed1, seed2 = _get_seed(seed, "normal")
     stdnormal = P.StandardNormal(seed1, seed2)
     random_normal = stdnormal(shape)
     value = random_normal * stddev + mean
@@ -94,7 +94,7 @@ def laplace(shape, mean, lambda_param, seed=None):
     lambda_param_dtype = F.dtype(lambda_param)
     const_utils.check_tensors_dtype_same(mean_dtype, mstype.float32, "laplace")
     const_utils.check_tensors_dtype_same(lambda_param_dtype, mstype.float32, "laplace")
-    seed1, seed2 = get_seed(seed, "laplace")
+    seed1, seed2 = _get_seed(seed, "laplace")
     stdlaplace = P.StandardLaplace(seed1, seed2)
     rnd = stdlaplace(shape)
     value = rnd * lambda_param + mean
@@ -144,7 +144,7 @@ def uniform(shape, minval, maxval, seed=None, dtype=mstype.float32):
     const_utils.check_valid_type(dtype, [mstype.int32, mstype.float32], 'uniform')
     const_utils.check_tensors_dtype_same(minval_dtype, dtype, "uniform")
     const_utils.check_tensors_dtype_same(maxval_dtype, dtype, "uniform")
-    seed1, seed2 = get_seed(seed, "uniform")
+    seed1, seed2 = _get_seed(seed, "uniform")
     if const_utils.is_same_type(dtype, mstype.int32):
         random_uniform = P.UniformInt(seed1, seed2)
         value = random_uniform(shape, minval, maxval)
@@ -176,7 +176,7 @@ def gamma(shape, alpha, beta, seed=None):
         >>> beta = Tensor(1.0, mstype.float32)
         >>> output = C.gamma(shape, alpha, beta, seed=5)
     """
-    seed1, seed2 = get_seed(seed, "gamma")
+    seed1, seed2 = _get_seed(seed, "gamma")
     random_gamma = P.Gamma(seed1, seed2)
     value = random_gamma(shape, alpha, beta)
     return value
@@ -200,7 +200,7 @@ def poisson(shape, mean, seed=None):
         >>> mean = Tensor(1.0, mstype.float32)
         >>> output = C.poisson(shape, mean, seed=5)
     """
-    seed1, seed2 = get_seed(seed, "poisson")
+    seed1, seed2 = _get_seed(seed, "poisson")
     random_poisson = P.Poisson(seed1, seed2)
     value = random_poisson(shape, mean)
     return value