!31930 clean static alarm for operator module with python.

Merge pull request !31930 from wangshuide/wsd_master_warn

mindspore/python/mindspore/nn/layer/normalization.py

@@ -191,18 +191,18 @@ class _BatchNorm(Cell):
                 self.is_global = True
                 global SYNC_BN_GROUP_NAME
                 if SYNC_BN_GROUP_NAME == "":
-                    SYNC_BN_GROUP_NAME = "sync_bn_group" + str(i)
+                    SYNC_BN_GROUP_NAME = "sync_bn_group%d" % i
                     management.create_group(SYNC_BN_GROUP_NAME, self.rank_list[i])
 
     def _create_sync_groups(self):
         for i in range(len(self.process_groups)):
-            validator.check_isinstance("process_groups[" + str(i) + "]", self.process_groups[i], list)
+            validator.check_isinstance("process_groups[%d]" % i, self.process_groups[i], list)
             self.group_device_num = len(self.process_groups[i])
             if self.rank_id in self.process_groups[i] and self.group_device_num > 1:
                 self.is_global = True
                 global SYNC_BN_GROUP_NAME
                 if SYNC_BN_GROUP_NAME == "":
-                    SYNC_BN_GROUP_NAME = "sync_bn_group" + str(i)
+                    SYNC_BN_GROUP_NAME = "sync_bn_group%d" % i
                     management.create_group(SYNC_BN_GROUP_NAME, self.process_groups[i])
 
     def construct(self, x):

mindspore/python/mindspore/nn/layer/rnn_cells.py

@@ -136,6 +136,7 @@ def _gru_cell(inputs, hidden, w_ih, w_hh, b_ih, b_hh):
 
     return hy
 
+
 class RNNCellBase(Cell):
     '''Basic class for RNN Cells'''
     def __init__(self, input_size: int, hidden_size: int, has_bias: bool, num_chunks: int):

mindspore/python/mindspore/nn/layer/rnn_utils.py

@@ -79,6 +79,7 @@ class _ReverseSequence(Cell):
 
     @staticmethod
     def make_shape(shape, dtype, range_dim):
+        """Calculates the shape according by the inputs."""
         output = P.Ones()(shape, mstype.float32)
         output = P.CumSum()(output, range_dim)
         output = P.Cast()(output, dtype)

mindspore/python/mindspore/nn/layer/thor_layer.py

@@ -19,13 +19,15 @@ import mindspore.common.dtype as mstype
 import mindspore.log as logger
 from mindspore.common.tensor import Tensor
 from mindspore.common.initializer import initializer, Initializer
+from mindspore.communication.management import get_group_size, get_rank
 from mindspore.ops import operations as P
 from mindspore.common.parameter import Parameter
 from mindspore._checkparam import Validator, Rel, twice
 from mindspore import context
 from mindspore.nn.cell import Cell
 from mindspore.nn.layer.activation import get_activation
-from mindspore.parallel._ps_context import _is_role_worker, _get_ps_context
+from mindspore.parallel._ps_context import _is_role_worker, _get_ps_context, \
+    _set_rank_id, _insert_hash_table_size, _set_cache_enable
 from mindspore.parallel._utils import _get_parallel_mode, _get_full_batch
 from mindspore.context import ParallelMode
 from mindspore.ops.primitive import constexpr
@@ -40,7 +42,8 @@ class DenseThor(Cell):
     The dense connected layer and saving the information needed for THOR.
 
     Applies dense connected layer for the input and saves the information A and G in the dense connected layer
-    needed for THOR, the detail can be seen in paper: https://www.aaai.org/AAAI21Papers/AAAI-6611.ChenM.pdf
+    needed for THOR, the detail can be seen in `THOR, Trace-based Hardware-driven layer-ORiented Natural
+    Gradient Descent Computation <https://www.aaai.org/AAAI21Papers/AAAI-6611.ChenM.pdf>`_.
     This layer implements the operation as:
 
     .. math::

mindspore/python/mindspore/ops/_vmap/vmap_base.py

@@ -466,6 +466,7 @@ def get_print_vmap_rule(prim, axis_size):
     """VmapRule for `Print` operation."""
     if isinstance(prim, str):
         prim = Primitive(prim)
+
     def vmap_rule(*args):
         vals = ()
         args_len = len(args)

mindspore/python/mindspore/ops/operations/custom_ops.py

@@ -574,7 +574,7 @@ class Custom(ops.PrimitiveWithInfer):
                     if isinstance(item, dict) and item.get("value") is None:
                         reg_info["attr"][i]["value"] = "all"
             reg_info["async_flag"] = reg_info.get("async_flag", False)
-            reg_info["binfile_name"] = self.func_name + ".so"
+            reg_info["binfile_name"] = "%s.so" % self.func_name
             reg_info["compute_cost"] = reg_info.get("compute_cost", 10)
             reg_info["kernel_name"] = self.func_name
             reg_info["partial_flag"] = reg_info.get("partial_flag", True)