clean side_effect_flag

mindspore/ops/operations/math_ops.py

@@ -4087,7 +4087,6 @@ class NPUAllocFloatStatus(PrimitiveWithInfer):
     @prim_attr_register
     def __init__(self):
         """Initialize NPUAllocFloatStatus"""
-        self.add_prim_attr("_side_effect_flag", True)
 
     def infer_shape(self):
         return [8]
@@ -4103,6 +4102,9 @@ class NPUGetFloatStatus(PrimitiveWithInfer):
     The flag is a tensor whose shape is `(8,)` and data type is `mindspore.dtype.float32`.
     If the sum of the flag equals to 0, there is no overflow happened. If the sum of the flag is bigger than 0, there
     is overflow happened.
+    In addition, there are strict sequencing requirements for use, i.e., before using the NPUGetFloatStatus operator,
+    need to ensure that the NPUClearFlotStatus and your compute has been executed.
+    We use Depend to ensure the execution order.
 
     Inputs:
         - **x** (Tensor) - The output tensor of `NPUAllocFloatStatus`.
@@ -4120,10 +4122,17 @@ class NPUGetFloatStatus(PrimitiveWithInfer):
         ``Ascend``
 
     Examples:
-        >>> alloc_status = ops.NPUAllocFloatStatus()
-        >>> get_status = ops.NPUGetFloatStatus()
-        >>> init = alloc_status()
-        >>> get_status(init)
+        >>> self.alloc_status = ops.NPUAllocFloatStatus()
+        >>> self.get_status = ops.NPUGetFloatStatus()
+        >>> self.clear_status = ops.NPUClearFloatStatus()
+        >>> init = self.alloc_status()
+        >>> init = F.Depend(init, input)  # Ensure clear_status after input
+        >>> clear_status = self.clear_status(init)
+        >>> input = F.Depend(input, clear_status)  # Ensure your compute after clear_status
+        >>> output = Compute(input)
+        >>> init = F.Depend(init, output)
+        >>> flag = self.get_status(init)  # Ensure get_status after your compute
+        >>> self.clear_status(init)
         >>> print(init)
         [0. 0. 0. 0. 0. 0. 0. 0.]
     """
@@ -4131,7 +4140,6 @@ class NPUGetFloatStatus(PrimitiveWithInfer):
     @prim_attr_register
     def __init__(self):
         """Initialize NPUGetFloatStatus"""
-        self.add_prim_attr("_side_effect_flag", True)
 
     def infer_shape(self, x_shape):
         cls_name = self.name
@@ -4151,6 +4159,9 @@ class NPUClearFloatStatus(PrimitiveWithInfer):
     Note:
         The flag is in the register on the `Ascend` device. It will be reset and can not be reused again after the
         `NPUClearFloatStatus` is called.
+        In addition, there are strict sequencing requirements for use, i.e., before using the NPUGetFloatStatus
+        operator, need to ensure that the NPUClearFlotStatus and your compute has been executed.
+        We use Depend to ensure the execution order.
 
         Examples: see `NPUGetFloatStatus`.
 
@@ -4165,12 +4176,17 @@ class NPUClearFloatStatus(PrimitiveWithInfer):
         ``Ascend``
 
     Examples:
-        >>> alloc_status = ops.NPUAllocFloatStatus()
-        >>> get_status = ops.NPUGetFloatStatus()
-        >>> clear_status = ops.NPUClearFloatStatus()
-        >>> init = alloc_status()
-        >>> flag = get_status(init)
-        >>> clear_status(init)
+        >>> self.alloc_status = ops.NPUAllocFloatStatus()
+        >>> self.get_status = ops.NPUGetFloatStatus()
+        >>> self.clear_status = ops.NPUClearFloatStatus()
+        >>> init = self.alloc_status()
+        >>> init = F.Depend(init, input)  # Ensure clear_status after input
+        >>> clear_status = self.clear_status(init)
+        >>> input = F.Depend(input, clear_status)  # Ensure your compute after clear_status
+        >>> output = Compute(input)
+        >>> init = F.Depend(init, output)
+        >>> flag = self.get_status(init)  # Ensure get_status after your compute
+        >>> self.clear_status(init)
         >>> print(init)
         [0. 0. 0. 0. 0. 0. 0. 0.]
     """
@@ -4178,7 +4194,6 @@ class NPUClearFloatStatus(PrimitiveWithInfer):
     @prim_attr_register
     def __init__(self):
         """Initialize NPUClearFloatStatus"""
-        self.add_prim_attr("_side_effect_flag", True)
 
     def infer_shape(self, x_shape):
         cls_name = self.name

model_zoo/official/nlp/pangu_alpha/src/pangu_alpha_wrapcell.py

@@ -189,9 +189,9 @@ class PanguAlphaTrainPipelineWithLossScaleCell(nn.Cell):
             self.grad_reducer = DistributedGradReducer(optimizer.parameters, False, self.degree)
         self.is_distributed = (self.parallel_mode != ParallelMode.STAND_ALONE)
         self.cast = P.Cast()
-        self.alloc_status = P.NPUAllocFloatStatus().add_prim_attr("_side_effect_flag", False)
-        self.get_status = P.NPUGetFloatStatus().add_prim_attr("_side_effect_flag", False)
-        self.clear_before_grad = P.NPUClearFloatStatus().add_prim_attr("_side_effect_flag", False)
+        self.alloc_status = P.NPUAllocFloatStatus()
+        self.get_status = P.NPUGetFloatStatus()
+        self.clear_before_grad = P.NPUClearFloatStatus()
         self.reduce_sum = P.ReduceSum(keep_dims=False)
         self.base = Tensor(1, mstype.float32)
         self.less_equal = P.LessEqual()