fix batchnorm issue in pynative auto mix precision

mindspore/_extends/builtin_operations.py

@@ -15,6 +15,7 @@
 """builtin_operations"""
 import numpy as np
 from mindspore.ops import functional as F
+from mindspore.ops import composite as C
 from mindspore.common.tensor import Tensor
 from mindspore.common.dtype import dtype_to_nptype, get_py_obj_dtype
 
@@ -174,11 +175,10 @@ def stop_gradient(x):
     return x
 
 
+hyper_map = C.HyperMap()
+
 def mixed_precision_cast(dst_type, x):
     """Implement `mixed_precision_cast`."""
-    if isinstance(x, tuple):
-        res = list()
-        for item in x:
-            res.append(F.cast(item, dst_type))
-        return tuple(res)
-    return F.cast(x, dst_type)
+    def cast_inner(data):
+        return F.cast(data, dst_type)
+    return hyper_map(cast_inner, x)

mindspore/common/parameter.py

@@ -61,6 +61,7 @@ class Parameter:
         self._is_init = False
         self._sliced = False
         self.is_param_ps = False
+        self._cast_type = None
         self.init_in_server = False
         if context.get_context("mode") == context.PYNATIVE_MODE:
             self.init_data()
@@ -103,6 +104,16 @@ class Parameter:
             raise ValueError("The type of the name should be `str` or `None`.")
         self._value.name = name_
 
+    @property
+    def cast_type(self):
+        return self._cast_type
+
+    @cast_type.setter
+    def cast_type(self, dst_type):
+        if dst_type not in (mstype.float16, mstype.float32, None):
+            raise ValueError("The type of the name should be type of [float32, float16] or `None`.")
+        self._cast_type = dst_type
+
     @property
     def sliced(self):
         """Get slice status of the parameter."""

mindspore/common/tensor.py

@@ -274,7 +274,7 @@ class SparseTensor:
     Outputs:
         SparseTensor, composed of `indices`, `values`, `dense_shape`.
 
-    Examples:     
+    Examples:
         >>> class Net(nn.Cell):
         >>>     def __init__(self, dense_shape):
         >>>         super(Net, self).__init__()

mindspore/nn/cell.py

@@ -240,12 +240,13 @@ class Cell:
         else:
             _pynative_exec.set_grad_flag(False)
         cast_inputs = list()
-        if hasattr(self, "_mindspore_flags") and self._mindspore_flags.get('fp16'):
-            for item in inputs:
-                cast_inputs.append(cast(item, mstype.float16))
-        if hasattr(self, "_mindspore_flags") and self._mindspore_flags.get('fp32'):
-            for item in inputs:
-                cast_inputs.append(cast(item, mstype.float32))
+        if hasattr(self, "_mindspore_flags"):
+            if self._mindspore_flags.get('fp16'):
+                for item in inputs:
+                    cast_inputs.append(cast(item, mstype.float16))
+            elif self._mindspore_flags.get('fp32'):
+                for item in inputs:
+                    cast_inputs.append(cast(item, mstype.float32))
         if cast_inputs:
             cast_inputs = tuple(cast_inputs)
         else:
@@ -285,6 +286,8 @@ class Cell:
             if context.get_context("mode") == context.PYNATIVE_MODE:
                 if name in self.__dict__:
                     del self.__dict__[name]
+                if name in params:
+                    del params[name]
                 params_list[name] = value
             else:
                 object.__setattr__(self, name, value)
@@ -496,10 +499,13 @@ class Cell:
         Args:
             param (Parameter): The parameter to cast.
         """
-        if hasattr(self, "_mindspore_flags") and self._mindspore_flags.get('fp16'):
-            return cast(param, mstype.float16)
-        if hasattr(self, "_mindspore_flags") and self._mindspore_flags.get('fp32'):
-            return cast(param, mstype.float32)
+        if hasattr(self, "_mindspore_flags"):
+            if self._mindspore_flags.get('fp16'):
+                param.cast_type = mstype.float16
+            elif self._mindspore_flags.get('fp32'):
+                param.cast_type = mstype.float32
+            else:
+                param.cast_type = None
         return param
 
     def insert_child_to_cell(self, child_name, child):

mindspore/ops/functional.py

@@ -182,3 +182,4 @@ tensor_operator_registry.register('__ge__', tensor_ge)
 tensor_operator_registry.register('shape', shape)
 #support GE backend for no compare operators
 tensor_operator_registry.register('vm_compare', BP.vm_compare)
+tensor_operator_registry.register('cast', cast)

mindspore/ops/operations/nn_ops.py

@@ -618,6 +618,7 @@ class FusedBatchNorm(Primitive):
         self.mode = validator.check_integer('mode', mode, [0, 1], Rel.IN, self.name)
         self.epsilon = validator.check_number_range('epsilon', epsilon, 0, 1, Rel.INC_RIGHT, self.name)
         self.momentum = validator.check_number_range('momentum', momentum, 0, 1, Rel.INC_BOTH, self.name)
+        self._update_parameter = True
 
 
 class BNTrainingReduce(PrimitiveWithInfer):

mindspore/ops/primitive.py

@@ -18,6 +18,8 @@
 import inspect
 import copy
 from mindspore.common.api import _wrap_func
+from mindspore.common import Parameter
+from mindspore.common._register_for_tensor import tensor_operator_registry
 from .._c_expression import Primitive_, real_run_op, prim_type
 from .._c_expression import signature_rw as sig_rw
 from .._c_expression import signature_kind as sig_kind
@@ -49,6 +51,7 @@ class Primitive(Primitive_):
         self.name = name
         self.attrs = {}
         self.init_attrs = {"name": name}
+        self._update_parameter = False
         Primitive_.__init__(self, name, self)
         if hasattr(self.__class__, '__mindspore_signature__'):
             sig = self._fill_signature(self.__class__.__mindspore_signature__)
@@ -189,6 +192,11 @@ class Primitive(Primitive_):
         # for checking output number with kernel implementation
         self.add_prim_attr("output_names", outputs)
 
+    @property
+    def update_parameter(self):
+        """ Whether the primitive will update the value of parameter."""
+        return self._update_parameter
+
 
 class PrimitiveWithInfer(Primitive):
     """
@@ -359,7 +367,20 @@ def constexpr(fn=None, get_instance=True, name=None):
 @_wrap_func
 def _run_op(obj, op_name, args):
     """Single op execution function supported by ge in PyNative mode."""
-    output = real_run_op(obj, op_name, args)
+    cast = tensor_operator_registry.get("cast")
+    if op_name == "Cast" or obj.update_parameter:
+        cast_args = args
+    else:
+        cast_args = list()
+        for arg in args:
+            if isinstance(arg, Parameter):
+                if arg.cast_type:
+                    cast_args.append(cast(arg, arg.cast_type))
+                else:
+                    cast_args.append(arg)
+            else:
+                cast_args.append(arg)
+    output = real_run_op(obj, op_name, tuple(cast_args))
     if not output:
         raise RuntimeError("Pynative run op %s failed!" % op_name)
     if len(output) == 1:

tests/st/pynative/test_pynative_resnet50.py

@@ -428,5 +428,4 @@ def test_pynative_resnet50():
         cost_time = end_time - start_time
         print("======step: ", step, " loss: ", loss_output.asnumpy(), " cost time: ", cost_time)
         if step > 1:
-            assert cost_time < 0.31
-        
+            assert cost_time < 0.32