!12781 fix bug of amp bn cast

From: @jojobugfree
Reviewed-by: 
Signed-off-by:

mindspore/ccsrc/pybind_api/ir/primitive_py.cc

@@ -75,7 +75,7 @@ PrimitivePy::~PrimitivePy() {
 void PrimitivePy::SetPyObj(const py::object &obj) { python_obj_ = obj; }
 void PrimitivePy::set_signatures(const std::vector<Signature> &signatures) {
   signatures_ = signatures;
-  set_has_signature(true);
+  set_has_signature(!signatures.empty());
 }
 
 py::function PrimitivePy::GetBpropFunction() {

mindspore/ops/operations/nn_ops.py

@@ -1303,8 +1303,18 @@ class BatchNorm(PrimitiveWithInfer):
          [ 1.00000000e+00, 1.00000000e+00]))
     """
 
+    __mindspore_signature__ = (
+        sig.make_sig('input_x', dtype=sig.sig_dtype.T1),
+        sig.make_sig('scale', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T2),
+        sig.make_sig('bias', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T2),
+        sig.make_sig('mean', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T3),
+        sig.make_sig('variance', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T3)
+    )
+
     @prim_attr_register
     def __init__(self, is_training=False, epsilon=1e-5, momentum=0.1, data_format="NCHW"):
+        if is_training is False:
+            self.set_signatures(tuple())
         validator.check_value_type('is_training', is_training, (bool,), self.name)
         validator.check_float_range(epsilon, 0, 1, Rel.INC_RIGHT, 'epsilon', self.name)
         validator.check_float_range(momentum, 0, 1, Rel.INC_BOTH, 'momentum', self.name)

tests/st/mix_precision/test_mix_precision.py

@@ -129,7 +129,7 @@ def test_sit_auto_mix_precision_model_o0():
     model.train(1, dataset1, dataset_sink_mode=False)
     contend = read_validateir_file('./test_amp_o0')
     castnum = re.findall("Cast", contend)
-    assert len(castnum) == 17
+    assert len(castnum) == 5
     model.predict(Tensor(input_data))
     contend = read_validateir_file('./test_amp_o0')
     castnum = re.findall("Cast", contend)

tests/ut/python/parallel/test_bn_prelu_cell.py

@@ -109,8 +109,8 @@ class FusedBatchNorm(nn.Cell):
                 self.bn_train(x,
                               self.gamma,
                               self.beta,
-                              None,
-                              None)
+                              self.moving_mean,
+                              self.moving_variance)
 
             mean_sub = self.sub_mean(self.moving_mean, batch_mean)
             temp_mean = self.mul_mean(mean_sub, self.momentum)