!6667 fix stream sync error for mixed precision on pynative

Merge pull request !6667 from chujinjin/fix_stream_sync_error_for_mixed_precision

mindspore/ccsrc/pipeline/pynative/pynative_execute.cc

@@ -260,7 +260,7 @@ py::object DoAutoCast(const py::object &arg, const TypeId &type_id) {
 py::object DoParamMixPrecisionCast(bool *is_cast, const py::object obj) {
   auto tensor = py::cast<tensor::TensorPtr>(obj);
   auto cast_type = tensor->cast_dtype();
-  py::object cast_output;
+  py::object cast_output = obj;
   if (cast_type != nullptr) {
     auto source_element = tensor->Dtype();
     if (source_element != nullptr && IsSubType(source_element, kFloat) && *source_element != *cast_type) {
@@ -282,6 +282,8 @@ py::object DoParamMixPrecisionCastTuple(bool *is_cast, const py::tuple tuple) {
       result[i] = DoParamMixPrecisionCast(is_cast, tuple[i]);
     } else if (py::isinstance<py::tuple>(tuple[i])) {
       result[i] = DoParamMixPrecisionCastTuple(is_cast, tuple[i]);
+    } else {
+      result[i] = tuple[i];
     }
   }
   return result;

mindspore/ops/operations/nn_ops.py

@@ -609,6 +609,13 @@ class FusedBatchNorm(Primitive):
         >>> op = P.FusedBatchNorm()
         >>> output = op(input_x, scale, bias, mean, variance)
     """
+    __mindspore_signature__ = (
+        sig.make_sig('input_x', dtype=sig.sig_dtype.T2),
+        sig.make_sig('scale', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
+        sig.make_sig('bias', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
+        sig.make_sig('mean', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
+        sig.make_sig('variance', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
+    )
 
     @prim_attr_register
     def __init__(self, mode=0, epsilon=1e-5, momentum=0.1):

tests/ut/python/ops/test_ops.py

@@ -1394,11 +1394,6 @@ test_case_nn_ops = [
         'desc_inputs': [[2, 16], [2, 16], [2, 16], [2, 16], [16]],
         'desc_bprop': [[2, 16], [16], [16]],
         'skip': ['backward']}),
-    ('FusedBatchNorm', {
-        'block': P.FusedBatchNorm(),
-        'desc_inputs': [[128, 64, 32, 64], [64], [64], [64], [64]],
-        'desc_bprop': [[128, 64, 32, 64], [64], [64], [64], [64]],
-        'skip': []}),
     ('FusedBatchNormGrad', {
         'block': G.FusedBatchNormGrad(),
         'desc_inputs': [[128, 64, 32, 64], [128, 64, 32, 64], [64], [64], [64]],