fix operation

mindspore/nn/layer/basic.py

@@ -352,6 +352,13 @@ def _is_float_dtype(dtype):
     return False
 
 
+@constexpr
+def _need_reduce_all(axis):
+    if axis == ():
+        return True
+    return False
+
+
 class ClipByNorm(Cell):
     r"""
     Clips tensor values to a maximum :math:`L_2`-norm.
@@ -424,7 +431,7 @@ class ClipByNorm(Cell):
             intermediate = x * clip_norm
 
         max_norm = self.max_op(l2norm, clip_norm)
-        if self.axis is None:
+        if _need_reduce_all(self.axis):
             max_norm = self.expand_dims(max_norm, -1)
         values_clip = self.cast(intermediate, mstype.float32) / max_norm
         values_clip = self.reshape(values_clip, self.shape(x))

mindspore/nn/layer/embedding.py

@@ -15,7 +15,7 @@
 """embedding"""
 import mindspore.common.dtype as mstype
 from mindspore import log as logger
-from mindspore.common.tensor import Tensor
+from mindspore.common.tensor import Tensor, MetaTensor
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore.common.parameter import Parameter
@@ -101,8 +101,11 @@ class Embedding(Cell):
         if padding_idx is not None:
             self.padding_idx = validator.check_int_range(padding_idx, 0, vocab_size, Rel.INC_BOTH,
                                                          "padding_idx", self.cls_name)
-            self.init_tensor = self.init_tensor.to_tensor().asnumpy()
+            if isinstance(self.init_tensor, MetaTensor):
+                self.init_tensor = self.init_tensor.to_tensor()
+            self.init_tensor = self.init_tensor.asnumpy()
             self.init_tensor[self.padding_idx] = 0
+            self.init_tensor = Tensor(self.init_tensor)
         self.embedding_table = Parameter(self.init_tensor, name='embedding_table')
         self.expand = P.ExpandDims()
         self.reshape_flat = P.Reshape()

mindspore/ops/composite/clip_ops.py

@@ -77,7 +77,9 @@ def _get_square_sum(x):
 apply_global_norm = C.MultitypeFuncGraph("apply_global_norm")
 @apply_global_norm.register("Tensor", "Tensor", "Tensor")
 def _apply_global_norm(clip_norm, global_norm, x):
+    x_dtype = F.dtype(x)
     x = x * clip_norm / global_norm
+    x = F.cast(x, x_dtype)
     return x
 
 

mindspore/ops/operations/nn_ops.py

@@ -6638,7 +6638,7 @@ class DynamicGRUV2(PrimitiveWithInfer):
             args = {'init_h': h_dtype, 'bias_input': binput_dtype}
             validator.check_tensors_dtypes_same_and_valid(args, valid_dtypes, self.name)
             b_dtype = binput_dtype
-        elif bhidden_dtype is not None:
+        if bhidden_dtype is not None:
             args = {'init_h': h_dtype, 'bias_hidden': bhidden_dtype}
             validator.check_tensors_dtypes_same_and_valid(args, valid_dtypes, self.name)
             b_dtype = bhidden_dtype