[Eager] fix spectral_norm interface under eager mode (#45655)

* [Eager] fix spectral_norm interface under eager mode

* fix spectral_norm yaml config

paddle/phi/api/yaml/legacy_api.yaml

@@ -2496,7 +2496,7 @@
   infer_meta :
     func : SpectralNormInferMeta
   kernel :
-    func : spectralnorm
+    func : spectral_norm
     data_type : weight
   backward : spectral_norm_grad
 

python/paddle/fluid/dygraph/nn.py

@@ -3163,6 +3163,10 @@ class SpectralNorm(layers.Layer):
         self.weight_v.stop_gradient = True
 
     def forward(self, weight):
+        if in_dygraph_mode():
+            return _C_ops.spectral_norm(weight, self.weight_u, self.weight_v,
+                                        self._dim, self._power_iters, self._eps)
+
         check_variable_and_dtype(weight, "weight", ['float32', 'float64'],
                                  'SpectralNorm')
         inputs = {'Weight': weight, 'U': self.weight_u, 'V': self.weight_v}

python/paddle/fluid/layers/nn.py

@@ -3933,7 +3933,6 @@ def spectral_norm(weight, dim=0, power_iters=1, eps=1e-12, name=None):
     dtype = weight.dtype
 
     # create intput and parameters
-    inputs = {'Weight': weight}
     input_shape = weight.shape
     assert weight.numel() > 0, "Any dimension of input cannot be equal to 0."
     assert dim < len(input_shape), ("The input `dim` should be less than the "
@@ -3947,14 +3946,19 @@ def spectral_norm(weight, dim=0, power_iters=1, eps=1e-12, name=None):
                                 dtype=dtype,
                                 default_initializer=Normal(0., 1.))
     u.stop_gradient = True
-    inputs['U'] = u
     v = helper.create_parameter(attr=ParamAttr(),
                                 shape=[w],
                                 dtype=dtype,
                                 default_initializer=Normal(0., 1.))
-    inputs['V'] = v
     v.stop_gradient = True
 
+    if in_dygraph_mode():
+        return _C_ops.spectral_norm(weight, u, v, dim, power_iters, eps)
+
+    inputs = {'Weight': weight}
+    inputs['U'] = u
+    inputs['V'] = v
+
     # create output
     out = helper.create_variable(dtype=dtype)