Fix prelu for compatibility with saved model of old version (#26052) (#26130)

* fix prelu for compatibility with saved model of old version

* reshape alpha

python/paddle/fluid/dygraph/nn.py

@@ -2265,7 +2265,8 @@ class PRelu(layers.Layer):
             #NOTE(zhiqiu): The _alpha_shape should be [1, channel] + [1] * len(input_shape[2:]), not [1, channel, 1, 1].
             # However, the suffix 1 in the list is useless, since the tensor is viewed as one demension array during kernel calculation. 
             # And, input_shape is not required when mode is 'channel', so it is simplified.
-            self._alpha_shape = [1, channel]
+            #NOTE(zhiqiu): Revert shape to [1, channel, 1, 1] for compatibility with saved model of old version.
+            self._alpha_shape = [1, channel, 1, 1]
         elif mode == 'element':
             assert isinstance(input_shape, (
                 list, tuple

python/paddle/fluid/layers/nn.py

@@ -10671,7 +10671,8 @@ def prelu(x, mode, param_attr=None, name=None):
         ) >= 2, "The size of input shape should be equal or larger than 2 in prelu() when mode is 'channel'"
         #NOTE(zhiqiu): The alpha_shape should be [1, channel] + [1] * len(x.shape[2:]).
         # To be consistent with Prelu, it is simplified.
-        alpha_shape = [1, x.shape[1]]
+        #NOTE(zhiqiu): Revert shape to [1, channel, 1, 1] for compatibility with saved model of old version.
+        alpha_shape = [1, x.shape[1], 1, 1]
     elif mode == 'element':
         assert len(
             x.shape

python/paddle/fluid/tests/unittests/test_prelu_op.py

@@ -51,21 +51,22 @@ class PReluTest(OpTest):
         if self.attrs == {'mode': "all"}:
             alpha_np = np.random.uniform(-1, -0.5, (1))
         elif self.attrs == {'mode': "channel"}:
-            alpha_np = np.random.uniform(-1, -0.5, [1, self.x_shape[1]])
+            alpha_np = np.random.uniform(-1, -0.5, [1, self.x_shape[1], 1, 1])
         else:
             alpha_np = np.random.uniform(-1, -0.5, [1] + self.x_shape[1:])
 
         self.inputs = {'X': x_np, 'Alpha': alpha_np}
 
-        # NOTE(zhiqu): reshape inputs['Alpha'] from [1, 100] to [1, 100, 1, 1] since np operands could not be broadcast together with shapes (2,100,3,4) (1,100) 
+        # NOTE(zhiqu): reshape inputs['Alpha'] from [1, 100, 1, 1] to [1, 100] + [1]*len(x.shape[2:])
+        # since np operands could not be broadcast together with shapes (1,100,2,2,2,3) (1,100,1,1) 	
+        reshaped_alpha = self.inputs['Alpha']
         if self.attrs == {'mode': "channel"}:
-            self.inputs['Alpha'] = np.reshape(
+            reshaped_alpha = np.reshape(
                 self.inputs['Alpha'],
                 [1, self.x_shape[1]] + [1] * len(self.x_shape[2:]))
 
         out_np = np.maximum(self.inputs['X'], 0.)
-        out_np = out_np + np.minimum(self.inputs['X'],
-                                     0.) * self.inputs['Alpha']
+        out_np = out_np + np.minimum(self.inputs['X'], 0.) * reshaped_alpha
         assert out_np is not self.inputs['X']
         self.outputs = {'Out': out_np}