For normalize op, remove special treatment of one-dimensional input, add check for one-dimensional input (#26747)

For normalize op, remove special treatment of one-dimensional input, add check for one-dimensional input

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

@@ -23,8 +23,6 @@ import numpy as np
 
 
 def p_normalize(x, axis=1, p=2, epsilon=1e-12, keepdims=True):
-    if len(x.shape) == 1:
-        axis = 0
     xp = np.power(np.abs(x), p)
     s = np.sum(xp, axis=axis, keepdims=keepdims)
     r = np.maximum(np.power(s, 1.0 / p), epsilon)
@@ -38,10 +36,10 @@ class TestNNFunctionalNormalize(unittest.TestCase):
         self.expected0 = p_normalize(self.input_np)
         self.expected1 = p_normalize(self.input_np, p=1.5)
         self.expected2 = p_normalize(self.input_np, axis=0)
-        self.expected3 = p_normalize(self.input_np2)
+        self.expected3 = p_normalize(self.input_np2, axis=0)
 
     def run_imperative(self):
-        x = paddle.to_variable(self.input_np)
+        x = paddle.to_tensor(self.input_np)
         y = F.normalize(x)
         self.assertTrue(np.allclose(y.numpy(), self.expected0))
 
@@ -51,10 +49,12 @@ class TestNNFunctionalNormalize(unittest.TestCase):
         y = F.normalize(x, axis=0)
         self.assertTrue(np.allclose(y.numpy(), self.expected2))
 
-        x = paddle.to_variable(self.input_np2)
-        y = F.normalize(x)
+        x = paddle.to_tensor(self.input_np2)
+        y = F.normalize(x, axis=0)
         self.assertTrue(np.allclose(y.numpy(), self.expected3))
 
+        self.assertRaises(BaseException, F.normalize, x)
+
     def run_static(self, use_gpu=False):
         x = paddle.data(name='input', shape=[10, 10], dtype='float32')
         x2 = paddle.data(name='input2', shape=[2], dtype='float32')
@@ -62,7 +62,7 @@ class TestNNFunctionalNormalize(unittest.TestCase):
         result1 = F.normalize(x, p=1.5)
         result2 = F.normalize(x, axis=0)
         result3 = F.normalize(x, name='aaa')
-        result4 = F.normalize(x2)
+        result4 = F.normalize(x2, axis=0)
 
         place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
         exe = fluid.Executor(place)
@@ -77,6 +77,7 @@ class TestNNFunctionalNormalize(unittest.TestCase):
         self.assertTrue(np.allclose(static_result[2], self.expected2))
         self.assertTrue('aaa' in result3.name)
         self.assertTrue(np.allclose(static_result[3], self.expected3))
+        self.assertRaises(ValueError, F.normalize, x2)
 
     def test_cpu(self):
         paddle.disable_static(place=paddle.fluid.CPUPlace())

python/paddle/nn/functional/norm.py

@@ -54,8 +54,7 @@ def normalize(x, p=2, axis=1, epsilon=1e-12, name=None):
     Args:
         x (Tensor): The input tensor could be N-D tensor, and the input data type could be float32 or float64.
         p (float|int, optional): The exponent value in the norm formulation. Default: 2
-        axis (int, optional): The axis on which to apply normalization. If ``x`` is 1-D tensor, ``axis`` is fixed to 0. If `axis < 0`, \
-            the dimension to normalization is `x.ndim + axis`. -1 is the last dimension.
+        axis (int, optional): The axis on which to apply normalization. If `axis < 0`, the dimension to normalization is `x.ndim + axis`. -1 is the last dimension. 
         epsilon (float, optional): Small float added to denominator to avoid dividing by zero. Default is 1e-12.
         name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
 
@@ -72,7 +71,7 @@ def normalize(x, p=2, axis=1, epsilon=1e-12, name=None):
 
             paddle.disable_static()
             x = np.arange(6, dtype=np.float32).reshape(2,3)
-            x = paddle.to_variable(x)
+            x = paddle.to_tensor(x)
             y = F.normalize(x)
             print(y.numpy())
             # [[0.         0.4472136  0.8944272 ]
@@ -88,8 +87,6 @@ def normalize(x, p=2, axis=1, epsilon=1e-12, name=None):
             # [[0.         0.24253564 0.37139067]
             # [1.         0.97014254 0.9284767 ]]
     """
-    if len(x.shape) == 1:
-        axis = 0
     if in_dygraph_mode():
         eps = fluid.dygraph.base.to_variable([epsilon], dtype=x.dtype)
         out = core.ops.p_norm(x, 'axis', axis, 'porder',
@@ -99,6 +96,10 @@ def normalize(x, p=2, axis=1, epsilon=1e-12, name=None):
     check_type(p, 'p', (float, int), 'normalize')
     check_type(axis, 'axis', (int), 'normalize')
     check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'normalize')
+    if len(x.shape) == 1 and axis != 0 and axis != -1:
+        raise ValueError(
+            "Axis must be 0 or -1 when x is a 1-D tensor, but received axis = {}".
+            format(axis))
 
     attrs = {
         'axis': axis,