fix 'is with a literal' warning (#39798)

* fix 'is with a literal'

* fix typo

python/paddle/fluid/dygraph/amp/auto_cast.py

@@ -161,7 +161,7 @@ def pure_fp16_initialize(models):
     for idx in range(len(models)):
         for layer in models[idx].sublayers(include_self=True):
             layer._casted_by_pure_fp16 = True
-            if (layer._dtype is 'float16') or isinstance(
+            if (layer._dtype == 'float16') or isinstance(
                     layer, (paddle.nn.BatchNorm, paddle.nn.BatchNorm1D,
                             paddle.nn.BatchNorm2D, paddle.nn.BatchNorm3D,
                             paddle.nn.LayerNorm)):

python/paddle/fluid/framework.py

@@ -2544,7 +2544,7 @@ class Operator(object):
                     warnings.warn("The Op(%s) is not support to set device." %
                                   type)
                 if 'force_cpu' in op_attrs:
-                    if (type is 'less_than' and op_attrs['force_cpu'] != None
+                    if (type == 'less_than' and op_attrs['force_cpu'] != None
                         ) or op_attrs['force_cpu'] != False:
                         warnings.warn(
                             "The Attr(force_cpu) of Op(%s) will be deprecated in the future, "

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

@@ -380,7 +380,7 @@ class OpTest(unittest.TestCase):
             hasattr(self, 'output_dtype') and
             self.output_dtype == np.uint16) or (
                 hasattr(self, 'mkldnn_data_type') and
-                getattr(self, 'mkldnn_data_type') is "bfloat16") or (
+                getattr(self, 'mkldnn_data_type') == "bfloat16") or (
                     hasattr(self, 'attrs') and
                     'mkldnn_data_type' in self.attrs and
                     self.attrs['mkldnn_data_type'] == 'bfloat16')

python/paddle/nn/functional/loss.py

@@ -131,7 +131,7 @@ def binary_cross_entropy(input, label, weight=None, reduction='mean',
     fluid.data_feeder.check_variable_and_dtype(
         label, 'label', ['float32', 'float64'], 'binary_cross_entropy')
 
-    sub_name = name if weight is None and reduction is 'none' else None
+    sub_name = name if weight is None and reduction == 'none' else None
     helper = LayerHelper("binary_cross_entropy", name=sub_name)
     out = helper.create_variable_for_type_inference(dtype=input.dtype)
     helper.append_op(
@@ -144,7 +144,7 @@ def binary_cross_entropy(input, label, weight=None, reduction='mean',
 
     if weight is not None:
         if isinstance(weight, paddle.static.Variable):
-            weight_name = name if reduction is 'none' else None
+            weight_name = name if reduction == 'none' else None
             out = paddle.multiply(out, weight, name=weight_name)
         else:
             raise ValueError(

python/paddle/nn/functional/norm.py

@@ -217,7 +217,7 @@ def batch_norm(x,
 
     helper = LayerHelper('batch_norm', **locals())
 
-    param_dtype = x.dtype if x.dtype is not 'float16' else 'float32'
+    param_dtype = x.dtype if x.dtype != 'float16' else 'float32'
     saved_mean = helper.create_variable_for_type_inference(
         dtype=param_dtype, stop_gradient=True)
     saved_variance = helper.create_variable_for_type_inference(

python/paddle/nn/layer/conv.py

@@ -162,7 +162,7 @@ class _ConvNd(Layer):
             main_str += ', stride={_stride}'
         if self._padding != 0:
             main_str += ', padding={_padding}'
-        if self._padding_mode is not 'zeros':
+        if self._padding_mode != 'zeros':
             main_str += ', padding_mode={_padding_mode}'
         if self.output_padding != 0:
             main_str += ', output_padding={output_padding}'

python/paddle/nn/layer/norm.py

@@ -668,7 +668,7 @@ class _BatchNormBase(Layer):
     def extra_repr(self):
         main_str = 'num_features={}, momentum={}, epsilon={}'.format(
             self._num_features, self._momentum, self._epsilon)
-        if self._data_format is not 'NCHW':
+        if self._data_format != 'NCHW':
             main_str += ', data_format={}'.format(self._data_format)
         if self._name is not None:
             main_str += ', name={}'.format(self._name)
@@ -1252,7 +1252,7 @@ class LocalResponseNorm(Layer):
     def extra_repr(self):
         main_str = 'size={}, alpha={}, beta={}, k={}'.format(
             self.size, self.alpha, self.beta, self.k)
-        if self.data_format is not 'NCHW':
+        if self.data_format != 'NCHW':
             main_str += ', data_format={}'.format(self.data_format)
         if self.name is not None:
             main_str += ', name={}'.format(self.name)

python/paddle/nn/layer/rnn.py

@@ -391,7 +391,7 @@ class SimpleRNNCell(RNNCellBase):
 
     def extra_repr(self):
         s = '{input_size}, {hidden_size}'
-        if self.activation is not "tanh":
+        if self.activation != "tanh":
             s += ', activation={activation}'
         return s.format(**self.__dict__)
 

python/paddle/nn/layer/vision.py

@@ -82,7 +82,7 @@ class PixelShuffle(Layer):
 
     def extra_repr(self):
         main_str = 'upscale_factor={}'.format(self._upscale_factor)
-        if self._data_format is not 'NCHW':
+        if self._data_format != 'NCHW':
             main_str += ', data_format={}'.format(self._data_format)
         if self._name is not None:
             main_str += ', name={}'.format(self._name)

python/paddle/tensor/linalg.py

@@ -2792,7 +2792,7 @@ def eigvalsh(x, UPLO='L', name=None):
             raise ValueError(
                 "The input matrix must be batches of square matrices. But received x's dimention: {}".
                 format(x_shape))
-        if UPLO is not 'L' and UPLO is not 'U':
+        if UPLO != 'L' and UPLO != 'U':
             raise ValueError(
                 "UPLO must be L or U. But received UPLO is: {}".format(UPLO))
 

python/paddle/tensor/math.py

@@ -3439,7 +3439,7 @@ def erfinv_(x, name=None):
     return _C_ops.erfinv_(x)
 
 def rad2deg(x, name=None):
-    """
+    r"""
     Convert each of the elements of input x from angles in radians to degrees.
     
     Equation:
@@ -3498,7 +3498,7 @@ def rad2deg(x, name=None):
         return out
 
 def deg2rad(x, name=None):
-    """
+    r"""
     Convert each of the elements of input x from degrees to angles in radians.
     
     Equation: