Fix some typos (test_fill_any_eqaul, LeraningRate, etc.)

test/legacy_test/test_fill_any_op.py

@@ -86,7 +86,7 @@ def test_fill_any_version(self):
             var.fill_(0)
             self.assertEqual(var.inplace_version, 3)
 
-    def test_fill_any_eqaul(self):
+    def test_fill_any_equal(self):
         with paddle.base.dygraph.guard():
             tensor = paddle.to_tensor(
                 np.random.random((20, 30)).astype(np.float32)

test/xpu/test_activation_op_xpu.py

@@ -664,7 +664,7 @@ def __init__(self):
         self.op_name = 'reciprocal'
         self.use_dynamic_create_class = False
 
-    class XPUTestRecipocal(TestActivationOPBase):
+    class XPUTestReciprocal(TestActivationOPBase):
         def set_case(self):
             self.op_type = "reciprocal"
             self.dtype = self.in_type

test/xpu/test_collective_api_base.py

@@ -606,19 +606,19 @@ def convertbf16(origin):
             result1 = []
             result2 = []
 
-            def is_empyt_list(x):
+            def is_empty_list(x):
                 if isinstance(x, list) and len(x) == 0:
                     return True
                 return False
 
             for i in range(tot_expert):
                 for arr in output1[i]:
-                    if is_empyt_list(arr):
+                    if is_empty_list(arr):
                         continue
                     result1.append(arr)
             for i in range(tot_expert):
                 for arr in output2[i]:
-                    if is_empyt_list(arr):
+                    if is_empty_list(arr):
                         continue
                     result2.append(arr)
 

test/xpu/test_collective_base_xpu.py

@@ -339,12 +339,12 @@ def check_with_place(
             np.testing.assert_allclose(tr0_out, need_result1, rtol=0, atol=0)
             np.testing.assert_allclose(tr1_out, need_result2, rtol=0, atol=0)
         elif col_type == "reduce_slicegather":
-            slicesize = input1.shape[0] // 2
-            tmp10 = input1[0:slicesize]
-            tmp11 = input2[0:slicesize]
+            slice_size = input1.shape[0] // 2
+            tmp10 = input1[0:slice_size]
+            tmp11 = input2[0:slice_size]
             need_result1 = np.concatenate((tmp10, tmp11), axis=1)
-            tmp20 = input1[slicesize:]
-            tmp21 = input2[slicesize:]
+            tmp20 = input1[slice_size:]
+            tmp21 = input2[slice_size:]
             need_result2 = np.concatenate((tmp20, tmp21), axis=1)
             np.testing.assert_allclose(tr0_out, need_result1)
             np.testing.assert_allclose(tr1_out, need_result2)

test/xpu/test_fill_any_op_xpu.py

@@ -88,7 +88,7 @@ def test_fill_any_version(self):
                 var.fill_(0)
                 self.assertEqual(var.inplace_version, 3)
 
-        def test_fill_any_eqaul(self):
+        def test_fill_any_equal(self):
             with paddle.base.dygraph.guard():
                 tensor = paddle.to_tensor(
                     np.random.random((20, 30)).astype(np.float32)

test/xpu/test_matmul_op_xpu.py

@@ -297,23 +297,23 @@ def dynamic_create_class(self):
         for dims in xpu_support_dims_list:
             dim_X = dims[0]
             dim_Y = dims[1]
-            for transose_x in [True, False]:
-                for transose_y in [True, False]:
+            for transpose_x in [True, False]:
+                for transpose_y in [True, False]:
                     for batch in batch_size:
                         no_need_check_grad = False
                         if batch >= 5:
                             no_need_check_grad = True
                         class_name = 'TestMatMulOp_dimX_{}_dim_Y_{}_transX_{}_transY_{}_batch_{}'.format(
-                            dim_X, dim_Y, transose_x, transose_y, batch
+                            dim_X, dim_Y, transpose_x, transpose_y, batch
                         )
                         shape_x, shape_y = generate_compatible_shapes(
-                            dim_X, dim_Y, transose_x, transose_y, batch
+                            dim_X, dim_Y, transpose_x, transpose_y, batch
                         )
                         attr_dict = {
                             'shape_X': shape_x,
                             'shape_Y': shape_y,
-                            'transpose_X': transose_x,
-                            'transpose_Y': transose_y,
+                            'transpose_X': transpose_x,
+                            'transpose_Y': transpose_y,
                             'no_need_check_grad': no_need_check_grad,
                             'op_type': "matmul",
                         }

test/xpu/test_prior_box_op_xpu.py

@@ -201,7 +201,7 @@ def init_test_output(self):
                                 ]
                                 idx += 1
 
-            # clip the prior's coordidate such that it is within[0, 1]
+            # clip the prior's coordinate such that it is within[0, 1]
             if self.clip:
                 out_boxes = np.clip(out_boxes, 0.0, 1.0)
             # set the variance.

test/xpu/test_refactor_op_xpu.py

@@ -115,7 +115,7 @@ def setUp(self):
             self.__class__.no_need_check_grad = True
 
             self.init_dtype()
-            self.init_inputshape()
+            self.init_input_shape()
             self.init_axis()
             self.init_direction()
 
@@ -145,7 +145,7 @@ def get_output(self):
                 )
                 self.sorted_x = np.sort(self.x, kind='heapsort', axis=self.axis)
 
-        def init_inputshape(self):
+        def init_input_shape(self):
             self.input_shape = (2, 2, 2, 3, 3)
 
         def init_dtype(self):

test/xpu/test_sgd_op_xpu.py

@@ -109,7 +109,7 @@ def check_with_place(self, place):
         param_array = np.full((height, self.row_numel), 5.0).astype("float32")
         param.set(param_array, place)
 
-        # create and initialize LeraningRate Variable
+        # create and initialize LearningRate Variable
         lr = scope.var('LearningRate').get_tensor()
         lr_array = np.full((1), 2.0).astype("float32")
         lr.set(lr_array, place)

test/xpu/test_sigmoid_cross_entropy_with_logits_op_xpu.py

@@ -86,7 +86,7 @@ def init_dtype(self):
     class TestSigmoidCrossEntropyWithLogitsOp2(
         TestSigmoidCrossEntropyWithLogitsOp
     ):
-        """Test sigmoid_cross_entropy_with_logit_op with probabalistic label"""
+        """Test sigmoid_cross_entropy_with_logit_op with probabilistic label"""
 
         def set_inputs(self):
             batch_size = 64
@@ -119,7 +119,7 @@ def set_output(self):
     class TestSigmoidCrossEntropyWithLogitsOp3(
         TestSigmoidCrossEntropyWithLogitsOp
     ):
-        """Test sigmoid_cross_entropy_with_logit_op with probabalistic label"""
+        """Test sigmoid_cross_entropy_with_logit_op with probabilistic label"""
 
         def set_inputs(self):
             batch_size = 64
@@ -148,7 +148,7 @@ def set_output(self):
     class TestSigmoidCrossEntropyWithLogitsOp4(
         TestSigmoidCrossEntropyWithLogitsOp
     ):
-        """Test sigmoid_cross_entropy_with_logit_op with probabalistic label"""
+        """Test sigmoid_cross_entropy_with_logit_op with probabilistic label"""
 
         def set_inputs(self):
             batch_size = 64
@@ -185,7 +185,7 @@ def set_output(self):
     class TestSigmoidCrossEntropyWithLogitsOp5(
         TestSigmoidCrossEntropyWithLogitsOp
     ):
-        """Test sigmoid_cross_entropy_with_logit_op with probabalistic label"""
+        """Test sigmoid_cross_entropy_with_logit_op with probabilistic label"""
 
         def set_inputs(self):
             batch_size = [10, 10]
@@ -278,7 +278,7 @@ def set_output(self):
     class TestSigmoidCrossEntropyWithLogitsNorm(
         TestSigmoidCrossEntropyWithLogitsOp
     ):
-        """Test sigmoid_cross_entropy_with_logit_op with probabalistic label"""
+        """Test sigmoid_cross_entropy_with_logit_op with probabilistic label"""
 
         def set_inputs(self):
             batch_size = [10, 10]

test/xpu/test_warpctc_op_xpu.py

@@ -116,7 +116,7 @@ def forward_a_sequence(self, softmax_a_sequence, labels_a_sequence):
         required_times = labels_a_sequence.shape[0]
         old_label = -1
         for i in range(labels_a_sequence.shape[0]):
-            # two contingous labels with the same value
+            # two contiguous labels with the same value
             if labels_a_sequence[i, 0] == old_label:
                 required_times = required_times + 1
             old_label = labels_a_sequence[i, 0]
@@ -425,7 +425,7 @@ def test_dygraph_with_lod():
             paddle.enable_static()
 
     class TestCTCLossAPICase(unittest.TestCase):
-        def test_functinal_api(self):
+        def test_functional_api(self):
             self.dtype = self.in_type
             self.place = paddle.XPUPlace(0)
             self.batch_size = 4