Fix typos in dy2st unittests (#56006)

test/dygraph_to_static/ifelse_simple_func.py

@@ -21,9 +21,9 @@ def add_fn(x):
     return x
 
 
-def loss_fn(x, lable):
+def loss_fn(x, label):
     loss = paddle.nn.functional.cross_entropy(
-        x, lable, reduction='none', use_softmax=False
+        x, label, reduction='none', use_softmax=False
     )
     return loss
 
@@ -57,7 +57,7 @@ def dyfunc_with_if_else(x_v, label=None):
 def dyfunc_with_if_else2(x, col=100):
     row = 0
     if abs(col) > x.shape[-1]:
-        # TODO: Don't support return non-Tensor in Tensor-dependent `if` stament currently.
+        # TODO: Don't support return non-Tensor in Tensor-dependent `if` statement currently.
         #  `x` is Tensor, `col` is not Tensor, and `col` is the return value of `true_fn` after transformed.
         # col = -1
         col = paddle.tensor.fill_constant(shape=[1], value=-1, dtype="int64")
@@ -136,7 +136,7 @@ def dyfunc_with_if_else_early_return2():
     return e, None
 
 
-def dyfunc_with_if_else_with_list_geneator(x):
+def dyfunc_with_if_else_with_list_generator(x):
     if 10 > 5:
         y = paddle.add_n(
             [paddle.full(shape=[2], fill_value=v) for v in range(5)]
@@ -151,7 +151,7 @@ def nested_if_else(x_v):
     feat_size = x_v.shape[-1]
     bias = paddle.tensor.fill_constant([feat_size], dtype='float32', value=1)
     if x_v.shape[0] != batch_size:
-        # TODO: Don't support return non-Tensor in Tensor-dependent `if` stament currently.
+        # TODO: Don't support return non-Tensor in Tensor-dependent `if` statement currently.
         #  `x_v.shape[0]` is not Tensor, and `batch_size` is the return value of `true_fn` after transformed.
         # col = -1
         # batch_size = x_v.shape[0]

test/dygraph_to_static/seq2seq_dygraph_model.py

@@ -728,7 +728,7 @@ class AttentionModel(paddle.nn.Layer):
 
         src_emb = self.src_embeder(self._transpose_batch_time(src))
 
-        # NOTE: modify model code about `enc_hidden` and `enc_cell` to transforme dygraph code successfully.
+        # NOTE: modify model code about `enc_hidden` and `enc_cell` to transform dygraph code successfully.
         # Because nested list can't be transformed now.
         enc_hidden_0 = to_variable(
             np.zeros((self.batch_size, self.hidden_size), dtype='float32')

test/dygraph_to_static/test_ifelse.py

@@ -26,7 +26,7 @@ from ifelse_simple_func import (
     dyfunc_with_if_else,
     dyfunc_with_if_else2,
     dyfunc_with_if_else3,
-    dyfunc_with_if_else_with_list_geneator,
+    dyfunc_with_if_else_with_list_generator,
     fluid,
     if_tensor_case,
     if_with_and_or,
@@ -116,7 +116,7 @@ class TestDygraphIfElse4(TestDygraphIfElse):
 class TestDygraphIfElseWithListGenerator(TestDygraphIfElse):
     def setUp(self):
         self.x = np.random.random([10, 16]).astype('float32')
-        self.dyfunc = dyfunc_with_if_else_with_list_geneator
+        self.dyfunc = dyfunc_with_if_else_with_list_generator
 
 
 class TestDygraphNestedIfElse(TestDygraphIfElse):

test/dygraph_to_static/test_jit_property_save.py

@@ -44,7 +44,7 @@ class TestPropertySave(unittest.TestCase):
             self.a.get_float(1)
 
     def test_set(self):
-        """test propety set."""
+        """test property set."""
         try:
             a = paddle.framework.core.Property()
             a.set_float('float', 10.0)

test/dygraph_to_static/test_jit_setitem.py

@@ -39,13 +39,13 @@ class TestSetItemBase(unittest.TestCase):
 
     def test_case(self):
         func = self.init_func()
-        dy_res = self.run_dygrah(func)
+        dy_res = self.run_dygraph(func)
         st_res = self.run_to_static(func)
 
         for dy_out, st_out in zip(dy_res, st_res):
             np.testing.assert_allclose(dy_out.numpy(), st_out.numpy())
 
-    def run_dygrah(self, func):
+    def run_dygraph(self, func):
         x = self.init_data()
         y = func(x)
         x_grad = paddle.grad(y, x)[0]
@@ -53,7 +53,7 @@ class TestSetItemBase(unittest.TestCase):
 
     def run_to_static(self, func):
         func = paddle.jit.to_static(func)
-        return self.run_dygrah(func)
+        return self.run_dygraph(func)
 
 
 class TestCase1(TestSetItemBase):
@@ -169,7 +169,7 @@ class TestCase11(TestSetItemBase):
 
         return foo
 
-    def run_dygrah(self, func):
+    def run_dygraph(self, func):
         x = self.init_data()
         value = paddle.ones((16, 32))
         value.stop_gradient = False
@@ -188,7 +188,7 @@ class TestCase12(TestSetItemBase):
 
         return foo
 
-    def run_dygrah(self, func):
+    def run_dygraph(self, func):
         x = self.init_data()
         value = paddle.ones((32,))
         value.stop_gradient = False

test/dygraph_to_static/test_origin_info.py

@@ -116,8 +116,8 @@ class TestOriginInfo(unittest.TestCase):
 
         for i in range(self.line_num):
             static_lineno = self.static_abs_lineno_list[i]
-            staic_loc = Location(static_filepath, static_lineno)
-            self.assertIn(staic_loc.line_location, origin_info_map)
+            static_loc = Location(static_filepath, static_lineno)
+            self.assertIn(static_loc.line_location, origin_info_map)
 
             dy_lineno = dygraph_abs_lineno_list[i]
             dy_col_offset = self.dy_abs_col_offset[i]
@@ -129,7 +129,7 @@ class TestOriginInfo(unittest.TestCase):
                 code,
             )
             self.assertEqual(
-                str(origin_info_map[staic_loc.line_location]), str(origin_info)
+                str(origin_info_map[static_loc.line_location]), str(origin_info)
             )
 
     def test_attach_origin_info(self):

test/dygraph_to_static/test_program_translator.py

@@ -261,23 +261,23 @@ class SwitchModeNet(paddle.nn.Layer):
 
 
 @paddle.jit.to_static
-def switch_mode_funciton():
+def switch_mode_function():
     return True
 
 
 class TestFunctionTrainEvalMode(unittest.TestCase):
     def test_switch_mode(self):
         paddle.disable_static()
-        switch_mode_funciton.eval()
-        switch_mode_funciton()
-        self.assertEqual(switch_mode_funciton._training, False)
-        _, partial_layer = switch_mode_funciton.program_cache.last()[-1]
+        switch_mode_function.eval()
+        switch_mode_function()
+        self.assertEqual(switch_mode_function._training, False)
+        _, partial_layer = switch_mode_function.program_cache.last()[-1]
         self.assertEqual(partial_layer.training, False)
 
-        switch_mode_funciton.train()
-        switch_mode_funciton()
-        self.assertEqual(switch_mode_funciton._training, True)
-        _, partial_layer = switch_mode_funciton.program_cache.last()[-1]
+        switch_mode_function.train()
+        switch_mode_function()
+        self.assertEqual(switch_mode_function._training, True)
+        _, partial_layer = switch_mode_function.program_cache.last()[-1]
         self.assertEqual(partial_layer.training, True)
 
     def test_raise_error(self):

test/dygraph_to_static/test_resnet_amp.py

@@ -70,7 +70,7 @@ def train(to_static, build_strategy=None):
 
                 with paddle.amp.auto_cast():
                     pred = resnet(img)
-                    # FIXME(Aurelius84): The followding cross_entropy seems to bring out a
+                    # FIXME(Aurelius84): The following cross_entropy seems to bring out a
                     # precision problem, need to figure out the underlying reason.
                     # If we remove it, the loss between dygraph and dy2stat is exactly same.
                     loss = paddle.nn.functional.cross_entropy(

test/dygraph_to_static/test_seq2seq.py

@@ -70,11 +70,11 @@ def train(args, attn_model=False):
                 dropout=args.dropout,
             )
 
-        gloabl_norm_clip = ClipGradByGlobalNorm(args.max_grad_norm)
+        global_norm_clip = ClipGradByGlobalNorm(args.max_grad_norm)
         optimizer = fluid.optimizer.SGD(
             args.learning_rate,
             parameter_list=model.parameters(),
-            grad_clip=gloabl_norm_clip,
+            grad_clip=global_norm_clip,
         )
 
         model.train()

test/dygraph_to_static/test_tensor_shape.py

@@ -486,7 +486,7 @@ class TestTensorShapeInWhile4(TestTensorShapeBasic):
         self.expected_slice_op_num = 0
 
 
-# 5. Test op num for negetive dim
+# 5. Test op num for negative dim
 class TestOpNumBasicWithTensorShape(unittest.TestCase):
     def setUp(self):
         self._set_input_spec()