Fix some typos (tranpose, wamup, etc.)

test/cinn/passes/test_transpose_floding_output_pass.py

@@ -79,7 +79,9 @@ def trans_out_func(self, builder, out):
         return builder.transpose(out_s, [0, 2, 1])
 
 
-class TestTransposeFoldingOutputPassInvlidTrans(TestTransposeFoldingOutputPass):
+class TestTransposeFoldingOutputPassInvalidTrans(
+    TestTransposeFoldingOutputPass
+):
     def expect_folding_number(self):
         return 1
 
@@ -88,7 +90,9 @@ def trans_out_func(self, builder, out):
         return builder.scale(out_t, scale=2.0)
 
 
-class TestTransposeFoldingOutputPassInvlidScale(TestTransposeFoldingOutputPass):
+class TestTransposeFoldingOutputPassInvalidScale(
+    TestTransposeFoldingOutputPass
+):
     def expect_folding_number(self):
         return 1
 

test/dygraph_to_static/test_pylayer.py

@@ -322,7 +322,7 @@ def _run_and_compare(self, *args, **kwargs):
                 dygraph_inp_kwargs[k].stop_gradient = False
                 static_inp_kwargs[k].stop_gradient = False
 
-        # Step2. Run the dygraph and the static seperately
+        # Step2. Run the dygraph and the static separately
         dygraph_res = self._run_dygraph(*dygraph_inp_args, **dygraph_inp_kwargs)
         static_res = self._run_static(*static_inp_args, **static_inp_kwargs)
 

test/ir/inference/test_mkldnn_reshape_transpose_matmul_v2_fuse_pass.py

@@ -25,7 +25,7 @@
 class TestReshapeTransposeMatmulV2OneDNNFusePass(InferencePassTest):
     def setUp(self):
         self.set_params()
-        self.tranpose_perm = [0, 2, 1, 3]
+        self.transpose_perm = [0, 2, 1, 3]
         self.pass_name = 'reshape_transpose_matmul_mkldnn_fuse_pass'
 
         with base.program_guard(self.main_program, self.startup_program):
@@ -37,7 +37,7 @@ def setUp(self):
             )
 
             reshape = paddle.reshape(data, shape=self.reshape_shape)
-            transpose = paddle.transpose(reshape, self.tranpose_perm)
+            transpose = paddle.transpose(reshape, self.transpose_perm)
 
             matmul = paddle.matmul(
                 transpose,

test/ir/inference/test_trt_multiclass_nms3_op.py

@@ -48,10 +48,10 @@ def multiclass_nms(
     In the NMS step, this operator greedily selects a subset of detection bounding
     boxes that have high scores larger than score_threshold, if providing this
     threshold, then selects the largest nms_top_k confidences scores if nms_top_k
-    is larger than -1. Then this operator pruns away boxes that have high IOU
+    is larger than -1. Then this operator prunes away boxes that have high IOU
     (intersection over union) overlap with already selected boxes by adaptive
     threshold NMS based on parameters of nms_threshold and nms_eta.
-    Aftern NMS step, at most keep_top_k number of total bboxes are to be kept
+    After NMS step, at most keep_top_k number of total bboxes are to be kept
     per image if keep_top_k is larger than -1.
     Args:
         bboxes (Tensor): Two types of bboxes are supported:
@@ -332,7 +332,7 @@ def test_background(self):
         self.run_test()
 
     def test_disable_varseqlen(self):
-        self.diable_tensorrt_varseqlen = False
+        self.disable_tensorrt_varseqlen = False
         self.run_test()
 
 

test/legacy_test/test_model.py

@@ -69,14 +69,14 @@ def forward(self, x):
         return y, 0
 
 
-class ModelOutter(paddle.nn.Layer):
+class ModelOuter(paddle.nn.Layer):
     def __init__(self):
         super().__init__()
         self.module1 = ModelInner()
         self.module2 = paddle.nn.Linear(4, 5)
 
     def forward(self, x):
-        y, dummpy = self.module1(x)
+        y, dumpy = self.module1(x)
         y = self.module2(y)
         return y, 3
 
@@ -704,7 +704,7 @@ def _get_param_from_state_dict(state_dict):
             model.summary(input_size=(20), dtype='float32')
 
     def test_summary_non_tensor(self):
-        paddle.summary(ModelOutter(), input_size=(-1, 3))
+        paddle.summary(ModelOuter(), input_size=(-1, 3))
 
     def test_summary_nlp(self):
         def _get_param_from_state_dict(state_dict):
@@ -1020,7 +1020,7 @@ def test_fit_by_epoch(self):
         base_lr = 1e-3
         boundaries = [5, 8]
         epochs = 10
-        wamup_epochs = 4
+        warmup_epochs = 4
 
         def make_optimizer(parameters=None):
             momentum = 0.9
@@ -1031,7 +1031,7 @@ def make_optimizer(parameters=None):
             )
             learning_rate = paddle.optimizer.lr.LinearWarmup(
                 learning_rate=learning_rate,
-                warmup_steps=wamup_epochs,
+                warmup_steps=warmup_epochs,
                 start_lr=base_lr / 5.0,
                 end_lr=base_lr,
                 verbose=True,
@@ -1071,7 +1071,7 @@ def make_optimizer(parameters=None):
 
         cnt = 0
         for b in boundaries:
-            if b + wamup_epochs <= epochs:
+            if b + warmup_epochs <= epochs:
                 cnt += 1
 
         np.testing.assert_allclose(
@@ -1104,7 +1104,7 @@ def make_optimizer(parameters=None):
 
         cnt = 0
         for b in boundaries:
-            if b + wamup_epochs <= epochs:
+            if b + warmup_epochs <= epochs:
                 cnt += 1
 
         np.testing.assert_allclose(

test/legacy_test/test_sparse_model.py

@@ -22,9 +22,9 @@
 
 class TestGradientAdd(unittest.TestCase):
     def sparse(self, sp_x):
-        indentity = sp_x
+        identity = sp_x
         out = nn.functional.relu(sp_x)
-        values = out.values() + indentity.values()
+        values = out.values() + identity.values()
         out = paddle.sparse.sparse_coo_tensor(
             out.indices(),
             values,
@@ -34,9 +34,9 @@ def sparse(self, sp_x):
         return out
 
     def dense(self, x):
-        indentity = x
+        identity = x
         out = paddle.nn.functional.relu(x)
-        out = out + indentity
+        out = out + identity
         return out
 
     def test(self):

test/mkldnn/test_requantize_mkldnn_op.py

@@ -296,7 +296,7 @@ def set_shifts(self):
         self.shift_out = 128.0
 
 
-# ---------------test non-four dimentional formats--------------------------
+# ---------------test non-four dimensional formats--------------------------
 
 
 class TestReQuantizeOp_2DimFormat(TestReQuantizeOp):

test/prim/model/bert.py

@@ -175,12 +175,12 @@ def forward(
         if token_type_ids is None:
             token_type_ids = paddle.zeros_like(input_ids, dtype="int64")
 
-        input_embedings = self.word_embeddings(input_ids)
+        input_embeddings = self.word_embeddings(input_ids)
         position_embeddings = self.position_embeddings(position_ids)
         token_type_embeddings = self.token_type_embeddings(token_type_ids)
 
         embeddings = (
-            input_embedings + position_embeddings + token_type_embeddings
+            input_embeddings + position_embeddings + token_type_embeddings
         )
         embeddings = self.layer_norm(embeddings)
         embeddings = self.dropout(embeddings)

test/tokenizer/bert_tokenizer.py

@@ -466,7 +466,7 @@ def get_special_tokens_mask(
         Args:
             token_ids_0 (List[int]):
                 A list of `inputs_ids` for the first sequence.
-            token_ids_1 (List[int], optinal):
+            token_ids_1 (List[int], optional):
                 Optional second list of IDs for sequence pairs. Defaults to None.
             already_has_special_tokens (bool, optional): Whether or not the token list is already
                 formatted with special tokens for the model. Defaults to None.