[CodeStyle][Typos][L-[1-7]] Fix typos(lable,lamda,labmda,lastest,Lanuch,lanuch,leyer,learing,Leafs,leafs)

_typos.toml

@@ -4,7 +4,7 @@ extend-exclude = [
     "third_party",
     "patches",
     "build",
-    "test/dataset/imikolov_test.py"
+    "test/dataset/imikolov_test.py",
 ]
 
 [default.extend-words]
@@ -210,16 +210,6 @@ interated = 'interated'
 Iteraion = 'Iteraion'
 IIT = 'IIT'
 iy = 'iy'
-lable = 'lable'
-lamda = 'lamda'
-labmda = 'labmda'
-lastest = 'lastest'
-Lanuch = 'Lanuch'
-lanuch = 'lanuch'
-leyer = 'leyer'
-learing = 'learing'
-Leafs = 'Leafs'
-leafs = 'leafs'
 occured = 'occured'
 Ocurred = 'Ocurred'
 occures = 'occures'

paddle/fluid/distributed/index_dataset/index_wrapper.cc

@@ -187,7 +187,7 @@ std::vector<uint64_t> TreeIndex::GetTravelCodes(uint64_t id, int start_level) {
   return res;
 }
 
-std::vector<IndexNode> TreeIndex::GetAllLeafs() {
+std::vector<IndexNode> TreeIndex::GetAllLeaves() {
   std::vector<IndexNode> res;
   res.reserve(id_codes_map_.size());
   for (auto& ite : id_codes_map_) {

paddle/fluid/distributed/index_dataset/index_wrapper.h

@@ -55,7 +55,7 @@ class TreeIndex : public Index {
                                          int level);
   std::vector<uint64_t> GetChildrenCodes(uint64_t ancestor, int level);
   std::vector<uint64_t> GetTravelCodes(uint64_t id, int start_level);
-  std::vector<IndexNode> GetAllLeafs();
+  std::vector<IndexNode> GetAllLeaves();
 
   std::unordered_map<uint64_t, IndexNode> data_;
   std::unordered_map<uint64_t, uint64_t> id_codes_map_;

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -280,7 +280,7 @@ class PDPattern {
  *    detector.mutable_pattern().AddEdge(node0, node1);
  *    // Create an handler, to define the behavior of treating the filtered
  *    // subgraphs that comply with the patterns.
- *    GraphPatternDetector::handle_t handler = some labmda
+ *    GraphPatternDetector::handle_t handler = some lambda
  *    // Execute the detector.
  *    detector(&graph, handler);
  */

paddle/fluid/pir/drr/README_cn.md

@@ -92,7 +92,7 @@ DRR PASS 包含以下三个部分：
 	<tr>
 		<td> <pre>void AddConstraint(
         const std::function&lt;bool(const MatchContext&)&gt;& constraint_fn)</pre></td>
-		<td> 在 SourcePattern 中定义一个约束，可以利用此接口和 lamda 表达式实现对 SourcePattern 的自定义约束</td>
+		<td> 在 SourcePattern 中定义一个约束，可以利用此接口和 lambda 表达式实现对 SourcePattern 的自定义约束</td>
 		<td> constraint_fn: 自定义的约束函数</td>
 	</tr>
 	<tr>

paddle/fluid/pybind/fleet_py.cc

@@ -303,7 +303,8 @@ void BindTreeIndex(py::module* m) {
       .def("total_node_nums",
            [](TreeIndex& self) { return self.TotalNodeNums(); })
       .def("emb_size", [](TreeIndex& self) { return self.EmbSize(); })
-      .def("get_all_leafs", [](TreeIndex& self) { return self.GetAllLeafs(); })
+      .def("get_all_leaves",
+           [](TreeIndex& self) { return self.GetAllLeaves(); })
       .def("get_nodes",
            [](TreeIndex& self, const std::vector<uint64_t>& codes) {
              return self.GetNodes(codes);

paddle/phi/kernels/fusion/gpu/fused_layernorm_residual_dropout_bias.h

@@ -40,7 +40,7 @@ using LayerNormScaleBiasT =
     typename std::conditional<ScaleBiasWithSameTypeX, T, U>::type;
 
 /**
- * @brief fused add_bias, dropout, add residual and leyer_norm into one
+ * @brief fused add_bias, dropout, add residual and layer_norm into one
  * operators. Currently only support forward
  */
 

paddle/phi/kernels/gpu/lars_momentum_kernel.cu

@@ -578,7 +578,7 @@ void LarsMomentumKernel(
                           reinterpret_cast<void*>(&epsilon_),
                           reinterpret_cast<void*>(&rescale_grad_),
                           reinterpret_cast<void*>(&multi_precision)};
-    // Lanuch all sm theads, and thead of each block synchronizedly cooperate.
+    // Launch all sm theads, and thead of each block synchronizedly cooperate.
     cudaLaunchCooperativeKernel(
         reinterpret_cast<void*>(MergedMomentumLarsKernel<T, MT>),
         lars_thread_config.grid_for_lars,
@@ -630,7 +630,7 @@ void LarsMomentumKernel(
         reinterpret_cast<void*>(&thresh),  // Just a placeholder
         reinterpret_cast<void*>(&numel),
         reinterpret_cast<void*>(&multi_precision)};
-    // Lanuch all sm theads.
+    // Launch all sm theads.
     cudaLaunchCooperativeKernel(
         reinterpret_cast<void*>(MomentumLarsKernel<T, MT>),
         lars_thread_config.grid_for_lars,

paddle/phi/kernels/gpu/top_k_grad_kernel.cu

@@ -73,7 +73,7 @@ void TopkGradKernel(const Context& dev_ctx,
   const int max_blocks = std::max(((max_threads - 1) / block_size + 1), 1);
   int grid_size = std::min(max_blocks, pre);
 
-  // lanuch the cuda kernel to assign the grad
+  // launch the cuda kernel to assign the grad
   phi::funcs::AssignGradWithAxis<T>
       <<<grid_size, block_size, 64 * 4, dev_ctx.stream()>>>(
           out_grad_data, indices_data, x_grad_data, pre, post, n, k);

python/paddle/distributed/fleet/dataset/index_dataset.py

@@ -50,8 +50,8 @@ def total_node_nums(self) -> int:
     def emb_size(self) -> int:
         return self._emb_size
 
-    def get_all_leafs(self) -> list[Any]:
-        return self._tree.get_all_leafs()
+    def get_all_leaves(self) -> list[Any]:
+        return self._tree.get_all_leaves()
 
     def get_nodes(self, codes: list[int]) -> list[Any]:
         return self._tree.get_nodes(codes)

python/paddle/optimizer/optimizer.py

@@ -701,7 +701,7 @@ def get_lr(self) -> float:
         """
         Get current learning rate of optimizer.
         If 'LRScheduler' is not used, the return value is all the same.
-        If 'LRScheduler' is used, the return value is the current scheduled learing rete.
+        If 'LRScheduler' is used, the return value is the current scheduled learning rete.
 
         Returns:
             float, The current learning rate of optimizer.

python/paddle/text/datasets/conll05.py

@@ -244,8 +244,8 @@ def _load_anno(self) -> None:
 
                 if len(label) == 0:  # end of sentence
                     for i in range(len(one_seg[0])):
-                        a_kind_lable = [x[i] for x in one_seg]
-                        labels.append(a_kind_lable)
+                        a_kind_label = [x[i] for x in one_seg]
+                        labels.append(a_kind_label)
 
                     if len(labels) >= 1:
                         verb_list = []

test/legacy_test/test_dist_tree_index.py

@@ -69,7 +69,7 @@ def test_tree_index(self):
                 [node.id() for node in tree.get_nodes(layer_node_codes[-1])]
             )
 
-        all_leaf_ids = [node.id() for node in tree.get_all_leafs()]
+        all_leaf_ids = [node.id() for node in tree.get_all_leaves()]
         self.assertEqual(sum(all_leaf_ids), sum(layer_node_ids[-1]))
 
         # get_travel

tools/final_ut_parallel_rule.py

@@ -100,7 +100,7 @@ def classify_cases_by_mem(rootPath):
         json.dump(all_tests_by_card, f)
 
     with open("/pre_test/ut_mem_map.json", 'r') as load_f:
-        new_lastest_mem = json.load(load_f)
+        new_latest_mem = json.load(load_f)
     no_parallel_case = '^job$'
     for cardType in all_tests_by_card:
         case_mem_0 = '^job$'
@@ -112,15 +112,15 @@ def classify_cases_by_mem(rootPath):
                 no_parallel_case = no_parallel_case + '|^' + case + '$'
                 continue
 
-            if case not in new_lastest_mem:
+            if case not in new_latest_mem:
                 continue
 
             # mem = 0
-            if new_lastest_mem[case]["mem_nvidia"] == 0:
+            if new_latest_mem[case]["mem_nvidia"] == 0:
                 case_mem_0 = case_mem_0 + '|^' + case + '$'
             # mem != 0
             else:
-                case_mem_1[case] = new_lastest_mem[case]["mem_nvidia"]
+                case_mem_1[case] = new_latest_mem[case]["mem_nvidia"]
 
         with open(f'/pre_test/{cardType}_mem0', 'w') as f:
             f.write(case_mem_0)