[CodeStyle][Typos][[N-[1-5],N-[9,10],N-[12-14]] Fix typo(namess, `n…

…ams`, `neccessary`, `Neet`, `neet`, `neeed`, `nedd`,`neighor`,`netwrok`,`normlize`,`noraml`,`numer`) (#70283)

* fix typos

* Update _typos.toml

* fix typos

_typos.toml

@@ -318,18 +318,6 @@ Multiplie = 'Multiplie'
 Muti = 'Muti'
 muti = 'muti'
 mutexs = 'mutexs'
-nams = 'nams'
-namess = 'namess'
-neccessary = 'neccessary'
-Neet = 'Neet'
-neet = 'neet'
-neeed = 'neeed'
-nedd = 'nedd'
-neighor = 'neighor'
-netwrok = 'netwrok'
-normlize = 'normlize'
-noraml = 'noraml'
-numer = 'numer'
 occured = 'occured'
 Ocurred = 'Ocurred'
 occures = 'occures'

paddle/fluid/framework/ir/embedding_fc_lstm_fuse_pass.cc

@@ -64,7 +64,7 @@ static int BuildFusion(Graph* graph,
 #define SET_IN(Key, node__) op_desc.SetInput(#Key, {node__->Name()});
     SET_IN(Ids, input);
     SET_IN(WeightH, weight_h);
-    // Neet to have this passed as We need Wc data for peephole connections
+    // Need to have this passed as We need Wc data for peephole connections
     SET_IN(Bias, bias);
 #undef SET_IN
 

paddle/fluid/framework/new_executor/new_executor_defs.h

@@ -350,7 +350,7 @@ static constexpr char kMemcpyH2D[] = "memcpy_h2d";
 static constexpr char kMemcpyD2H[] = "memcpy_d2h";
 static constexpr char kFetchVarName[] = "fetch";
 
-// static_ref_ is the numer of last live ops calculated to statically after
+// static_ref_ is the number of last live ops calculated to statically after
 // `build` the Instructions. dynamic_ref_  is the runtime version ref which will
 // be decreased by one dynamically after the execution of an op (in last ops
 // list). var_ is the related variable
@@ -381,7 +381,7 @@ class VarRefInfo {
   Variable* var_;
 };
 
-// static_dep_ is the numer of dependencies (ops that must run before it) of
+// static_dep_ is the number of dependencies (ops that must run before it) of
 // each op which is calculated to statically. static_dep_  is the runtime
 // version dep which will be decreased by one dynamically after the execution of
 // one dependency op.

paddle/fluid/operators/controlflow/pylayer_op.cc

@@ -219,7 +219,7 @@ class PyLayerBackwardOp : public PyLayerOp {
 
     core_->Run({}, false);
 
-    // NOTE: It's neccessary. The reason of associating `inside_grads` and
+    // NOTE: It's necessary. The reason of associating `inside_grads` and
     // `outside_grads` at runtime `RunImpl` instead of `assign` op at block is
     // that the Var name of grad_op's outputs may be changed in the
     // `append_backward` function (e.g. `_addup_repetitive_outputs_`).

paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/infer_sym_slice_utils.h

@@ -139,7 +139,7 @@ inline ExprVec GetSliceDims(const ExprVec &in_dims,
   for (size_t i = 0; i < axes.size(); ++i) {
     auto out_dim = ends[i] - starts[i];
     int64_t axis = axes[i];
-    // If in_dims[axis] or ends[i] have symbol, nedd get Min(in_dims[axis] -
+    // If in_dims[axis] or ends[i] have symbol, need get Min(in_dims[axis] -
     // start[i], ends[i] - start[i] )
     if (!out_dim.isa<int64_t>() &&
         (!in_dims[axis].isa<int64_t>() || !ends[i].isa<int64_t>())) {

paddle/phi/infermeta/spmd_rules/stack.cc

@@ -53,7 +53,7 @@ SpmdInfo StackInferSpmd(const std::vector<DistMetaTensor>& x, int axis) {
 
   auto non_empty_index = all_empty ? 0 : non_empty_iter - tensor_shapes.begin();
   auto ndim = tensor_shapes[non_empty_index].size();
-  // normlize dim
+  // normalize dim
   auto dim = axis < 0 ? static_cast<int64_t>(ndim) + axis + 1 : axis;
   std::vector<TensorDistAttr> input_attrs;
   std::transform(

paddle/phi/kernels/gpu/grid_sample_grad_kernel.cu

@@ -535,7 +535,7 @@ __global__ void GridSampler3DCudaBackwardKernel(const int nthreads,
       auto iy_nearest = static_cast<int>(std::round(iy));
       auto iz_nearest = static_cast<int>(std::round(iz));
 
-      // assign nearest neighor pixel value to output pixel
+      // assign nearest neighbor pixel value to output pixel
       int gOut_offset = n * gOut_sN + d * gOut_sD + h * gOut_sH + w * gOut_sW;
       T* gInp_ptr_NC = grad_input + n * inp_sN;
       for (int c = 0; c < out_c;

paddle/phi/kernels/gpu/grid_sample_kernel.cu

@@ -306,7 +306,7 @@ __global__ void GridSample3DCudaKernel(const int nthreads,
       int iy_nearest = static_cast<int>(std::round(iy));
       int iz_nearest = static_cast<int>(std::round(iz));
 
-      // assign nearest neighor pixel value to output pixel
+      // assign nearest neighbor pixel value to output pixel
       auto inp_ptr_NC = input + n * inp_sN;
       auto out_ptr_NCDHW =
           output + n * out_sN + d * out_sD + h * out_sH + w * out_sW;

python/paddle/audio/functional/window.py

@@ -213,7 +213,7 @@ def _taylor(
     signs[1::2] = -1
     m2 = ma * ma
     for mi in range(len(ma)):
-        numer = signs[mi] * paddle.prod(
+        number = signs[mi] * paddle.prod(
             1 - m2[mi] / s2 / (A**2 + (ma - 0.5) ** 2)
         )
         if mi == 0:
@@ -227,7 +227,7 @@ def _taylor(
                 * paddle.prod(1 - m2[mi] / m2[mi + 1 :])
             )
 
-        Fm[mi] = numer / denom
+        Fm[mi] = number / denom
 
     def W(n):
         return 1 + 2 * paddle.matmul(

python/paddle/distributed/auto_parallel/process_mesh.py

@@ -355,7 +355,7 @@ def __ne__(self, other: ProcessMesh | core.ProcessMesh) -> None:
         return not self.__eq__(other)
 
     def __str__(self) -> str:
-        str = f"shape {self.shape}, process_ids {self.process_ids}, dim_nams {self.dim_names}"
+        str = f"shape {self.shape}, process_ids {self.process_ids}, dim_names {self.dim_names}"
         return str
 
     def __hash__(self) -> int:

python/paddle/jit/api.py

@@ -1435,7 +1435,7 @@ def save(
         if combine_params:
             if use_pir_api():
                 # NOTE(Ruting): concrete_program has been pruned when init partialProgramLayer,
-                # so we do not neet to prune again.
+                # so we do not need to prune again.
 
                 for var in concrete_program.main_program.list_vars():
                     if var.persistable:

test/dygraph_to_static/predictor_utils.py

@@ -74,7 +74,7 @@ def _get_analysis_outputs(self, config):
         Args:
             config (AnalysisConfig): predictor configs
         Returns:
-            outs (numpy array): forward netwrok prediction outputs
+            outs (numpy array): forward network prediction outputs
         '''
         predictor = create_paddle_predictor(config)
         names = predictor.get_input_names()

test/legacy_test/test_dataset_consistency_inspection.py

@@ -445,7 +445,7 @@ def test_var_consistency_insepection(self):
                 )
             )
 
-        # context_feat_namess
+        # context_feat_names
         for feat_name in range(len_sparse_query + 16, len_sparse_query + 18):
             slot_data.append(
                 paddle.static.data(
@@ -471,7 +471,7 @@ def test_var_consistency_insepection(self):
                 )
             )
 
-        # neg context_feat_namess
+        # neg context_feat_names
         for feat_name in range(len_sparse_query + 33, len_sparse_query + 35):
             slot_data.append(
                 paddle.static.data(

test/legacy_test/test_log_normal.py

@@ -22,7 +22,7 @@
 paddle.seed(10)
 
 
-def log_noraml_mean(mean, std):
+def log_normal_mean(mean, std):
     return np.exp(mean + np.power(std, 2) / 2.0)
 
 
@@ -152,7 +152,7 @@ def test_api(self):
 
             mean = np.mean(ret, axis=0, keepdims=True)
             var = np.var(ret, axis=0, keepdims=True)
-            mean_ref = log_noraml_mean(self.mean, self.std)
+            mean_ref = log_normal_mean(self.mean, self.std)
             var_ref = log_normal_var(self.mean, self.std)
             np.testing.assert_allclose(mean_ref, mean, rtol=0.2, atol=0.2)
             np.testing.assert_allclose(var_ref, var, rtol=0.2, atol=0.2)

test/legacy_test/test_log_normal_inplace.py

@@ -21,7 +21,7 @@
 from paddle import base
 
 
-def log_noraml_mean(mean, std):
+def log_normal_mean(mean, std):
     return np.exp(mean + np.power(std, 2) / 2.0)
 
 
@@ -106,7 +106,7 @@ def test_log_normal_inplace_op_distribution(self):
         tensor.log_normal_(self.mean, self.std)
         mean = np.mean(tensor.numpy())
         var = np.var(tensor.numpy())
-        mean_ref = log_noraml_mean(self.mean, self.std)
+        mean_ref = log_normal_mean(self.mean, self.std)
         var_ref = log_normal_var(self.mean, self.std)
         np.testing.assert_allclose(mean_ref, mean, rtol=0.2, atol=0.2)
         np.testing.assert_allclose(var_ref, var, rtol=0.2, atol=0.2)

tools/jetson_infer_op.py

@@ -118,7 +118,7 @@ def add_import_skip_return(file, pattern_import, pattern_skip, pattern_return):
             match_obj = pattern_2.search(line)
             if match_obj is not None:
                 file_data += (
-                    "@skip_check_grad_ci(reason='jetson do n0t neeed this !')\n"
+                    "@skip_check_grad_ci(reason='jetson do n0t need this !')\n"
                 )
                 print("### add @skip_check_grad_ci ####")