[CodeStyle][Typos][D-[24-30]] Fix typos(defferent,differenciation,diffrent,differnt,difficults,dimensinal,dimenstions,demension,dimention,dimenstion)

_typos.toml

@@ -49,16 +49,6 @@ deciamls = 'deciamls'
 decalared = 'decalared'
 decompse = 'decompse'
 decompositing = 'decompositing'
-defferent = 'defferent'
-differenciation = 'differenciation'
-differnt = 'differnt'
-diffrent = 'diffrent'
-difficults = 'difficults'
-dimensinal = 'dimensinal'
-dimenstions = 'dimenstions'
-dimenstion = 'dimenstion'
-dimention = 'dimention'
-demension = 'demension'
 Direcly = 'Direcly'
 direcly = 'direcly'
 direcotory = 'direcotory'

paddle/cinn/operator_fusion/graph_transformer/operation.h

@@ -113,7 +113,7 @@ struct MergeReduceTreeAndTrivialOperation {
     merged_node->set_fusion_iters(
         graph->iters_fusion_policy()->SingleDownstreamItersFusion(node,
                                                                   downstream));
-    // TODO(huangjiyi): Support relationship analysis for defferent iters, for
+    // TODO(huangjiyi): Support relationship analysis for different iters, for
     // example the input iters and output iters of reshape op.
     auto sig = merged_node->fusion_iters();
     const auto upstream_iters = node->fusion_iters();

paddle/cinn/operator_fusion/pir_graph_analyzing/dim_relation.h

@@ -19,7 +19,7 @@
 namespace cinn::fusion {
 
 // TODO(@wuzhanfei) ops like a = b + b, the Value b is used by AddOp twice
-// Currently we can not mark them as two differnt DimUsage
+// Currently we can not mark them as two different DimUsage
 
 struct DimUsage {
   pir::Value v_;

paddle/common/flags.cc

@@ -1106,25 +1106,25 @@ PHI_DEFINE_EXPORTED_string(cinn_subgraph_graphviz_dir,
  * Since Version: 3.0 Beta
  * Value Range: bool, default=false
  * Example: FLAGS_cinn_specify_input_dynamic_dim=true will use file set by
- * FLAGS_cinn_input_dynamic_dim_spec_file to specify input dynamic dimention.
+ * FLAGS_cinn_input_dynamic_dim_spec_file to specify input dynamic dimension.
  */
 PHI_DEFINE_EXPORTED_bool(cinn_specify_input_dynamic_dim,
                          false,
-                         "Whether to specify input dynamic dimention.");
+                         "Whether to specify input dynamic dimension.");
 
 /*
  * CINN related FLAG
  * Name: FLAGS_cinn_input_dynamic_dim_spec_file
  * Since Version: 3.0 Beta
  * Value Range: string, default=""
  * Example: FLAGS_cinn_input_dynamic_dim_spec_file="./config.json",
- * FLAGS_cinn_specify_input_dynamic_dim=true would use input dynamic dimention
- * predefined in ./config.json to specify input dynamic dimention.
+ * FLAGS_cinn_specify_input_dynamic_dim=true would use input dynamic dimension
+ * predefined in ./config.json to specify input dynamic dimension.
  */
 PHI_DEFINE_EXPORTED_string(
     cinn_input_dynamic_dim_spec_file,
     "",
-    "File path of predefined input dynamic dimention specification.");
+    "File path of predefined input dynamic dimension specification.");
 
 #endif
 

paddle/fluid/pir/serialize_deserialize/include/deserialize_utils.h

@@ -428,7 +428,7 @@ pir::Attribute AttrTypeReader::ReadPaddleOperatorAttr(
                                         std::vector<int64_t>>(attr_json, ctx);
   } else if (attr_name == paddle::dialect::ScalarAttribute::name()) {
     VLOG(8) << "Parse ScalarAttribute .";
-    // this func's return type is pir::Attribute which is diffrent
+    // this func's return type is pir::Attribute which is different
     // from paddle::dialect::ScalarAttribute
     return pir::deserializeAttrFromJson_scalarAttr(attr_json, ctx);
   } else if (attr_name == paddle::dialect::DataTypeAttribute::name()) {

paddle/fluid/pybind/imperative.cc

@@ -1162,7 +1162,7 @@ void BindImperative(py::module *m_ptr) {
 
     count (Tensor): The count tensor, and the data type should be `int64` currently.
                     Besides, `count` should be placed on CPUPlace. The shape of `count`
-                    should be one-dimensinal.
+                    should be one-dimensional.
 
   Examples:
         .. code-block:: python
@@ -1395,7 +1395,7 @@ void BindImperative(py::module *m_ptr) {
 
     count (Tensor): The count tensor, and the data type should be `int64` currently.
                     Besides, `count` should be placed on CPUPlace. The shape of `count`
-                    should be one-dimensinal.
+                    should be one-dimensional.
 
   Examples:
         .. code-block:: python

paddle/phi/backends/onednn/onednn_reuse.h

@@ -1751,7 +1751,7 @@ class PoolingOneDNNHandler
       const OneDNNContext& dev_ctx, const std::string& unique_name) {
     dnnl::memory::desc workspace_md = this->fwd_pd_->workspace_desc();
     // Pooling Workspace has to be passed to Grad op that
-    // may be executed by diffrent thread, hence
+    // may be executed by different thread, hence
     // for that one we use key that does not contain TID
     std::string workspace_key = CreateKey(dev_ctx,
                                           workspace_md.get_dims(),

paddle/phi/infermeta/spmd_rules/utils.cc

@@ -299,7 +299,7 @@ void AlignDimsSharding(std::vector<TensorDistAttr>* input_attrs_ptr,
     return false;
   };
 
-  // a dim can not be sharded twice along diffrent mesh_dim
+  // a dim can not be sharded twice along different mesh_dim
   std::set<char> sharded_axis;
   std::map<int32_t, ReduceType> partial_dim_to_type;
   std::map<int32_t, char> mesh_dim_to_axis;

paddle/phi/infermeta/unary.cc

@@ -4773,7 +4773,7 @@ void SumInferMeta(const MetaTensor& x,
 }
 
 void DetInferMeta(const MetaTensor& x, MetaTensor* out, MetaConfig config) {
-  // remove the last two demension
+  // remove the last two dimension
   auto out_dim = common::vectorize<int>(x.dims());
   out_dim.pop_back();
   out_dim.pop_back();

paddle/phi/kernels/gpu/flash_attn_grad_kernel.cu

@@ -143,11 +143,11 @@ static void kvReduceForGQA(const Context& ctx,
   PADDLE_ENFORCE_EQ(
       dk->strides()[2],
       1,
-      common::errors::InvalidArgument("headdim dimention must be contiguous"));
+      common::errors::InvalidArgument("headdim dimension must be contiguous"));
   PADDLE_ENFORCE_EQ(
       dk_tmp.strides()[3],
       1,
-      common::errors::InvalidArgument("headdim dimention must be contiguous"));
+      common::errors::InvalidArgument("headdim dimension must be contiguous"));
   const int64_t reduceDimSize = dk_tmp.dims()[2];
   const size_t blockNum =
       std::min((static_cast<int64_t>(dk_tmp.dims()[0] + 31) / 32),
@@ -177,19 +177,19 @@ static void kvReduceBatchedForGQA(const Context& ctx,
   PADDLE_ENFORCE_EQ(
       dk->strides()[3],
       1,
-      common::errors::InvalidArgument("headdim dimention must be contiguous"));
+      common::errors::InvalidArgument("headdim dimension must be contiguous"));
   PADDLE_ENFORCE_EQ(
       dk_tmp.strides()[4],
       1,
-      common::errors::InvalidArgument("headdim dimention must be contiguous"));
+      common::errors::InvalidArgument("headdim dimension must be contiguous"));
   PADDLE_ENFORCE_EQ(dk->strides()[0],
                     dk->strides()[1] * dk->dims()[1],
                     common::errors::InvalidArgument(
-                        "batchsize dimention must be contiguous"));
+                        "batchsize dimension must be contiguous"));
   PADDLE_ENFORCE_EQ(dk_tmp.strides()[0],
                     dk_tmp.strides()[1] * dk_tmp.dims()[1],
                     common::errors::InvalidArgument(
-                        "batchsize dimention must be contiguous"));
+                        "batchsize dimension must be contiguous"));
   const int64_t reduceDimSize = dk_tmp.dims()[3];
   const size_t blockNum = std::min(
       (static_cast<int64_t>(dk_tmp.dims()[0] * dk_tmp.dims()[1] + 31) / 32),

paddle/phi/kernels/gpu/flash_attn_utils.h

@@ -100,7 +100,7 @@ static std::vector<int64_t> GetAttnSparseMaskDims(
         rank,
         4,
         common::errors::InvalidArgument(
-            "The number of dimenstions of startend_row_indices is expected to "
+            "The number of dimensions of startend_row_indices is expected to "
             "be greater or equal to 4, but recieved %d. The shape of "
             "startend_row_indices is [%s]",
             rank,

test/cpp/inference/api/full_pascalvoc_test_preprocess.py

@@ -81,7 +81,7 @@ def convert_pascalvoc_local2bin(args):
 
     boxes = []
     lbls = []
-    difficults = []
+    difficulties = []
     object_nums = []
 
     for line in lines:
@@ -127,12 +127,12 @@ def convert_pascalvoc_local2bin(args):
 
         lbls.extend(bbox_labels[:, 0])
         boxes.extend(bbox_labels[:, 1:5])
-        difficults.extend(bbox_labels[:, -1])
+        difficulties.extend(bbox_labels[:, -1])
 
     f1.write(np.array(object_nums).astype('uint64').tobytes())
     f1.write(np.array(lbls).astype('int64').tobytes())
     f1.write(np.array(boxes).astype('float32').tobytes())
-    f1.write(np.array(difficults).astype('int64').tobytes())
+    f1.write(np.array(difficulties).astype('int64').tobytes())
     f1.close()
 
     object_nums_sum = sum(object_nums)
@@ -168,7 +168,7 @@ def convert_pascalvoc_tar2bin(tar_path, data_out_path):
     gt_labels = {}
     boxes = []
     lbls = []
-    difficults = []
+    difficulties = []
     object_nums = []
 
     # map label to number (index)
@@ -254,7 +254,7 @@ def convert_pascalvoc_tar2bin(tar_path, data_out_path):
             continue
         lbls.extend(bbox_labels[:, 0])
         boxes.extend(bbox_labels[:, 1:5])
-        difficults.extend(bbox_labels[:, -1])
+        difficulties.extend(bbox_labels[:, -1])
 
         if line_idx % per_percentage:
             print_processbar(line_idx / per_percentage)
@@ -265,7 +265,7 @@ def convert_pascalvoc_tar2bin(tar_path, data_out_path):
     f1.write(np.array(object_nums).astype('uint64').tobytes())
     f1.write(np.array(lbls).astype('int64').tobytes())
     f1.write(np.array(boxes).astype('float32').tobytes())
-    f1.write(np.array(difficults).astype('int64').tobytes())
+    f1.write(np.array(difficulties).astype('int64').tobytes())
     f1.close()
     print_processbar(100)
     print("Conversion finished!\n")

test/cpp/pir/cinn/tile_config_performance_test.cc

@@ -631,13 +631,13 @@ void TestPerformanceForTileConfig(int spatial_left_bound,
                             best_tile_config_map,
                             iter_space_type);
     }  // end of r_dimension_lower loop
-  }    // end of s_dimention_lower loop
+  }    // end of s_dimension_lower loop
   if (test_single_large) {
     // (II) Test in the single large areas,
     // i.e., S:[4096-32768]*R:[2-1024], S:[2-1024]*R:[4096-32768]
     for (int s_dimension_lower = 2; s_dimension_lower < 1024;
          s_dimension_lower += spatial_tile_config) {
-      // adjust the tile size for the spatial dimension dymaically
+      // adjust the tile size for the spatial dimension dynamically
       spatial_tile_config =
           get_tile_size_config_in_large_area(s_dimension_lower);
       spatial_tile_width = (is_spatial_dynamic ? spatial_tile_config : 1);

test/ipu/test_eval_model_ipu.py

@@ -120,7 +120,7 @@ def _test_optimizer(self, run_ipu=True):
             return np.array(result)
 
     def test(self):
-        # cpu and ipu dimenstion mismatch, cpu:(100, 1, 1), ipu:(100, 1)
+        # cpu and ipu dimension mismatch, cpu:(100, 1, 1), ipu:(100, 1)
         ipu_loss = self._test_optimizer(True).flatten()
         cpu_loss = self._test_optimizer(False).flatten()
         self.assertTrue(ipu_loss[0] == ipu_loss[99])

test/ipu/test_lr_sheduler_ipu.py

@@ -81,7 +81,7 @@ def run_model(self, run_ipu=True):
     def test_training(self):
         data = np.random.rand(1, 3, 10, 10).astype(np.float32)
         self.feed = {'image': data}
-        # cpu and ipu dimenstion mismatch, cpu:(100, 1, 1), ipu:(100, 1)
+        # cpu and ipu dimension mismatch, cpu:(100, 1, 1), ipu:(100, 1)
         ipu_loss = self.run_model(True).flatten()
         cpu_loss = self.run_model(False).flatten()
 

test/ipu/test_weight_decay_ipu.py

@@ -120,7 +120,7 @@ def exclude_fn(param):
             return np.array(result)
 
     def test(self):
-        # cpu and ipu dimenstion mismatch, cpu:(100, 1, 1), ipu:(100, 1)
+        # cpu and ipu dimension mismatch, cpu:(100, 1, 1), ipu:(100, 1)
         ipu_loss = self._test_optimizer(True).flatten()
         cpu_loss = self._test_optimizer(False).flatten()
 

test/legacy_test/gradient_checker.py

@@ -541,7 +541,7 @@ def double_grad_check(
 
 
 # TODO(jiabin): We currently support only triple grad check here, extend this to support
-# higher order differenciation later.
+# higher order differentiation later.
 
 
 # check triple grad and two outputs of the triple Kernel

test/legacy_test/test_group_norm_op_v2.py

@@ -26,7 +26,7 @@ def group_norm_naive_for_general_dimension(
     x, scale, bias, epsilon, groups, channel_last=False
 ):
     # original version group norm only support 4-D tensor
-    # this function generalizes to support differnt dimensions tensor (>= 2-D)
+    # this function generalizes to support different dimensions tensor (>= 2-D)
     if channel_last:
         shape = list(range(x.ndim))
         shape.insert(1, shape.pop(-1))