[CodeStyle][Typos][F-[12-17],F-[19-24],F-[26-28]] Fix typo(Flattend,flattend,flattern,Flattern,filpped,flaot,follwed,folowing,formater,formating,foramt,formt,formate,forwad,forwrad,forword,founf,framwork,frequence,fron,fullfill) (#70646)

---------

Co-authored-by: Nyakku Shigure <[email protected]>

Author: yinfan98

CONTRIBUTING.md

@@ -39,7 +39,7 @@ PaddlePaddle uses this [Git branching model](http://nvie.com/posts/a-successful-
    pre-commit install
    ```
 
-   Our pre-commit configuration requires clang-format 3.8 for auto-formating C/C++ code and yapf for Python.
+   Our pre-commit configuration requires clang-format 3.8 for auto-formatting C/C++ code and yapf for Python.
 
    Once installed, `pre-commit` checks the style of code and documentation in every commit.  We will see something like the following when you run `git commit`:
 
@@ -52,7 +52,7 @@ PaddlePaddle uses this [Git branching model](http://nvie.com/posts/a-successful-
    Check for broken symlinks................................................Passed
    Detect Private Key...................................(no files to check)Skipped
    Fix End of Files.....................................(no files to check)Skipped
-   clang-formater.......................................(no files to check)Skipped
+   clang-format.........................................(no files to check)Skipped
    [my-cool-stuff c703c041] add test file
     1 file changed, 0 insertions(+), 0 deletions(-)
     create mode 100644 233

_typos.toml

@@ -46,27 +46,6 @@ dobule = 'dobule'
 Dowloading = 'Dowloading'
 downsteram = 'downsteram'
 fetchs = 'fetchs'
-Flattend = 'Flattend'
-flattend = 'flattend'
-flattern = 'flattern'
-Flattern = 'Flattern'
-filpped = 'filpped'
-flaot = 'flaot'
-follwed = 'follwed'
-folowing = 'folowing'
-formater = 'formater'
-formating = 'formating'
-foramt = 'foramt'
-formate = 'formate'
-formt = 'formt'
-forwrad = 'forwrad'
-forwad = 'forwad'
-forword = 'forword'
-founf = 'founf'
-framwork = 'framwork'
-frequence = 'frequence'
-fron = 'fron'
-fullfill = 'fullfill'
 Indexs = 'Indexs'
 indexs = 'indexs'
 indiates = 'indiates'

paddle/cinn/common/ir_util.h

@@ -191,7 +191,7 @@ inline void UnpackReduction(const ir::IndexExpr &expr, FLeaf fleaf) {
 }
 
 /*!
- * \brief Flattern the expression into a vector of expressions splited by `Add`
+ * \brief Flatten the expression into a vector of expressions splited by `Add`
  * or `Mul`.
  *
  * For example (Add):

paddle/cinn/runtime/cuda/cuda_util.cc

@@ -1742,7 +1742,7 @@ void cinn_call_cholesky_nvgpu(void *v_args,
   cinn_buffer_t *x = args[0].operator cinn_buffer_t *();
   cinn_buffer_t *out = args[1].operator cinn_buffer_t *();
   // In cuSOLVER, dense matrix stores in COL_MAJOR, thus FILL_MODE needs to be
-  // filpped. See also:
+  // flipped. See also:
   // https://docs.nvidia.com/cuda/cusolver/index.html#matrix-dense-format
   cublasFillMode_t uplo =
       upper ? CUBLAS_FILL_MODE_LOWER : CUBLAS_FILL_MODE_UPPER;

paddle/fluid/framework/data_transform.cc

@@ -173,7 +173,7 @@ phi::GetKernelTypeForVarContext BuildGetKernelTypeForVarContext(
   if (has_infer_varkernel_fn) {
     for (auto &attr : fluid_attrs) {
       switch (attr.second.index()) {
-        case 3:  // string type in framwork::Attribute
+        case 3:  // string type in framework::Attribute
           (*phi_attrs)[attr.first] = PADDLE_GET_CONST(std::string, attr.second);
           break;
         default:

paddle/fluid/framework/new_executor/pir_interpreter.cc

@@ -979,7 +979,7 @@ void PirInterpreter::BuildInstruction() {
 }
 
 std::string PirInterpreter::DebugInstructions() {
-  // log formate: var[101] = pd_op.relu(var[100]) or for inplace op var[100] =
+  // log format: var[101] = pd_op.relu(var[100]) or for inplace op var[100] =
   // pd_op.relu_(var[100])
   std::stringstream ss;
   ss << "{outputs}"

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -3467,9 +3467,9 @@ struct CustomGenericPluginTeller : public Teller {
                    "SetTrtInferShapeFn.";
         return false;
       }
-      auto& trt_supports_formate_config =
+      auto& trt_supports_format_config =
           OpMetaInfoHelper::GetTrtSupportsFormatConfig(op_info);
-      if (trt_supports_formate_config.empty()) {
+      if (trt_supports_format_config.empty()) {
         VLOG(3)
             << op_type
             << " has no trt supportsFormatCombination config. Please set by "

paddle/fluid/inference/tensorrt/plugin/custom_generic_plugin.cu

@@ -311,9 +311,9 @@ bool CustomGenericPlugin::supportsFormatCombination(
   auto& op_meta_info_map = OpMetaInfoMap::Instance();
   const auto& meta_info_map = op_meta_info_map.GetMap();
   auto& op_info = meta_info_map.at(op_desc_.Type()).front();
-  auto& supports_formate_config =
+  auto& supports_format_config =
       OpMetaInfoHelper::GetTrtSupportsFormatConfig(op_info);
-  PADDLE_ENFORCE_NE(supports_formate_config.empty(),
+  PADDLE_ENFORCE_NE(supports_format_config.empty(),
                     true,
                     common::errors::InvalidArgument(
                         "The %s op has no tensorrt plugin "
@@ -325,7 +325,7 @@ bool CustomGenericPlugin::supportsFormatCombination(
   size_t output_num = OpMetaInfoHelper::GetOutputs(op_info).size();
   std::vector<std::vector<std::pair<std::string, std::string>>>
       format_combinations;
-  for (auto& config : supports_formate_config) {
+  for (auto& config : supports_format_config) {
     auto format_combination = parseConfig(op_desc_.Type(), config);
     PADDLE_ENFORCE_EQ(input_num + output_num,
                       format_combination.size(),

paddle/fluid/inference/tensorrt/plugin/gelu_op_plugin.cu

@@ -77,7 +77,7 @@ __device__ half do_tanh<half>(half a) {
   return __float2half(tmp);
 }
 
-// the kernel below is not aligned with fluid fp32 forwrad ones, use it for
+// the kernel below is not aligned with fluid fp32 forward ones, use it for
 // fp16.
 template <typename T, unsigned TPB>
 __global__ void no_exact_gelu_kernel(

paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/multiary_infer_sym.cc

@@ -2631,7 +2631,7 @@ bool GroupNormOpInferSymbolicShape(
     channel_idx = 1;
   } else {
     PADDLE_THROW(common::errors::Unimplemented(
-        "GroupNorm only suport NHWC and NCHW data formt"));
+        "GroupNorm only suport NHWC and NCHW data format"));
   }
 
   symbol::DimExpr channel_dim = x_shape.shape()[channel_idx];

paddle/phi/kernels/cpu/unique_kernel.cc

@@ -83,15 +83,15 @@ void UniqueRawKernel(const Context& context,
   if (axis.empty()) {
     phi::VisitDataTypeTiny(
         dtype,
-        phi::funcs::UniqueFlattendTensorFunctor<Context, T>(context,
-                                                            x,
-                                                            out,
-                                                            indices,
-                                                            index,
-                                                            counts,
-                                                            return_index,
-                                                            return_inverse,
-                                                            return_counts));
+        phi::funcs::UniqueFlattenedTensorFunctor<Context, T>(context,
+                                                             x,
+                                                             out,
+                                                             indices,
+                                                             index,
+                                                             counts,
+                                                             return_index,
+                                                             return_inverse,
+                                                             return_counts));
   } else {
     int axis_value = axis[0];
     axis_value = (axis_value == -1) ? (x.dims().size() - 1) : axis_value;

paddle/phi/kernels/funcs/math_cuda_utils.h

@@ -298,7 +298,7 @@ __inline__ __device__ T PartialWarpReduceMin(T val, warp_mask_t lane_mask) {
   T warp_val = __shfl_sync(lane_mask, val, 0, warpSize);
 #else
   T warp_val = __shfl(
-      val, 0, warpSize);  // To fullfill the data in each thread of this warp.
+      val, 0, warpSize);  // To fulfill the data in each thread of this warp.
 #endif
   warp_val = val;
 

paddle/phi/kernels/funcs/unique_functor.h

@@ -130,15 +130,15 @@ static bool Equal(const DenseTensor& a, const DenseTensor& b) {
 }
 
 template <typename Context, typename InT, typename IndexT>
-static void UniqueFlattendTensor(const Context& context,
-                                 const DenseTensor& in,
-                                 DenseTensor* out,
-                                 DenseTensor* indices,
-                                 DenseTensor* index,
-                                 DenseTensor* count,
-                                 bool return_index,
-                                 bool return_inverse,
-                                 bool return_counts) {
+static void UniqueFlattenedTensor(const Context& context,
+                                  const DenseTensor& in,
+                                  DenseTensor* out,
+                                  DenseTensor* indices,
+                                  DenseTensor* index,
+                                  DenseTensor* count,
+                                  bool return_index,
+                                  bool return_inverse,
+                                  bool return_counts) {
   const InT* in_data = in.data<InT>();
   std::set<InT> unique(in_data, in_data + in.numel());
   out->Resize(common::make_ddim({static_cast<int64_t>(unique.size())}));
@@ -327,7 +327,7 @@ static void UniqueDim(const Context& context,
 }
 
 template <typename Context, typename InT>
-struct UniqueFlattendTensorFunctor {
+struct UniqueFlattenedTensorFunctor {
   const Context& ctx_; /*  */
   const DenseTensor& in_;
   DenseTensor* out_;
@@ -338,15 +338,15 @@ struct UniqueFlattendTensorFunctor {
   const bool return_inverse_;
   const bool return_counts_;
 
-  UniqueFlattendTensorFunctor(const Context& context,
-                              const DenseTensor& in,
-                              DenseTensor* out,
-                              DenseTensor* indices,
-                              DenseTensor* index,
-                              DenseTensor* count,
-                              bool return_index,
-                              bool return_inverse,
-                              bool return_counts)
+  UniqueFlattenedTensorFunctor(const Context& context,
+                               const DenseTensor& in,
+                               DenseTensor* out,
+                               DenseTensor* indices,
+                               DenseTensor* index,
+                               DenseTensor* count,
+                               bool return_index,
+                               bool return_inverse,
+                               bool return_counts)
       : ctx_(context),
         in_(in),
         out_(out),
@@ -359,15 +359,15 @@ struct UniqueFlattendTensorFunctor {
 
   template <typename IndexT>
   void apply() const {
-    UniqueFlattendTensor<Context, InT, IndexT>(ctx_,
-                                               in_,
-                                               out_,
-                                               indices_,
-                                               index_,
-                                               count_,
-                                               return_index_,
-                                               return_inverse_,
-                                               return_counts_);
+    UniqueFlattenedTensor<Context, InT, IndexT>(ctx_,
+                                                in_,
+                                                out_,
+                                                indices_,
+                                                index_,
+                                                count_,
+                                                return_index_,
+                                                return_inverse_,
+                                                return_counts_);
   }
 };
 

paddle/phi/kernels/gpu/rms_norm_funcs.h

@@ -14,7 +14,7 @@ limitations under the License. */
 
 /* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. */
 
-/*This code is copied fron NVIDIA apex:
+/*This code is copied from NVIDIA apex:
  *     https://github.com/NVIDIA/apex
  *     with minor changes. */
 

paddle/phi/kernels/gpu/rms_norm_grad_kernel.cu

@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 /* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. */
-/*This code is copied fron NVIDIA apex:
+/*This code is copied from NVIDIA apex:
  *     https://github.com/NVIDIA/apex
  *     with minor changes. */
 

paddle/phi/kernels/gpu/unique_consecutive_functor.h

@@ -32,7 +32,7 @@
 
 namespace phi {
 
-// The core logic of computing Unique Consecutive for a flattend Tensor
+// The core logic of computing Unique Consecutive for a flattened Tensor
 template <typename Context,
           typename InT,
           typename IndexT,
@@ -113,7 +113,7 @@ static void UniqueConsecutiveFlattenedCUDATensor(const Context& context,
   }
 }
 
-// functor for processing a flattend Tensor
+// functor for processing a flattened Tensor
 template <typename Context, typename InT>
 struct UniqueConsecutiveFlattenedCUDAFunctor {
   const Context& ctx_;