[CodeStyle][Typos][A-[11-19]] Fix typo(addtional、addess、adjancent、admissable、afer、Ags、aggragate、alis、algorithem、alogrithm)

_typos.toml

@@ -25,7 +25,6 @@ expection = 'expection'
 pacakage = 'pacakage'
 normlized = 'normlized'
 Runing = 'Runing'
-Ags = 'Ags'
 Becasue = 'Becasue'
 craeted = 'craeted'
 oreder = 'oreder'
@@ -154,7 +153,6 @@ CACH = 'CACH'
 endianess = 'endianess'
 VAILD = 'VAILD'
 ues = 'ues'
-algorithem = 'algorithem'
 aer = 'aer'
 elemenents = 'elemenents'
 CANN = 'CANN'
@@ -258,7 +256,6 @@ dateset = 'dateset'
 arised = 'arised'
 optimzed = 'optimzed'
 encouter = 'encouter'
-alis = 'alis'
 feeded = 'feeded'
 poped = 'poped'
 parmeter = 'parmeter'
@@ -358,7 +355,6 @@ accessable = 'accessable'
 Wheter = 'Wheter'
 processer = 'processer'
 recored = 'recored'
-afer = 'afer'
 wraper = 'wraper'
 Partitial = 'Partitial'
 leafs = 'leafs'
@@ -478,7 +474,6 @@ subsituted = 'subsituted'
 automaticly = 'automaticly'
 Minium = 'Minium'
 sequnece = 'sequnece'
-adjancent = 'adjancent'
 payed = 'payed'
 linke = 'linke'
 nagative = 'nagative'
@@ -556,7 +551,6 @@ splited = 'splited'
 instrinsics = 'instrinsics'
 outputing = 'outputing'
 hadler = 'hadler'
-aggragate = 'aggragate'
 qucik = 'qucik'
 alog = 'alog'
 exsit = 'exsit'
@@ -664,7 +658,6 @@ usefull = 'usefull'
 sqaure = 'sqaure'
 adn = 'adn'
 intialize = 'intialize'
-addtional = 'addtional'
 Taget = 'Taget'
 parm = 'parm'
 thrads = 'thrads'
@@ -686,7 +679,6 @@ upsupported = 'upsupported'
 settting = 'settting'
 templat = 'templat'
 priorites = 'priorites'
-admissable = 'admissable'
 optmize = 'optmize'
 bondary = 'bondary'
 Traning = 'Traning'
@@ -732,7 +724,6 @@ mannualy = 'mannualy'
 learing = 'learing'
 noraml = 'noraml'
 padd = 'padd'
-alogrithm = 'alogrithm'
 Requred = 'Requred'
 astroid = 'astroid'
 Fidn = 'Fidn'
@@ -785,7 +776,6 @@ distrubuted = 'distrubuted'
 Localy = 'Localy'
 PARM = 'PARM'
 thi = 'thi'
-addess = 'addess'
 Oll = 'Oll'
 Auxillary = 'Auxillary'
 Infor = 'Infor'

paddle/cinn/backends/codegen_gpu_dev.cc

@@ -150,15 +150,15 @@ void CodeGenGpuDev::Visit(const ir::_LoweredFunc_ *op) {
 
   auto alloca_temp_buffers = op->PrepareAllocTempBufferExprs();
   auto temp_buffer_alias = GenerateBufferAliasExprs(op, op->temp_bufs);
-  auto alis_var_exprs = op->CudaAliasVarExprs();
+  auto alias_var_exprs = op->CudaAliasVarExprs();
   auto dealloc_temp_buffers =
       FilterDeallocTempBuffers(op->PrepareDeallocTempBufferExprs());
 
 #define APPEND_TO_NEW_BODY(field__) \
   new_body.insert(std::end(new_body), std::begin(field__), std::end(field__));
   APPEND_TO_NEW_BODY(alloca_temp_buffers)
   APPEND_TO_NEW_BODY(temp_buffer_alias)
-  APPEND_TO_NEW_BODY(alis_var_exprs)
+  APPEND_TO_NEW_BODY(alias_var_exprs)
 
   new_body.push_back(op->body);
   APPEND_TO_NEW_BODY(dealloc_temp_buffers);

paddle/cinn/poly/stage.cc

@@ -956,7 +956,7 @@ Iterator Stage::Fuse(const std::vector<Iterator> &levels) {
           offsets.back() + 1,
           offset,
           ::common::errors::InvalidArgument(
-              "level offsets.back and level offset should be adjancent"));
+              "level offsets.back and level offset should be adjacent"));
     AssertAxisIsNotLocked(offset);
     offsets.push_back(offset);
     new_iter_name += utils::StringFormat("%s_", level.id.c_str());

paddle/fluid/framework/fleet/heter_ps/graph_gpu_ps_table_inl.cu

@@ -987,7 +987,7 @@ int GpuPsGraphTable::init_cpu_table(
  gpu i triggers a neighbor_sample task,
  when this task is done,
  this function is called to move the sample result on other gpu back
- to gup i and aggragate the result.
+ to gup i and aggregate the result.
  the sample_result is saved on src_sample_res and the actual sample size for
  each node is saved on actual_sample_size.
  the number of actual sample_result for

paddle/fluid/framework/ir/graph_helper.cc

@@ -557,7 +557,7 @@ void ReplaceAllReduceOp(const Node &node,
   // ### v0 = op0(grad0)
   // ### v1 = op1(grad1)
   // We should add the following dependency to ensure that op0 and op1 both run
-  // afer c_sum_allreduce:
+  // after c_sum_allreduce:
   // ### grad0 =  depend(grad0, fused_grad)
   // ### grad1 = depend(grad1, fused_grad)
   if (is_fused) {

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

@@ -3256,8 +3256,8 @@ bool SlogdetOpInferSymbolicShape(
   infer_context->AddEqualCstr(x_shape[x_shape_size - 1],
                               x_shape[x_shape_size - 2]);
   std::vector<symbol::DimExpr> out_shape = {2};
-  size_t addtional_dims = x_shape.size() - 2;
-  for (size_t i = 0; i < addtional_dims; i++) {
+  size_t additional_dims = x_shape.size() - 2;
+  for (size_t i = 0; i < additional_dims; i++) {
     out_shape.push_back(x_shape[i]);
   }
   infer_context->SetShapeOrDataForValue(

paddle/fluid/pir/transforms/general/transfer_layout_pass.cc

@@ -492,7 +492,7 @@ struct FlowGraph {
     return (heights[dst_node()] > 0);
   }
 
-  // cf is the admissable flow in current path
+  // cf is the admissible flow in current path
   float FindBlockingFlow(Node src, float cf) {
     if (src == dst_node() || abs(cf) < 1e-9) {
       return cf;

paddle/phi/backends/onednn/onednn_context.cc

@@ -139,7 +139,7 @@ struct OneDNNContext::Impl {
   void LinkEntryWithExecutor(BlobPtr_t<KeyBlob> pblob,
                              KeyBlob::iterator it) const {
     // Take current input shape from TLS
-    // Take current executor addess from TLS
+    // Take current executor address from TLS
     // and for this executor's items add the one defined with arguments
     auto key_it =
         p_exec_items_

paddle/phi/kernels/gpu/rms_norm_kernel.cu

@@ -286,7 +286,7 @@ struct DirectStore {
 
 template <typename T>
 inline __device__ void WelfordCombine(T val, T* mean, T* m2, T* count) {
-  // Use Welford Online algorithem to compute mean and variance
+  // Use Welford Online algorithm to compute mean and variance
   // For more details you can refer to:
   // https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford's_online_algorithm
   *count += 1;

paddle/phi/kernels/gpudnn/conv_cudnn_v7.h

@@ -97,7 +97,7 @@ struct SearchAlgorithmBase {};
 // cuDNN convolution forward algorithm searcher, consisted of three searching
 // modes, namely: deterministic, heuristic and exhaustive_search mode.
 // As well as one workspace size acquirsition function with respect to
-// the chosen alogrithm.
+// the chosen algorithm.
 template <>
 struct SearchAlgorithmBase<ConvKind::kForward> {
   using PerfT = cudnnConvolutionFwdAlgoPerf_t;
@@ -271,7 +271,7 @@ struct SearchAlgorithmBase<ConvKind::kForward> {
 // mode. Specially, there are 2 pattens of exhaustive search mode, one for
 // HALF precision only, one for the rest.
 // As well as one workspace size acquirsition function with
-// respect to the chosen alogrithm.
+// respect to the chosen algorithm.
 template <>
 struct SearchAlgorithmBase<ConvKind::kBackwardData> {
   using PerfT = cudnnConvolutionBwdDataAlgoPerf_t;
@@ -454,7 +454,7 @@ struct SearchAlgorithmBase<ConvKind::kBackwardData> {
 // cuDNN convution backward filter-algorithm searcher, consisted of three
 // algorithm searching modes, namely: deterministic, heuristic, and
 // exhaustive_search mode. As well as one workspace size acquirsition function
-// with respect to the chosen alogrithm.
+// with respect to the chosen algorithm.
 template <>
 struct SearchAlgorithmBase<ConvKind::kBackwardFilter> {
   using PerfT = cudnnConvolutionBwdFilterAlgoPerf_t;

python/paddle/audio/datasets/dataset.py

@@ -40,7 +40,7 @@ def __init__(
         **kwargs,
     ):
         """
-        Ags:
+        Args:
             files (:obj:`List[str]`): A list of absolute path of audio files.
             labels (:obj:`List[int]`): Labels of audio files.
             feat_type (:obj:`str`, `optional`, defaults to `raw`):