[CodeStyle][Typos][O-[1-2],O-[4-5],O[10-14]] Fix typos (occured,Ocurred,occures,offets,offseted,oeprations,Opeartion,operaion,oppotunity,optimzed,optmize)

_typos.toml

@@ -65,19 +65,8 @@ insid = 'insid'
 insepection = 'insepection'
 intall = 'intall'
 instanciate = 'instanciate'
-occured = 'occured'
-Ocurred = 'Ocurred'
-occures = 'occures'
-offets = 'offets'
-offseted = 'offseted'
 Operants = 'Operants'
 operants = 'operants'
-oeprations = 'oeprations'
-Opeartion = 'Opeartion'
-operaion = 'operaion'
-oppotunity = 'oppotunity'
-optimzed = 'optimzed'
-optmize = 'optmize'
 optin = 'optin'
 Optin = 'Optin'
 rder = 'rder'

paddle/cinn/hlir/dialect/operator/ir/generate_shape_util.cc

@@ -587,7 +587,7 @@ std::vector<pir::Value> GetMinimalInputs(
     const std::vector<pir::Value>& input_tensors) {
   std::unordered_set<symbol::DimExpr> handled_dim_exprs;
   std::unordered_set<pir::Value> first_occurred_input_tensors;
-  auto TryCollectFirstOcurredInput_tensor =
+  auto TryCollectFirstOccurredInput_tensor =
       [&](pir::Value input_tensor,
           const std::vector<symbol::DimExpr>& dim_exprs) {
         for (const auto& dim_expr : dim_exprs) {
@@ -601,11 +601,11 @@ std::vector<pir::Value> GetMinimalInputs(
     const auto& shape_or_data_dim_exprs =
         ShapeOrDataDimExprs4Value(input_tensor);
     if (shape_or_data_dim_exprs.data().has_value()) {
-      TryCollectFirstOcurredInput_tensor(
+      TryCollectFirstOccurredInput_tensor(
           input_tensor, shape_or_data_dim_exprs.data().value());
     }
-    TryCollectFirstOcurredInput_tensor(input_tensor,
-                                       shape_or_data_dim_exprs.shape());
+    TryCollectFirstOccurredInput_tensor(input_tensor,
+                                        shape_or_data_dim_exprs.shape());
   }
   std::vector<pir::Value> ret{};
   ret.reserve(input_tensors.size());

paddle/cinn/ir/utils/ir_copy.cc

@@ -273,7 +273,7 @@ struct IRCopyVisitor : public ir::IRVisitorRequireReImpl<Expr> {
     auto domain = Visit(op->domain);
     auto buffer_expr = Expr(op->buffer);
     // TODO(Superjomn) copy the operation.
-    auto operaion = op->operation;
+    auto operation = op->operation;
     auto name = op->name;
     auto tensor = make_shared<_Tensor_>();
 
@@ -289,7 +289,7 @@ struct IRCopyVisitor : public ir::IRVisitorRequireReImpl<Expr> {
     tensor->domain = domain;
     tensor->shape = shape;
     tensor->reduce_axis = op->reduce_axis;
-    tensor->operation = operaion;
+    tensor->operation = operation;
     tensor->name = name;
     tensor->set_type(op->type());
     tensor->axis_ = op->axis_;

paddle/fluid/framework/fleet/heter_ps/heter_comm_inl.h

@@ -3141,7 +3141,7 @@ void HeterComm<KeyType, ValType, GradType, GPUAccessor>::
 
   // local use end scatter(len is node num), reote use before scatter(len is
   // pull size) recompute offsets and parts
-  VLOG(2) << "begin recalc local and remote size and offets";
+  VLOG(2) << "begin recalc local and remote size and offsets";
   for (int i = 0; i < node_size_; i++) {
     size_t local_size = 0;
     size_t remote_size = 0;

paddle/fluid/inference/tensorrt/convert/op_converter.h

@@ -818,7 +818,7 @@ class OpConverter {
       //     layer->getOutput(i)->getDimensions().nbDims,
       //     0,
       //     common::errors::InvalidArgument(
-      //         "Error occures in Paddle-TRT layer with output name: %s",
+      //         "Error occurred in Paddle-TRT layer with output name: %s",
       //         output_tensor_names[i].c_str()));
     }
     layer->setName((layer_name + ")").c_str());

paddle/fluid/pir/utils/general_functions.cc

@@ -139,7 +139,7 @@ pir::Type TranslateToIrDataType(phi::DataType dtype) {
   return data_type;
 }
 
-pir::Operation* CreateOpeartionByName(const std::string& op_name,
+pir::Operation* CreateOperationByName(const std::string& op_name,
                                       const std::vector<pir::Value>& inputs,
                                       const pir::AttributeMap& attrs,
                                       const pir::PatternRewriter& rewriter) {

paddle/fluid/pir/utils/general_functions.h

@@ -96,7 +96,7 @@ pir::Type TranslateToIrDataType(phi::DataType dtype);
  *
  * @return pir::Operation*
  */
-pir::Operation* CreateOpeartionByName(const std::string& op_name,
+pir::Operation* CreateOperationByName(const std::string& op_name,
                                       const std::vector<pir::Value>& inputs,
                                       const pir::AttributeMap& attrs,
                                       const pir::PatternRewriter& rewriter);

paddle/phi/infermeta/fusion.cc

@@ -4398,7 +4398,7 @@ void CrossAttentionXPUInferMeta(
       phi::make_ddim({input_q_dims[0], input_q_dims[1], head_num * head_dim}));
   qkv->set_dtype(out_dtype);
   qkv->set_layout(input_q.layout());
-  // TODO(Terry) optmize the max value num
+  // TODO(Terry) optimize the max value num
   // unable to pass few PR-CIs, so just use a constant value
   // int xpu2_max_value_num = phi::backends::xpu::get_xpu_max_ptr_size(-1);
   const int xpu2_max_value_num = 6;

paddle/phi/kernels/fusion/cutlass/cutlass_extensions/gemm/threadblock/dq_mma_base.h

@@ -114,7 +114,7 @@ class DqMmaBase {
                               Shape::kN / WarpGemm::kN,
                               Shape::kK / WarpGemm::kK>;
 
-  /// Number of warp-level GEMM oeprations
+  /// Number of warp-level GEMM operations
   static int const kWarpGemmIterations =
       (WarpGemm::kK / Operator::Policy::MmaShape::kK);
 

paddle/phi/kernels/fusion/cutlass/memory_efficient_attention/gemm/custom_mma_base.h

@@ -106,7 +106,7 @@ class CustomMmaBase {
                               Shape::kN / WarpGemm::kN,
                               Shape::kK / WarpGemm::kK>;
 
-  /// Number of warp-level GEMM oeprations
+  /// Number of warp-level GEMM operations
   static int const kWarpGemmIterations =
       (WarpGemm::kK / Operator::Policy::MmaShape::kK);
 

paddle/phi/kernels/fusion/cutlass/memory_efficient_attention/gemm/mma_from_smem.h

@@ -179,7 +179,7 @@ class MmaBaseFromSharedMemory {
                               Shape::kK / WarpGemm::kK>;
   using WarpCount1 = WarpCount;
 
-  /// Number of warp-level GEMM oeprations
+  /// Number of warp-level GEMM operations
   static int const kWarpGemmIterations =
       (WarpGemm::kK / Operator::Policy::MmaShape::kK);
   static int const kWarpGemmIterations1 = kWarpGemmIterations;

paddle/pir/src/core/op_operand_impl.cc

@@ -61,7 +61,7 @@ void OpOperandImpl::RemoveFromUdChain() {
   if (!source_) return;
   if (!prev_use_addr_) return;
   if (prev_use_addr_ == source_.impl()->first_use_addr()) {
-    /// NOTE: In ValueImpl, first_use_offseted_by_index_ use lower three bits
+    /// NOTE: In ValueImpl, first_use_offsetted_by_index_ use lower three bits
     /// storage index information, so need to be updated using the set_first_use
     /// method here.
     source_.impl()->set_first_use(next_use_);

paddle/pir/src/core/program.cc

@@ -47,7 +47,7 @@ int64_t GetUniqueRandomId() {
       return random_id;
     }
   }
-  LOG(FATAL) << "Fatal bug occured in GetUniqueRandomId().";
+  LOG(FATAL) << "Fatal bug occurred in GetUniqueRandomId().";
 }
 
 }  // namespace

paddle/pir/src/core/value_impl.cc

@@ -28,7 +28,7 @@ namespace pir::detail {
 void ValueImpl::set_first_use(OpOperandImpl *first_use) {
   uint32_t offset = kind();
   uintptr_t ptr = reinterpret_cast<uintptr_t>(first_use) + offset;
-  first_use_offseted_by_kind_ = reinterpret_cast<OpOperandImpl *>(ptr);
+  first_use_offsetted_by_kind_ = reinterpret_cast<OpOperandImpl *>(ptr);
   VLOG(10) << "The index of this value is: " << offset
            << ". The address of this value is: " << this
            << ". This value first use is: " << first_use << ".";
@@ -58,7 +58,7 @@ ValueImpl::ValueImpl(Type type, uint32_t kind) : id_(GenerateId()) {
           kind));
   type_ = type;
   uintptr_t ptr = reinterpret_cast<uintptr_t>(nullptr) + kind;
-  first_use_offseted_by_kind_ = reinterpret_cast<OpOperandImpl *>(ptr);
+  first_use_offsetted_by_kind_ = reinterpret_cast<OpOperandImpl *>(ptr);
   VLOG(10) << "Construct a ValueImpl whose's kind is " << kind
            << ". The value_impl address is: " << this;
 }

paddle/pir/src/core/value_impl.h

@@ -44,12 +44,12 @@ class alignas(8) ValueImpl {
 
   OpOperandImpl *first_use() const {
     return reinterpret_cast<OpOperandImpl *>(
-        reinterpret_cast<uintptr_t>(first_use_offseted_by_kind_) & (~0x07));
+        reinterpret_cast<uintptr_t>(first_use_offsetted_by_kind_) & (~0x07));
   }
 
   void set_first_use(OpOperandImpl *first_use);
 
-  OpOperandImpl **first_use_addr() { return &first_use_offseted_by_kind_; }
+  OpOperandImpl **first_use_addr() { return &first_use_offsetted_by_kind_; }
 
   bool use_empty() const { return first_use() == nullptr; }
 
@@ -60,10 +60,10 @@ class alignas(8) ValueImpl {
   std::string PrintUdChain();
 
   ///
-  /// \brief Interface functions of "first_use_offseted_by_kind_" attribute.
+  /// \brief Interface functions of "first_use_offsetted_by_kind_" attribute.
   ///
   uint32_t kind() const {
-    return reinterpret_cast<uintptr_t>(first_use_offseted_by_kind_) & 0x07;
+    return reinterpret_cast<uintptr_t>(first_use_offsetted_by_kind_) & 0x07;
   }
 
   template <typename T>
@@ -93,7 +93,7 @@ class alignas(8) ValueImpl {
   /// output(OpInlineResultImpl); (2) index = 6: represent the position >=6
   /// outline output(OpOutlineResultImpl); (3) index = 7 is reserved.
   ///
-  OpOperandImpl *first_use_offseted_by_kind_ = nullptr;
+  OpOperandImpl *first_use_offsetted_by_kind_ = nullptr;
 
   const uint64_t id_ = 0;
 };

python/paddle/distributed/auto_parallel/static/pir_pass.py

@@ -231,7 +231,7 @@ class ReshardPasses:
 
     @staticmethod
     def decompose_reshard_pass(dist_program):
-        # split composed reshard op into atomic reshard ops, which would increase the oppotunity of reshard Re-Use in following fold_reshard_pass.
+        # split composed reshard op into atomic reshard ops, which would increase the opportunity of reshard Re-Use in following fold_reshard_pass.
         del_ops = []
         for op in dist_program.global_block().ops:
             if op.name() != 'dist_op.reshard':

python/paddle/optimizer/optimizer.py

@@ -2008,7 +2008,7 @@ def _add_param_group(self, param_group):
         Add a param group to parameter_list.
 
         Args:
-            param_group (dict): The group of Tensors to be optimzed with
+            param_group (dict): The group of Tensors to be optimized with
             different optimization options.
         """
         params = param_group['params']
@@ -2052,7 +2052,7 @@ def _update_param_group(self, parameters):
         """
         Update the param group with new entry
         Args:
-            parameters (dict): The extra group of Tensors to be optimzed with
+            parameters (dict): The extra group of Tensors to be optimized with
             different optimization options. Only used in child class.
         """
         pass

test/cpp/pir/core/paddle_fatal_test.cc

@@ -20,7 +20,7 @@
 class FatalClass {
  public:
   FatalClass() {}
-  ~FatalClass() { PADDLE_FATAL("fatal occured in deconstructor!"); }
+  ~FatalClass() { PADDLE_FATAL("fatal occurred in deconstructor!"); }
 };
 
 void throw_exception_in_func() {
@@ -32,7 +32,7 @@ void terminate_in_func() { FatalClass test_case; }
 
 TEST(paddle_fatal_test, base) {
   EXPECT_FALSE(::common::enforce::IsPaddleFatalSkip());
-  EXPECT_DEATH(terminate_in_func(), "fatal occured in deconstructor!.*");
+  EXPECT_DEATH(terminate_in_func(), "fatal occurred in deconstructor!.*");
   EXPECT_THROW(throw_exception_in_func(), common::enforce::EnforceNotMet);
   EXPECT_FALSE(::common::enforce::IsPaddleFatalSkip());
   ::common::enforce::SkipPaddleFatal(true);