Fix typos (#53960)

paddle/fluid/framework/async_executor.cc

@@ -113,14 +113,14 @@ void AsyncExecutor::RunFromFile(const ProgramDesc& main_program,
   }
 
   /*
-    readerDesc: protobuf description for reader initlization
+    readerDesc: protobuf description for reader initialization
     argument: class_name, batch_size, use_slot, queue_size, buffer_size,
     padding_index
 
     reader:
     1) each thread has a reader, reader will read input data and
     put it into input queue
-    2) each reader has a Next() iterface, that can fetch an instance
+    2) each reader has a Next() interface, that can fetch an instance
     from the input queue
    */
   // todo: should be factory method for creating datafeed

paddle/fluid/framework/attribute_checker.h

@@ -44,7 +44,7 @@ class EqualGreaterThanChecker {
     PADDLE_ENFORCE_GE(
         value,
         lower_bound_,
-        platform::errors::OutOfRange("Check for attribute valur equal or "
+        platform::errors::OutOfRange("Check for attribute value equal or "
                                      "greater than a certain value failed."));
   }
 
@@ -92,7 +92,7 @@ class TypedAttrVarInfoChecker {
                         true,
                         platform::errors::InvalidArgument(
                             "Required dtype of Attribute(%s) shall be "
-                            "int32|int64, but recevied %s.",
+                            "int32|int64, but received %s.",
                             var_desc->Name(),
                             dtype));
     }

paddle/fluid/framework/block_desc.cc

@@ -316,7 +316,7 @@ void BlockDesc::MoveFrom(BlockDesc *block) {
         // NOTE(GhostScreaming): don't use program->proto()->blocks_size(),
         // previous assignment of new Variable in vars_ use std::move,
         // which makes 'var_ptr' which holded by 'block' a nullptr.
-        // block->Program()->proto() will calls Flush() at firtst,
+        // block->Program()->proto() will calls Flush() at first,
         // a null var_ptr will cause segmentation fault.
         int block_size = static_cast<int>(program->Size());
         for (int i = 0; i < block_size; ++i) {

paddle/fluid/framework/ir/coalesce_grad_tensor_pass.cc

@@ -53,7 +53,7 @@ namespace ir {
 static constexpr double kMB = 1048576.0;
 
 // SetFuseParameterGroupsSize and SetFuseParameterMemorySize are used in unit
-// test, because it is invalid that seting 'FLAGS_fuse_parameter_memory_size'
+// test, because it is invalid that setting 'FLAGS_fuse_parameter_memory_size'
 // and 'FLAGS_fuse_parameter_groups_size' in unit test.
 void SetFuseParameterGroupsSize(int group_size) {
   FLAGS_fuse_parameter_groups_size = group_size;
@@ -567,7 +567,7 @@ class CoalesceGradTensorPass : public ir::Pass {
     // coalesce_tensor op needs to be executed again after the execution
     // of DropScope().
 
-    // we can make fused_output persistable, so the memeory is not cleared
+    // we can make fused_output persistable, so the memory is not cleared
     // and coalesce_tensor op do nothing if the inputs are already continue.
 
     result->Get<details::ProgramDescs>(details::kProgramDescs).emplace_back();

paddle/fluid/framework/ir/constant_folding_pass.cc

@@ -62,7 +62,7 @@ void ConstantFoldingPass::ApplyImpl(ir::Graph *graph) const {
   PADDLE_ENFORCE_NOT_NULL(
       scope,
       platform::errors::Fatal(
-          "scope must not be null when applying constant floding."));
+          "scope must not be null when applying constant folding."));
 
   std::vector<std::string> blacklist{"feed", "matrix_multiply", "save"};
 
@@ -75,7 +75,7 @@ void ConstantFoldingPass::ApplyImpl(ir::Graph *graph) const {
       continue;
 
     bool input_persis = true;
-    // map is used to record how many time a name string occures in the whole
+    // map is used to record how many time a name string occurs in the whole
     // graph's nodes
     std::unordered_map<std::string, int> map;
     for (auto in_node : op_node->inputs) {

paddle/fluid/framework/ir/conv_bn_fuse_pass_tester.cc

@@ -54,7 +54,7 @@ void TestMain(const std::string& conv_type) {
   // ------------------------------------------------------------------
   // (in, filters, bias_0)            conv           ->   conv_out
   // (conv_out, scale,
-  //  bias_1, mean, varaince)         batch_norm     ->   (...)
+  //  bias_1, mean, variance)         batch_norm     ->   (...)
   Layers layers;
   auto* in = layers.data("in", {1, 3, 20, 20});
   auto* filters = layers.data("filters", {3, 3, 2, 2}, true);

paddle/fluid/framework/ir/fuse_optimizer_ops_pass/fuse_momentum_op_pass.cc

@@ -44,7 +44,7 @@ class FuseMomentumOpPass : public FuseOptimizerOpPass {
     PADDLE_ENFORCE_GT(
         momentum_ops.size(),
         static_cast<size_t>(0),
-        platform::errors::InvalidArgument("Momentum ops must not be empyt."));
+        platform::errors::InvalidArgument("Momentum ops must not be empty."));
 
     // Check attributions
     // NOTE: If new attribution is added, the following code maybe need change.

paddle/fluid/framework/ir/fuse_optimizer_ops_pass/fuse_sgd_op_pass.cc

@@ -43,7 +43,7 @@ class FuseSgdOpPass : public FuseOptimizerOpPass {
     PADDLE_ENFORCE_GT(
         sgd_ops.size(),
         static_cast<size_t>(0),
-        platform::errors::InvalidArgument("SGD ops must not be empyt."));
+        platform::errors::InvalidArgument("SGD ops must not be empty."));
 
     // NOTE: fused_var is only exist in scope, so the graph doesn't have
     // fused_var node.

paddle/fluid/framework/ir/fusion_group/cuda_resources.h

@@ -35,7 +35,7 @@ __device__ inline double Sqrt(double x) { return sqrt(x); }
 
 )";
 
-// List some bulit-in functions of __half implemented in cuda_fp16.hpp
+// List some built-in functions of __half implemented in cuda_fp16.hpp
 static constexpr char predefined_cuda_functions_fp16[] = R"(
 #define __HALF_TO_US(var) *(reinterpret_cast<unsigned short *>(&(var)))
 #define __HALF_TO_CUS(var) *(reinterpret_cast<const unsigned short *>(&(var)))

paddle/fluid/framework/ir/fusion_group/operation.cc

@@ -134,7 +134,7 @@ void OperationMap::InsertUnaryElementwiseOperations() {
 
   // cast:
   //  out = static_cast<T>(x)
-  // TODO(wangchaochaohu): This is not the compelete definition of
+  // TODO(wangchaochaohu): This is not the complete definition of
   //  cast Op, We need refine it later.
   insert_handler("cast", "${0}", {});
 

paddle/fluid/operators/collective/allreduce_op.cc

@@ -39,7 +39,7 @@ class AllReduceDelOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() {
     AddInput("X", "(Tensor), tensor to be allreduced.");
     AddOutput("Out", "(Tensor) the result of allreduced.");
-    AddAttr<int>("reduce_type", "(int) determin the reduce type.")
+    AddAttr<int>("reduce_type", "(int) determine the reduce type.")
         .SetDefault(0);
     AddAttr<bool>(
         "sync_mode",

paddle/fluid/operators/collective/barrier_op.cc

@@ -33,7 +33,7 @@ class BarrierOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<int>("ring_id", "(int default 0) communication ring id.")
         .SetDefault(0);
     AddComment(R"DOC(
-Barrier Operator - Barrier among all pariticapitors.)DOC");
+Barrier Operator - Barrier among all participators.)DOC");
   }
 };
 

paddle/fluid/operators/collective/c_comm_init_all_op.cc

@@ -124,7 +124,7 @@ class CCommInitAllOpMaker : public framework::OpProtoAndCheckerMaker {
     AddComment(R"DOC(
 CCommInitAll operator
 
-Initialize all collective communicatoin context
+Initialize all collective communication context
 )DOC");
     AddAttr<std::vector<int>>(
         "devices",

paddle/fluid/operators/collective/c_comm_init_multitrainer_op.cc

@@ -79,7 +79,7 @@ class CCommInitMultiTrainerOp : public framework::OperatorBase {
 class CCommInitMultiTrainerOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
-    AddInput("X", "Raw variable contains a NCCL UniqueId instaces.");
+    AddInput("X", "Raw variable contains a NCCL UniqueId instances.");
     AddComment(R"DOC(
 CCommInitMultiTrainer operator
 

paddle/fluid/operators/collective/c_comm_init_op.cc

@@ -115,17 +115,17 @@ class CCommInitOp : public framework::OperatorBase {
 class CCommInitOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
-    AddInput("X", "Raw variable contains a NCCL UniqueId instaces.");
+    AddInput("X", "Raw variable contains a NCCL UniqueId instances.");
     AddComment(R"DOC(
 CCommInit operator
 
-Initialize collective communicatoin context within this trainer
+Initialize collective communication context within this trainer
 )DOC");
     AddAttr<int>("nranks", "(int) The number of ranks of distributed trainers");
     AddAttr<int>("rank",
                  "(int) The rank of the trainer in distributed training.");
     AddAttr<int>("device_id",
-                 "(int) The deivce_id on which to initialize the communicator."
+                 "(int) The device_id on which to initialize the communicator."
                  "Now, you only have to set this attr manually for pipeline "
                  "training. Otherwise, make it as default.")
         .SetDefault(-1);

paddle/fluid/operators/collective/c_embedding_op.cc

@@ -58,7 +58,7 @@ class CEmbeddingOp : public framework::OperatorWithKernel {
         (height > 0 && width > 0 && start_idx >= 0),
         true,
         platform::errors::InvalidArgument(
-            "height:%ld width:%ld start_idx:%ld must not have negtive values",
+            "height:%ld width:%ld start_idx:%ld must not have negative values",
             height,
             width,
             start_idx));
@@ -142,7 +142,7 @@ class CEmbeddingOpGrad : public framework::OperatorWithKernel {
         (height > 0 && width > 0 && start_idx >= 0),
         true,
         platform::errors::InvalidArgument(
-            "height:%ld width:%ld start_idx:%ld must not have negtive values",
+            "height:%ld width:%ld start_idx:%ld must not have negative values",
             height,
             width,
             start_idx));

paddle/fluid/operators/collective/c_gen_bkcl_id_op.cc

@@ -89,7 +89,7 @@ class CGenBKCLIdOp : public framework::OperatorBase {
 class CGenBKCLIdOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
-    AddOutput("Out", "Raw variable contains a BKCL UniqueId instaces.");
+    AddOutput("Out", "Raw variable contains a BKCL UniqueId instances.");
     AddComment(R"DOC(
 CGenBKCLId operator
 

paddle/fluid/operators/collective/c_gen_nccl_id_op.cc

@@ -103,7 +103,7 @@ class CGenNCCLIdOp : public framework::OperatorBase {
 class CGenNCCLIdOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
-    AddOutput("Out", "Raw variable contains a NCCL UniqueId instaces.");
+    AddOutput("Out", "Raw variable contains a NCCL UniqueId instances.");
     AddComment(R"DOC(
 CGenNCCLId operator
 

paddle/fluid/operators/collective/gen_bkcl_id_op.cc

@@ -165,7 +165,7 @@ class GenBKCLIdOp : public framework::OperatorBase {
 class GenBKCLIdOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
-    AddOutput("BKCLID", "Raw variable contains a BKCL UniqueId instaces.");
+    AddOutput("BKCLID", "Raw variable contains a BKCL UniqueId instances.");
     AddComment(R"DOC(
 GenBKCLId operator
 
@@ -186,11 +186,11 @@ For trainer 1~n: start a gRPC server to get the UniqueId, once got, stop the ser
         .SetDefault(1);
     AddAttr<bool>("use_hierarchical_allreduce",
                   "(bool default false) "
-                  "Wheter to use hierarchical allreduce.")
+                  "Whether to use hierarchical allreduce.")
         .SetDefault(false);
     AddAttr<int>("hierarchical_allreduce_inter_nranks",
                  "(int default 1) "
-                 "Wheter to use hierarchical allreduce.")
+                 "Whether to use hierarchical allreduce.")
         .SetDefault(-1);
   }
 };

paddle/fluid/operators/collective/gen_nccl_id_op.cc

@@ -222,7 +222,7 @@ class GenNCCLIdOp : public framework::OperatorBase {
 class GenNCCLIdOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
-    AddOutput("NCCLID", "Raw variable contains a NCCL UniqueId instaces.");
+    AddOutput("NCCLID", "Raw variable contains a NCCL UniqueId instances.");
     AddComment(R"DOC(
 GenNCCLId operator
 
@@ -243,11 +243,11 @@ For trainer 1~n: start a gRPC server to get the UniqueId, once got, stop the ser
         .SetDefault(1);
     AddAttr<bool>("use_hierarchical_allreduce",
                   "(bool default false) "
-                  "Wheter to use hierarchical allreduce.")
+                  "Whether to use hierarchical allreduce.")
         .SetDefault(false);
     AddAttr<int>("hierarchical_allreduce_inter_nranks",
                  "(int default 1) "
-                 "Wheter to use hierarchical allreduce.")
+                 "Whether to use hierarchical allreduce.")
         .SetDefault(-1);
   }
 };

paddle/fluid/operators/collective/global_gather_op.cc

@@ -77,7 +77,7 @@ class GlobalGatherOpMaker : public framework::OpProtoAndCheckerMaker {
         .SetDefault(false);
     AddComment(R"DOC(
 Global Gather Operator
-Gather data in X to n_expert * world_size exeperts according to
+Gather data in X to n_expert * world_size experts according to
 local_count and receive tensors from n_expert * world_size experts according
 to global_count.
 )DOC");

paddle/fluid/operators/collective/global_scatter_op.cc

@@ -81,7 +81,7 @@ class GlobalScatterOpMaker : public framework::OpProtoAndCheckerMaker {
     AddComment(R"DOC(
 Global Scatter Operator
 Scatter data in X which has been put together belong to one expert
-to n_expert * world_size exeperts according to local_count
+to n_expert * world_size experts according to local_count
 and receive tensors from n_expert * world_size experts according
 to global_count.
 )DOC");

paddle/fluid/platform/gen_comm_id_helper.cc

@@ -314,7 +314,7 @@ static int ConnectAddr(const std::string& ep, const CommHead head) {
     CHECK_SYS_CALL(SocketSend(sock, phead, sizeof(head)), "send");
     ret_val = SocketRecv(sock, buffer, sizeof(head));
     if (ret_val > 0 && memcmp(buffer, phead, sizeof(head)) == 0) {
-      // recv same message from recver, indicating that the link is correct
+      // recv same message from receiver, indicating that the link is correct
       break;  // accept client
     } else {
       VLOG(3) << "socket read failed with ret_val=" << ret_val;

paddle/fluid/platform/init.cc

@@ -304,9 +304,9 @@ void SignalHandle(const char *data, int size) {
       signal_info.replace(start_pos, useless_substr.length(), "");
       *signal_msg_dunmer_ptr << "  [SignalInfo: " << signal_info << "]\n";
 
-      // NOTE3: Final singal error message print.
+      // NOTE3: Final signal error message print.
       // Here does not throw an exception,
-      // otherwise it will casue "terminate called recursively"
+      // otherwise it will cause "terminate called recursively"
       std::ostringstream sout;
       sout << "\n\n--------------------------------------\n";
       sout << "C++ Traceback (most recent call last):";

paddle/fluid/pybind/cuda_streams_py.cc

@@ -209,7 +209,7 @@ void BindCudaStream(py::module *m_ptr) {
           Default: None.
 
       Returns:
-          The recored event.
+          The record event.
 
       Examples:
         .. code-block:: python
@@ -263,7 +263,7 @@ void BindCudaStream(py::module *m_ptr) {
               auto place_tmp = platform::CUDAPlace(curr_device_id);
               new (&self) phi::CUDAStream(place_tmp, priority - 2, stream_flag);
             } else {
-              // seting priority 1(high) and 2(normal) correspond to the actual
+              // setting priority 1(high) and 2(normal) correspond to the actual
               // cuda stream priority -1 and 0.
               new (&self) phi::CUDAStream(*place, priority - 2, stream_flag);
             }
@@ -295,7 +295,7 @@ void BindCudaStream(py::module *m_ptr) {
             }
 
             auto stream_flag = phi::CUDAStream::StreamFlag::kStreamNonBlocking;
-            // seting priority 1(high) and 2(normal) correspond to the actual
+            // setting priority 1(high) and 2(normal) correspond to the actual
             // cuda stream priority -1 and 0.
             new (&self) phi::CUDAStream(
                 platform::CUDAPlace(device), priority - 2, stream_flag);

paddle/fluid/pybind/custom_device_py.cc

@@ -313,7 +313,7 @@ void BindCustomDevicePy(py::module *m_ptr) {
           Default: None.
 
       Returns:
-          The recored event.
+          The record event.
 
       Examples:
         .. code-block:: python

paddle/fluid/pybind/eager.cc

@@ -763,7 +763,7 @@ int TensorInit(PyObject* self, PyObject* args, PyObject* kwargs) {
     }
   } else if (args_num == (Py_ssize_t)1 || args_num == (Py_ssize_t)2 ||
              args_num == (Py_ssize_t)3) {
-    // 1 to 3 position args, remainting arguments are kwargs
+    // 1 to 3 position args, remaining arguments are kwargs
     PyObject* arg0_ptr = PyTuple_GET_ITEM(args, 0);
     if (pybind11::detail::npy_api::get().PyArray_Check_(arg0_ptr)) {
       VLOG(6) << "Calling case3's or case4's initializer.";
@@ -798,7 +798,7 @@ int TensorInit(PyObject* self, PyObject* args, PyObject* kwargs) {
           "constructor."));
     }
   } else if (args_num == (Py_ssize_t)4) {
-    // 4 position args, remainting arguments are kwargs
+    // 4 position args, remaining arguments are kwargs
     PyObject* arg0_ptr = PyTuple_GET_ITEM(args, 0);
     if (pybind11::detail::npy_api::get().PyArray_Check_(arg0_ptr)) {
       VLOG(6) << "Calling case3's or case4's initializer.";
@@ -808,7 +808,7 @@ int TensorInit(PyObject* self, PyObject* args, PyObject* kwargs) {
     } else {
       PADDLE_THROW(platform::errors::InvalidArgument(
           "Incompatible constructor arguments, "
-          "there are 4 position args and remainting arguments arg kwargs,"
+          "there are 4 position args and remaining arguments arg kwargs,"
           "but the first position args should be PyArray. "
           "Please check your code and make sure the first position args is "
           "PyArray."));
@@ -856,7 +856,7 @@ int TensorInit(PyObject* self, PyObject* args, PyObject* kwargs) {
             "Please check your code and make sure you call the existed "
             "constructor."));
       }
-    } else {  // five position args, remainting arguments are kwargs
+    } else {  // five position args, remaining arguments are kwargs
       PyObject* arg0_ptr = PyTuple_GET_ITEM(args, 0);
       if (pybind11::detail::npy_api::get().PyArray_Check_(arg0_ptr)) {
         VLOG(6) << "Calling case3's or case4's initializer";
@@ -866,7 +866,7 @@ int TensorInit(PyObject* self, PyObject* args, PyObject* kwargs) {
       } else {
         PADDLE_THROW(platform::errors::InvalidArgument(
             "Incompatible constructor arguments, "
-            "there are 5 position args and remainting arguments are kwargs,"
+            "there are 5 position args and remaining arguments are kwargs,"
             "but the first position args should be PyArray. "
             "Please check your code and make sure the first position args is "
             "PyArray."));
@@ -879,11 +879,11 @@ int TensorInit(PyObject* self, PyObject* args, PyObject* kwargs) {
       AutoInitTensorByPyArray(
           py_tensor_ptr, kws_map, args, flag_kwargs, args_num);
       return 0;
-    } else {  // six position args, remainting arguments are kwargs, but this
+    } else {  // six position args, remaining arguments are kwargs, but this
               // is not a right way
       PADDLE_THROW(platform::errors::InvalidArgument(
           "Incompatible constructor arguments, "
-          "there are 6 position args and the remainting arguments are kwargs. "
+          "there are 6 position args and the remaining arguments are kwargs. "
           "Please check your code and make sure the first position args is "
           "PyArray."));
     }
@@ -1050,7 +1050,7 @@ int StringTensorInit(PyObject* self, PyObject* args, PyObject* kwargs) {
       }
     }
   } else if (args_num == (Py_ssize_t)1) {  // case 3 ~ 6
-    // 1 position args, remainting arguments are kwargs
+    // 1 position args, remaining arguments are kwargs
     PyObject* arg0_ptr = PyTuple_GET_ITEM(args, 0);
     if (pybind11::detail::npy_api::get().PyArray_Check_(arg0_ptr)) {
       VLOG(6) << "Calling case3's or case4's string initializer.";

paddle/fluid/pybind/eager_legacy_op_function_generator.cc

@@ -131,7 +131,7 @@ static PyObject * %s(PyObject *self, PyObject *args, PyObject *kwargs)
 
 const char* PYBIND_ITEM_TEMPLATE = R"(  {"%s", (PyCFunction)(void(*)(void))%s, METH_VARARGS | METH_KEYWORDS, "C++ interface function for %s in dygraph."},)";
 
-// These operators will skip automatical code generatrion and
+// These operators will skip automatical code generation and
 // need to be handwritten in CUSTOM_HANDWRITE_OP_FUNC_FILE
 std::unordered_set<std::string> CUSTOM_HANDWRITE_OPS_SET = {"run_program"};
 
@@ -216,7 +216,7 @@ std::string GenerateOpFunctionsBody(
   for (auto& output : op_proto->outputs()) {
     auto& out_name = output.name();
 
-    // skip those dispensable oututs
+    // skip those dispensable outputs
     if (output.dispensable() && !FindOutsMap(op_type, out_name)) {
       continue;
     }
@@ -341,7 +341,7 @@ std::string GenerateOpFunctionsBody(
     function_args = paddle::string::Sprintf(FUNCTION_ARGS, input_args);
   }
 
-  // generate op funtcion body
+  // generate op function body
   auto op_function_str = paddle::string::Sprintf(OP_FUNCTION_TEMPLATE,
                                                  func_name,
                                                  ins_cast_str,
@@ -410,7 +410,7 @@ GenerateOpFunctions() {
       continue;
     }
     auto& op_type = op_proto->type();
-    // Skip operators that will be handwriten in CUSTOM_HANDWRITE_OP_FUNC_FILE.
+    // Skip operators that will be handwritten in CUSTOM_HANDWRITE_OP_FUNC_FILE.
     if (CUSTOM_HANDWRITE_OPS_SET.count(op_type)) {
       continue;
     }

paddle/fluid/pybind/eager_method.cc

@@ -295,7 +295,7 @@ static PyObject* tensor_method_numpy(TensorObject* self,
       VLOG(6) << "Getting DenseTensor's numpy value";
       auto dense_tensor =
           std::dynamic_pointer_cast<phi::DenseTensor>(self->tensor.impl());
-      // TODO(qili93): temporary for ascned npu performance to be removed along
+      // TODO(qili93): temporary for ascend npu performance to be removed along
       // with npu_identity op
       paddle::Tensor temp_tensor(std::make_shared<phi::DenseTensor>());
       if (dense_tensor->storage_properties_initialized()) {
@@ -422,7 +422,7 @@ static void IncreaseTensorReferenceCountUntilCopyComplete(
   // Note(dev): This is an empty callback, the only way is to "reference"
   // inner memory Holder, so it will not be destructed until the kernels
   // launched at current stream of given place is finished, such as
-  // CUDAPinned Mem -> CUDA by cudamemcpyAsync.
+  // CUDAPinned Mem -> CUDA by cudaMemcpyAsync.
   auto callback = [tensor, place_]() {
     VLOG(3) << "Run callback of Tensor:" << tensor.name() << " at place "
             << place_;

paddle/fluid/pybind/eager_properties.cc

@@ -41,8 +41,8 @@ extern PyTypeObject* p_tensor_type;
 
 PyObject* tensor_properties_get_name(TensorObject* self, void* closure) {
   EAGER_TRY
-  // NOTE(dev): [why not use egr::Controller::Instance::GernerateUniqueName()?]
-  // Beacause Controller must holder a tracer, but 'tensor.name' maybe called
+  // NOTE(dev): [why not use egr::Controller::Instance::GenerateUniqueName()?]
+  // Because Controller must holder a tracer, but 'tensor.name' maybe called
   // everywhere such as static graph mode in @to_static, which means tracer is
   // None.
   static egr::UniqueNameGenerator name_generator;

paddle/fluid/pybind/eager_py_layer.cc

@@ -137,7 +137,7 @@ PyObject* pylayer_method_apply(PyObject* cls,
       PyObject_GetAttrString(cls, "_backward_function");
   if (!backward_function) {
     PADDLE_THROW(paddle::platform::errors::InvalidArgument(
-        "Get _backward_function faild."));
+        "Get _backward_function failed."));
   }
   PyLayerObject* ctx = reinterpret_cast<PyLayerObject*>(
       PyObject_CallFunctionObjArgs(backward_function, nullptr));
@@ -255,7 +255,7 @@ PyObject* pylayer_method_apply(PyObject* cls,
   auto forward_fn = PyObject_GetAttrString(cls, "forward");
   if (!forward_fn) {
     PADDLE_THROW(paddle::platform::errors::InvalidArgument(
-        "Get forward function faild."));
+        "Get forward function failed."));
   }
   bool trace_backward = egr::Controller::Instance().HasGrad();
   egr::Controller::Instance().SetHasGrad(false);

paddle/fluid/pybind/eager_utils.cc

@@ -1362,7 +1362,7 @@ paddle::experimental::Scalar CastNumpy2Scalar(PyObject* obj,
       return paddle::experimental::Scalar(value);
     } else {
       PADDLE_THROW(platform::errors::InvalidArgument(
-          "%s(): argument (position %d) is numpy.ndarry, the inner elements "
+          "%s(): argument (position %d) is numpy.ndarray, the inner elements "
           "must be "
           "numpy.float32/float64 now, but got %s",
           op_type,

paddle/fluid/pybind/imperative.cc

@@ -481,7 +481,7 @@ static void VarBaseCopy(std::shared_ptr<imperative::VarBase> &src,  // NOLINT
     }
   } else {
     PADDLE_THROW(platform::errors::InvalidArgument(
-        "The destion Tensor(%s) can not copy when it is not empty.",
+        "The destination Tensor(%s) can not copy when it is not empty.",
         dst.Name()));
   }
 }
@@ -538,7 +538,7 @@ void BindImperative(py::module *m_ptr) {
                   "lists with different lengths.\n  * Check the reader "
                   "function passed to 'set_(sample/sample_list/batch)"
                   "_generator' to locate the data causes this issue."));
-          // 2. construcct LoDTensor
+          // 2. construct LoDTensor
           phi::DenseTensor t;
           SetTensorFromPyArray<platform::CPUPlace>(
               &t, array, platform::CPUPlace(), true);
@@ -578,7 +578,7 @@ void BindImperative(py::module *m_ptr) {
                 "lists with different lengths.\n  * Check the reader "
                 "function passed to 'set_(sample/sample_list/batch)"
                 "_generator' to locate the data causes this issue."));
-        // 2. construcct LoDTensor
+        // 2. construct LoDTensor
         phi::DenseTensor t;
         SetTensorFromPyArray<platform::CPUPlace>(
             &t, array, platform::CPUPlace(), true);
@@ -787,7 +787,7 @@ void BindImperative(py::module *m_ptr) {
             // inplace operator for the VarBase self.
             self->BumpInplaceVersion();
 
-            // 1. Check argumnets
+            // 1. Check arguments
             bool parse_index = true;
 
             // Check whether _index can be parsed.
@@ -1512,7 +1512,7 @@ void BindImperative(py::module *m_ptr) {
                } else {
                  PADDLE_THROW(platform::errors::Unimplemented(
                      "Imperative SelectedRows allreduce is not supported when "
-                     "paddle is compiled with NCCL verison lower than v2.2.12. "
+                     "paddle is compiled with NCCL version lower than v2.2.12. "
                      "You can set is_sparse=False for the Layer containing "
                      "this argument, such as Embedding(is_sparse=False)."));
                }
@@ -1586,7 +1586,7 @@ void BindImperative(py::module *m_ptr) {
              This hook will be called every time the gradient of current Tensor has been fully calculated.
 
              There are two differences with `_register_grad_hook`:
-             1. This backward hook will be executed after the gradient accumulation completed across batchs,
+             1. This backward hook will be executed after the gradient accumulation completed across batches,
                 but the hook registered by `_register_grad_hook` will be executed the gradient accumulation
                 completed in current batch.
              2. This backward hook function should have the following signature:

paddle/fluid/pybind/jit.cc

@@ -193,7 +193,7 @@ static PyObject *custom_eval_frame_shim(PyFrameObject *frame, int throw_flag) {
 
 static PyObject *set_eval_frame(PyObject *new_callback, PyThreadState *tstate) {
   // Change the eval frame callback and return the old one
-  //  - None: disables: diable custom callback.
+  //  - None: disables: disable custom callback.
   //  - Python callable(): enables custom callback.
   //  NOTE: Cache is not supported now
   PyObject *old_callback = eval_frame_callback_get();

paddle/fluid/pybind/parallel_executor.cc

@@ -301,7 +301,7 @@ void BindParallelExecutor(pybind11::module &m) {  // NOLINT
             PADDLE_ENFORCE_NE(self.IsFinalized(),
                               true,
                               platform::errors::PreconditionNotMet(
-                                  "BuildStrategy has been finlaized, cannot be "
+                                  "BuildStrategy has been finalized, cannot be "
                                   "configured again."));
             self.reduce_ = strategy;
           },
@@ -332,7 +332,7 @@ void BindParallelExecutor(pybind11::module &m) {  // NOLINT
             PADDLE_ENFORCE_NE(self.IsFinalized(),
                               true,
                               platform::errors::PreconditionNotMet(
-                                  "BuildStrategy has been finlaized, cannot be "
+                                  "BuildStrategy has been finalized, cannot be "
                                   "configured again."));
             self.gradient_scale_ = strategy;
           },
@@ -384,7 +384,7 @@ void BindParallelExecutor(pybind11::module &m) {  // NOLINT
             PADDLE_ENFORCE_NE(self.IsFinalized(),
                               true,
                               platform::errors::PreconditionNotMet(
-                                  "BuildStrategy has been finlaized, cannot be "
+                                  "BuildStrategy has been finalized, cannot be "
                                   "configured again."));
             self.debug_graphviz_path_ = path;
           },
@@ -412,7 +412,7 @@ void BindParallelExecutor(pybind11::module &m) {  // NOLINT
             PADDLE_ENFORCE_NE(self.IsFinalized(),
                               true,
                               platform::errors::PreconditionNotMet(
-                                  "BuildStrategy has been finlaized, cannot be "
+                                  "BuildStrategy has been finalized, cannot be "
                                   "configured again."));
             self.enable_sequential_execution_ = b;
           },
@@ -439,7 +439,7 @@ void BindParallelExecutor(pybind11::module &m) {  // NOLINT
             PADDLE_ENFORCE_NE(self.IsFinalized(),
                               true,
                               platform::errors::PreconditionNotMet(
-                                  "BuildStrategy has been finlaized, cannot be "
+                                  "BuildStrategy has been finalized, cannot be "
                                   "configured again."));
             self.remove_unnecessary_lock_ = b;
           },
@@ -515,7 +515,7 @@ void BindParallelExecutor(pybind11::module &m) {  // NOLINT
             PADDLE_ENFORCE_NE(self.IsFinalized(),
                               true,
                               platform::errors::PreconditionNotMet(
-                                  "BuildStrategy has been finlaized, "
+                                  "BuildStrategy has been finalized, "
                                   "cannot be configured again."));
             self.build_cinn_pass_ = b;
           },
@@ -544,7 +544,7 @@ void BindParallelExecutor(pybind11::module &m) {  // NOLINT
             PADDLE_ENFORCE_NE(self.IsFinalized(),
                               true,
                               platform::errors::PreconditionNotMet(
-                                  "BuildStrategy has been finlaized, cannot be "
+                                  "BuildStrategy has been finalized, cannot be "
                                   "configured again."));
             self.fuse_elewise_add_act_ops_ = b;
           },
@@ -570,7 +570,7 @@ void BindParallelExecutor(pybind11::module &m) {  // NOLINT
             PADDLE_ENFORCE_NE(self.IsFinalized(),
                               true,
                               platform::errors::PreconditionNotMet(
-                                  "BuildStrategy has been finlaized, cannot be "
+                                  "BuildStrategy has been finalized, cannot be "
                                   "configured again."));
             self.fuse_gemm_epilogue_ = b;
           },
@@ -596,7 +596,7 @@ void BindParallelExecutor(pybind11::module &m) {  // NOLINT
             PADDLE_ENFORCE_NE(self.IsFinalized(),
                               true,
                               platform::errors::PreconditionNotMet(
-                                  "BuildStrategy has been finlaized, cannot be "
+                                  "BuildStrategy has been finalized, cannot be "
                                   "configured again."));
             self.fuse_adamw_ = b;
           },
@@ -618,7 +618,7 @@ void BindParallelExecutor(pybind11::module &m) {  // NOLINT
             PADDLE_ENFORCE_NE(self.IsFinalized(),
                               true,
                               platform::errors::PreconditionNotMet(
-                                  "BuildStrategy has been finlaized, cannot be "
+                                  "BuildStrategy has been finalized, cannot be "
                                   "configured again."));
             self.fused_attention_ = b;
           },
@@ -644,7 +644,7 @@ void BindParallelExecutor(pybind11::module &m) {  // NOLINT
             PADDLE_ENFORCE_NE(self.IsFinalized(),
                               true,
                               platform::errors::PreconditionNotMet(
-                                  "BuildStrategy has been finlaized, cannot be "
+                                  "BuildStrategy has been finalized, cannot be "
                                   "configured again."));
             self.fused_feedforward_ = b;
           },
@@ -670,7 +670,7 @@ void BindParallelExecutor(pybind11::module &m) {  // NOLINT
             PADDLE_ENFORCE_NE(self.IsFinalized(),
                               true,
                               platform::errors::PreconditionNotMet(
-                                  "BuildStrategy has been finlaized, cannot be "
+                                  "BuildStrategy has been finalized, cannot be "
                                   "configured again."));
             self.sequential_run_ = b;
           },
@@ -695,7 +695,7 @@ void BindParallelExecutor(pybind11::module &m) {  // NOLINT
             PADDLE_ENFORCE_NE(self.IsFinalized(),
                               true,
                               platform::errors::PreconditionNotMet(
-                                  "BuildStrategy has been finlaized, cannot be "
+                                  "BuildStrategy has been finalized, cannot be "
                                   "configured again."));
             self.fuse_bn_act_ops_ = b;
           },
@@ -721,7 +721,7 @@ void BindParallelExecutor(pybind11::module &m) {  // NOLINT
             PADDLE_ENFORCE_NE(self.IsFinalized(),
                               true,
                               platform::errors::PreconditionNotMet(
-                                  "BuildStrategy has been finlaized, cannot be "
+                                  "BuildStrategy has been finalized, cannot be "
                                   "configured again."));
             self.fuse_bn_add_act_ops_ = b;
           },
@@ -747,7 +747,7 @@ void BindParallelExecutor(pybind11::module &m) {  // NOLINT
             PADDLE_ENFORCE_NE(self.IsFinalized(),
                               true,
                               platform::errors::PreconditionNotMet(
-                                  "BuildStrategy has been finlaized, cannot be "
+                                  "BuildStrategy has been finalized, cannot be "
                                   "configured again."));
             self.enable_auto_fusion_ = b;
           },
@@ -776,7 +776,7 @@ void BindParallelExecutor(pybind11::module &m) {  // NOLINT
             PADDLE_ENFORCE_NE(self.IsFinalized(),
                               true,
                               platform::errors::PreconditionNotMet(
-                                  "BuildStrategy has been finlaized, cannot be "
+                                  "BuildStrategy has been finalized, cannot be "
                                   "configured again."));
             self.fuse_relu_depthwise_conv_ = b;
           },
@@ -807,7 +807,7 @@ void BindParallelExecutor(pybind11::module &m) {  // NOLINT
             PADDLE_ENFORCE_NE(self.IsFinalized(),
                               true,
                               platform::errors::PreconditionNotMet(
-                                  "BuildStrategy has been finlaized, "
+                                  "BuildStrategy has been finalized, "
                                   "cannot be configured again."));
             self.fuse_broadcast_ops_ = b;
           },
@@ -839,7 +839,7 @@ void BindParallelExecutor(pybind11::module &m) {  // NOLINT
             PADDLE_ENFORCE_NE(self.IsFinalized(),
                               true,
                               platform::errors::PreconditionNotMet(
-                                  "BuildStrategy has been finlaized, "
+                                  "BuildStrategy has been finalized, "
                                   "cannot be configured again."));
             self.fuse_all_optimizer_ops_ = b;
           })
@@ -850,7 +850,7 @@ void BindParallelExecutor(pybind11::module &m) {  // NOLINT
             PADDLE_ENFORCE_NE(self.IsFinalized(),
                               true,
                               platform::errors::PreconditionNotMet(
-                                  "BuildStrategy has been finlaized, cannot be "
+                                  "BuildStrategy has been finalized, cannot be "
                                   "configured again."));
             self.sync_batch_norm_ = b;
           },

paddle/fluid/pybind/protobuf.cc

@@ -434,8 +434,8 @@ void BindOpDesc(pybind11::module *m) {
       .def("set_serialized_attr",
            [](pd::OpDesc &self,
               const std::string &name,
-              const pybind11::bytes &seriralized) {
-             std::string ser(seriralized);
+              const pybind11::bytes &serialized) {
+             std::string ser(serialized);
              self.SetAttr(name, ser);
            })
       .def("_block_attr_id", &pd::OpDesc::GetBlockAttrId)
@@ -484,8 +484,8 @@ void BindOpDesc(pybind11::module *m) {
                  return self.to<bool>();
                case phi::DataType::COMPLEX64:
                case phi::DataType::COMPLEX128:
-                 // to paddle's complex to avoid ambiguious
-                 // when converting bfloat16 or float16 to std::copmplex<double>
+                 // to paddle's complex to avoid ambiguous
+                 // when converting bfloat16 or float16 to std::complex<double>
                  return static_cast<std::complex<double>>(
                      self.to<phi::dtype::complex<double>>());
                default:

paddle/fluid/pybind/reader_py.cc

@@ -320,7 +320,7 @@ class MultiDeviceFeedReader {
     PADDLE_ENFORCE_EQ(status,
                       Status::kSuccess,
                       platform::errors::NotFound(
-                          "The function executed sucessfully, but "
+                          "The function executed successfully, but "
                           "the result status is not Status::kSuccess"));
   }
 

paddle/ir/builtin_attribute_storage.h

@@ -46,7 +46,7 @@ namespace ir {
   };
 
 ///
-/// \brief Define Parameteric AttributeStorage for StrAttribute.
+/// \brief Define Parametric AttributeStorage for StrAttribute.
 ///
 struct StrAttributeStorage : public AttributeStorage {
   using ParamKey = std::string;

paddle/ir/ir_context.cc

@@ -66,7 +66,7 @@ class IrContextImpl {
     std::lock_guard<ir::SpinLock> guard(registed_abstract_types_lock_);
     auto iter = registed_abstract_types_.find(type_id);
     if (iter != registed_abstract_types_.end()) {
-      VLOG(4) << "Fonund a cached abstract_type of: [TypeId_hash="
+      VLOG(4) << "Found a cached abstract_type of: [TypeId_hash="
               << std::hash<ir::TypeId>()(type_id)
               << ", AbstractType_ptr=" << iter->second << "].";
       return iter->second;
@@ -89,7 +89,7 @@ class IrContextImpl {
     std::lock_guard<ir::SpinLock> guard(registed_abstract_attributes_lock_);
     auto iter = registed_abstract_attributes_.find(type_id);
     if (iter != registed_abstract_attributes_.end()) {
-      VLOG(4) << "Fonund a cached abstract_attribute of: [TypeId_hash="
+      VLOG(4) << "Found a cached abstract_attribute of: [TypeId_hash="
               << std::hash<ir::TypeId>()(type_id)
               << ", AbstractAttribute_ptr=" << iter->second << "].";
       return iter->second;
@@ -110,7 +110,7 @@ class IrContextImpl {
     std::lock_guard<ir::SpinLock> guard(registed_op_infos_lock_);
     auto iter = registed_op_infos_.find(name);
     if (iter != registed_op_infos_.end()) {
-      VLOG(4) << "Fonund a cached operation of: [name=" << name
+      VLOG(4) << "Found a cached operation of: [name=" << name
               << ", OpInfoImpl ptr=" << iter->second << "].";
       return iter->second;
     }
@@ -129,11 +129,11 @@ class IrContextImpl {
     std::lock_guard<ir::SpinLock> guard(registed_dialect_lock_);
     auto iter = registed_dialect_.find(name);
     if (iter != registed_dialect_.end()) {
-      VLOG(4) << "Fonund a cached dialect of: [name=" << name
+      VLOG(4) << "Found a cached dialect of: [name=" << name
               << ", dialect_ptr=" << iter->second << "].";
       return iter->second;
     }
-    LOG(WARNING) << "No cache fonund dialect of: [name=" << name << "].";
+    LOG(WARNING) << "No cache found dialect of: [name=" << name << "].";
     return nullptr;
   }
 
@@ -156,7 +156,7 @@ class IrContextImpl {
   // AttributeStorage uniquer and cache instances.
   StorageManager registed_attribute_storage_manager_;
 
-  // The dialcet registered in the context.
+  // The dialect registered in the context.
   std::unordered_map<std::string, Dialect *> registed_dialect_;
   ir::SpinLock registed_dialect_lock_;
 

paddle/ir/operation.cc

@@ -118,7 +118,7 @@ void Operation::destroy() {
   }
   reinterpret_cast<Operation *>(base_ptr)->~Operation();
   base_ptr += sizeof(Operation);
-  // 2.3. Deconstruct OpOpOerand.
+  // 2.3. Deconstruct OpOperand.
   for (size_t idx = 0; idx < num_operands_; idx++) {
     reinterpret_cast<detail::OpOperandImpl *>(base_ptr)->~OpOperandImpl();
     base_ptr += sizeof(detail::OpOperandImpl);

paddle/ir/operation.h

@@ -39,7 +39,7 @@ class alignas(8) Operation final {
   static Operation *create(const OperationArgument &op_argument);
 
   ///
-  /// \brief Destroy the operation objects and free memeory by create().
+  /// \brief Destroy the operation objects and free memory by create().
   ///
   void destroy();
 

paddle/ir/storage_manager.cc

@@ -72,7 +72,7 @@ StorageManager::StorageBase *StorageManager::GetParametricStorageImpl(
     std::function<bool(const StorageBase *)> equal_func,
     std::function<StorageBase *()> constructor) {
   std::lock_guard<ir::SpinLock> guard(parametric_instance_lock_);
-  VLOG(4) << "Try to get a parameteretric storage of: [TypeId_hash="
+  VLOG(4) << "Try to get a parametric storage of: [TypeId_hash="
           << std::hash<ir::TypeId>()(type_id) << ", param_hash=" << hash_value
           << "].";
   if (parametric_instance_.find(type_id) == parametric_instance_.end())

paddle/ir/type_base.h

@@ -77,7 +77,7 @@ class AbstractType {
  private:
   ///
   /// \brief The constructor is set to private and provides the user with the
-  /// get method to obtain and manage the AstractType.
+  /// get method to obtain and manage the AbstractType.
   ///
   /// \param type_id The type id of the AbstractType.
   /// \param dialect The Dialect which the type registered to.

paddle/ir/value_impl.h

@@ -55,7 +55,7 @@ class OpOperandImpl {
 };
 
 ///
-/// \brief ValueImpl is the base class of all drived Value classes such as
+/// \brief ValueImpl is the base class of all derived Value classes such as
 /// OpResultImpl. This class defines all the information and usage interface in
 /// the IR Value. Each Value include three attributes:
 /// (1) type: ir::Type; (2) UD-chain of value: OpOperandImpl*, first operand

python/paddle/audio/backends/wave_backend.py

@@ -180,7 +180,7 @@ def save(
         filepath: saved path
         src: the audio tensor
         sample_rate: the number of samples of audio per second.
-        channels_first: src channel infomation
+        channels_first: src channel information
             if True, means input tensor is (channels, time)
             if False, means input tensor is (time, channels)
         encoding: audio encoding format, wave_backend only support PCM16 now.
@@ -216,7 +216,7 @@ def save(
 
     # only support PCM16
     if bits_per_sample not in (None, 16):
-        raise ValueError("Invalid bits_per_sample, only supprt 16 bit")
+        raise ValueError("Invalid bits_per_sample, only support 16 bit")
 
     sample_width = int(bits_per_sample / 8)  # 2
 

python/paddle/audio/datasets/dataset.py

@@ -44,7 +44,7 @@ class AudioClassificationDataset(paddle.io.Dataset):
             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`):
-                It identifies the feature type that user wants to extrace of an audio file.
+                It identifies the feature type that user wants to extract an audio file.
         """
         super().__init__()
 

python/paddle/autograd/autograd.py

@@ -187,7 +187,7 @@ class Hessian(Jacobian):
 class _Jacobian:
     """The base class for computing Jacobian matrix.
 
-    ``_Jacobian`` implementes the core logic of multidimensional index and lazy
+    ``_Jacobian`` implements the core logic of multidimensional index and lazy
     evaluation for Jacobian matrix, subclass only need to overwrite following
     methods:
 
@@ -436,7 +436,7 @@ def _multi_index(indexes, shape):
                     index.start + shape[i] if index.start < 0 else index.start,
                     index.stop + shape[i] if index.stop < 0 else index.stop,
                     # Negative step means index backward, no need to convert to
-                    # positive interger.
+                    # positive integer.
                     index.step,
                 )
             )
@@ -690,9 +690,9 @@ def _grad_for_jacobian(ys, xs, v=None):
             inputs.
     """
     if paddle.in_dynamic_mode():
-        # paddle.grad returns a list though the inputs is a signle Tensor. The
+        # paddle.grad returns a list though the inputs is a single Tensor. The
         # follow code snippet fixes the problem by return the first element of
-        # xs_grad when the xs is a signle Tensor.
+        # xs_grad when the xs is a single Tensor.
         xs_grad = paddle.grad(ys, xs, v, create_graph=True, allow_unused=True)
         if (
             isinstance(xs, paddle.fluid.framework.Variable)

python/paddle/batch.py

@@ -67,7 +67,7 @@ def batch(reader, batch_size, drop_last=False):
     batch_size = int(batch_size)
     if batch_size <= 0:
         raise ValueError(
-            "batch_size should be a positive integeral value, "
+            "batch_size should be a positive integer value, "
             "but got batch_size={}".format(batch_size)
         )
 

python/paddle/distributed/auto_parallel/operators/dist_dropout.py

@@ -82,7 +82,7 @@ class DistributedDropoutImpl0(DistributedElementwiseImpl0):
                 and src_op.attr("seed")
             ):
                 _logger.info(
-                    "Auto Parallel Random Control Skiped Since manul seed is set by user: {}".format(
+                    "Auto Parallel Random Control Skipped Since manul seed is set by user: {}".format(
                         src_op
                     )
                 )
@@ -90,7 +90,7 @@ class DistributedDropoutImpl0(DistributedElementwiseImpl0):
                 pass
             # NOTE Adopt for recompute
             # If user already set seed, We should not modify it. But if the seed is added by recompute pass, it should be under control.
-            # TODO  in future recompute pass should happen after parallel partitione. and remove this at that time.
+            # TODO  in future recompute pass should happen after parallel partition. and remove this at that time.
             elif len(kwargs['Seed']) > 0 or len(src_op.input("Seed")) > 0:
                 seed_var_name = kwargs['Seed'][0]
                 if seed_var_name.startswith('rc_seed'):
@@ -115,7 +115,7 @@ class DistributedDropoutImpl0(DistributedElementwiseImpl0):
                     pre_op._set_attr("force_cpu", True)
                 else:
                     _logger.info(
-                        "Auto Parallel Random Control Skiped Since manul seed is set by user: {}".format(
+                        "Auto Parallel Random Control Skipped Since manul seed is set by user: {}".format(
                             src_op
                         )
                     )

python/paddle/distributed/auto_parallel/operators/dist_fused_dropout_add.py

@@ -87,7 +87,7 @@ class DistributedDropoutImpl0(DistributedElementwiseImpl0):
                 and src_op.attr("seed")
             ):
                 _logger.info(
-                    "Auto Parallel Random Control Skiped Since manul seed is set by user: {}".format(
+                    "Auto Parallel Random Control Skipped Since manul seed is set by user: {}".format(
                         src_op
                     )
                 )
@@ -120,7 +120,7 @@ class DistributedDropoutImpl0(DistributedElementwiseImpl0):
                     pre_op._set_attr("force_cpu", True)
                 else:
                     _logger.info(
-                        "Auto Parallel Random Control Skiped Since manul seed is set by user: {}".format(
+                        "Auto Parallel Random Control Skipped Since manul seed is set by user: {}".format(
                             src_op
                         )
                     )