Fix alreadly already, etc

paddle/fluid/framework/feed_fetch_method.cc

@@ -97,7 +97,7 @@ FetchType& GetFetchVariable(const Scope& scope,
                             const std::string& var_name,
                             size_t index) {
   // Since we want to fetch FetchType from a variable, the variable must
-  // be created alreadly.
+  // be created already.
   Variable* g_fetch_value = scope.FindVar(var_name);
   PADDLE_ENFORCE_NOT_NULL(g_fetch_value,
                           platform::errors::NotFound(

paddle/fluid/framework/infershape_utils.cc

@@ -859,7 +859,7 @@ CompatInferMetaContext BuildInferMetaContext(InferShapeContext* ctx,
                   attr_names[i]));
           }
         } else {
-          // do nothing, skip currnet attr
+          // do nothing, skip current attr
         }
     }
   }

paddle/fluid/framework/infershape_utils.h

@@ -85,7 +85,7 @@ class CompatMetaTensor : public phi::MetaTensor {
         is_runtime_,
         true,
         platform::errors::Unavailable(
-            "Only can get phi::DenseTensor from MetaTensor in rumtime."));
+            "Only can get phi::DenseTensor from MetaTensor in runtime."));
     auto* var = PADDLE_GET_CONST(Variable*, var_);
     PADDLE_ENFORCE_EQ(
         var->IsType<phi::SelectedRows>(),

paddle/fluid/framework/lod_tensor.h

@@ -166,8 +166,8 @@ std::pair<LoD, std::pair<size_t, size_t>> GetSubLoDAndAbsoluteOffset(
     const LoD& lod, size_t start_idx, size_t end_idx, size_t start_level);
 
 /*
- * Serialize/Desiralize phi::DenseTensor to std::ostream
- * You can pass ofstream or ostringstream to serilize to file
+ * Serialize/Deserialize phi::DenseTensor to std::ostream
+ * You can pass ofstream or ostringstream to serialize to file
  * or to a in memory string. GPU tensor will be copied to CPU.
  */
 void SerializeToStream(std::ostream& os,

paddle/fluid/framework/op_desc.cc

@@ -322,7 +322,7 @@ class CompileTimeInferShapeContext : public InferShapeContext {
     PADDLE_ENFORCE_EQ(arg_names.size(),
                       1UL,
                       platform::errors::InvalidArgument(
-                          "The iutput(%s) should hold only one element, but "
+                          "The input(%s) should hold only one element, but "
                           "now it holds %d elements.",
                           name,
                           arg_names.size()));

paddle/fluid/framework/op_version_proto.h

@@ -55,7 +55,7 @@ class OpVersionMap {
 };
 
 // get version id for operators with version id in paddle 2.4.2, this is used
-// for converting ProgramDesc in 2.4 comtabible format
+// for converting ProgramDesc in 2.4 compatible format
 const std::unordered_map<std::string, uint32_t>& GetLegacyOpVersions();
 
 }  // namespace pb

paddle/fluid/framework/op_version_registry.h

@@ -378,11 +378,12 @@ class PassVersionCheckerRegistrar {
   static PassVersionCheckerRegistrar& GetInstance();
 
   PassVersionCheckers& Register(const std::string& pass_name) {
-    PADDLE_ENFORCE_EQ(pass_version_checkers_map_.find(pass_name),
-                      pass_version_checkers_map_.end(),
-                      platform::errors::AlreadyExists(
-                          "PassVersionCheckers(%s) has alredy been registered.",
-                          pass_name.c_str()));
+    PADDLE_ENFORCE_EQ(
+        pass_version_checkers_map_.find(pass_name),
+        pass_version_checkers_map_.end(),
+        platform::errors::AlreadyExists(
+            "PassVersionCheckers(%s) has already been registered.",
+            pass_name.c_str()));
     return pass_version_checkers_map_[pass_name];
   }
   bool IsPassCompatible(const std::string& fuse_pass_name) const {

paddle/fluid/framework/operator.cc

@@ -2677,7 +2677,7 @@ Scope* OperatorWithKernel::PrepareData(
       // target_kernel_type.
       // Have a discussion with @Superjomn or the inference developers if some
       // changes on this logic for this macro might not tested on the other
-      // scenerios.
+      // scenarios.
       // If this op is not called by an Executor or ParallelExecutor, it should
       // called by a NaiveExecutor, the NaiveExecutor will cache the scopes and
       // variables, that behavior a lot different.
@@ -3088,10 +3088,10 @@ static void SetDnnAttrIntoDeviceContext(
     phi::DeviceContext* dev_ctx,
     const Attribute& attr,
     const std::string& attr_name,
-    const operators::ExtraAttrPropertySet& attr_propertys) {
+    const operators::ExtraAttrPropertySet& attr_properties) {
 #ifdef PADDLE_WITH_DNNL
   if (phi::OneDNNContext::classof(dev_ctx) &&
-      attr_propertys.Support(operators::ExtraAttrProperty::ONEDNN)) {
+      attr_properties.Support(operators::ExtraAttrProperty::ONEDNN)) {
     VLOG(4) << "Runtime attr `" << attr_name << "` is passed to OneDNNContext.";
     phi::OneDNNContext* one_dnn_ctx = static_cast<phi::OneDNNContext*>(dev_ctx);
     switch (AttrTypeID(attr)) {
@@ -3124,7 +3124,7 @@ static void SetDnnAttrIntoDeviceContext(
 #endif
 #ifdef PADDLE_WITH_CUDA
   if (phi::GPUContext::classof(dev_ctx) &&
-      attr_propertys.Support(operators::ExtraAttrProperty::GPUDNN)) {
+      attr_properties.Support(operators::ExtraAttrProperty::GPUDNN)) {
     VLOG(4) << "Runtime attr `" << attr_name << "` is passed to GPUDNNContext.";
     phi::GPUContext* gpu_dnn_ctx = static_cast<phi::GPUContext*>(dev_ctx);
     switch (AttrTypeID(attr)) {
@@ -3585,8 +3585,8 @@ void OperatorWithKernel::BuildPhiKernelContext(
   for (const auto& attr_iter : runtime_attrs) {
     auto& attr_name = attr_iter.first;
     auto& attr = attr_iter.second;
-    auto attr_propertys = paddle::operators::GetExtraAttrProperties(attr_name);
-    SetDnnAttrIntoDeviceContext(dev_ctx, attr, attr_name, attr_propertys);
+    auto attr_properties = paddle::operators::GetExtraAttrProperties(attr_name);
+    SetDnnAttrIntoDeviceContext(dev_ctx, attr, attr_name, attr_properties);
   }
   // TODO(chenweihang): Since the pass will still `SetAttr` in the OpDesc,
   // we try to add these Attrs to the RuntimeAttrs, but these OpDesc will lose
@@ -3600,8 +3600,8 @@ void OperatorWithKernel::BuildPhiKernelContext(
   for (const auto& attr_iter : attrs) {
     auto& attr_name = attr_iter.first;
     auto& attr = attr_iter.second;
-    auto attr_propertys = paddle::operators::GetExtraAttrProperties(attr_name);
-    SetDnnAttrIntoDeviceContext(dev_ctx, attr, attr_name, attr_propertys);
+    auto attr_properties = paddle::operators::GetExtraAttrProperties(attr_name);
+    SetDnnAttrIntoDeviceContext(dev_ctx, attr, attr_name, attr_properties);
   }
   VLOG(4) << "Done runtime attributes";
 #endif

paddle/fluid/framework/parallel_executor.cc

@@ -1314,7 +1314,7 @@ void ParallelExecutor::InitExecutorPrivateMemberInfo(
     device_name = "XPU";
   } else {
     PADDLE_THROW(
-        platform::errors::Unavailable("Only CPU/CUDA/XPU is supportted. "
+        platform::errors::Unavailable("Only CPU/CUDA/XPU is supported. "
                                       "please use CPU/CUDA/XPU backend."));
   }
 

paddle/fluid/framework/program_utils.cc

@@ -158,9 +158,9 @@ void ProgramProcessor::AddDepToBlockOp(const BlockDesc &block) {
       } else if (op_type.compare("conditional_block") == 0) {
         op_input_var_vec = &((*op_inputs)["kInputs"]);
       } else {
-        // Only support while_op and conditinal_block_op now
+        // Only support while_op and conditional_block_op now
         LOG(WARNING)
-            << "Currently, only support while_op and conditinal_block_op.\n";
+            << "Currently, only support while_op and conditional_block_op.\n";
         continue;
       }
 

paddle/fluid/framework/proto_desc.h

@@ -26,7 +26,7 @@ constexpr int kNoneBlockIndex = -1;
 constexpr int kNoneProcessMeshIndex = -1;
 
 // If a attribute name has a certain suffix, it means that the
-// atrribute is a distributed-related attribute for auto parallel.
+// attribute is a distributed-related attribute for auto parallel.
 // e.g., "mesh_id@AUTO_PARALLEL".
 constexpr char kAutoParallelSuffix[] = "@AUTO_PARALLEL";
 

paddle/fluid/framework/prune.cc

@@ -168,7 +168,7 @@ int GetOpRole(const proto::OpDesc& op_desc) {
     }
   }
   // If attr op_role is not found, it may be operator created in c++ test, like
-  // prune_test.cc. In that case, the op_role should be defaut value, which is
+  // prune_test.cc. In that case, the op_role should be default value, which is
   // kNotSpecified.
   return static_cast<int>(OpRole::kNotSpecified);
 }
@@ -261,7 +261,7 @@ void prune_impl(const proto::ProgramDesc& input,
   for (auto op_iter = ops.rbegin(); op_iter != ops.rend(); ++op_iter) {
     auto& op_desc = *op_iter;
 
-    // TODO(wanghaipeng03) reconstruct the follwing if/else block
+    // TODO(wanghaipeng03) reconstruct the following if/else block
     //                     to extract common code
     //
     // bool should_run_flag = false;
@@ -281,7 +281,7 @@ void prune_impl(const proto::ProgramDesc& input,
     //
     // should_run.push_back(should_run_flag);
     // if (should_run_flag) {
-    //   for (auto & var: op_desc.iputs()) {
+    //   for (auto & var: op_desc.inputs()) {
     //     for (....) {
     //       if (.....) {
     //         dependent_vars->insert(argu);
@@ -304,7 +304,7 @@ void prune_impl(const proto::ProgramDesc& input,
           add_dependent_var(argu);
         }
       }
-      // NOTE(dev): All attibute with VarDesc type is considered as Input,
+      // NOTE(dev): All attribute with VarDesc type is considered as Input,
       // so they shall be added into dependent_vars.
       for (auto& attr : op_desc.attrs()) {
         if (attr.type() == proto::AttrType::VAR) {
@@ -391,7 +391,7 @@ void prune_impl(const proto::ProgramDesc& input,
           std::vector<int> sub_indices;
           GetSubBlocksIndices(*op, &sub_indices);
           for (auto& sub_index : sub_indices) {
-            // create a copy of dependent_vars to avoid being overwrited by the
+            // create a copy of dependent_vars to avoid being overwritten by the
             // other sub_block
             std::unordered_set<std::string> dependent_vars_copy =
                 sub_block_dependent_vars;
@@ -438,7 +438,7 @@ void prune_impl(const proto::ProgramDesc& input,
         add_var_names(arg);
       }
     }
-    // NOTE(dev): All attibute with VarDesc type is considered as Input,
+    // NOTE(dev): All attribute with VarDesc type is considered as Input,
     // so they shall be added into dependent_vars.
     for (auto& attr : op.attrs()) {
       if (attr.type() == proto::AttrType::VAR) {