fix typo word (#22784)

cmake/configure.cmake

@@ -48,7 +48,7 @@ if(WIN32)
   SET(CMAKE_C_RESPONSE_FILE_LINK_FLAG "@")
   SET(CMAKE_CXX_RESPONSE_FILE_LINK_FLAG "@")
 
-  # set defination for the dll export
+  # set definition for the dll export
   if (NOT MSVC)
     message(FATAL "Windows build only support msvc. Which was binded by the nvcc compiler of NVIDIA.")
   endif(NOT MSVC)

cmake/third_party.cmake

@@ -174,7 +174,7 @@ if(${CMAKE_VERSION} VERSION_GREATER "3.5.2")
     set(SHALLOW_CLONE "GIT_SHALLOW TRUE") # adds --depth=1 arg to git clone of External_Projects
 endif()
 
-########################### include third_party accoring to flags ###############################
+########################### include third_party according to flags ###############################
 include(external/zlib)      # download, build, install zlib
 include(external/gflags)    # download, build, install gflags
 include(external/glog)      # download, build, install glog

paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_pass.cc

@@ -857,7 +857,7 @@ int DistSSAGraphBuilder::CreateRPCOp(ir::Graph *result, ir::Node *node) const {
     op_dev_id = GetVarDeviceID(node->inputs[0]->Name());
     PADDLE_ENFORCE(!ir::IsControlDepVar(*node->inputs[0]),
                    "This hack no longer holds, please fix.");
-    // the variable name which contains .block means it was splited by
+    // the variable name which contains .block means it was split by
     // split_byref op
     if (strategy_.reduce_ ==
             details::BuildStrategy::ReduceStrategy::kAllReduce &&

paddle/fluid/framework/operator.cc

@@ -990,7 +990,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
   }
 
   if (!transfered_inplace_vars.empty()) {
-    // there is inplace variable has been transfered.
+    // there is inplace variable has been transferred.
     TransferInplaceVarsBack(scope, transfered_inplace_vars, *transfer_scope);
   }
   if (FLAGS_enable_unused_var_check) {

paddle/fluid/framework/operator.h

@@ -517,7 +517,8 @@ class OperatorWithKernel : public OperatorBase {
                RuntimeContext* runtime_ctx) const;
 
   /**
-   * Transfer data from scope to a transfered scope. If there is no data need to
+   * Transfer data from scope to a transferred scope. If there is no data need
+   * to
    * be tranfered, it returns nullptr.
    *
    * * transfered_inplace_vars is a output vector.

paddle/fluid/framework/parallel_executor.cc

@@ -87,18 +87,18 @@ class ParallelExecutorPrivate {
   inline bool HasGarbageCollectors() const { return !gcs_.empty(); }
 
   /**
-   * NOTE(zengjinle): the feeded variables of users should not be reused,
-   * because users may feed them into another network. Changing the feeded
+   * NOTE(zengjinle): the fed variables of users should not be reused,
+   * because users may feed them into another network. Changing the fed
    * variables that users can visit may cause calculation wrong, which is
    * a very subtle bug when traning networks. However, these variables
    * can be garbage collected.
    *
    * ParallelExecutor provides 2 methods to feed variables:
    *
-   *  - FeedTensorsIntoLocalScopes: this method would share memory of feeded
+   *  - FeedTensorsIntoLocalScopes: this method would share memory of fed
    *                                variables, so we have to skip these.
    *
-   *  - FeedAndSplitTensorIntoLocalScopes: this method would copy data of feeded
+   *  - FeedAndSplitTensorIntoLocalScopes: this method would copy data of fed
    *                                       variables, so we do not need to skip
    *                                       them.
    */

paddle/fluid/framework/reader.h

@@ -53,10 +53,10 @@ class ReaderBase {
   // they are readers just before read op.
   std::unordered_set<ReaderBase*> GetEndPoints();
 
-  // Returns the shapes of the feeded variables
+  // Returns the shapes of the fed variables
   const std::vector<DDim>& Shapes() const { return shapes_; }
 
-  // Returns the dtypes of the feeded variables
+  // Returns the dtypes of the fed variables
   const std::vector<proto::VarType::Type>& VarTypes() const {
     return var_types_;
   }
@@ -80,13 +80,13 @@ class ReaderBase {
 
   mutable std::mutex mu_;
 
-  // The shapes of the feeded variables.
+  // The shapes of the fed variables.
   std::vector<DDim> shapes_;
 
-  // The dtypes of the feeded variables.
+  // The dtypes of the fed variables.
   std::vector<proto::VarType::Type> var_types_;
 
-  // Whether to check the shape and dtype of feeded variables.
+  // Whether to check the shape and dtype of fed variables.
   // For Backward compatibility, variables created by old API fluid.layers.data
   // doesn't check shape but fluid.data checks.
   std::vector<bool> need_check_feed_;

paddle/fluid/imperative/tests/test_prepare_op.cc

@@ -210,7 +210,7 @@ TEST(test_prepare_op, test_prepare_data_same_place) {
                                             attr_map);
   framework::RuntimeContext ctx = PrepareRuntimeContext(ins, outs);
 
-  // test if it never transfered on GPU place
+  // test if it never transferred on GPU place
   PreparedOp prepared_op = PreparedOp::Prepare(
       ins, outs, dynamic_cast<framework::OperatorWithKernel&>(*op), cpu_place,
       &attr_map);

paddle/fluid/inference/CMakeLists.txt

@@ -14,7 +14,7 @@
 #
 
 if(WITH_TESTING)
-  include(tests/test.cmake) # some generic cmake funtion for inference
+  include(tests/test.cmake) # some generic cmake function for inference
 endif()
 
 # TODO(panyx0718): Should this be called paddle_fluid_inference_api_internal?

paddle/fluid/inference/api/demo_ci/README.md

@@ -12,7 +12,7 @@ There are several demos:
     - Each line contains a single record
     - Each record's format is
     ```
-    <space splitted floats as data>\t<space splitted ints as shape>
+    <space split floats as data>\t<space split ints as shape>
     ```
 
 To build and execute the demos, simply run 

paddle/fluid/inference/api/demo_ci/trt_mobilenet_demo.cc

@@ -23,10 +23,9 @@ limitations under the License. */
 DECLARE_double(fraction_of_gpu_memory_to_use);
 DEFINE_string(modeldir, "", "Directory of the inference model.");
 DEFINE_string(refer, "", "path to reference result for comparison.");
-DEFINE_string(
-    data, "",
-    "path of data; each line is a record, format is "
-    "'<space splitted floats as data>\t<space splitted ints as shape'");
+DEFINE_string(data, "",
+              "path of data; each line is a record, format is "
+              "'<space split floats as data>\t<space split ints as shape'");
 
 namespace paddle {
 namespace demo {

paddle/fluid/inference/api/demo_ci/vis_demo.cc

@@ -25,10 +25,9 @@ DECLARE_double(fraction_of_gpu_memory_to_use);
 #endif
 DEFINE_string(modeldir, "", "Directory of the inference model.");
 DEFINE_string(refer, "", "path to reference result for comparison.");
-DEFINE_string(
-    data, "",
-    "path of data; each line is a record, format is "
-    "'<space splitted floats as data>\t<space splitted ints as shape'");
+DEFINE_string(data, "",
+              "path of data; each line is a record, format is "
+              "'<space split floats as data>\t<space split ints as shape'");
 DEFINE_bool(use_gpu, false, "Whether use gpu.");
 #ifdef PADDLE_WITH_SHARED_LIB
 DECLARE_bool(profile);

paddle/fluid/operators/beam_search_op.cc

@@ -52,7 +52,7 @@ class BeamSearchOpMaker : public framework::OpProtoAndCheckerMaker {
               "A LoDTensor containing the accumulated scores corresponding to "
               "Output(selected_ids).");
     AddOutput("parent_idx",
-              "A Tensor preserving the selected_ids' parent indice in pre_ids.")
+              "A Tensor preserving the selected_ids' parent index in pre_ids.")
         .AsDispensable();
 
     // Attributes stored in AttributeMap

paddle/fluid/operators/cudnn_lstm_op.cc

@@ -119,7 +119,7 @@ class CudnnLSTMOpMaker : public framework::OpProtoAndCheckerMaker {
         .SetDefault(0.0);
     AddAttr<bool>("is_bidirec",
                   "is_bidirec"
-                  "if it is bidirection rnn"
+                  "if it is bidirectional rnn"
                   "The will affect the shape of the Out, last_h, and last_c")
         .SetDefault(false);
     AddAttr<int>("input_size", "input size ot the Input Tensor").SetDefault(10);

paddle/fluid/operators/detection/density_prior_box_op.cc

@@ -35,7 +35,7 @@ class DensityPriorBoxOp : public framework::OperatorWithKernel {
           platform::errors::InvalidArgument(
               "The input tensor Input's height"
               "of DensityPriorBoxOp should be smaller than input tensor Image's"
-              "hight. But received Input's height = %d, Image's height = %d",
+              "height. But received Input's height = %d, Image's height = %d",
               input_dims[2], image_dims[2]));
 
       PADDLE_ENFORCE_LT(

paddle/fluid/operators/distributed/communicator.cc

@@ -543,7 +543,7 @@ std::unordered_set<int64_t> GeoSgdCommunicator::SparseIdsMerge(
     const std::string &splited_var_name) {
   // every batch has some sparse id, merge them into one unoredered_set
   VLOG(4) << "Sparse Ids merge var: " << var_name
-          << " splited var: " << splited_var_name;
+          << " split var: " << splited_var_name;
   auto before_run_ids_merge_ = GetCurrentUS();
   auto origin_var_name = DeltaVarToVar(var_name);
   auto splited_var_index = GetSplitedVarIndex(var_name, splited_var_name);
@@ -567,9 +567,8 @@ void GeoSgdCommunicator::SendUpdateDenseVars(
   // var_name: param.delta
   auto origin_var_name = DeltaVarToVar(var_name);
   auto splited_var_index = GetSplitedVarIndex(var_name, splited_var_name);
-  VLOG(4) << "Dense var: " << var_name
-          << " 's splited var: " << splited_var_name
-          << " splited var index: " << splited_var_index;
+  VLOG(4) << "Dense var: " << var_name << " 's split var: " << splited_var_name
+          << " split var index: " << splited_var_index;
   auto before_run_send_dense = GetCurrentUS();
   auto cpu_ctx = paddle::platform::CPUDeviceContext();
 
@@ -592,7 +591,7 @@ void GeoSgdCommunicator::SendUpdateDenseVars(
     begin_loc = absolute_section_[origin_var_name][splited_var_index];
     dimension = total_element / vars_first_dimension_[origin_var_name];
     total_element = section * dimension;
-    VLOG(4) << "Dense splited var: " << splited_var_name
+    VLOG(4) << "Dense split var: " << splited_var_name
             << " section: " << section << " dimension: " << dimension
             << " begin loc: " << begin_loc << " total_element "
             << total_element;
@@ -600,12 +599,12 @@ void GeoSgdCommunicator::SendUpdateDenseVars(
 
   auto *var_x_data = var_x_tensor.mutable_data<float>(var_x_tensor.place()) +
                      begin_loc * dimension;
-  VLOG(4) << "Dense splited var: " << splited_var_name << " var_x_data[0] "
+  VLOG(4) << "Dense split var: " << splited_var_name << " var_x_data[0] "
           << var_x_data[0] << " var_x_data[end] "
           << var_x_data[total_element - 1];
   auto *var_y_data = var_y_tensor.mutable_data<float>(var_y_tensor.place()) +
                      begin_loc * dimension;
-  VLOG(4) << "Dense splited var: " << splited_var_name << " var_y_data[0] "
+  VLOG(4) << "Dense split var: " << splited_var_name << " var_y_data[0] "
           << var_y_data[0] << " var_y_data[end] "
           << var_y_data[total_element - 1];
 
@@ -616,14 +615,14 @@ void GeoSgdCommunicator::SendUpdateDenseVars(
   var_z_tensor->mutable_data<float>(dims, cpu_ctx.GetPlace());
   auto *var_z_data = var_z_tensor->mutable_data<float>(cpu_ctx.GetPlace());
 
-  VLOG(4) << "Dense splited var: " << splited_var_name << "var_z_data[0] "
+  VLOG(4) << "Dense split var: " << splited_var_name << "var_z_data[0] "
           << var_z_data[0] << " var_z_data[end] "
           << var_z_data[total_element - 1];
 
   // calc sub = var_training - var_old
   auto blas = math::GetBlas<paddle::platform::CPUDeviceContext, float>(cpu_ctx);
   blas.VSUB(total_element, var_x_data, var_y_data, var_z_data);
-  VLOG(4) << "Dense splited var: " << splited_var_name << " var_z_data[0] "
+  VLOG(4) << "Dense split var: " << splited_var_name << " var_z_data[0] "
           << var_z_data[0] << " var_z_data[end] "
           << var_z_data[total_element - 1];
 
@@ -633,7 +632,7 @@ void GeoSgdCommunicator::SendUpdateDenseVars(
 
   // calc var_old += var_delta
   blas.VADD(total_element, var_y_data, var_z_data, var_y_data);
-  VLOG(4) << "Dense splited var: " << splited_var_name << " var_y_data[0] "
+  VLOG(4) << "Dense split var: " << splited_var_name << " var_y_data[0] "
           << var_y_data[0] << " var_y_data[end] "
           << var_y_data[total_element - 1];
 
@@ -763,7 +762,7 @@ void GeoSgdCommunicator::RecvUpdateDenseVars(
     section = dims[0];
     begin_loc = absolute_section_[origin_var_name][splited_var_index];
     dimension = total_element / section;
-    VLOG(4) << "Dense splited var: " << splited_var_name
+    VLOG(4) << "Dense split var: " << splited_var_name
             << " section: " << section << " dimension: " << dimension
             << " begin loc: " << begin_loc << " total_element "
             << total_element;
@@ -771,18 +770,18 @@ void GeoSgdCommunicator::RecvUpdateDenseVars(
 
   auto *var_x_data = var_x_tensor.mutable_data<float>(var_x_tensor.place()) +
                      begin_loc * dimension;
-  VLOG(4) << "Dense splited var: " << splited_var_name << " var_x_data[0] "
+  VLOG(4) << "Dense split var: " << splited_var_name << " var_x_data[0] "
           << var_x_data[0] << " var_x_data[end] "
           << var_x_data[total_element - 1];
 
   auto *var_y_data = var_y_tensor.mutable_data<float>(var_y_tensor.place()) +
                      begin_loc * dimension;
-  VLOG(4) << "Dense splited var: " << splited_var_name << " var_y_data[0] "
+  VLOG(4) << "Dense split var: " << splited_var_name << " var_y_data[0] "
           << var_y_data[0] << " var_y_data[end] "
           << var_y_data[total_element - 1];
 
   auto *var_z_data = var_z_tensor.mutable_data<float>(cpu_ctx.GetPlace());
-  VLOG(4) << "Dense splited var: " << splited_var_name << " var_z_data[0] "
+  VLOG(4) << "Dense split var: " << splited_var_name << " var_z_data[0] "
           << var_z_data[0] << " var_z_data[end] "
           << var_z_data[total_element - 1];
 
@@ -793,7 +792,7 @@ void GeoSgdCommunicator::RecvUpdateDenseVars(
   auto *var_y_sub_data =
       var_y_sub_tensor->mutable_data<float>(cpu_ctx.GetPlace());
 
-  VLOG(4) << "Dense splited var: " << splited_var_name << " var_y_sub_data[0] "
+  VLOG(4) << "Dense split var: " << splited_var_name << " var_y_sub_data[0] "
           << var_y_sub_data[0] << " var_y_sub_data[end] "
           << var_y_sub_data[total_element - 1];
 
@@ -801,19 +800,19 @@ void GeoSgdCommunicator::RecvUpdateDenseVars(
 
   // calc sub = pserver - old
   blas.VSUB(total_element, var_z_data, var_y_data, var_y_sub_data);
-  VLOG(4) << "Dense splited var: " << splited_var_name << " var_y_sub_data[0] "
+  VLOG(4) << "Dense split var: " << splited_var_name << " var_y_sub_data[0] "
           << var_y_sub_data[0] << " var_y_sub_data[end] "
           << var_y_sub_data[total_element - 1];
 
   // calc train += sub
   blas.VADD(total_element, var_x_data, var_y_sub_data, var_x_data);
-  VLOG(4) << "Dense splited var: " << splited_var_name << " var_x_data[0] "
+  VLOG(4) << "Dense split var: " << splited_var_name << " var_x_data[0] "
           << var_x_data[0] << " var_x_data[end] "
           << var_x_data[total_element - 1];
 
   // calc old = pserver
   blas.VCOPY(total_element, var_z_data, var_y_data);
-  VLOG(4) << "Dense splited var: " << splited_var_name << " var_y_data[0] "
+  VLOG(4) << "Dense split var: " << splited_var_name << " var_y_data[0] "
           << var_y_data[0] << " var_y_data[end] "
           << var_y_data[total_element - 1];
 
@@ -824,7 +823,7 @@ void GeoSgdCommunicator::RecvUpdateDenseVars(
 
 void GeoSgdCommunicator::RecvUpdateSparseVars(
     const std::string &var_name, const std::string &splited_var_name) {
-  // step 1: recv splited var from pserver
+  // step 1: recv split var from pserver
   auto splited_var_index = GetSplitedVarIndex(var_name, splited_var_name);
   auto origin_var_name = DeltaVarToVar(var_name);
   auto origin_splited_var_name = DeltaVarToVar(splited_var_name);

paddle/fluid/operators/distributed_ops/recv_op.cc

@@ -122,7 +122,7 @@ This operator can get variables from server side.
     AddAttr<std::vector<std::string>>(
         "recv_varnames",
         "(vector<string>) "
-        "the splited parameter varnames to be recved from pserver")
+        "the split parameter varnames to be recved from pserver")
         .SetDefault(std::vector<std::string>{});
     AddAttr<int>("do_not_run", "if recv need to really run").SetDefault(0);
   }

paddle/fluid/operators/distributed_ops/send_op.cc

@@ -116,7 +116,7 @@ This operator will send variables to listen_and_serve op at the parameter server
     AddAttr<std::vector<std::string>>(
         "send_varnames",
         "(vector<string>) "
-        "the splited output varnames to send to pserver")
+        "the split output varnames to send to pserver")
         .SetDefault(std::vector<std::string>{});
     AddAttr<int>("num",
                  "(int, default 0)"

paddle/fluid/operators/fused/conv_fusion_op.cc

@@ -28,7 +28,7 @@ namespace operators {
 //         x is Input,
 //         z is ResidualData,
 //         bias is Bias
-// When `split_channels` is set, y will be splitted into multiple outputs,
+// When `split_channels` is set, y will be split into multiple outputs,
 // each output has split_channels[i] number of channels.
 class Conv2DFusionOpMaker : public Conv2DOpMaker {
  protected:

paddle/fluid/operators/hierarchical_sigmoid_op.cc

@@ -162,7 +162,7 @@ class HierarchicalSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
         .SetDefault({});
     AddAttr<std::vector<std::string>>(
         "table_names",
-        "(string vector, the splited table names that will be fetched from "
+        "(string vector, the split table names that will be fetched from "
         "parameter server)"
         "in the order of input variables for mapping")
         .SetDefault({});

paddle/fluid/operators/lookup_table_op.cc

@@ -113,7 +113,7 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
         .SetDefault({});
     AddAttr<std::vector<std::string>>(
         "table_names",
-        "(string vector, the splited table names that will be fetched from "
+        "(string vector, the split table names that will be fetched from "
         "parameter server)"
         "in the order of input variables for mapping")
         .SetDefault({});

paddle/fluid/operators/lookup_table_v2_op.cc

@@ -100,7 +100,7 @@ class LookupTableV2OpMaker : public framework::OpProtoAndCheckerMaker {
         .SetDefault({});
     AddAttr<std::vector<std::string>>(
         "table_names",
-        "(string vector, the splited table names that will be fetched from "
+        "(string vector, the split table names that will be fetched from "
         "parameter server)"
         "in the order of input variables for mapping")
         .SetDefault({});

paddle/fluid/operators/math/blas_impl.h

@@ -777,11 +777,11 @@ void Blas<DeviceContext>::MatMul(const framework::Tensor &mat_a,
  * When user calls this API, the multiplication of two big matrixes is split
  * into multiplication of several (head_number_) small matrixes. e.g. if Mat A
  * is [3, 24] and Mat B is [24, 4], when multiple A and B with head_number as
- * 4, Mat A will be splitted as 4 matrix of [3, 6] and Mat B will be
- * (horizontally) splitted as 4 matrix of [6, 4]. The result of final matrix
+ * 4, Mat A will be split as 4 matrix of [3, 6] and Mat B will be
+ * (horizontally) split as 4 matrix of [6, 4]. The result of final matrix
  * will be 4 matrix of [3, 4], i.e. [3, 16].
  * Another example is A is [3, 8], B is [2, 16], head_number is 4. In this
- * case, A will be splitted as [3, 2], B will be (vertically) splitted as
+ * case, A will be split as [3, 2], B will be (vertically) split as
  * [2, 4]. The final result will be 4 matrix of 4 matrix of [3,4], i.e. [3, 16]
  */
 template <typename DeviceContext>

paddle/fluid/operators/math/matrix_bit_code.h

@@ -106,7 +106,7 @@ class SimpleCode {
       : c_(static_cast<size_t>(ids[code]) + num_classes) {}
   /**
    * Here the id of root should be 1 rather than 0, thus the encoding of class c
-   * is `c + num_classes` and all siblings can get the same weight indice using
+   * is `c + num_classes` and all siblings can get the same weight index using
    * prefixes.
    * Weight index is the prefixes of encoding, thus leave out the right most
    * bit in calc_index.
@@ -133,7 +133,7 @@ class CustomCode {
   }
   /**
    * Here the id of root should be 1 rather than 0, thus the encoding of class c
-   * is `c + num_classes` and all siblings can get the same weight indice using
+   * is `c + num_classes` and all siblings can get the same weight index using
    * prefixes.
    * Weight index is the prefixes of encoding, thus leave out the right most
    * bit in calc_index.

paddle/fluid/operators/mkldnn/fc_mkldnn_op.cc

@@ -55,11 +55,12 @@ class FCPrimitiveFactory {
     }  // Otherwise, create a new one.
 
     auto in_col_dims = ctx.Attr<int>("in_num_col_dims");
-    PADDLE_ENFORCE_LE(in_col_dims, 2,
-                      platform::errors::Unimplemented(
-                          "DNNL FC doesn't support in_num_col_dims paramter to "
-                          "be higher than "
-                          "2."));
+    PADDLE_ENFORCE_LE(
+        in_col_dims, 2,
+        platform::errors::Unimplemented(
+            "DNNL FC doesn't support in_num_col_dims parameter to "
+            "be higher than "
+            "2."));
     if (in_col_dims == 2) {
       PADDLE_ENFORCE_EQ(
           input->dims().size(), 3,