[CodeStyle][Typos][P-[42-48]] Fix typo (producted,progam,progrss,propogated,Propogation,protocal,PROTOCAL,pthon,pyrhon)

CMakeLists.txt

@@ -283,7 +283,7 @@ option(WITH_MULTINODE_TESTING "Test multinode apis and ops" OFF)
 option(WITH_MKL "Compile PaddlePaddle with MKL support." ${AVX_FOUND})
 option(WITH_SYSTEM_BLAS "Use system blas library" OFF)
 option(WITH_DISTRIBUTE "Compile with distributed support" OFF)
-option(WITH_BRPC_RDMA "Use brpc rdma as the rpc protocal" OFF)
+option(WITH_BRPC_RDMA "Use brpc rdma as the rpc protocol" OFF)
 option(ON_INFER "Turn on inference optimization and inference-lib generation"
        ON)
 option(WITH_CPP_DIST "Install PaddlePaddle C++ distribution" OFF)

_typos.toml

@@ -35,7 +35,6 @@ clen = 'clen'
 datas = 'datas'
 dota = 'dota'
 dout = "dout"
-eles = 'eles'
 entrys = 'entrys'
 feeded = 'feeded'
 grad = "grad"
@@ -58,15 +57,6 @@ ouput = 'ouput'
 outpout = 'outpout'
 ouptut = 'ouptut'
 Ouput = 'Ouput'
-producted = 'producted'
-progam = 'progam'
-progrss = 'progrss'
-propogated = 'propogated'
-Propogation = 'Propogation'
-protocal = 'protocal'
-PROTOCAL = 'PROTOCAL'
-pyrhon = 'pyrhon'
-pthon = 'pthon'
 regist = 'regist'
 Regist = 'Regist'
 Registe = 'Registe'

paddle/fluid/inference/tensorrt/convert/matrix_multiply_op.cc

@@ -114,11 +114,11 @@ class MatrixMultiplyOpConverter : public OpConverter {
         before_shape_tensors.push_back(
             GetEleTensorOfShape(input_shape_tensor, i));
       }
-      nvinfer1::ITensor* producted = Add1DConstantLayer(1);
+      nvinfer1::ITensor* produced = Add1DConstantLayer(1);
       for (int i = x_num_col_dims; i < x_rank; ++i) {
-        producted = Prod(producted, GetEleTensorOfShape(input_shape_tensor, i));
+        produced = Prod(produced, GetEleTensorOfShape(input_shape_tensor, i));
       }
-      before_shape_tensors.push_back(producted);
+      before_shape_tensors.push_back(produced);
       nvinfer1::ITensor* before_shape_tensor = Concat(before_shape_tensors);
       auto* reshape_before_layer =
           TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input1);

paddle/phi/infermeta/spmd_rules/dim_trans.cc

@@ -174,7 +174,7 @@ std::shared_ptr<DimTrans> make_split(const std::shared_ptr<DimTrans> dim,
 }
 
 // Given a `dim_trans` of an output axis, get the input axis
-// whose dim mapping should be propogated to it.
+// whose dim mapping should be propagated to it.
 // If the returned input axis is none, the output axis's
 // dim mapping should be set to -1 (replicated). For an axis
 // that is flattened from input axes, return the leftmost

paddle/phi/infermeta/spmd_rules/gather.cc

@@ -70,7 +70,7 @@ SpmdInfo GatherInferSpmdBase(const DistMetaTensor& x,
     out_axes[axis] = 'k';
   }
 
-  // Step2: Sharding Propogation
+  // Step2: Sharding Propagation
   // Step2.1: Merge input shardings
   std::vector<int64_t> x_dims_mapping(x_dims_mapping_src);
   if (axis < x_ndim) {
@@ -132,7 +132,7 @@ SpmdInfo GatherInferSpmdReverseBase(const DistMetaTensor& x,
     out_axes[axis] = 'k';
   }
 
-  // Step2: Sharding Propogation
+  // Step2: Sharding Propagation
   // Step2.1: Merge output shardings
   std::unordered_map<std::string, int64_t> axis_to_dim_map =
       ShardingMergeForTensors({{out_axes, out_dims_mapping_src}});

paddle/phi/infermeta/spmd_rules/nonzero.cc

@@ -31,7 +31,7 @@ SpmdInfo NonZeroInferSpmd(const DistMetaTensor& x) {
   std::string x_axes(x_ndim, '1');
   std::string output_axes(2, '1');
 
-  // Step2: Sharding Propogation
+  // Step2: Sharding Propagation
   // Step2.1: Merge input shardings
   std::unordered_map<std::string, int64_t> axis_to_dim_map =
       ShardingMergeForTensors({{x_axes, x_dims_mapping_src}});
@@ -65,7 +65,7 @@ SpmdInfo NonZeroInferSpmdReverse(const DistMetaTensor& x,
   std::string x_axes(x_ndim, '1');
   std::string output_axes(2, '1');
 
-  // Step2: Sharding Propogation
+  // Step2: Sharding Propagation
   // Step2.1: Merge input shardings
   std::unordered_map<std::string, int64_t> axis_to_dim_map =
       ShardingMergeForTensors({{output_axes, output_dims_mapping_src}});

paddle/phi/infermeta/spmd_rules/scatter.cc

@@ -57,7 +57,7 @@ SpmdInfo ScatterBaseInferSpmd(const DistMetaTensor& x,
   std::string out_axes = GetBroadcastAxes(x_ndim, x_ndim, alphabet);
   out_axes[0] = '1';
 
-  // Step2: Sharding Propogation
+  // Step2: Sharding Propagation
   // Step2.1: Merge input shardings
   std::unordered_map<std::string, int64_t> axis_to_dim_map =
       ShardingMergeForTensors({{index_axes, index_dims_mapping_src},
@@ -139,7 +139,7 @@ SpmdInfo ScatterBaseInferSpmdReverse(const DistMetaTensor& x,
       GetBroadcastAxes(updates_ndim, updates_ndim, alphabet);
   std::string out_axes = GetBroadcastAxes(out_ndim, out_ndim, alphabet);
 
-  // Step2: Sharding Propogation
+  // Step2: Sharding Propagation
   // Step2.1: Merge output shardings
   // the batch axis of output must be replicated
   // TODO(zhangyichen): consider the case when the output is partial

paddle/scripts/fast_install.sh

@@ -340,7 +340,7 @@ function checkLinuxPython(){
         if [ "$python_path" != "" ];then
           break
         else
-          echo "输入路径有误,未找到pyrhon"
+          echo "输入路径有误,未找到 python"
         fi
     done
   fi

python/paddle/distributed/launch/controllers/master.py

@@ -23,7 +23,7 @@
 from paddle.distributed.launch.utils.kv_client import KVClient
 from paddle.distributed.launch.utils.kv_server import KVServer
 
-ETCD_PROTOCAL = 'etcd://'
+ETCD_PROTOCOL = 'etcd://'
 
 
 def _cmp_by_ip(x):
@@ -39,7 +39,7 @@ class Master:
 
     MAIN = "main"
     STANDBY = "standby"
-    PATICIPANT = "participant"
+    PARTICIPANT = "participant"
 
     def __init__(self, ctx):
         self.ctx = ctx
@@ -64,7 +64,7 @@ def sync_peers(self, prefix, key, value, size, rank=-1) -> (list, int):
 
     @classmethod
     def factory(cls, ctx):
-        if ctx.args.master and ctx.args.master.startswith(ETCD_PROTOCAL):
+        if ctx.args.master and ctx.args.master.startswith(ETCD_PROTOCOL):
             return ETCDMaster(ctx)
         else:
             return HTTPMaster(ctx)
@@ -75,7 +75,7 @@ def lazy_init(self):
         if self.initialized:
             return
 
-        self.role = Master.PATICIPANT
+        self.role = Master.PARTICIPANT
 
         if self.ctx.args.master:
             self.endpoint = self.ctx.args.master

python/paddle/jit/dy2static/convert_operators.py

@@ -675,7 +675,7 @@ def convert_super(super_fn):
 class VariableTuple:
     """
     this class will cause enumerate can't be wrapped by other iterator change function.
-    this will be fixed when list<Variable> is producted.
+    this will be fixed when list<Variable> is produced.
     VariableTuple can only deal with variables which is fixed.
     """
 

python/paddle/tensor/linalg.py

@@ -2747,7 +2747,7 @@ def mv(x: Tensor, vec: Tensor, name: str | None = None) -> Tensor:
             For more information, please refer to :ref:`api_guide_Name`. Default is None.
 
     Returns:
-        Tensor: The tensor which is producted by x and vec.
+        Tensor: The tensor which is produced by x and vec.
 
     Examples:
         .. code-block:: python

test/deprecated/legacy_test/auto_parallel_op_test.py

@@ -428,7 +428,7 @@ class AutoParallelForwardChecker:
     def __init__(
         self,
         op_type,
-        pthon_api,
+        python_api,
         dtype,
         placements_map,
         inputs,
@@ -441,7 +441,7 @@ def __init__(
         self.checker_name = "AutoParallelForwardChecker"
         self.init_checker(
             op_type,
-            pthon_api,
+            python_api,
             dtype,
             placements_map,
             inputs,
@@ -455,7 +455,7 @@ def __init__(
     def init_checker(
         self,
         op_type,
-        pthon_api,
+        python_api,
         dtype,
         placements_map,
         inputs,
@@ -466,7 +466,7 @@ def init_checker(
         python_out_sig=None,
     ):
         self.op_type = op_type
-        self.public_python_api = pthon_api
+        self.public_python_api = python_api
         self.dtype = np.dtype(dtype)
         self.placements_map = placements_map
         self.inputs = inputs
@@ -667,7 +667,7 @@ class AutoParallelGradChecker(AutoParallelForwardChecker):
     def __init__(
         self,
         op_type,
-        pthon_api,
+        python_api,
         dtype,
         placements_map,
         inputs,
@@ -683,7 +683,7 @@ def __init__(
     ):
         super().__init__(
             op_type,
-            pthon_api,
+            python_api,
             dtype,
             placements_map,
             inputs,

test/legacy_test/auto_parallel_op_test.py

@@ -427,7 +427,7 @@ class AutoParallelForwardChecker:
     def __init__(
         self,
         op_type,
-        pthon_api,
+        python_api,
         dtype,
         placements_map,
         inputs,
@@ -440,7 +440,7 @@ def __init__(
         self.checker_name = "AutoParallelForwardChecker"
         self.init_checker(
             op_type,
-            pthon_api,
+            python_api,
             dtype,
             placements_map,
             inputs,
@@ -454,7 +454,7 @@ def __init__(
     def init_checker(
         self,
         op_type,
-        pthon_api,
+        python_api,
         dtype,
         placements_map,
         inputs,
@@ -465,7 +465,7 @@ def init_checker(
         python_out_sig=None,
     ):
         self.op_type = op_type
-        self.public_python_api = pthon_api
+        self.public_python_api = python_api
         self.dtype = np.dtype(dtype)
         self.placements_map = placements_map
         self.inputs = inputs
@@ -666,7 +666,7 @@ class AutoParallelGradChecker(AutoParallelForwardChecker):
     def __init__(
         self,
         op_type,
-        pthon_api,
+        python_api,
         dtype,
         placements_map,
         inputs,
@@ -682,7 +682,7 @@ def __init__(
     ):
         super().__init__(
             op_type,
-            pthon_api,
+            python_api,
             dtype,
             placements_map,
             inputs,