[CodeStyle][Typos][V-[1-11],W-1] Fix typos (vaccum,valud,validat,VAILD,valus,valuse,Varible,varaible,vecotr,vesion,verson,Vetical,vunerability,varn) (#70680)

enkilee · web-flow · commit 044b37e1f21e · 2025-01-13T14:15:56.000+08:00
* fix

* fix
diff --git a/SECURITY.md b/SECURITY.md
@@ -25,7 +25,7 @@ PaddlePaddle always take code security seriously. However, due to the complexity
 
 ### Code security tools
 
-PaddlePaddle security team attaches great importance to the security of the framework. In order to find and fix security issues as soon as possible, we are continuously conducting code security audit and developing automatic vunerability discovery tools. We have already open sourced some of them to the community, hoping this could encourage people to contribute and improve the safety and robustness of PaddlePaddle. [This tool](https://github.com/PaddlePaddle/PaddleSleeve/tree/main/CodeSecurity) includes two parts. The dynamic part includes some op fuzzer samples. And the static part includes some CodeQL samples. Both of them are aim to find vulnerabilities in PaddlePaddle framework codebase. By referring the samples, security researchers can write their own fuzzers or QLs to test more PaddlePaddle modules, and find more code security issues.
+PaddlePaddle security team attaches great importance to the security of the framework. In order to find and fix security issues as soon as possible, we are continuously conducting code security audit and developing automatic vulnerability discovery tools. We have already open sourced some of them to the community, hoping this could encourage people to contribute and improve the safety and robustness of PaddlePaddle. [This tool](https://github.com/PaddlePaddle/PaddleSleeve/tree/main/CodeSecurity) includes two parts. The dynamic part includes some op fuzzer samples. And the static part includes some CodeQL samples. Both of them are aim to find vulnerabilities in PaddlePaddle framework codebase. By referring the samples, security researchers can write their own fuzzers or QLs to test more PaddlePaddle modules, and find more code security issues.
 
 ### Reporting vulnerabilities
 
diff --git a/_typos.toml b/_typos.toml
@@ -43,6 +43,7 @@ Halfs = 'Halfs'
 kinf = 'kinf'
 pash = 'pash'
 unpacket = "unpacket"
+vaccum = 'vaccum'
 
 # These words need to be fixed
 Indexs = 'Indexs'
@@ -292,19 +293,6 @@ unsed = 'unsed'
 uesd = 'uesd'
 usefull = 'usefull'
 usless = 'usless'
-vaccum = 'vaccum'
-valud = 'valud'
-VAILD = 'VAILD'
-valus = 'valus'
-valuse = 'valuse'
-Varible = 'Varible'
-varaible = 'varaible'
-vecotr = 'vecotr'
-verson = 'verson'
-vesion = 'vesion'
-Vetical = 'Vetical'
-vunerability = 'vunerability'
-varn = 'varn'
 warpped = 'warpped'
 warpper = 'warpper'
 Warpper = 'Warpper'
diff --git a/paddle/cinn/hlir/framework/pir/fusion_info.cc b/paddle/cinn/hlir/framework/pir/fusion_info.cc
@@ -175,7 +175,7 @@ void FusionInfo::ParseOpInfos(const OpLoweringGroup& group) {
 void FusionInfo::ParseInputDimExprs(const OpLoweringGroup& group) {
   // NOTE(Aurelius84): [Why try get DimExpr from Group firstly? ]
   // In case of BroadcastTree, we will clone many Groups containing same ops.
-  // But its input valus is defining outside and will have same DimExprs in
+  // But its input values is defining outside and will have same DimExprs in
   // global ShapeAnalysis, which leading hash conflict unexpected.
   const auto TryGetDimExprsFromGroup = [&](const ::pir::Value& value) -> bool {
     if (!group.HasShapeOrDataExprs(value)) return false;
diff --git a/paddle/cinn/operator_fusion/pir_graph_analyzing/dim_relation.cc b/paddle/cinn/operator_fusion/pir_graph_analyzing/dim_relation.cc
@@ -20,12 +20,12 @@
 namespace cinn::fusion {
 
 ValueUsage GetValueUsage(const pir::Value& v, const size_t usage_idx) {
-  ValueUsage valud_dim;
+  ValueUsage value_dim;
   size_t rank = GetRank(v);
   for (size_t i = 0; i < rank; ++i) {
-    valud_dim.emplace_back(v, i, usage_idx);
+    value_dim.emplace_back(v, i, usage_idx);
   }
-  return valud_dim;
+  return value_dim;
 }
 
 static std::vector<ValueUsage> GetInputValueUsage(pir::Operation* op) {
diff --git a/paddle/fluid/framework/data_feed.cu b/paddle/fluid/framework/data_feed.cu
@@ -1804,7 +1804,7 @@ int GraphDataGenerator::FillSlotFeature(uint64_t *d_walk,
       slot_num);
 
   std::vector<std::shared_ptr<phi::Allocation>> ins_slot_num(slot_num, nullptr);
-  std::vector<uint64_t *> ins_slot_num_vecotr(slot_num, NULL);
+  std::vector<uint64_t *> ins_slot_num_vector(slot_num, NULL);
   std::shared_ptr<phi::Allocation> d_ins_slot_num_vector =
       memory::AllocShared(place_, (slot_num) * sizeof(uint64_t *));
   uint64_t **d_ins_slot_num_vector_ptr =
@@ -1815,15 +1815,15 @@ int GraphDataGenerator::FillSlotFeature(uint64_t *d_walk,
     if ((*feed_info_)[feed_vec_idx + 2 * i].type[0] == 'u') {
       ins_slot_num[ii] =
           memory::AllocShared(place_, key_num * sizeof(uint64_t));
-      ins_slot_num_vecotr[ii] =
+      ins_slot_num_vector[ii] =
           reinterpret_cast<uint64_t *>(ins_slot_num[ii]->ptr());
       ii++;
     }
   }
   if (slot_num > 0) {
     CUDA_CHECK(
         cudaMemcpyAsync(reinterpret_cast<char *>(d_ins_slot_num_vector_ptr),
-                        ins_slot_num_vecotr.data(),
+                        ins_slot_num_vector.data(),
                         sizeof(uint64_t *) * slot_num,
                         cudaMemcpyHostToDevice,
                         train_stream_));
@@ -1844,7 +1844,7 @@ int GraphDataGenerator::FillSlotFeature(uint64_t *d_walk,
     size_t temp_storage_bytes = 0;
     CUDA_CHECK(cub::DeviceScan::InclusiveSum(NULL,
                                              temp_storage_bytes,
-                                             ins_slot_num_vecotr[0],
+                                             ins_slot_num_vector[0],
                                              slot_lod_tensor_ptr_[0] + 1,
                                              key_num,
                                              train_stream_));
@@ -1863,7 +1863,7 @@ int GraphDataGenerator::FillSlotFeature(uint64_t *d_walk,
             slot_lod_tensor_ptr_[ii], 0, sizeof(uint64_t), train_stream_));
         CUDA_CHECK(cub::DeviceScan::InclusiveSum(d_temp_storage->ptr(),
                                                  temp_storage_bytes,
-                                                 ins_slot_num_vecotr[ii],
+                                                 ins_slot_num_vector[ii],
                                                  slot_lod_tensor_ptr_[ii] + 1,
                                                  key_num,
                                                  train_stream_));
@@ -1893,7 +1893,7 @@ int GraphDataGenerator::FillSlotFeature(uint64_t *d_walk,
             d_feature_list_ptr,
             d_feature_size_prefixsum_ptr,
             d_each_ins_slot_num_inner_prefix_ptr,
-            ins_slot_num_vecotr[ii],
+            ins_slot_num_vector[ii],
             slot_lod_tensor_ptr_[ii],
             slot_tensor_ptr_[ii],
             ii,
@@ -2109,7 +2109,7 @@ int GraphDataGenerator::FillFloatFeature(uint64_t *d_walk,
 
   std::vector<std::shared_ptr<phi::Allocation>> ins_slot_num(float_slot_num_,
                                                              nullptr);
-  std::vector<uint64_t *> ins_slot_num_vecotr(float_slot_num_, NULL);
+  std::vector<uint64_t *> ins_slot_num_vector(float_slot_num_, NULL);
   std::shared_ptr<phi::Allocation> d_ins_slot_num_vector =
       memory::AllocShared(place_, (float_slot_num_) * sizeof(uint64_t *));
   uint64_t **d_ins_slot_num_vector_ptr =
@@ -2120,7 +2120,7 @@ int GraphDataGenerator::FillFloatFeature(uint64_t *d_walk,
     if ((*feed_info_)[feed_vec_idx + 2 * i].type[0] == 'f') {
       ins_slot_num[ii] =
           memory::AllocShared(place_, key_num * sizeof(uint64_t));
-      ins_slot_num_vecotr[ii] =
+      ins_slot_num_vector[ii] =
           reinterpret_cast<uint64_t *>(ins_slot_num[ii]->ptr());
       ii++;
     }
@@ -2129,7 +2129,7 @@ int GraphDataGenerator::FillFloatFeature(uint64_t *d_walk,
   if (float_slot_num_ > 0) {
     CUDA_CHECK(
         cudaMemcpyAsync(reinterpret_cast<char *>(d_ins_slot_num_vector_ptr),
-                        ins_slot_num_vecotr.data(),
+                        ins_slot_num_vector.data(),
                         sizeof(uint64_t *) * float_slot_num_,
                         cudaMemcpyHostToDevice,
                         train_stream_));
@@ -2152,7 +2152,7 @@ int GraphDataGenerator::FillFloatFeature(uint64_t *d_walk,
     size_t temp_storage_bytes = 0;
     CUDA_CHECK(cub::DeviceScan::InclusiveSum(NULL,
                                              temp_storage_bytes,
-                                             ins_slot_num_vecotr[0],
+                                             ins_slot_num_vector[0],
                                              slot_lod_tensor_ptr_[0] + 1,
                                              key_num,
                                              train_stream_));
@@ -2170,7 +2170,7 @@ int GraphDataGenerator::FillFloatFeature(uint64_t *d_walk,
             slot_lod_tensor_ptr_[ii], 0, sizeof(uint64_t), train_stream_));
         CUDA_CHECK(cub::DeviceScan::InclusiveSum(d_temp_storage->ptr(),
                                                  temp_storage_bytes,
-                                                 ins_slot_num_vecotr[ii],
+                                                 ins_slot_num_vector[ii],
                                                  slot_lod_tensor_ptr_[ii] + 1,
                                                  key_num,
                                                  train_stream_));
@@ -2200,7 +2200,7 @@ int GraphDataGenerator::FillFloatFeature(uint64_t *d_walk,
             d_feature_list_ptr,
             d_feature_size_prefixsum_ptr,
             d_each_ins_slot_num_inner_prefix_ptr,
-            ins_slot_num_vecotr[ii],
+            ins_slot_num_vector[ii],
             slot_lod_tensor_ptr_[ii],
             slot_tensor_ptr_[ii],
             ii,
diff --git a/paddle/fluid/pir/dialect/op_generator/op_gen.py b/paddle/fluid/pir/dialect/op_generator/op_gen.py
@@ -1191,7 +1191,7 @@ def GenOneDnnExtraAttrsDefaultValue(onednn_extra_args):
 """
     ARRAY_ATTRIBUTE_TEMPLATE = """  std::vector<pir::Attribute> vec_{attr_name};
 {{
-    std::vector<{cpp_type}> vec_values = {attr_valuse};
+    std::vector<{cpp_type}> vec_values = {attr_values};
     for (size_t i = 0; i < static_cast<size_t>(vec_values.size()); i++) {{
         {create_attribute}
         vec_{attr_name}.push_back(attr_{attr_name});
@@ -1215,7 +1215,7 @@ def GenOneDnnExtraAttrsDefaultValue(onednn_extra_args):
                     cpp_type=onednn_extra_args[idx]['typename'].replace(
                         '[]', ''
                     ),
-                    attr_valuse=onednn_extra_args[idx]['default_value'],
+                    attr_values=onednn_extra_args[idx]['default_value'],
                     create_attribute=INTARRAY_STR_TEMPLATE.format(
                         attr_name=onednn_extra_args[idx]['name'],
                         op_attribute_type=inner_attribute_type,
@@ -1228,7 +1228,7 @@ def GenOneDnnExtraAttrsDefaultValue(onednn_extra_args):
                     cpp_type=onednn_extra_args[idx]['typename'].replace(
                         '[]', ''
                     ),
-                    attr_valuse=onednn_extra_args[idx]['default_value'],
+                    attr_values=onednn_extra_args[idx]['default_value'],
                     create_attribute=SCALAR_STR_TEMPLATE.format(
                         attr_name=onednn_extra_args[idx]['name'],
                         attr="vec_values[i]",
@@ -1240,7 +1240,7 @@ def GenOneDnnExtraAttrsDefaultValue(onednn_extra_args):
                     cpp_type=onednn_extra_args[idx]['typename'].replace(
                         '[]', ''
                     ),
-                    attr_valuse=onednn_extra_args[idx]['default_value'],
+                    attr_values=onednn_extra_args[idx]['default_value'],
                     create_attribute=STR_TEMPLATE.format(
                         attr_name=onednn_extra_args[idx]['name'],
                         op_attribute_type=inner_attribute_type,
diff --git a/paddle/fluid/platform/device/ipu/ipu_utils.h b/paddle/fluid/platform/device/ipu/ipu_utils.h
@@ -104,7 +104,7 @@ const popart::DataType OnnxDType2PopartType(const ONNXDataType type);
 const ONNXDataType VarType2OnnxDType(const VarType::Type type);
 // VarType::Type to String in Popart
 const std::string VarType2PopartStr(const VarType::Type type);
-// Get bool from environment varaible
+// Get bool from environment variable
 const bool GetBoolEnv(const std::string& str);
 // Request number of ipus must be pow(2, n)
 const int RequestIpus(const int num_ipus);
diff --git a/paddle/phi/kernels/funcs/norm_utils.cu.h b/paddle/phi/kernels/funcs/norm_utils.cu.h
@@ -667,12 +667,12 @@ void NormDoubleGradFunctor(const DeviceContext &ctx,
 }
 
 template <typename T, typename BnT>
-__device__ __forceinline__ void BlockReduceByVetical(BnT x_sum,
-                                                     BnT x_square_sum,
-                                                     BnT *smem_sum,
-                                                     BnT *smem_square_sum,
-                                                     BnT *x_sum_out,
-                                                     BnT *x_square_sum_out) {
+__device__ __forceinline__ void BlockReduceByVertical(BnT x_sum,
+                                                      BnT x_square_sum,
+                                                      BnT *smem_sum,
+                                                      BnT *smem_square_sum,
+                                                      BnT *x_sum_out,
+                                                      BnT *x_square_sum_out) {
   int tid = threadIdx.x + threadIdx.y * blockDim.x;
 #pragma unroll
   for (int offset = blockDim.y / 2; offset > 0; offset >>= 1) {
@@ -733,7 +733,7 @@ __device__ __forceinline__ void ReduceSumPost(const int C,  // channels
     }
 
     // vertical block sum
-    funcs::BlockReduceByVetical<T, BnT>(
+    funcs::BlockReduceByVertical<T, BnT>(
         *sum1, *sum2, &cache1[0], &cache2[0], sum1, sum2);
   }
 }
diff --git a/paddle/phi/kernels/funcs/sync_batch_norm_utils.h b/paddle/phi/kernels/funcs/sync_batch_norm_utils.h
@@ -193,7 +193,7 @@ __global__ void KeBackwardLocalStats2D(const T *dy,
       auto x_i = static_cast<BatchNormParamType<T>>(x[id]);
       sum2 += g * (x_i - mean);
     }
-    funcs::BlockReduceByVetical<T, BatchNormParamType<T>>(
+    funcs::BlockReduceByVertical<T, BatchNormParamType<T>>(
         sum1, sum2, &smem_sum[0], &smem_square_sum[0], &sum1, &sum2);
 
     if (gridDim.y > 1) {
@@ -304,7 +304,7 @@ static __global__ void KeBNBackwardScaleBias2D(
       db_sum += dy_i;
     }
 
-    funcs::BlockReduceByVetical<T, BatchNormParamType<T>>(
+    funcs::BlockReduceByVertical<T, BatchNormParamType<T>>(
         ds_sum, db_sum, &smem_sum[0], &smem_square_sum[0], &ds_sum, &db_sum);
 
     if (gridDim.y > 1) {
diff --git a/paddle/phi/kernels/fusion/fp8_gemm/fp8_gemm_with_cublasLt/cublaslt_gemm.h b/paddle/phi/kernels/fusion/fp8_gemm/fp8_gemm_with_cublasLt/cublaslt_gemm.h
@@ -328,7 +328,7 @@ void cublaslt_fp8_fp8_fp16_gemm(
     PADDLE_ENFORCE_EQ(bias->dims()[0] == n,
                       true,
                       common::errors::InvalidArgument(
-                          "FP8 gemm bias_vecotr_dim needs to equal "
+                          "FP8 gemm bias_vector_dim needs to equal "
                           "to n, n = %d, but bias_vector_dim = %d",
                           n,
                           bias->dims()[0]));
@@ -385,7 +385,7 @@ void cublaslt_fp8_fp8_bf16_gemm(
     PADDLE_ENFORCE_EQ(bias->dims()[0] == n,
                       true,
                       common::errors::InvalidArgument(
-                          "FP8 gemm bias_vecotr_dim needs to equal "
+                          "FP8 gemm bias_vector_dim needs to equal "
                           "to n, n = %d, but bias_vector_dim = %d",
                           n,
                           bias->dims()[0]));
diff --git a/paddle/phi/kernels/gpu/batch_norm_grad_kernel.cu b/paddle/phi/kernels/gpu/batch_norm_grad_kernel.cu
@@ -286,12 +286,12 @@ static __global__ void BNBackward2DChannelLastStage1(
     }
 
     // vertical block sum
-    funcs::BlockReduceByVetical<T, BatchNormParamType<T>>(x_sum,
-                                                          x_square_sum,
-                                                          &smem_sum[0],
-                                                          &smem_square_sum[0],
-                                                          &x_sum,
-                                                          &x_square_sum);
+    funcs::BlockReduceByVertical<T, BatchNormParamType<T>>(x_sum,
+                                                           x_square_sum,
+                                                           &smem_sum[0],
+                                                           &smem_square_sum[0],
+                                                           &x_sum,
+                                                           &x_square_sum);
 
     if (gridDim.y > 1) {
       __shared__ bool is_last_block_done;
@@ -366,7 +366,7 @@ static __global__ void BNBackward2DChannelLastStage2(
     }
 
     // vertical block sum
-    funcs::BlockReduceByVetical<T, BatchNormParamType<T>>(
+    funcs::BlockReduceByVertical<T, BatchNormParamType<T>>(
         ds_sum, db_sum, &smem_ds_sum[0], &smem_db_sum[0], &ds_sum, &db_sum);
 
     if (gridDim.y > 1) {
diff --git a/paddle/phi/kernels/gpu/batch_norm_kernel.cu b/paddle/phi/kernels/gpu/batch_norm_kernel.cu
@@ -247,12 +247,12 @@ static __global__ void BNForwardTraining2DChannelLastCompStat(
     }
 
     // vertical block sum
-    funcs::BlockReduceByVetical<T, BatchNormParamType<T>>(x_sum,
-                                                          x_square_sum,
-                                                          &smem_sum[0],
-                                                          &smem_square_sum[0],
-                                                          &x_sum,
-                                                          &x_square_sum);
+    funcs::BlockReduceByVertical<T, BatchNormParamType<T>>(x_sum,
+                                                           x_square_sum,
+                                                           &smem_sum[0],
+                                                           &smem_square_sum[0],
+                                                           &x_sum,
+                                                           &x_square_sum);
 
     if (gridDim.y > 1) {
       __shared__ bool is_last_block_done;
diff --git a/python/paddle/distributed/fleet/base/util_factory.py b/python/paddle/distributed/fleet/base/util_factory.py
@@ -738,28 +738,30 @@ def need_highlight(name: str) -> bool:
         # TODO(gongwb): format the var.type
         # create var
         if var.persistable:
-            varn = graph.add_param(
+            var_name = graph.add_param(
                 var.name,
                 str(var.type).replace("\n", "<br />", 1),
                 highlight=need_highlight(var.name),
             )
         else:
-            varn = graph.add_arg(var.name, highlight=need_highlight(var.name))
-        vars[var.name] = varn
+            var_name = graph.add_arg(
+                var.name, highlight=need_highlight(var.name)
+            )
+        vars[var.name] = var_name
 
     def add_op_link_var(op, var, op2var=False):
         for arg in var.arguments:
             if arg not in vars:
                 # add missing variables as argument
                 vars[arg] = graph.add_arg(arg, highlight=need_highlight(arg))
-            varn = vars[arg]
+            var_name = vars[arg]
             highlight = need_highlight(op.description) or need_highlight(
-                varn.description
+                var_name.description
             )
             if op2var:
-                graph.add_edge(op, varn, highlight=highlight)
+                graph.add_edge(op, var_name, highlight=highlight)
             else:
-                graph.add_edge(varn, op, highlight=highlight)
+                graph.add_edge(var_name, op, highlight=highlight)
 
     for op in desc.ops:
         opn = graph.add_op(op.type, highlight=need_highlight(op.type))
diff --git a/python/setup.py.in b/python/setup.py.in
@@ -40,7 +40,7 @@ def git_commit() -> str:
     return str(git_commit)
 
 def _get_version_detail(idx):
-    assert idx < 3, "vesion info consists of %(major)d.%(minor)d.%(patch)d, \
+    assert idx < 3, "version info consists of %(major)d.%(minor)d.%(patch)d, \
         so detail index must less than 3"
 
     if re.match(r'@TAG_VERSION_REGEX@', '@PADDLE_VERSION@'):
diff --git a/python/setup_cinn.py.in b/python/setup_cinn.py.in
@@ -24,7 +24,7 @@ def git_commit():
     return str(git_commit)
 
 def _get_version_detail(idx):
-    assert idx < 3, "vesion info consists of %(major)d.%(minor)d.%(patch)d, \
+    assert idx < 3, "version info consists of %(major)d.%(minor)d.%(patch)d, \
         so detail index must less than 3"
 
     if re.match(r'${TAG_VERSION_REGEX}', '${PADDLE_VERSION}'):
diff --git a/test/cpp/utils/span_test.cc b/test/cpp/utils/span_test.cc
@@ -996,7 +996,7 @@ TEST(iterator, span) {
     PADDLE_ENFORCE_EQ(
         std::is_sorted(vec.cbegin(), vec.cend()),
         true,
-        common::errors::Fatal("Varible `vec` should be sorted, please check"));
+        common::errors::Fatal("Variable `vec` should be sorted, please check"));
   }
 
   {
@@ -1006,7 +1006,7 @@ TEST(iterator, span) {
         std::equal(s.rbegin(), s.rend(), vec.crbegin()),
         true,
         common::errors::Fatal(
-            "Varible `s` is not equal to its self by using rbegin(), rend() "
+            "Variable `s` is not equal to its self by using rbegin(), rend() "
             "and crbegin() with std::equal, please check related function"));
   }
 }
diff --git a/test/deprecated/ir/inference/test_trt_convert_depthwise_conv2d_deprecated.py b/test/deprecated/ir/inference/test_trt_convert_depthwise_conv2d_deprecated.py
diff --git a/test/ir/inference/test_trt_convert_pool2d.py b/test/ir/inference/test_trt_convert_pool2d.py
diff --git a/tools/gen_ut_cmakelists.py b/tools/gen_ut_cmakelists.py

Original file line number	Diff line number	Diff line change
`@@ -20,12 +20,12 @@`
`20`	`20`	`namespace cinn::fusion {`
`21`	`21`
`22`	`22`	`ValueUsage GetValueUsage(const pir::Value& v, const size_t usage_idx) {`
`23`		`- ValueUsage valud_dim;`
	`23`	`+ ValueUsage value_dim;`
`24`	`24`	`size_t rank = GetRank(v);`
`25`	`25`	`for (size_t i = 0; i < rank; ++i) {`
`26`		`- valud_dim.emplace_back(v, i, usage_idx);`
	`26`	`+ value_dim.emplace_back(v, i, usage_idx);`
`27`	`27`	`}`
`28`		`- return valud_dim;`
	`28`	`+ return value_dim;`
`29`	`29`	`}`
`30`	`30`
`31`	`31`	`static std::vector<ValueUsage> GetInputValueUsage(pir::Operation* op) {`
Original file line number	Diff line number	Diff line change
`@@ -193,7 +193,7 @@ __global__ void KeBackwardLocalStats2D(const T *dy,`
`193`	`193`	`auto x_i = static_cast<BatchNormParamType<T>>(x[id]);`
`194`	`194`	`sum2 += g * (x_i - mean);`
`195`	`195`	`}`
`196`		`- funcs::BlockReduceByVetical<T, BatchNormParamType<T>>(`
	`196`	`+ funcs::BlockReduceByVertical<T, BatchNormParamType<T>>(`
`197`	`197`	`sum1, sum2, &smem_sum[0], &smem_square_sum[0], &sum1, &sum2);`
`198`	`198`
`199`	`199`	`if (gridDim.y > 1) {`
`@@ -304,7 +304,7 @@ static __global__ void KeBNBackwardScaleBias2D(`
`304`	`304`	`db_sum += dy_i;`
`305`	`305`	`}`
`306`	`306`
`307`		`- funcs::BlockReduceByVetical<T, BatchNormParamType<T>>(`
	`307`	`+ funcs::BlockReduceByVertical<T, BatchNormParamType<T>>(`
`308`	`308`	`ds_sum, db_sum, &smem_sum[0], &smem_square_sum[0], &ds_sum, &db_sum);`
`309`	`309`
`310`	`310`	`if (gridDim.y > 1) {`
Original file line number	Diff line number	Diff line change
`@@ -996,7 +996,7 @@ TEST(iterator, span) {`
`996`	`996`	`PADDLE_ENFORCE_EQ(`
`997`	`997`	`std::is_sorted(vec.cbegin(), vec.cend()),`
`998`	`998`	`true,`
`999`		- common::errors::Fatal("Varible `vec` should be sorted, please check"));
	`999`	+ common::errors::Fatal("Variable `vec` should be sorted, please check"));
`1000`	`1000`	`}`
`1001`	`1001`
`1002`	`1002`	`{`
`@@ -1006,7 +1006,7 @@ TEST(iterator, span) {`
`1006`	`1006`	`std::equal(s.rbegin(), s.rend(), vec.crbegin()),`
`1007`	`1007`	`true,`
`1008`	`1008`	`common::errors::Fatal(`
`1009`		- "Varible `s` is not equal to its self by using rbegin(), rend() "
	`1009`	+ "Variable `s` is not equal to its self by using rbegin(), rend() "
`1010`	`1010`	`"and crbegin() with std::equal, please check related function"));`
`1011`	`1011`	`}`
`1012`	`1012`	`}`