Fix transfrom transform, etc

paddle/fluid/operators/pull_gpups_sparse_op.h

@@ -30,7 +30,7 @@ static void PullGpuPSSparseFunctor(const framework::ExecutionContext &ctx) {
   auto embedding_size_vec = ctx.Attr<std::vector<int>>("size");
   const auto slot_size = inputs.size();
   std::vector<const uint64_t *> all_keys(slot_size);
-  // GpuPSPS only supports float now
+  // GpuPS only supports float now
   std::vector<float *> all_values(slot_size);
   std::vector<int64_t> slot_lengths(slot_size);
   for (size_t i = 0; i < slot_size; i++) {
@@ -80,7 +80,7 @@ static void PushGpuPSSparseFunctor(const framework::ExecutionContext &ctx) {
                         cur_batch_size,
                         platform::errors::PreconditionNotMet(
                             "The batch size of all input slots should be same, "
-                            "please cheack"));
+                            "please check"));
     }
     const float *grad_value = d_output[i]->data<float>();
     all_grad_values[i] = grad_value;

paddle/fluid/operators/py_func_op.cc

@@ -119,7 +119,7 @@ static void CallPythonFunc(py::object *callable,
       out->ShareDataWith(*py_out_tensor);
     } catch (py::cast_error &) {
       PADDLE_THROW(platform::errors::InvalidArgument(
-          "py::cast to phi::DenseTensor error. The %d-th output expection is "
+          "py::cast to phi::DenseTensor error. The %d-th output exception is "
           "phi::DenseTensor",
           i));
     }

paddle/fluid/operators/randperm_op.h

@@ -29,7 +29,7 @@ namespace paddle {
 namespace operators {
 
 template <typename T>
-static inline void random_permate(T* data_ptr, int num, unsigned int seed) {
+static inline void random_permute(T* data_ptr, int num, unsigned int seed) {
   auto engine = phi::GetCPURandomEngine(seed);
   for (int i = 0; i < num; ++i) {
     data_ptr[i] = static_cast<T>(i);
@@ -50,13 +50,13 @@ class RandpermKernel : public framework::OpKernel<T> {
 
     if (platform::is_cpu_place(ctx.GetPlace())) {
       T* out_data = out_tensor->mutable_data<T>(platform::CPUPlace());
-      random_permate<T>(out_data, n, seed);
+      random_permute<T>(out_data, n, seed);
 
     } else {
       phi::DenseTensor tmp_tensor;
       tmp_tensor.Resize(common::make_ddim({n}));
       T* tmp_data = tmp_tensor.mutable_data<T>(platform::CPUPlace());
-      random_permate<T>(tmp_data, n, seed);
+      random_permute<T>(tmp_data, n, seed);
       framework::TensorCopy(tmp_tensor, ctx.GetPlace(), out_tensor);
     }
   }

paddle/fluid/operators/read_file_op.cc

@@ -46,7 +46,7 @@ class ReadFileOpMaker : public framework::OpProtoAndCheckerMaker {
     AddComment(R"DOC(
 This operator read a file.
 )DOC");
-    AddAttr<std::string>("filename", "Path of the file to be readed.")
+    AddAttr<std::string>("filename", "Path of the file to be read.")
         .SetDefault({});
   }
 };

paddle/fluid/operators/repeat_interleave_op.cc

@@ -77,7 +77,7 @@ class RepeatInterleaveOp : public framework::OperatorWithKernel {
     } else if (repeats > 0) {
       output_dim[dim] = input_dim[dim] * repeats;
     }
-    VLOG(3) << "infershap out " << output_dim[dim];
+    VLOG(3) << "infershape out " << output_dim[dim];
     ctx->SetOutputDim("Out", common::make_ddim(output_dim));
     auto type = ctx->GetInputsVarType("X")[0];
     if (type == framework::proto::VarType::LOD_TENSOR) {
@@ -124,7 +124,7 @@ class RepeatInterleaveOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() override {
     AddInput("X", "(Tensor) the input tensor.");
     AddInput("RepeatsTensor",
-             "the 1-D tensor containing the repeats alongsize the axis.")
+             "the 1-D tensor containing the repeats alongside the axis.")
         .AsDispensable();
     AddOutput("Out", "the output tensor.");
     AddAttr<int>("Repeats", "the number of repetitions for each element.")

paddle/fluid/operators/reshape_op.cc

@@ -581,7 +581,7 @@ class Reshape2CompositeGradOpMaker : public prim::CompositeGradOpMakerBase {
 
     auto *dx_ptr = this->GetOutputPtr(&dx);
     std::string dx_name = this->GetOutputName(dx);
-    VLOG(6) << "Runing reshape2_grad composite func";
+    VLOG(6) << "Running reshape2_grad composite func";
     prim::reshape_grad<prim::DescTensor>(x, out_grad, dx_ptr);
     this->RecoverOutputName(dx, dx_name);
   }

paddle/fluid/operators/split_op.cc

@@ -222,7 +222,7 @@ class SplitCompositeGradOpMaker : public prim::CompositeGradOpMakerBase {
           "We don't support dynamic index or sections from tensor for split "
           "composite grad for now. "));
     } else {
-      VLOG(6) << "Runing split_grad composite func";
+      VLOG(6) << "Running split_grad composite func";
       prim::split_grad<prim::DescTensor>(out_grad, axis, dx_ptr);
       this->RecoverOutputName(input_grad, dx_name);
     }

paddle/fluid/operators/sum_op.cc

@@ -127,7 +127,7 @@ class SumOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() override {
     AddInput(
         "X",
-        "A Varaible list. The shape and data type of the list elements"
+        "A Variable list. The shape and data type of the list elements"
         "should be consistent. Variable can be multi-dimensional Tensor"
         "or phi::DenseTensor, and data types can be: float32, float64, int32, "
         "int64.")

paddle/fluid/operators/svd_helper.h

@@ -271,7 +271,7 @@ struct DiagAndFillFunctor {
 
 template <typename DeviceContext, typename T, typename ValueType = T>
 struct DeviceIndependenceTensorOperations {
-  // 1. Device indenpendence, for kernel reuse.
+  // 1. Device independence, for kernel reuse.
   // 2. Input and output is always tensor type.
   // 3. output phi::DenseTensor is alway allocated
   // 4. Basic phi::DenseTensor operator is supported
@@ -315,7 +315,7 @@ struct DeviceIndependenceTensorOperations {
   }
 
   phi::DenseTensor Transpose(const phi::DenseTensor& x) {
-    // transpose the last two dimision
+    // transpose the last two dimension
     phi::DenseTensor ret;
     auto x_dim = x.dims();
     auto x_vec = common::vectorize<int>(x_dim);
@@ -745,15 +745,15 @@ struct DeviceIndependenceTensorOperations {
       const framework::AttributeMap& attrs,
       std::vector<int> out_shape,
       NameOutTensor out_str = {"Out"}) {
-    // varialble set dims must be phi::DenseTensor / SelectedRowTensor
+    // variable set dims must be phi::DenseTensor / SelectedRowTensor
     framework::Scope& local_scope = context.scope().NewScope();
     framework::VariableNameMap op_outputs;
     for (auto out_name : out_str) {
       local_scope.Var("tmp_" + out_name)->GetMutable<phi::DenseTensor>();
       op_outputs[out_name].emplace_back("tmp_" + out_name);
     }
     auto out_var = local_scope.Var("tmp_Out");  // return the Out
-    // create Out phi::DenseTensor and allocat memory
+    // create Out phi::DenseTensor and allocate memory
     out_var->GetMutable<phi::DenseTensor>()->mutable_data<T>(
         common::make_ddim(out_shape), context.GetPlace());
     // common::make_ddim(out_shape)

paddle/fluid/operators/tdm_sampler_op.h

@@ -214,9 +214,9 @@ void TDMSamplerInner(const framework::ExecutionContext &context,
         label_vec[i * sample_res_length + offset] = 0;
         mask_vec[i * sample_res_length + offset] = 1;
         VLOG(3) << "TDM: node id: " << travel_data[start_offset + layer_idx]
-                << " Res append negitive "
+                << " Res append negative "
                 << output_vec[i * sample_res_length + offset]
-                << " Label append negitive "
+                << " Label append negative "
                 << label_vec[i * sample_res_length + offset]
                 << " Mask append value "
                 << mask_vec[i * sample_res_length + offset];

paddle/fluid/operators/teacher_student_sigmoid_loss_op.cc

@@ -173,7 +173,7 @@ class TeacherStudentSigmoidLossGradientOp
           platform::errors::InvalidArgument(
               "When Attr(soft_label) == false, the 2nd dimension of "
               "Input(Label) should be 1. But received Input(Label)'s 2nd "
-              "dimemsion "
+              "dimension "
               "is [%d]",
               label_dims[1]));
     }

paddle/fluid/operators/tile_op.cc

@@ -185,7 +185,7 @@ class TileCompositeGradOpMaker : public prim::CompositeGradOpMakerBase {
           "We don't support RepeatTimes from tensor or repeat_times_tensor for "
           "tile composite grad for now. "));
     } else {
-      VLOG(6) << "Runing tile_grad composite func";
+      VLOG(6) << "Running tile_grad composite func";
       prim::tile_grad<prim::DescTensor>(
           x, out_grad, paddle::experimental::IntArray(repeat_times), dx_ptr);
       this->RecoverOutputName(x_grad, dx_name);

paddle/fluid/operators/top_k_op.h

@@ -46,7 +46,7 @@ class TopkKernel : public framework::OpKernel<T> {
     T* output_data = output->mutable_data<T>(ctx.GetPlace());
     int64_t* indices_data = indices->mutable_data<int64_t>(ctx.GetPlace());
 
-    // reshape input to a flattern matrix(like flat_inner_dims)
+    // reshape input to a flatten matrix(like flat_inner_dims)
     framework::DDim inputdims = input->dims();
     const size_t row =
         common::product(common::slice_ddim(inputdims, 0, inputdims.size() - 1));

paddle/fluid/operators/top_k_op_xpu.cc

@@ -60,7 +60,7 @@ class TopkXPUKernel : public framework::OpKernel<T> {
     int* indices_int_data = RAII_GUARD.alloc_l3_or_gm<int>(indices->numel());
     PADDLE_ENFORCE_XDNN_NOT_NULL(indices_int_data);
 
-    // reshape input to a flattern matrix(like flat_inner_dims)
+    // reshape input to a flatten matrix(like flat_inner_dims)
     framework::DDim inputdims = input->dims();
     const size_t row =
         common::product(common::slice_ddim(inputdims, 0, inputdims.size() - 1));

paddle/fluid/operators/transfer_layout_op.h

@@ -110,7 +110,7 @@ class TransferLayoutFunctor {
         }
         VLOG(4) << "TransDataLayoutFromOneDNN: " << in_layout << "->"
                 << target_layout;
-        // Case2 - transfrom from ONEDNN OPKernel to Non-ONEDNN OPKernel
+        // Case2 - transform from ONEDNN OPKernel to Non-ONEDNN OPKernel
         // Do transform via ONEDNN lib
         phi::funcs::TransDataLayoutFromOneDNN(in_layout,
                                               target_layout,
@@ -119,11 +119,11 @@ class TransferLayoutFunctor {
                                               dev_ctx_.GetPlace());
       }
     } else {
-      // Case3 - transfrom between Non-ONEDNN OPKernels
+      // Case3 - transform between Non-ONEDNN OPKernels
       TransDataLayout(dev_ctx_, in_tensor, &out_tensor);
     }
 #else
-    // Case3 - transfrom between Non-ONEDNN OPKernels
+    // Case3 - transform between Non-ONEDNN OPKernels
     TransDataLayout(dev_ctx_, in_tensor, &out_tensor);
 #endif
     framework::SetTensorToVariable(*in_, out_tensor, out_);

paddle/fluid/operators/transpose_op.cc

@@ -202,7 +202,7 @@ class Transpose2CompositeGradOpMaker : public prim::CompositeGradOpMakerBase {
     std::string dx_name = this->GetOutputName(dx);
     std::vector<int> axis =
         static_cast<std::vector<int>>(this->Attr<std::vector<int>>("axis"));
-    VLOG(6) << "Runing transpose2_grad composite func";
+    VLOG(6) << "Running transpose2_grad composite func";
     prim::transpose_grad<prim::DescTensor>(out_grad, axis, dx_ptr);
     this->RecoverOutputName(dx, dx_name);
   }

paddle/fluid/prim/utils/static/composite_grad_desc_maker.h

@@ -72,7 +72,7 @@ class CompositeGradOpMakerBase {
   virtual ~CompositeGradOpMakerBase() = default;
 
   virtual std::vector<std::unique_ptr<framework::OpDesc>> operator()() {
-    VLOG(3) << "Runing Composite Grad func for " << fwd_op_.Type() << "_grad ";
+    VLOG(3) << "Running Composite Grad func for " << fwd_op_.Type() << "_grad ";
     this->Apply();
     std::vector<std::unique_ptr<framework::OpDesc>> ops;
     // TODO(jiabin): Support multiple blocks later