[CodeStyle][Typos][A-46,A-47,N-15] Fix typos (axises, aixs, numberic) (#69856)

Author: SigureMo

_typos.toml

@@ -26,9 +26,6 @@ UE = "UE"
 unpacket = "unpacket"
 
 # These words need to be fixed
-axises = 'axises'
-Axises = 'Axises'
-aixs = 'aixs'
 beacuse = 'beacuse'
 becasue = 'becasue'
 Becasue = 'Becasue'
@@ -382,7 +379,6 @@ Normlized = 'Normlized'
 normlize = 'normlize'
 noraml = 'noraml'
 numer = 'numer'
-Numberic = 'Numberic'
 occured = 'occured'
 Ocurred = 'Ocurred'
 occures = 'occures'

paddle/cinn/common/axis.cc

@@ -23,7 +23,7 @@
 namespace cinn {
 namespace common {
 
-static const std::vector<std::string> kAxises({
+static const std::vector<std::string> kAxes({
     "i",  // level 0
     "j",  // level 1
     "k",  // level 2
@@ -49,12 +49,12 @@ static const std::vector<std::string> kAxises({
 });
 
 std::string axis_name(int level) {
-  if (level < kAxises.size()) {
-    return kAxises[level];
+  if (level < kAxes.size()) {
+    return kAxes[level];
   }
   // upper level
-  int repeat_num = 1 + (level / kAxises.size());
-  const auto& base_axis = kAxises[level % kAxises.size()];
+  int repeat_num = 1 + (level / kAxes.size());
+  const auto& base_axis = kAxes[level % kAxes.size()];
 
   // if the level greater than kAxis, repeat the axis, like:
   // level == 22 ==> axis = "ii"
@@ -89,7 +89,7 @@ std::vector<ir::Expr> GenDefaultAxisAsExpr(int naxis) {
 }
 
 static const std::set<std::string>& axis_set() {
-  static std::set<std::string> x(kAxises.begin(), kAxises.end());
+  static std::set<std::string> x(kAxes.begin(), kAxes.end());
   return x;
 }
 
@@ -102,13 +102,13 @@ bool IsAxisNameReserved(const std::string& x) {
     return true;
   }
   if (!axis_set().count(std::string(1, x[0]))) {
-    // all char in axis should in kAxises
+    // all char in axis should in kAxes
     return false;
   }
   bool is_repeat_axis = true;
   for (int i = 1; i < x.size(); ++i) {
     if (x[i] != x[0]) {
-      // the axis are repeat with the char in kAxises
+      // the axis are repeat with the char in kAxes
       is_repeat_axis = false;
       break;
     }

paddle/cinn/hlir/dialect/operator/transforms/reduce_as_to_sum_pass.cc

@@ -152,7 +152,7 @@ class ReduceAsOpPattern
     size_t x_rank = x_shape.size();
     size_t y_rank = y_shape.size();
 
-    // Get reduc aixs and
+    // Get reduc axis and
     int64_t compare_offset = x_rank - y_rank;
 
     for (size_t i = 0; i < y_rank; ++i) {

paddle/cinn/lang/compute.cc

@@ -155,9 +155,9 @@ ir::Tensor Compute(const std::vector<Expr> &domain,
                    std::function<Expr(const std::vector<Expr> &)> fn,
                    const std::string &name,
                    const std::vector<Expr> &shape) {
-  auto axises = cinn::common::GenDefaultAxis(domain.size());
+  auto axes = cinn::common::GenDefaultAxis(domain.size());
   std::vector<Expr> _axis;
-  for (auto &x : axises) _axis.push_back(x);
+  for (auto &x : axes) _axis.push_back(x);
   Expr fn_body = fn(_axis);
 
   std::vector<Var> reduce_axis;

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

@@ -39,7 +39,7 @@ class SliceOpConverter : public OpConverter {
         PADDLE_GET_CONST(std::vector<int>, op_desc.GetAttr("starts"));
     std::vector<int> ends =
         PADDLE_GET_CONST(std::vector<int>, op_desc.GetAttr("ends"));
-    std::vector<int> decrease_axises =
+    std::vector<int> decrease_axes =
         PADDLE_GET_CONST(std::vector<int>, op_desc.GetAttr("decrease_axis"));
     auto input_dims = input->getDimensions();
     nvinfer1::ILayer* layer = nullptr;
@@ -139,15 +139,15 @@ class SliceOpConverter : public OpConverter {
     layer->setInput(1, *start_tensor);
     layer->setInput(2, *size_tensor);
 
-    if (!decrease_axises.empty()) {
+    if (!decrease_axes.empty()) {
       std::vector<int32_t> gather_indices;
       for (int i = 0; i < trt_size_dims.nbDims; i++) {
-        if (decrease_axises.end() !=
-            std::find(decrease_axises.begin(), decrease_axises.end(), i))
+        if (decrease_axes.end() !=
+            std::find(decrease_axes.begin(), decrease_axes.end(), i))
           continue;
         gather_indices.push_back(i);
       }
-      if (gather_indices.empty()) gather_indices.push_back(decrease_axises[0]);
+      if (gather_indices.empty()) gather_indices.push_back(decrease_axes[0]);
       auto real_size_tensor = Gather(size_tensor, gather_indices);
       layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *layer->getOutput(0));
       layer->setInput(1, *real_size_tensor);

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

@@ -39,7 +39,7 @@ class StridedSliceOpConverter : public OpConverter {
         PADDLE_GET_CONST(std::vector<int>, op_desc.GetAttr("ends"));
     std::vector<int> strides =
         PADDLE_GET_CONST(std::vector<int>, op_desc.GetAttr("strides"));
-    std::vector<int> decrease_axises =
+    std::vector<int> decrease_axes =
         PADDLE_GET_CONST(std::vector<int>, op_desc.GetAttr("decrease_axis"));
 
     nvinfer1::ILayer* layer = nullptr;
@@ -96,15 +96,15 @@ class StridedSliceOpConverter : public OpConverter {
     layer->setInput(2, *size_tensor);
     layer->setInput(3, *step_tensor);
 
-    if (!decrease_axises.empty()) {
+    if (!decrease_axes.empty()) {
       std::vector<int32_t> gather_indices;
       for (int i = 0; i < trt_size_dims.nbDims; i++) {
-        if (decrease_axises.end() !=
-            std::find(decrease_axises.begin(), decrease_axises.end(), i))
+        if (decrease_axes.end() !=
+            std::find(decrease_axes.begin(), decrease_axes.end(), i))
           continue;
         gather_indices.push_back(i);
       }
-      if (gather_indices.empty()) gather_indices.push_back(decrease_axises[0]);
+      if (gather_indices.empty()) gather_indices.push_back(decrease_axes[0]);
       auto real_size_tensor = Gather(size_tensor, gather_indices);
       layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *layer->getOutput(0));
       layer->setInput(1, *real_size_tensor);

python/paddle/tensor/stat.py

@@ -721,7 +721,7 @@ def _compute_quantile(
         axis (int|list, optional): The axis along which to calculate quantile. ``axis`` should be int or list of int.
             ``axis`` should be in range [-D, D), where D is the dimensions of ``x`` .
             If ``axis`` is less than 0, it works the same way as :math:`axis + D`.
-            If ``axis`` is a list, quantile is calculated over all elements of given axises.
+            If ``axis`` is a list, quantile is calculated over all elements of given axes.
             If ``axis`` is None, quantile is calculated over all elements of ``x``. Default is None.
         keepdim (bool, optional): Whether to reserve the reduced dimension(s)
             in the output Tensor. If ``keepdim`` is True, the dimensions of
@@ -905,7 +905,7 @@ def quantile(
         axis (int|list, optional): The axis along which to calculate quantile. ``axis`` should be int or list of int.
             ``axis`` should be in range [-D, D), where D is the dimensions of ``x`` .
             If ``axis`` is less than 0, it works the same way as :math:`axis + D`.
-            If ``axis`` is a list, quantile is calculated over all elements of given axises.
+            If ``axis`` is a list, quantile is calculated over all elements of given axes.
             If ``axis`` is None, quantile is calculated over all elements of ``x``. Default is None.
         keepdim (bool, optional): Whether to reserve the reduced dimension(s)
             in the output Tensor. If ``keepdim`` is True, the dimensions of
@@ -989,7 +989,7 @@ def nanquantile(
         axis (int|list, optional): The axis along which to calculate quantile. ``axis`` should be int or list of int.
             ``axis`` should be in range [-D, D), where D is the dimensions of ``x`` .
             If ``axis`` is less than 0, it works the same way as :math:`axis + D`.
-            If ``axis`` is a list, quantile is calculated over all elements of given axises.
+            If ``axis`` is a list, quantile is calculated over all elements of given axes.
             If ``axis`` is None, quantile is calculated over all elements of ``x``. Default is None.
         keepdim (bool, optional): Whether to reserve the reduced dimension(s)
             in the output Tensor. If ``keepdim`` is True, the dimensions of

test/custom_op/test_custom_concat.py

@@ -119,7 +119,7 @@ def setUp(self):
             np.array([[1, 2, 3], [4, 5, 6]]),
             np.array([[11, 12, 13], [14, 15, 16]]),
         ]
-        self.axises = [0, 1]
+        self.axes = [0, 1]
 
     def check_output(self, out, pd_out, name):
         np.testing.assert_array_equal(
@@ -130,7 +130,7 @@ def check_output(self, out, pd_out, name):
 
     def test_dynamic(self):
         for dtype in self.dtypes:
-            for axis in self.axises:
+            for axis in self.axes:
                 out, grad_inputs = concat_dynamic(
                     custom_ops.custom_concat, dtype, self.np_inputs, axis
                 )
@@ -144,7 +144,7 @@ def test_dynamic(self):
 
     def test_static(self):
         for dtype in self.dtypes:
-            for axis in self.axises:
+            for axis in self.axes:
                 out, x1_grad, x2_grad = concat_static(
                     custom_ops.custom_concat, dtype, self.np_inputs, axis
                 )
@@ -158,7 +158,7 @@ def test_static(self):
 
     def test_dynamic_with_attr(self):
         for dtype in self.dtypes:
-            for axis in self.axises:
+            for axis in self.axes:
                 out, grad_inputs = concat_dynamic(
                     custom_ops.custom_concat_with_attr,
                     dtype,
@@ -176,7 +176,7 @@ def test_dynamic_with_attr(self):
 
     def test_static_with_attr(self):
         for dtype in self.dtypes:
-            for axis in self.axises:
+            for axis in self.axes:
                 out, x1_grad, x2_grad = concat_static(
                     custom_ops.custom_concat_with_attr,
                     dtype,

test/legacy_test/test_kthvalue_op.py

@@ -121,7 +121,7 @@ def init_dtype(self):
 
 class TestKthvalueOpKernels(unittest.TestCase):
     def setUp(self):
-        self.axises = [2, -1]
+        self.axes = [2, -1]
 
     def test_kthvalue_op(self):
         paddle.disable_static()
@@ -132,7 +132,7 @@ def test_cpu_kernel():
             paddle.set_device('cpu')
             inputs = np.random.random(shape)
             tensor = paddle.to_tensor(inputs)
-            for axis in self.axises:
+            for axis in self.axes:
                 value_expect, indice_expect = cal_kthvalue(inputs, k, axis)
                 v, inds = paddle.kthvalue(tensor, k, axis)
                 np.testing.assert_allclose(v.numpy(), value_expect, rtol=1e-05)
@@ -146,7 +146,7 @@ def test_gpu_kernel():
             paddle.set_device('gpu')
             inputs = np.random.random(shape)
             tensor = paddle.to_tensor(inputs)
-            for axis in self.axises:
+            for axis in self.axes:
                 value_expect, indice_expect = cal_kthvalue(inputs, k, axis)
                 v, inds = paddle.kthvalue(tensor, k, axis)
                 np.testing.assert_allclose(v.numpy(), value_expect, rtol=1e-05)

test/legacy_test/test_mode_op.py

@@ -187,15 +187,15 @@ def init_args(self):
 
 class TestModeOpKernels(unittest.TestCase):
     def setUp(self):
-        self.axises = [-1, 1]
+        self.axes = [-1, 1]
         np.random.seed(666)
         self.inputs = np.ceil(np.random.rand(2, 10, 10) * 1000)
 
     def test_mode_op(self):
         def test_cpu_kernel():
             paddle.set_device('cpu')
             tensor = paddle.to_tensor(self.inputs)
-            for axis in self.axises:
+            for axis in self.axes:
                 value_expect, indice_expect = cal_mode(self.inputs, axis)
                 v, inds = paddle.mode(tensor, axis)
                 np.testing.assert_allclose(v.numpy(), value_expect, rtol=1e-05)
@@ -209,7 +209,7 @@ def test_cpu_kernel():
         def test_gpu_kernel():
             paddle.set_device('gpu')
             tensor = paddle.to_tensor(self.inputs)
-            for axis in self.axises:
+            for axis in self.axes:
                 value_expect, indice_expect = cal_mode(self.inputs, axis)
                 v, inds = paddle.mode(tensor, axis)
                 np.testing.assert_allclose(v.numpy(), value_expect, rtol=1e-05)