c53-58

_typos.toml

@@ -41,13 +41,13 @@ caculate = 'caculate'
 calcualtion = 'calcualtion'
 checkings = 'checkings'
 childs = 'childs'
-Continous = 'Continous'
-contibute = 'contibute'
-controled = 'controled'
-contorl = 'contorl'
-converage = 'converage'
-Converage = 'Converage'
-convertion = 'convertion'
+#Continous = 'Continous'
+#contibute = 'contibute'
+#controled = 'controled'
+#contorl = 'contorl'
+#converage = 'converage'
+#Converage = 'Converage'
+#convertion = 'convertion'
 Conver = 'Conver'
 convience = 'convience'
 coodinate = 'coodinate'

paddle/fluid/operators/print_op.cc

@@ -186,6 +186,6 @@ REGISTER_OPERATOR(print,
 
 REGISTER_OP_VERSION(print).AddCheckpoint(
     R"ROC(Upgrade print add a new attribute [print_tensor_layout] to "
-             "contorl whether to print tensor's layout.)ROC",
+             "control whether to print tensor's layout.)ROC",
     paddle::framework::compatible::OpVersionDesc().NewAttr(
         "print_tensor_layout", "Whether to print the tensor's layout.", true));

paddle/phi/backends/onednn/onednn_reuse.h

@@ -1166,7 +1166,7 @@ class BinaryOneDNNHandler : public OneDNNHandlerNoCachingT<T, dnnl::binary> {
       float scale_y,
       float scale_out,
       dnnl::post_ops post_ops = dnnl::post_ops{}) {
-    // Scales set in attributes for inputs contibute to the output equation
+    // Scales set in attributes for inputs contribute to the output equation
     // in the following way (assuming no broadcasting takes place):
     // output_i = scale_0 * x_i <+ or *> scale_1 * y_i;
     // Hence we have to create scales that will:

paddle/phi/kernels/funcs/weight_only_gemv.cu

@@ -394,7 +394,7 @@ struct WeightLayoutDetails<WeightOnlyQuantType::Int4b> {
   // 20 21 28 29 6 7 14 15 22 23 30 31
   static constexpr int kShuffleSize = 32;
   static constexpr int kShuffleBasicTile = 2;
-  static constexpr int kShuffleContinous = 4;
+  static constexpr int kShuffleContinuous = 4;
   static constexpr int kShuffleStrided = 4;
 
   // The rearrangement here counteracts the effect of
@@ -456,7 +456,7 @@ struct WeightLayoutDetails<WeightOnlyQuantType::Int8b> {
   // 13 14 15 weight 0 1 8 9 2 3 10 11 4 5 12 13 6 7 14 15
   static constexpr int kShuffleSize = 16;
   static constexpr int kShuffleBasicTile = 2;
-  static constexpr int kShuffleContinous = 2;
+  static constexpr int kShuffleContinuous = 2;
   static constexpr int kShuffleStrided = 4;
 
   // The rearrangement here counteracts the effect of
@@ -504,7 +504,7 @@ struct WeightOnlyKernelDetails {
 
   static constexpr int kShuffleSize = Layout::kShuffleSize;
   static constexpr int kShuffleBasicTile = Layout::kShuffleBasicTile;
-  static constexpr int kShuffleContinous = Layout::kShuffleContinous;
+  static constexpr int kShuffleContinuous = Layout::kShuffleContinuous;
   static constexpr int kShuffleStrided = Layout::kShuffleStrided;
 
   // using Converter = typename Layout::Converter;
@@ -848,14 +848,14 @@ struct WeightPostProcessor<T, WeightOnlyQuantType::Int4b, Details> {
                                              int idx) {
     using HALF_2_TYPE = typename CUDA_HALF_2_TYPE_TARIS<T>::type;
 #pragma unroll
-    for (int i = 0; i < Details::kShuffleContinous; ++i) {
+    for (int i = 0; i < Details::kShuffleContinuous; ++i) {
 #pragma unroll
       for (int j = 0; j < Details::kShuffleStrided; ++j) {
         // Dequantize the weights and arrange the shuffled elements back to
         // the correct order in the register array
         HALF_2_TYPE v = *reinterpret_cast<HALF_2_TYPE*>(
             weights_vec + i * Details::kShuffleBasicTile +
-            j * Details::kShuffleContinous * Details::kShuffleBasicTile);
+            j * Details::kShuffleContinuous * Details::kShuffleBasicTile);
         v = HalfMulAdd<HALF_2_TYPE>::apply(
             v,
             ConvertDstFunc_2<HALF_2_TYPE>::apply(scale[idx]),

python/paddle/jit/dy2static/program_translator.py

@@ -1034,7 +1034,7 @@ def concrete_program_specify_input_spec(
         # if specific the `input_spec`, the length of program_cache will always 1,
         # else, return the last one.
         cached_program_len = len(self._program_cache)
-        # If specific `input_spec`, apply convertion from dygraph layers into static Program.
+        # If specific `input_spec`, apply conversion from dygraph layers into static Program.
         # NOTE(jiabin): is_prim_infer indicates this method called by paddle.jit.save and it is worked in prim mode
 
         desired_input_spec = input_spec

test/cpp/inference/api/analyzer_capi_ner_tester.cc

@@ -111,7 +111,7 @@ TEST(PD_ZeroCopyRun, zero_copy_run) {
            "%s",
            PD_GetOutputName(predictor, 0));
 
-  // not necessary, just for converage tests
+  // not necessary, just for coverage tests
   output.lod.data = std::malloc(sizeof(size_t));
 
   PD_GetZeroCopyOutput(predictor, &output);

test/cpp/inference/infer_ut/README.md

@@ -24,7 +24,7 @@ busybox bash ./run.sh $PADDLE_ROOT $TURN_ON_MKL $TEST_GPU_CPU $DATA_DIR
 - `$TEST_GPU_CPU`: test both GPU/CPU mode or only CPU mode
 - `$DATA_DIR`: download data path
 
-now only support 4 kinds of tests which controled by `--gtest_filter` argument, test suite name should be same as following.
+now only support 4 kinds of tests which controlled by `--gtest_filter` argument, test suite name should be same as following.
 - `TEST(gpu_tester_*, test_name)`
 - `TEST(cpu_tester_*, test_name)`
 - `TEST(mkldnn_tester_*, test_name)`

test/dygraph_to_static/test_logging_utils.py

@@ -87,7 +87,7 @@ def test_set_code_level(self):
             paddle.jit.set_code_level(3.3)
 
     def test_log_api(self):
-        # test api for CI Converage
+        # test api for CI Coverage
         logging_utils.set_verbosity(1, True)
 
         logging_utils.warn("warn")

test/legacy_test/test_lbfgs_class.py

@@ -89,7 +89,7 @@ def func(w, x):
             np.testing.assert_allclose(net.w, weight, rtol=1e-05)
 
     def test_inf_minima_incubate(self):
-        # not converage
+        # not converge
         input = np.random.rand(1).astype(np.float32)
 
         def outputs1(x):
@@ -169,7 +169,7 @@ def error_func1():
         self.assertRaises(TypeError, error_func1)
 
     def test_error2_incubate(self):
-        # not converage
+        # not converge
         input = np.random.rand(1).astype(np.float32)
 
         def outputs2(x):
@@ -339,7 +339,7 @@ def func(w, x):
             np.testing.assert_allclose(net.w, weight, rtol=1e-05)
 
     def test_inf_minima(self):
-        # not converage
+        # not converge
         input = np.random.rand(1).astype(np.float32)
 
         def outputs1(x):
@@ -419,7 +419,7 @@ def error_func1():
         self.assertRaises(TypeError, error_func1)
 
     def test_error2(self):
-        # not converage
+        # not converge
         input = np.random.rand(1).astype(np.float32)
 
         def outputs2(x):

tools/gen_pybind11_stub.py

@@ -71,7 +71,7 @@
 # ref:
 # - https://pybind11.readthedocs.io/en/latest/advanced/misc.html#avoiding-cpp-types-in-docstrings
 # - https://pybind11.readthedocs.io/en/latest/advanced/functions.html#default-arguments-revisited
-# we can add some mappings for convertion, e.g. {'paddle::Tensor': 'paddle.Tensor'}
+# we can add some mappings for conversion, e.g. {'paddle::Tensor': 'paddle.Tensor'}
 PYBIND11_ATTR_MAPPING = {}
 
 # some bad full expression pybind11-stubgen can not catch as invalid exp