fix typos for `True` and `False` (#47477)

* fix typo `Fasle`/`Flase` -> `Flase`

* fix typo `Ture` -> `True`

paddle/fluid/framework/naive_executor.h

@@ -53,7 +53,7 @@ class NaiveExecutor {
                bool with_feed_fetch_ops);
 
   // Create variables before head.
-  // Create parameters if persistable is ture, or create the temporary variables
+  // Create parameters if persistable is true, or create the temporary variables
   // instead.
   void CreateVariables(const ProgramDesc& desc,
                        int block_id,

paddle/fluid/memory/allocation/allocator_facade.cc

@@ -238,7 +238,7 @@ class AllocatorFacadePrivate {
         // releate to non-default stream (i.e., the stream users pass in). The
         // default stream Allocator is built in the structure of
         // AllocatorFacadePrivate, while the non-default stream is build in a
-        // manner in GetAllocator function with 'create_if_not_found = ture'.
+        // manner in GetAllocator function with 'create_if_not_found = true'.
         // We make special treatment for the default stream for performance
         // reasons. Since most Alloc calls are for default stream in
         // application, treating it separately can avoid lots of overhead of

paddle/fluid/operators/detection/yolo_box_op.cc

@@ -237,7 +237,7 @@ class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
          .. math::
 
             score_{conf} = \begin{case}
-                             obj, \text{if } iou_aware == flase \\
+                             obj, \text{if } iou_aware == false \\
                              obj^{1 - iou_aware_factor} * iou^{iou_aware_factor}, \text{otherwise}
                            \end{case}
 

paddle/fluid/operators/fused/fused_dropout_helper.h

@@ -28,7 +28,7 @@ namespace operators {
  * Support two Dropouts in the use senarieo.
  * This warpper can be used in FFN op.
  * The DropoutParam will be used in the fused_dropout_act_bias,
- * fused_residual_dropout_bias(pre_layer_norm=ture) or
+ * fused_residual_dropout_bias(pre_layer_norm=true) or
  * fused_layernorm_residual_dropout_bias(pre_layer_norm=false).
  */
 struct DropoutParam {

paddle/fluid/operators/select_op_helper.h

@@ -37,7 +37,7 @@ inline int GetBranchNumber(const phi::DenseTensor &mask) {
   if (platform::is_cpu_place(mask.place())) {
     return mask.data<int>()[0];
   }
-  // when platform::is_gpu_place(mask.place()) is ture
+  // when platform::is_gpu_place(mask.place()) is true
   std::unique_ptr<phi::DenseTensor> cpu_mask{new phi::DenseTensor()};
 #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
   framework::TensorCopySync(mask, platform::CPUPlace(), cpu_mask.get());

paddle/fluid/operators/tensorrt/tensorrt_engine_op.h

@@ -269,7 +269,7 @@ class TensorRTEngineOp : public framework::OperatorBase {
       if (param_names_.count(x)) continue;
       runtime_input_names_.emplace_back(x);
     }
-    // calibration_mode is ture represents we need to
+    // calibration_mode is true represents we need to
     // generate the calibration table data.
     calibration_mode_ =
         (enable_int8_ && calibration_data_.size() == 0 && use_calib_mode_);

paddle/fluid/operators/unique_op.cc

@@ -119,7 +119,7 @@ class UniqueOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() override {
     AddInput("X",
              "Input tensor. It should be a 1-D tensor when Attr(is_sorted)"
-             " is fasle or a N-D tensor when Attr(is_sorted) is true.");
+             " is false or a N-D tensor when Attr(is_sorted) is true.");
     AddAttr<int>("dtype", "data type for output index");
     AddOutput("Out", "A unique subsequence for input tensor.");
     AddOutput("Index",

python/paddle/fluid/contrib/sparsity/asp.py

@@ -322,7 +322,7 @@ def prune_model(model, n=2, m=4, mask_algo='mask_1d', with_mask=True):
         m (int, optional): m of `n:m` sparse pattern. Default is 4.
         mask_algo (string, optional): The function name to generate spase mask. Default is `mask_1d`.
                                       The vaild inputs should be one of 'mask_1d', 'mask_2d_greedy' and 'mask_2d_best'.
-        with_mask (bool, optional): To prune mask Variables related to parameters or not. Ture is purning also, False is not. Default is True.
+        with_mask (bool, optional): To prune mask Variables related to parameters or not. True is purning also, False is not. Default is True.
     Returns:
         dictionary: A dictionary with key: `parameter name` (string) and value: its corresponding mask Variable.
     Examples:

python/paddle/fluid/dygraph/dygraph_to_static/convert_operators.py

@@ -373,7 +373,7 @@ def _run_paddle_cond(
     pred, true_fn, false_fn, get_args, set_args, return_name_ids, push_pop_names
 ):
     """
-    Paddle cond API will evaluate both ture_fn and false_fn codes.
+    Paddle cond API will evaluate both true_fn and false_fn codes.
     """
     helper = GetterSetterHelper(
         get_args, set_args, return_name_ids, push_pop_names

python/paddle/fluid/dygraph/nn.py

@@ -1268,7 +1268,7 @@ class InstanceNorm(layers.Layer):
         if param_attr == False or bias_attr == False:
             assert (
                 bias_attr == param_attr
-            ), "param_attr and bias_attr must be set to Fasle at the same time in InstanceNorm"
+            ), "param_attr and bias_attr must be set to False at the same time in InstanceNorm"
         self._epsilon = epsilon
         self._param_attr = param_attr
         self._bias_attr = bias_attr

python/paddle/fluid/layers/control_flow.py

@@ -207,7 +207,7 @@ def select_input_with_buildin_type(inputs, mask, name):
         inputs = [to_static_variable(false_var), to_static_variable(true_var)]
         warnings.warn(
             "Return results from different branches in cond are not same type: "
-            "false_var returned by fasle_fn is '{}' and true_var of true_fn is "
+            "false_var returned by false_fn is '{}' and true_var of true_fn is "
             "'{}'".format(type(false_var), type(true_var))
         )
     elif (
@@ -230,7 +230,7 @@ def select_input_with_buildin_type(inputs, mask, name):
     else:
         raise TypeError(
             "Unsupported return type of true_fn and false_fn in cond: false_var "
-            "returned by fasle_fn is '{}' and true_var of true_fn is '{}'".format(
+            "returned by false_fn is '{}' and true_var of true_fn is '{}'".format(
                 type(false_var), type(true_var)
             )
         )
@@ -2835,7 +2835,7 @@ def cond(pred, true_fn=None, false_fn=None, name=None, return_names=None):
             "true_fn returns non-None while false_fn returns None"
         )
 
-    # Merge ture and false output if they are not None
+    # Merge true and false output if they are not None
     if return_names is None:
         is_dy2staic = False
         return_names = ["no name"] * len(_to_sequence_except_dict(true_output))

python/paddle/fluid/layers/nn.py

@@ -3678,7 +3678,7 @@ def instance_norm(
     if param_attr is False:
         assert (
             bias_attr is False
-        ), "param_attr and bias_attr must be set to Fasle at the same time in instance_norm"
+        ), "param_attr and bias_attr must be set to False at the same time in instance_norm"
 
     helper = LayerHelper('instance_norm', **locals())
     dtype = helper.input_dtype()

python/paddle/nn/layer/norm.py

@@ -74,7 +74,7 @@ class _InstanceNormBase(Layer):
         if weight_attr == False or bias_attr == False:
             assert (
                 weight_attr == bias_attr
-            ), "weight_attr and bias_attr must be set to Fasle at the same time in InstanceNorm"
+            ), "weight_attr and bias_attr must be set to False at the same time in InstanceNorm"
         self._epsilon = epsilon
         self._weight_attr = weight_attr
         self._bias_attr = bias_attr
@@ -779,11 +779,11 @@ class BatchNorm1D(_BatchNormBase):
         momentum(float, optional): The value used for the moving_mean and moving_var computation. Default: 0.9.
         weight_attr(ParamAttr|bool, optional): The parameter attribute for Parameter `scale`
             of batch_norm. If it is set to None or one attribute of ParamAttr, batch_norm
-            will create ParamAttr as weight_attr. If it is set to Fasle, the weight is not learnable.
+            will create ParamAttr as weight_attr. If it is set to False, the weight is not learnable.
             If the Initializer of the weight_attr is not set, the parameter is initialized with ones. Default: None.
         bias_attr(ParamAttr|bool, optional): The parameter attribute for the bias of batch_norm.
             If it is set to None or one attribute of ParamAttr, batch_norm
-            will create ParamAttr as bias_attr. If it is set to Fasle, the weight is not learnable.
+            will create ParamAttr as bias_attr. If it is set to False, the weight is not learnable.
             If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None.
         data_format(str, optional): Specify the input data format, may be "NC", "NCL" or "NLC". Default "NCL".
         use_global_stats(bool|None, optional): Whether to use global mean and variance. If set to False, use the statistics of one mini-batch, if set to True, use the global statistics, if set to None, use global statistics in the test phase and use the statistics of one mini-batch in the training phase. Default: None.
@@ -892,11 +892,11 @@ class BatchNorm2D(_BatchNormBase):
         momentum(float, optional): The value used for the moving_mean and moving_var computation. Default: 0.9.
         weight_attr(ParamAttr|bool, optional): The parameter attribute for Parameter `scale`
             of batch_norm. If it is set to None or one attribute of ParamAttr, batch_norm
-            will create ParamAttr as weight_attr. If it is set to Fasle, the weight is not learnable.
+            will create ParamAttr as weight_attr. If it is set to False, the weight is not learnable.
             If the Initializer of the weight_attr is not set, the parameter is initialized with ones. Default: None.
         bias_attr(ParamAttr|bool, optional): The parameter attribute for the bias of batch_norm.
             If it is set to None or one attribute of ParamAttr, batch_norm
-            will create ParamAttr as bias_attr. If it is set to Fasle, the weight is not learnable.
+            will create ParamAttr as bias_attr. If it is set to False, the weight is not learnable.
             If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None.
         data_format(str, optional): Specify the input data format, the data format can be "NCHW" or "NHWC". Default: NCHW.
         use_global_stats(bool|None, optional): Whether to use global mean and variance. If set to False, use the statistics of one mini-batch, if set to True, use the global statistics, if set to None, use global statistics in the test phase and use the statistics of one mini-batch in the training phase. Default: None.
@@ -978,11 +978,11 @@ class BatchNorm3D(_BatchNormBase):
         momentum(float, optional): The value used for the moving_mean and moving_var computation. Default: 0.9.
         weight_attr(ParamAttr|bool, optional): The parameter attribute for Parameter `scale`
             of batch_norm. If it is set to None or one attribute of ParamAttr, batch_norm
-            will create ParamAttr as weight_attr. If it is set to Fasle, the weight is not learnable.
+            will create ParamAttr as weight_attr. If it is set to False, the weight is not learnable.
             If the Initializer of the weight_attr is not set, the parameter is initialized with ones. Default: None.
         bias_attr(ParamAttr|bool, optional): The parameter attribute for the bias of batch_norm.
             If it is set to None or one attribute of ParamAttr, batch_norm
-            will create ParamAttr as bias_attr. If it is set to Fasle, the weight is not learnable.
+            will create ParamAttr as bias_attr. If it is set to False, the weight is not learnable.
             If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None.
         data_format(str, optional): Specify the input data format, the data format can be "NCDHW" or "NDHWC. Default: NCDHW.
         use_global_stats(bool|None, optional): Whether to use global mean and variance. If set to False, use the statistics of one mini-batch, if set to True, use the global statistics, if set to None, use global statistics in the test phase and use the statistics of one mini-batch in the training phase. Default: None.

python/paddle/sparse/nn/layer/norm.py

@@ -61,11 +61,11 @@ class BatchNorm(paddle.nn.BatchNorm1D):
         epsilon(float, optional): The small value added to the variance to prevent division by zero. Default: 1e-5.
         weight_attr(ParamAttr|bool, optional): The parameter attribute for Parameter `scale`
             of batch_norm. If it is set to None or one attribute of ParamAttr, batch_norm
-            will create ParamAttr as weight_attr. If it is set to Fasle, the weight is not learnable.
+            will create ParamAttr as weight_attr. If it is set to False, the weight is not learnable.
             If the Initializer of the weight_attr is not set, the parameter is initialized with Xavier. Default: None.
         bias_attr(ParamAttr|bool, optional): The parameter attribute for the bias of batch_norm.
             If it is set to None or one attribute of ParamAttr, batch_norm
-            will create ParamAttr as bias_attr. If it is set to Fasle, the weight is not learnable.
+            will create ParamAttr as bias_attr. If it is set to False, the weight is not learnable.
             If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None.
         data_format(str, optional): Specify the input data format, may be "NC", "NCL" or "NLC". Default "NCL".
         use_global_stats(bool|None, optional): Whether to use global mean and variance. If set to False, use the statistics of one mini-batch, if set to True, use the global statistics, if set to None, use global statistics in the test phase and use the statistics of one mini-batch in the training phase. Default: None.

python/paddle/vision/ops.py

@@ -347,7 +347,7 @@ def yolo_box(
     .. math::
 
         score_{conf} = \begin{case}
-                         obj, \text{if } iou_aware == flase \\
+                         obj, \text{if } iou_aware == false \\
                          obj^{1 - iou_aware_factor} * iou^{iou_aware_factor}, \text{otherwise}
                        \end{case}