Fix excuted executed (PaddlePaddle#61153)

* Fix

* Fix

python/paddle/static/nn/static_pylayer.py

@@ -196,7 +196,7 @@ def _rename_var_recursively_(cur_block, var_old_to_new):
                 op._rename_output(old_var_name, new_var_name)
 
     # NOTE(MarioLulab): block attr type with the name of "blocks" or "sub_block" indicates
-    # the block might be excuted. We should rename the var name in these blocks recursively
+    # the block might be executed. We should rename the var name in these blocks recursively
     block_attr_names = ["blocks", "sub_block"]
 
     for op in cur_block.ops:

python/paddle/static/quantization/post_training_quantization.py

@@ -1116,10 +1116,10 @@ def _init_sampling_act_histogram(self):
             if var_name not in self._sampling_act_histogram:
                 min_val = self._sampling_act_abs_min_max[var_name][0]
                 max_val = self._sampling_act_abs_min_max[var_name][1]
-                hist, hist_edeges = np.histogram(
+                hist, hist_edges = np.histogram(
                     [], bins=self._histogram_bins, range=(min_val, max_val)
                 )
-                self._sampling_act_histogram[var_name] = [hist, hist_edeges]
+                self._sampling_act_histogram[var_name] = [hist, hist_edges]
 
     def _calculate_kl_hist_threshold(self):
         '''
@@ -1155,16 +1155,16 @@ def _calculate_kl_hist_threshold(self):
                 var_name not in self._sampling_act_histogram
             ):
                 continue
-            hist, hist_edeges = self._sampling_act_histogram[var_name]
+            hist, hist_edges = self._sampling_act_histogram[var_name]
             if self._algo == "KL":
-                bin_width = hist_edeges[1] - hist_edeges[0]
+                bin_width = hist_edges[1] - hist_edges[0]
                 self._quantized_var_threshold[var_name] = cal_kl_threshold(
                     hist, bin_width, self._activation_bits
                 )
             elif self._algo == "hist":
                 self._quantized_var_threshold[
                     var_name
-                ] = self._get_hist_scaling_factor(hist, hist_edeges)
+                ] = self._get_hist_scaling_factor(hist, hist_edges)
 
     def _update_program(self):
         '''
@@ -1995,7 +1995,7 @@ def _mul_channel_wise_dequantization(self, quantized_weight_data, scales):
 
     def _calculate_threshold(self, input, threshold_rate, histogram_bins=5000):
         input_abs = np.abs(input)
-        hist, hist_edeges = np.histogram(
+        hist, hist_edges = np.histogram(
             input_abs, bins=histogram_bins, range=(0, np.max(input_abs))
         )
         hist = hist / float(sum(hist))
@@ -2006,5 +2006,5 @@ def _calculate_threshold(self, input, threshold_rate, histogram_bins=5000):
             if hist_sum >= 1.0 - threshold_rate:
                 hist_index = i + 1
                 break
-        bin_width = hist_edeges[1] - hist_edeges[0]
+        bin_width = hist_edges[1] - hist_edges[0]
         return hist_index * bin_width

python/paddle/static/quantization/quant2_int8_mkldnn_pass.py

@@ -84,7 +84,7 @@ def __init__(
         self._gru_ops = ['fusion_gru', 'multi_gru']
         self._lstm_ops = ['fusion_lstm']
         self._weight_thresholds = {}
-        # Collect the Input and Output sclaes from Fake quant models
+        # Collect the Input and Output scales from Fake quant models
         self._var_quant_scales = {}
         self._max_range = {}
         self._s8_max = 127

python/paddle/static/quantization/quantization_pass.py

@@ -165,7 +165,7 @@ def __init__(
                 preprocess method works or not. The function's input is non-quantized
                 activation and function returns processed activation to be quantized.
                 If None, the activation will be quantized directly. Default is None.
-            optimizer_func(function): Fuction return a optimizer. When 'is_test' is
+            optimizer_func(function): Function return a optimizer. When 'is_test' is
                 False and user want to use self-defined quantization function and
                 preprocess function, this function must be set. Default is None.
             executor(base.Executor): If user want to use self-defined quantization
@@ -414,7 +414,7 @@ def _has_weight(op):
         if not self._is_test:
             self._create_global_step(graph)
         ops = graph.all_op_nodes()
-        # Do the preproccess of quantization, such as skipping some ops
+        # Do the preprocess of quantization, such as skipping some ops
         # for not being quantized.
         for op in ops:
             if (
@@ -3597,7 +3597,7 @@ def _var_name_order(self, graph):
 
     def _insert_quant_dequant(self, graph, var_node, op):
         """
-        Insert per tensort quantize_linear and dequantize_linear node between var_node and op
+        Insert per tensor quantize_linear and dequantize_linear node between var_node and op
         """
         insert_quant_pass = InsertQuantizeLinear(
             self._place,

python/paddle/tensor/attribute.py

@@ -212,7 +212,7 @@ def is_floating_point(x):
 
 
 def is_integer(x):
-    """Return whether x is a tensor of integeral data type.
+    """Return whether x is a tensor of integral data type.
 
     Args:
         x (Tensor): The input tensor.

python/paddle/tensor/einsum.py

@@ -646,10 +646,10 @@ def plan_einsum(operands, g_view, g_shape, g_supports, g_count, n_bcast):
         #       I... are aligned and not to be combined immediately
         #       J... are not aligned and not to be combined immediately
         #       K... are aligned and should be immediately combined
-        # At this point the non-trivial broadcast dimensinos in K are already reduced
+        # At this point the non-trivial broadcast dimensions in K are already reduced
         # and removed. That means all K dimensions are aligned and their sizes are not 1.
         # We then inspect the layout of I,J,K plus the above observation to make
-        # specializatoin decisions.  The current strategy is set as follows:
+        # specialization decisions.  The current strategy is set as follows:
         #  (1) if I... J... K... are all empty, it's multiplying a scalar
         #  (2) if K... are empty, better use a broadcast
         #  (3) if I... J... empty and K... not empty, a vector-vector multiply (or a dot)
@@ -748,7 +748,7 @@ def parse_fake_shape(equation, operands, labels):
     def fake_shape(ori_label, label, op):
         """
         1. ori_label is the original labels, not aligned by '....'
-        2. if the '...' is evalulated to empty list, there is no '.' in label
+        2. if the '...' is evaluated to empty list, there is no '.' in label
         """
         assert len(op.shape) == len(label), (
             "length of shape and length of label must be the same, but received %d != %d"
@@ -802,7 +802,7 @@ def einsum_v2(equation, *operands):
     """
     einsum v2 implementation.
     1. Implement C++ EinsumOp.
-    2. V2 create the EinsumOp to calculate, so just a little verifty work in python.
+    2. V2 create the EinsumOp to calculate, so just a little verify work in python.
     3. V2 use opt_einsum.contract_path to optimize the multivariable einsum.
     """
     n_op = len(operands)

python/paddle/tensor/math.py

@@ -1668,7 +1668,7 @@ def nan_to_num_(x, nan=0.0, posinf=None, neginf=None, name=None):
     Please refer to :ref:`api_paddle_nan_to_num`.
     """
     # NOTE(tiancaishaonvjituizi): it seems that paddle handles the dtype of python float number
-    # incorrectly, so we have to explicitly contruct tensors here
+    # incorrectly, so we have to explicitly construct tensors here
     posinf_value = paddle.full_like(x, float("+inf"))
     neginf_value = paddle.full_like(x, float("-inf"))
     nan = paddle.full_like(x, nan)
@@ -2716,7 +2716,7 @@ def _check_input(x):
             if len(x.shape) < 2:
                 raise ValueError(
                     "The input of inverse is expected to be a Tensor whose number "
-                    "of dimensions is no less than 2. But reviced: %d, "
+                    "of dimensions is no less than 2. But received: %d, "
                     "x's shape: %s." % (len(x.shape), x.shape)
                 )
 
@@ -6419,7 +6419,7 @@ def take(x, index, mode='raise', name=None):
     Args:
         x (Tensor): An N-D Tensor, its data type should be int32, int64, float32, float64.
         index (Tensor): An N-D Tensor, its data type should be int32, int64.
-        mode (str, optional): Specifies how out-of-bounds index will behave. the candicates are ``'raise'``, ``'wrap'`` and ``'clip'``.
+        mode (str, optional): Specifies how out-of-bounds index will behave. the candidates are ``'raise'``, ``'wrap'`` and ``'clip'``.
 
             - ``'raise'``: raise an error (default);
             - ``'wrap'``: wrap around;

python/paddle/tensor/random.py

@@ -225,7 +225,7 @@ def standard_gamma(x, name=None):
         out_i \sim Gamma (alpha = x_i, beta = 1.0)
 
     Args:
-        x(Tensor):  A tensor with rate parameter of standrad gamma Distribution. The data type
+        x(Tensor):  A tensor with rate parameter of standard gamma Distribution. The data type
             should be bfloat16, float16, float32, float64.
         name(str, optional): The default value is None. Normally there is no
             need for user to set this property. For more information, please
@@ -779,7 +779,7 @@ def normal(mean=0.0, std=1.0, shape=None, name=None):
 
     out = out * std + mean
     if not in_dynamic_or_pir_mode():
-        out.stop_grediant = True
+        out.stop_gradient = True
     return out
 
 
@@ -788,7 +788,7 @@ def normal_(x, mean=0.0, std=1.0, name=None):
     """
     This is the inplace version of api ``normal``, which returns a Tensor filled
     with random values sampled from a normal distribution. The output Tensor will
-    be inplaced with input ``x``. Please refer to :ref:`api_tensor_noraml`.
+    be inplaced with input ``x``. Please refer to :ref:`api_tensor_normal`.
 
     Args:
         x(Tensor): The input tensor to be filled with random values.
@@ -799,7 +799,7 @@ def normal_(x, mean=0.0, std=1.0, name=None):
         std (float|Tensor, optional): The  standard deviation of the output Tensor's normal distribution.
             If ``std`` is float, all elements of the output Tensor shared the same standard deviation.
             If ``std`` is a Tensor(data type supports float32, float64), it has per-element standard deviations.
-            Defaule is 1.0
+            Default is 1.0
         name(str, optional): The default value is None. Normally there is no
             need for user to set this property. For more information, please
             refer to :ref:`api_guide_Name`.

python/paddle/tensor/search.py

@@ -1239,7 +1239,7 @@ def top_p_sampling(x, ps, threshold=None, seed=None, name=None):
     Args:
         x(Tensor): A N-D Tensor with type float32, float16 and bfloat16.
         ps(Tensor): A 1-D Tensor with type float32, float16 and bfloat16.
-            it is the cumulative probalitity threshold to limit low probality input.
+            it is the cumulative probability threshold to limit low probability input.
         threshold(Tensor): A 1-D Tensor with type float32, float16 and bfloat16.
             it is the absolute probability threshold to limit input, it will take effect simultaneously with `ps`, if not set, the default value is 0.f.
         seed(int, optional): the random seed,

python/paddle/utils/cpp_extension/extension_utils.py

@@ -331,7 +331,7 @@ def clean_object_if_change_cflags(so_path, extension):
     """
     If already compiling source before, we should check whether cflags
     have changed and delete the built object to re-compile the source
-    even though source file content keeps unchanaged.
+    even though source file content keeps unchanged.
     """
 
     def serialize(path, version_info):
@@ -938,7 +938,7 @@ def get_build_directory(verbose=False):
 
 def parse_op_info(op_name):
     """
-    Parse input names and outpus detail information from registered custom op
+    Parse input names and outputs detail information from registered custom op
     from OpInfoMap.
     """
     if op_name not in OpProtoHolder.instance().op_proto_map:
@@ -1356,7 +1356,7 @@ def _jit_compile(file_path, verbose=False):
 
 def parse_op_name_from(sources):
     """
-    Parse registerring custom op name from sources.
+    Parse registering custom op name from sources.
     """
 
     def regex(content):