Fix typos, test=document_fix (#53916)

python/paddle/amp/debugging.py

@@ -315,7 +315,7 @@ def enable_operator_stats_collection():
     """
     Enable to collect the number of operators for different data types.
     The statistical data are categorized according to four data types, namely
-    float32, float16, bfloat16 and others. This funciton is used in pair with
+    float32, float16, bfloat16 and others. This function is used in pair with
     the corresponding disable function.
 
     Examples:
@@ -351,7 +351,7 @@ def enable_operator_stats_collection():
 def disable_operator_stats_collection():
     """
     Disable the collection the number of operators for different data types.
-    This funciton is used in pair with the corresponding enable function.
+    This function is used in pair with the corresponding enable function.
     The statistical data are categorized according to four data types, namely
     float32, float16, bfloat16 and others, and will be printed after the
     function call.

python/paddle/device/__init__.py

@@ -135,7 +135,7 @@ def XPUPlace(dev_id):
 
 def get_cudnn_version():
     """
-    This funciton return the version of cudnn. the retuen value is int which represents the
+    This function return the version of cudnn. the retuen value is int which represents the
     cudnn version. For example, if it return 7600, it represents the version of cudnn is 7.6.
 
     Returns:
@@ -270,7 +270,7 @@ def set_device(device):
 
 def get_device():
     """
-    This funciton can get the current global device of the program is running.
+    This function can get the current global device of the program is running.
     It's a string which is like 'cpu', 'gpu:x', 'xpu:x' and 'npu:x'. if the global device is not
     set, it will return a string which is 'gpu:x' when cuda is avaliable or it
     will return a string which is 'cpu' when cuda is not avaliable.

python/paddle/distributed/fleet/base/distributed_strategy.py

@@ -2388,7 +2388,7 @@ class DistributedStrategy:
         """
 
         The workspace limit size in MB unit for choosing cuDNN convolution algorithms.
-        The inner funciton of cuDNN obtain the fastest suited algorithm that fits within this memory limit.
+        The inner function of cuDNN obtain the fastest suited algorithm that fits within this memory limit.
         Usually, large workspace size may lead to choose faster algorithms,
         but significant increasing memory workspace. Users need to trade-off between memory and speed.
         Default Value: 4000

python/paddle/distribution/beta.py

@@ -120,7 +120,7 @@ class Beta(exponential_family.ExponentialFamily):
         return paddle.exp(self.log_prob(value))
 
     def log_prob(self, value):
-        """Log probability density funciton evaluated at value
+        """Log probability density function evaluated at value
 
         Args:
             value (Tensor): Value to be evaluated

python/paddle/distribution/kl.py

@@ -73,7 +73,7 @@ def register_kl(cls_p, cls_q):
     functions registered by ``register_kl``, according to multi-dispatch pattern.
     If an implemention function is found, it will return the result, otherwise,
     it will raise ``NotImplementError`` exception. Users can register
-    implemention funciton by the decorator.
+    implemention function by the decorator.
 
     Args:
         cls_p (Distribution): The Distribution type of Instance p. Subclass derived from ``Distribution``.

python/paddle/distribution/transform.py

@@ -66,7 +66,7 @@ class Transform:
 
     Suppose :math:`X` is a K-dimensional random variable with probability
     density function :math:`p_X(x)`. A new random variable :math:`Y = f(X)` may
-    be defined by transforming :math:`X` with a suitably well-behaved funciton
+    be defined by transforming :math:`X` with a suitably well-behaved function
     :math:`f`. It suffices for what follows to note that if `f` is one-to-one and
     its inverse :math:`f^{-1}` have a well-defined Jacobian, then the density of
     :math:`Y` is

python/paddle/fluid/dygraph/learning_rate_scheduler.py

@@ -1234,7 +1234,7 @@ class LambdaDecay(_LearningRateEpochDecay):
     :api_attr: imperative
 
     Sets the learning rate of ``optimizer`` to the initial lr times a multiplicative factor, and this multiplicative
-    factor is computed by function ``lr_lambda`` . ``lr_lambda`` is funciton which receives ``epoch`` .
+    factor is computed by function ``lr_lambda`` . ``lr_lambda`` is function which receives ``epoch`` .
 
     The algorithm can be described as the code below.
 

python/paddle/fluid/multiprocess_utils.py

@@ -44,7 +44,7 @@ def _clear_multiprocess_queue_set():
 def _cleanup():
     # NOTE: inter-process Queue shared memory objects clear function
     _clear_multiprocess_queue_set()
-    # NOTE: main process memory map files clear funciton
+    # NOTE: main process memory map files clear function
     core._cleanup_mmap_fds()
 
 

python/paddle/fluid/tests/unittests/cc_imp_py_test.cc

@@ -25,7 +25,7 @@ TEST(CC, IMPORT_PY) {
   ASSERT_FALSE(PyRun_SimpleString("import paddle"));
   ASSERT_FALSE(PyRun_SimpleString("print(paddle.to_tensor(1))"));
 
-  // 2. C/C++ Run Python funciton
+  // 2. C/C++ Run Python function
   PyRun_SimpleString("import sys");
   PyRun_SimpleString("import os");
   PyRun_SimpleString("sys.path.append(os.getcwd())");

python/paddle/io/multiprocess_utils.py

@@ -45,7 +45,7 @@ def _clear_multiprocess_queue_set():
 def _cleanup():
     # NOTE: inter-process Queue shared memory objects clear function
     _clear_multiprocess_queue_set()
-    # NOTE: main process memory map files clear funciton
+    # NOTE: main process memory map files clear function
     core._cleanup_mmap_fds()
 
 

python/paddle/optimizer/lr.py

@@ -1138,7 +1138,7 @@ class StepDecay(LRScheduler):
 
 class LambdaDecay(LRScheduler):
     """
-    Sets the learning rate of ``optimizer`` by function ``lr_lambda`` . ``lr_lambda`` is funciton which receives ``epoch`` .
+    Sets the learning rate of ``optimizer`` by function ``lr_lambda`` . ``lr_lambda`` is function which receives ``epoch`` .
 
     The algorithm can be described as the code below.