Fix some typos (tanspose, vlaue, etc.) (#61743)

co63oc · web-flow · commit 34f5d8cddddd · 2024-02-19T15:58:43.000+08:00
diff --git a/python/paddle/distribution/categorical.py b/python/paddle/distribution/categorical.py
@@ -170,7 +170,7 @@ def sample(self, shape):
         )
 
         # multinomial sample shape is (logits.shape[:-1], num_samples), need to
-        # tanspose to (num_samples, logits.shape[:-1])
+        # transpose to (num_samples, logits.shape[:-1])
         permute = list(range(sample_index.dim()))
         permute.insert(0, permute.pop(-1))
         sample_index = sample_index.transpose(permute)
@@ -273,7 +273,7 @@ def probs(self, value):
 
         If ``logits`` is 2-D or higher dimension, the last dimension will be regarded as
         category, and the others represents the different distributions.
-        At the same time, if ``vlaue`` is 1-D Tensor, ``value`` will be broadcast to the
+        At the same time, if ``value`` is 1-D Tensor, ``value`` will be broadcast to the
         same number of distributions as ``logits``.
         If ``value`` is not 1-D Tensor, ``value`` should have the same number distributions
         with ``logits. That is, ``value[:-1] = logits[:-1]``.
diff --git a/python/paddle/distribution/multinomial.py b/python/paddle/distribution/multinomial.py
@@ -74,7 +74,7 @@ def __init__(self, total_count, probs):
 
         if probs.dim() < 1:
             raise ValueError(
-                'probs parameter shoule not be none and over one dimension'
+                'probs parameter should not be none and over one dimension'
             )
 
         self.probs = probs / probs.sum(-1, keepdim=True)
@@ -115,7 +115,7 @@ def prob(self, value):
         return paddle.exp(self.log_prob(value))
 
     def log_prob(self, value):
-        """probability mass function evaluated at value
+        """probability mass function evaluated at value.
 
         Args:
             value (Tensor): value to be evaluated.
diff --git a/python/paddle/distribution/multivariate_normal.py b/python/paddle/distribution/multivariate_normal.py
@@ -212,7 +212,7 @@ def _check_positive_definite(self, value):
         ).all()
         if not is_square:
             raise ValueError(
-                "covariance_matrix or precision_matrix must be a sqaure matrix"
+                "covariance_matrix or precision_matrix must be a square matrix"
             )
         new_perm = list(range(len(value.shape)))
         new_perm[-1], new_perm[-2] = new_perm[-2], new_perm[-1]
diff --git a/python/paddle/distribution/uniform.py b/python/paddle/distribution/uniform.py
@@ -45,7 +45,7 @@ class Uniform(distribution.Distribution):
     * :math:`Z`: is the normalizing constant.
 
     The parameters `low` and `high` must be shaped in a way that supports
-    `Boardcasting` (e.g., `high - low` is a valid operation).
+    `Broadcasting` (e.g., `high - low` is a valid operation).
 
     Note:
         If you want know more about broadcasting, please refer to `Introduction to Tensor`_ .
diff --git a/python/paddle/framework/io.py b/python/paddle/framework/io.py
@@ -566,7 +566,7 @@ def _parse_every_object(obj, condition_func, convert_func):
             (str, np.ndarray, core.eager.Tensor, core.LoDTensor),
         ):
             raise NotImplementedError(
-                "The iteratable objects supported are tuple, list, dict, OrderedDict, string. But received {}.".format(
+                "The iterable objects supported are tuple, list, dict, OrderedDict, string. But received {}.".format(
                     type(obj)
                 )
             )
diff --git a/python/paddle/hapi/model.py b/python/paddle/hapi/model.py
@@ -2469,7 +2469,7 @@ def _verify_spec(self, specs, shapes=None, dtypes=None, is_input=False):
             ]
         else:
             out_specs = to_list(specs)
-        # Note: checks each element has specificed `name`.
+        # Note: checks each element has specified `name`.
         if out_specs is not None:
             for i, spec in enumerate(out_specs):
                 assert isinstance(spec, Input)
diff --git a/python/paddle/incubate/autograd/composite_rules.py b/python/paddle/incubate/autograd/composite_rules.py
@@ -290,7 +290,7 @@ def mean_composite(x, axis, keepdim):
 @REGISTER_COMPOSITE('expand_v2')
 def expand_v2_composite(x, shape):
     """
-    define composite rule of op expnad_v2, expand_v2->expand
+    define composite rule of op expand_v2, expand_v2->expand
     repeat_times = shape / x.shape
     out = tile(x, repeat_times = repeat_times)
     """
@@ -324,7 +324,7 @@ def expand_v2_composite(x, shape):
 @REGISTER_COMPOSITE('expand_as_v2')
 def expand_as_v2_composite(x, y, target_shape):
     """
-    define composite rule of op expnad_as_v2, expand_as_v2->expand_as
+    define composite rule of op expand_as_v2, expand_as_v2->expand_as
     repeat_times = target_shape / x.shape
     out = tile(x, repeat_times = repeat_times)
     """
@@ -420,7 +420,7 @@ def dropout_composite(x, seed_tensor, p, is_test, mode, seed, fix_seed):
 
     if upscale_in_train:
         if not is_test:
-            # Process p=1.0 for avoid devide zero error (x*mask/(1.0-p))
+            # Process p=1.0 for avoid divide zero error (x*mask/(1.0-p))
             if p == 1.0:
                 return 0.0 * x, zeros(x.shape, core.VarDesc.VarType.UINT8)
             else:
diff --git a/python/paddle/incubate/autograd/functional.py b/python/paddle/incubate/autograd/functional.py
@@ -194,7 +194,7 @@ class Jacobian:
 
     Notes:
 
-        Eclipsis index is not supported currently.
+        Ellipsis index is not supported currently.
 
     Warning:
         This API is in beta, the signatures could be changed in future version.
@@ -327,7 +327,7 @@ def shape(self):
 class _Jacobian:
     """The base class for computing Jacobian matrix.
 
-    ``_Jacobian`` implementes the core logic of multidimensional index and lazy
+    ``_Jacobian`` implements the core logic of multidimensional index and lazy
     evaluation for Jacobian matrix, subclass only need to overwrite following
     methods:
 
@@ -517,7 +517,7 @@ def _multi_index(indexes, shape):
                     index.start + shape[i] if index.start < 0 else index.start,
                     index.stop + shape[i] if index.stop < 0 else index.stop,
                     # Negative step means index backward, no need to convert to
-                    # positive interger.
+                    # positive integer.
                     index.step,
                 )
             )
diff --git a/python/paddle/incubate/autograd/generate_op_map.py b/python/paddle/incubate/autograd/generate_op_map.py
@@ -55,7 +55,7 @@ def generate_code(
     ops_yaml_path, ops_legacy_yaml_path, ops_compat_yaml_path, phi_ops_map_path
 ):
     """
-    Generate dictiorary and save to file phi_ops_map.py. The target file records gap
+    Generate dictionary and save to file phi_ops_map.py. The target file records gap
     of description between current op and standard ones.
     """
     dct = {}
diff --git a/python/paddle/incubate/autograd/primapi.py b/python/paddle/incubate/autograd/primapi.py
@@ -256,11 +256,11 @@ def to_prim(
         )
     if not isinstance(blacklist, (set, frozenset)):
         raise TypeError(
-            f'Expected type of blacklisst is set|frozenset, but got {type(blacklist)}.'
+            f'Expected type of blacklist is set|frozenset, but got {type(blacklist)}.'
         )
     if not isinstance(whitelist, (set, frozenset)):
         raise TypeError(
-            f'Expected type of whiltelist is set|frozenset, but got {type(whitelist)}.'
+            f'Expected type of whitelist is set|frozenset, but got {type(whitelist)}.'
         )
 
     blacklist = prim_config["forward_blacklist"] | blacklist
diff --git a/python/paddle/incubate/autograd/primops.py b/python/paddle/incubate/autograd/primops.py
@@ -144,7 +144,7 @@ def batch_norm(
         momentum (float, optional): The value used for the running_mean and
             running_var computation. Can be set to None for cumulative moving
             average (i.e. simple average). Defaults to 0.9.
-        use_run_stat (bool, optional): Whether or not using runing statistics.
+        use_run_stat (bool, optional): Whether or not using running statistics.
             Defaults to False.
     """
     reduce_axes = tuple(i for i in range(len(x.shape)) if i != axis)
diff --git a/python/paddle/incubate/autograd/utils.py b/python/paddle/incubate/autograd/utils.py
@@ -236,8 +236,8 @@ def _get_args_values(op, phi_name):
 def prepare_python_api_arguments(op):
     """
     Generate all args inputs of composite op. Because inputs of composite op is
-    the same as phi op desribed in ops.yaml. So we need to map origin op to phi op
-    and then push input data and attrs of origin op to correspondng phi op.
+    the same as phi op described in ops.yaml. So we need to map origin op to phi op
+    and then push input data and attrs of origin op to corresponding phi op.
     """
     if op.input_names is None:
         return []
diff --git a/python/paddle/incubate/distributed/fleet/parameter_server/distribute_transpiler/distributed_strategy.py b/python/paddle/incubate/distributed/fleet/parameter_server/distribute_transpiler/distributed_strategy.py
@@ -108,7 +108,7 @@ def get_communicator_flags(self):
                     'WARNING: In {} mode, communicator_max_merge_var_num '
                     'must be equal to CPU_NUM. But received, '
                     'communicator_max_merge_var_num = {}, CPU_NUM = '
-                    '{}. communicator_max_merge_var_num will be fored to {}.'.format(
+                    '{}. communicator_max_merge_var_num will be forced to {}.'.format(
                         mode_str, max_merge_var_num, num_threads, num_threads
                     )
                 )
@@ -120,7 +120,7 @@ def get_communicator_flags(self):
                     'WARNING: In {} mode, communicator_send_queue_size '
                     'must be equal to CPU_NUM. But received, '
                     'communicator_send_queue_size = {}, CPU_NUM = '
-                    '{}. communicator_send_queue_size will be fored to {}.'.format(
+                    '{}. communicator_send_queue_size will be forced to {}.'.format(
                         mode_str, send_queue_size, num_threads, num_threads
                     )
                 )
diff --git a/python/paddle/incubate/distributed/fleet/parameter_server/ir/public.py b/python/paddle/incubate/distributed/fleet/parameter_server/ir/public.py
@@ -329,7 +329,7 @@ def get_optimize_varname_on_ps(self, param_name):
                         "Beta2Tensor",
                     ]:
                         continue
-                    # check varibale shape related param, e.g: Moment1
+                    # check variable shape related param, e.g: Moment1
                     optimize_var_names += (
                         self._get_optimizer_param_related_var_name(
                             op, op.type, key
@@ -426,7 +426,7 @@ def get_grad_var_ep(slices):
 
     def get_trainer_send_context(self):
         send_ctx = {}
-        distibuted_varnames = get_sparse_tablenames(
+        distributed_varnames = get_sparse_tablenames(
             self.origin_main_program, True
         )
         idx = 0
@@ -446,7 +446,7 @@ def get_trainer_send_context(self):
                 param_name = param.merged_var.name
 
                 is_distributed = (
-                    True if param_name in distibuted_varnames else False
+                    True if param_name in distributed_varnames else False
                 )
 
                 ctx = self.build_ctx(
@@ -468,7 +468,7 @@ def get_trainer_send_context(self):
                 param, grad = pairs
                 param_name = param.name
                 is_distributed = (
-                    True if param_name in distibuted_varnames else False
+                    True if param_name in distributed_varnames else False
                 )
 
                 param_ctx = self.build_ctx(
@@ -509,7 +509,7 @@ def get_trainer_send_context(self):
 
     def get_communicator_send_context(self):
         send_ctx = {}
-        distibuted_varnames = get_sparse_tablenames(
+        distributed_varnames = get_sparse_tablenames(
             self.origin_main_program, True
         )
         idx = 0
@@ -526,7 +526,7 @@ def get_communicator_send_context(self):
                 param = pairs[0]
                 param_name = param.merged_var.name
                 is_distributed = (
-                    True if param_name in distibuted_varnames else False
+                    True if param_name in distributed_varnames else False
                 )
 
                 ctx = self.build_ctx(
@@ -554,7 +554,7 @@ def get_communicator_send_context(self):
                 param_name = param.merged_var.name
 
                 is_distributed = (
-                    True if param_name in distibuted_varnames else False
+                    True if param_name in distributed_varnames else False
                 )
 
                 ctx = self.build_ctx(
@@ -577,7 +577,7 @@ def get_communicator_recv_context(
     ):
         # recv_type
         # 1 : DENSE 2. SPARSE 3. DISTRIBUTED 4. ALL
-        distibuted_varnames = get_sparse_tablenames(
+        distributed_varnames = get_sparse_tablenames(
             self.origin_main_program, True
         )
         sparse_varnames = []
@@ -607,7 +607,7 @@ def get_communicator_recv_context(
         for pairs in self.origin_sparse_pairs:
             param, grad = pairs
 
-            if param.name in distibuted_varnames:
+            if param.name in distributed_varnames:
                 ctx = self.build_ctx(
                     param, self.param_var_mapping, False, True, False, True
                 )
@@ -638,15 +638,15 @@ def get_the_one_trainer_send_context(self, split_dense_table):
             trainer_id = self.get_role_id()
             idx = 0
 
-            distibuted_varnames = get_sparse_tablenames(
+            distributed_varnames = get_sparse_tablenames(
                 self.origin_main_program, True
             )
             for merged in self.merged_sparse_pairs:
                 param, grad = merged
                 grad_name = grad.merged_var.name
                 param_name = param.merged_var.name
                 is_distributed = (
-                    True if param_name in distibuted_varnames else False
+                    True if param_name in distributed_varnames else False
                 )
 
                 var = self.origin_main_program.global_block().vars[
@@ -781,7 +781,7 @@ def get_the_one_send_context(
             send_ctx, idx, merged_dense_pairs, trainer_id, split_dense_table
         )
 
-        distibuted_varnames = get_sparse_tablenames(
+        distributed_varnames = get_sparse_tablenames(
             self.origin_main_program, True
         )
         for merged in merged_sparse_pairs:
@@ -794,7 +794,7 @@ def get_the_one_send_context(
                 splited_varname.append(f"{param_name}.block{i}")
 
             is_distributed = (
-                True if param_name in distibuted_varnames else False
+                True if param_name in distributed_varnames else False
             )
 
             var = self.origin_main_program.global_block().vars[
@@ -1397,7 +1397,7 @@ def _get_lr_param_dict(opt_ops):
 
 
 def _get_lr_scheduler_program(lr_scheduler, lr_param_dict, lr_decay_steps):
-    schedler_decay = [
+    scheduler_decay = [
         'NoamDecay',
         'NaturalExpDecay',
         'InverseTimeDecay',
@@ -1479,7 +1479,7 @@ def _get_lr_scheduler_program(lr_scheduler, lr_param_dict, lr_decay_steps):
     else:
         raise ValueError(
             "Not supported current LearningRate strategy, please use follow decay strategy: {}".format(
-                schedler_decay
+                scheduler_decay
             )
         )
 
diff --git a/python/paddle/incubate/distributed/fleet/parameter_server/ir/trainer_pass.py b/python/paddle/incubate/distributed/fleet/parameter_server/ir/trainer_pass.py

Original file line number	Diff line number	Diff line change
`@@ -212,7 +212,7 @@ def _check_positive_definite(self, value):`
`212`	`212`	`).all()`
`213`	`213`	`if not is_square:`
`214`	`214`	`raise ValueError(`
`215`		`- "covariance_matrix or precision_matrix must be a sqaure matrix"`
	`215`	`+ "covariance_matrix or precision_matrix must be a square matrix"`
`216`	`216`	`)`
`217`	`217`	`new_perm = list(range(len(value.shape)))`
`218`	`218`	`new_perm[-1], new_perm[-2] = new_perm[-2], new_perm[-1]`
Original file line number	Diff line number	Diff line change
`@@ -566,7 +566,7 @@ def _parse_every_object(obj, condition_func, convert_func):`
`566`	`566`	`(str, np.ndarray, core.eager.Tensor, core.LoDTensor),`
`567`	`567`	`):`
`568`	`568`	`raise NotImplementedError(`
`569`		`- "The iteratable objects supported are tuple, list, dict, OrderedDict, string. But received {}.".format(`
	`569`	`+ "The iterable objects supported are tuple, list, dict, OrderedDict, string. But received {}.".format(`
`570`	`570`	`type(obj)`
`571`	`571`	`)`
`572`	`572`	`)`
Original file line number	Diff line number	Diff line change
`@@ -2469,7 +2469,7 @@ def _verify_spec(self, specs, shapes=None, dtypes=None, is_input=False):`
`2469`	`2469`	`]`
`2470`	`2470`	`else:`
`2471`	`2471`	`out_specs = to_list(specs)`
`2472`		- # Note: checks each element has specificed `name`.
	`2472`	+ # Note: checks each element has specified `name`.
`2473`	`2473`	`if out_specs is not None:`
`2474`	`2474`	`for i, spec in enumerate(out_specs):`
`2475`	`2475`	`assert isinstance(spec, Input)`
Original file line number	Diff line number	Diff line change
`@@ -256,11 +256,11 @@ def to_prim(`
`256`	`256`	`)`
`257`	`257`	`if not isinstance(blacklist, (set, frozenset)):`
`258`	`258`	`raise TypeError(`
`259`		`- f'Expected type of blacklisst is set\|frozenset, but got {type(blacklist)}.'`
	`259`	`+ f'Expected type of blacklist is set\|frozenset, but got {type(blacklist)}.'`
`260`	`260`	`)`
`261`	`261`	`if not isinstance(whitelist, (set, frozenset)):`
`262`	`262`	`raise TypeError(`
`263`		`- f'Expected type of whiltelist is set\|frozenset, but got {type(whitelist)}.'`
	`263`	`+ f'Expected type of whitelist is set\|frozenset, but got {type(whitelist)}.'`
`264`	`264`	`)`
`265`	`265`
`266`	`266`	`blacklist = prim_config["forward_blacklist"] \| blacklist`