Fix typos orignal original (#70784)

co63oc · web-flow · commit aac751f1c7b9 · 2025-01-20T11:12:06.000+08:00
diff --git a/paddle/cinn/operator_fusion/pir_graph_analyzing/shardable_axes_base.cc b/paddle/cinn/operator_fusion/pir_graph_analyzing/shardable_axes_base.cc
@@ -263,7 +263,7 @@ ShardableAxesSignature CreateSignatureForBroadcast(
     pir::Operation* op, pir::ShapeConstraintIRAnalysis* shape_analysis) {
   ShardableAxesSignature result = ShardableAxesSignature();
 
-  const auto& broad_cast_value = GetBroadcastOpInputOuputValue(op);
+  const auto& broad_cast_value = GetBroadcastOpInputOutputValue(op);
   PADDLE_ENFORCE_EQ(broad_cast_value.has_value(),
                     true,
                     ::common::errors::PreconditionNotMet(
diff --git a/paddle/cinn/operator_fusion/utils.cc b/paddle/cinn/operator_fusion/utils.cc
@@ -128,7 +128,7 @@ std::pair<std::vector<int64_t>, bool> GetSliceAxis(pir::Operation* slice_op) {
   return std::make_pair(slice_axis, keepdim);
 }
 
-std::optional<std::pair<pir::Value, pir::Value>> GetBroadcastOpInputOuputValue(
+std::optional<std::pair<pir::Value, pir::Value>> GetBroadcastOpInputOutputValue(
     pir::Operation* op) {
   auto* mut_op = const_cast<pir::Operation*>(op);
   if (op->isa<paddle::dialect::ExpandOp>()) {
@@ -149,7 +149,7 @@ std::vector<std::pair<size_t, size_t>> GetNonBroadCastDims(pir::Operation* op) {
   auto* shape_analysis =
       &pir::ShapeAnalysisManager::Instance().Get(op->GetParentProgram());
 
-  const auto& broad_cast_value = GetBroadcastOpInputOuputValue(op);
+  const auto& broad_cast_value = GetBroadcastOpInputOutputValue(op);
   CHECK(broad_cast_value.has_value());
 
   const auto& [input_value, output_value] = broad_cast_value.value();
diff --git a/paddle/cinn/operator_fusion/utils.h b/paddle/cinn/operator_fusion/utils.h
@@ -80,7 +80,7 @@ std::pair<std::vector<int64_t>, bool> GetSliceAxis(pir::Operation* slice_op);
 
 bool GetReduceOpKeepDims(pir::Operation* reduce_op);
 
-std::optional<std::pair<pir::Value, pir::Value>> GetBroadcastOpInputOuputValue(
+std::optional<std::pair<pir::Value, pir::Value>> GetBroadcastOpInputOutputValue(
     pir::Operation* op);
 
 std::vector<std::pair<size_t, size_t>> GetNonBroadCastDims(pir::Operation* op);
diff --git a/paddle/fluid/imperative/reducer.cc b/paddle/fluid/imperative/reducer.cc
@@ -721,7 +721,7 @@ void Reducer::MarkVarReady(const size_t var_index, const bool is_used_var) {
         "parameters participate in the backward calculation "
         "again at a later time (e.g. after the forward function, "
         "the loss calculation uses the unused "
-        "paramters of the forward and trigger backward), "
+        "parameters of the forward and trigger backward), "
         "its gradient will be wrong.";
 
     PADDLE_ENFORCE_EQ(has_marked_unused_vars_,
diff --git a/paddle/phi/backends/onednn/onednn_reuse.h b/paddle/phi/backends/onednn/onednn_reuse.h
@@ -296,7 +296,7 @@ class OneDNNHandlerT {
   }
 
   // If your primitive descriptor requires attributes, pass them as a
-  // first argument and paramters to descriptor constructor in the following
+  // first argument and parameters to descriptor constructor in the following
   // arguments. Otherwise, all arguments will be forwarded to descriptor
   // constructor, including the first one.
   template <typename Arg, typename... Args>
@@ -607,7 +607,7 @@ class OneDNNHandlerNoCachingT {
 
  protected:
   // If your primitive descriptor requires attributes, pass them as a
-  // first argument and paramters to descriptor constructor in the following
+  // first argument and parameters to descriptor constructor in the following
   // arguments. Otherwise, all arguments will be forwarded to descriptor
   // constructor, including the first one.
   template <typename Arg, typename... Args>
diff --git a/paddle/phi/infermeta/spmd_rules/optimizer.cc b/paddle/phi/infermeta/spmd_rules/optimizer.cc
@@ -93,7 +93,7 @@ SpmdInfo AdamInferSpmdDynamic(
           ? CopyTensorDistAttrForOutput(master_param.dist_attr())
           : TensorDistAttr();
   // If skip_update is on global_mesh, it should be reshard into
-  // local mesh. (currently occurs in static mode pipeline parellel)
+  // local mesh. (currently occurs in static mode pipeline parallel)
   auto skip_update_dist_attr = TensorDistAttr();
   if (skip_update.initialized()) {
     skip_update_dist_attr = skip_update.dist_attr();
diff --git a/paddle/utils/small_vector.h b/paddle/utils/small_vector.h
@@ -219,7 +219,7 @@ class small_vector_template_common
   /// Check whether Elt will be invalidated by resizing the vector to NewSize.
   void assertSafeToReferenceAfterResize(const void *Elt, size_t NewSize) {
     (void)Elt;
-    (void)NewSize;  // just remove [-Wunused-paremeter]
+    (void)NewSize;  // just remove [-Wunused-parameter]
     assert(isSafeToReferenceAfterResize(Elt, NewSize) &&
            "Attempting to reference an element of the vector in an operation "
            "that invalidates it");
diff --git a/python/paddle/distributed/auto_parallel/static/helper.py b/python/paddle/distributed/auto_parallel/static/helper.py
@@ -465,7 +465,7 @@ def init(self, main_program, place, dist_context):
 
         amp_stragety = dist_context.strategy.amp
         amp_config = copy.deepcopy(amp_stragety.to_dict())
-        need_cast_paramter = amp_stragety.enable and amp_config["level"] in [
+        need_cast_parameter = amp_stragety.enable and amp_config["level"] in [
             "o2",
             "o3",
         ]
@@ -516,14 +516,14 @@ def init(self, main_program, place, dist_context):
                     param.numpy(), dist_attr
                 )
                 param_tensor.set(sliced_param, place)
-                if not need_cast_paramter:
+                if not need_cast_parameter:
                     param.get_tensor()._clear()
             elif param.is_dist():
                 dense_tensor = global_scope().var(param.name).get_tensor()
                 dense_tensor._share_data_with(param.get_tensor().get_tensor())
 
         # transform the parameter in eager mode for amp.
-        if need_cast_paramter:
+        if need_cast_parameter:
             for param in self.concrete_program.parameters:
                 amp_dtype = amp_config["dtype"]
                 scope_var = global_scope().find_var(param.name)
diff --git a/python/paddle/jit/api.py b/python/paddle/jit/api.py
@@ -650,7 +650,7 @@ def _get_output_vars(outputs, output_spec, with_hook=False):
     )
     output_spec_is_not_value_error = (
         "tensor `%s` is not support in pir mode, "
-        "because pir value has no name sometimes, especially as ouptut,"
+        "because pir value has no name sometimes, especially as output,"
         "so we can't check tensor's name with output var name, please"
         "change as pir.value(to_static layer's output)"
         "or int(the position of to_static layer's output)"
diff --git a/python/paddle/optimizer/adadelta.py b/python/paddle/optimizer/adadelta.py
@@ -69,7 +69,7 @@ class Adadelta(Optimizer):
         parameters (list|tuple|None, optional): List/Tuple of ``Tensor`` to update to minimize ``loss``. \
             This parameter is required in dygraph mode. And you can specify different options for \
             different parameter groups such as the learning rate, weight decay, etc, \
-            then the parameters are list of dict. Note that the learning_rate in paramter groups \
+            then the parameters are list of dict. Note that the learning_rate in parameter groups \
             represents the scale of base learning_rate. \
             The default value is None in static graph mode, at this time all parameters will be updated.
         weight_decay (int|float|WeightDecayRegularizer|None, optional): The strategy of regularization. \
diff --git a/python/paddle/optimizer/optimizer.py b/python/paddle/optimizer/optimizer.py
@@ -288,7 +288,7 @@ def __init__(
         # Dictionary of accumulators. Some optimizer subclasses need to
         # allocate and manage extra tensors associated with the parameters
         # to train. These tensors are called accumulators.
-        # {accum_name : { paramter_name : accumulator_for_parameter, ...}, ...}
+        # {accum_name : { parameter_name : accumulator_for_parameter, ...}, ...}
         self._accumulators = defaultdict(lambda: {})
         self.helper = None
         self._opti_name_list = []
diff --git a/test/legacy_test/test_imperative_container_parameterdict.py b/test/legacy_test/test_imperative_container_parameterdict.py
@@ -45,7 +45,7 @@ def forward(self, x):
 
 
 class TestImperativeContainerParameterDict(unittest.TestCase):
-    def paramter_dict(self):
+    def parameter_dict(self):
         self.place = (
             paddle.CUDAPlace(0)
             if paddle.is_compiled_with_cuda()
@@ -67,23 +67,23 @@ def paramter_dict(self):
             )
             res = model(x)
             self.assertListEqual(res.shape, [5, 3])
-            parmeter = OrderedDict(
+            parameter = OrderedDict(
                 [
                     (
                         't' + str(num_stacked_param),
                         paddle.create_parameter(shape=[3, 4], dtype='float32'),
                     )
                 ]
             )
-            model.params.update(parmeter)
+            model.params.update(parameter)
             self.assertEqual(len(model.params), num_stacked_param + 1)
             res = model(x)
             self.assertListEqual(res.shape, [5, 4])
             loss = paddle.mean(res)
             loss.backward()
 
-    def test_paramter_dict(self):
-        self.paramter_dict()
+    def test_parameter_dict(self):
+        self.parameter_dict()
 
 
 if __name__ == '__main__':
diff --git a/test/legacy_test/test_imperative_container_parameterlist.py b/test/legacy_test/test_imperative_container_parameterlist.py
@@ -39,7 +39,7 @@ def forward(self, x):
 
 
 class TestImperativeContainerParameterList(unittest.TestCase):
-    def paramter_list(self, use_base_api):
+    def parameter_list(self, use_base_api):
         data_np = np.random.uniform(-1, 1, [5, 2]).astype('float32')
         with base.dygraph.guard():
             x = paddle.to_tensor(data_np)
@@ -65,9 +65,9 @@ def paramter_list(self, use_base_api):
             loss = paddle.mean(res)
             loss.backward()
 
-    def test_paramter_list(self):
-        self.paramter_list(False)
-        self.paramter_list(True)
+    def test_parameter_list(self):
+        self.parameter_list(False)
+        self.parameter_list(True)
 
 
 if __name__ == '__main__':
diff --git a/test/legacy_test/test_imperative_named_members.py b/test/legacy_test/test_imperative_named_members.py
@@ -32,7 +32,7 @@ def forward(self, x):
         return x
 
 
-class MyLayerDuplicatedParamters(paddle.nn.Layer):
+class MyLayerDuplicatedParameters(paddle.nn.Layer):
     def __init__(self):
         super().__init__()
         self.fc = paddle.nn.Linear(1, 1)
@@ -189,7 +189,7 @@ def __init__(self):
 class TestImperativeNamedParametersDuplicated(unittest.TestCase):
     def test_not_remove_duplicate(self):
         with base.dygraph.guard():
-            model = MyLayerDuplicatedParamters()
+            model = MyLayerDuplicatedParameters()
 
             named_parameters = list(
                 model.named_parameters(remove_duplicate=False)
@@ -198,7 +198,7 @@ def test_not_remove_duplicate(self):
 
     def test_remove_duplicate(self):
         with base.dygraph.guard():
-            model = MyLayerDuplicatedParamters()
+            model = MyLayerDuplicatedParameters()
 
             named_parameters = list(
                 model.named_parameters(remove_duplicate=True)
diff --git a/test/legacy_test/test_initializer.py b/test/legacy_test/test_initializer.py
@@ -299,15 +299,15 @@ def test_uniform_initializer_random_seed(self):
 
                 block = startup.global_block()
 
-                checked_paramter_names = []
+                checked_parameter_names = []
                 for op in block.ops:
                     if self.set_parameter_op_name != op.name():
                         continue
 
                     parameter_name = op.attrs()["parameter_name"]
                     if parameter_name == "param1":
                         # get "param1"
-                        checked_paramter_names.append(parameter_name)
+                        checked_parameter_names.append(parameter_name)
                         seed = (
                             op.operand(0)
                             .source()
@@ -317,7 +317,7 @@ def test_uniform_initializer_random_seed(self):
                         self.assertEqual(seed, 123)
                     elif parameter_name == "param2":
                         # get "param2"
-                        checked_paramter_names.append(parameter_name)
+                        checked_parameter_names.append(parameter_name)
                         seed = (
                             op.operand(0)
                             .source()
@@ -326,8 +326,8 @@ def test_uniform_initializer_random_seed(self):
                         )
                         self.assertEqual(seed, 456)
 
-                self.assertIn("param1", checked_paramter_names)
-                self.assertIn("param2", checked_paramter_names)
+                self.assertIn("param1", checked_parameter_names)
+                self.assertIn("param2", checked_parameter_names)
 
     def test_uniform_initializer(self, dtype="float32"):
         with paddle.pir_utils.IrGuard():

Original file line number	Diff line number	Diff line change
`@@ -650,7 +650,7 @@ def _get_output_vars(outputs, output_spec, with_hook=False):`
`650`	`650`	`)`
`651`	`651`	`output_spec_is_not_value_error = (`
`652`	`652`	"tensor `%s` is not support in pir mode, "
`653`		`- "because pir value has no name sometimes, especially as ouptut,"`
	`653`	`+ "because pir value has no name sometimes, especially as output,"`
`654`	`654`	`"so we can't check tensor's name with output var name, please"`
`655`	`655`	`"change as pir.value(to_static layer's output)"`
`656`	`656`	`"or int(the position of to_static layer's output)"`