Fix (#70011)

co63oc · web-flow · commit d6b74b78d7ec · 2024-12-09T09:44:18.000+08:00
diff --git a/python/paddle/base/data_feeder.py b/python/paddle/base/data_feeder.py
@@ -270,7 +270,7 @@ def check_shape(
         check_dtype(shape.dtype, 'shape', expected_tensor_dtype, op_name)
 
 
-class DataToLoDTensorConverter:
+class DataToDenseTensorConverter:
     def __init__(self, place, lod_level, shape, dtype):
         self.place = place
         self.lod_level = lod_level
@@ -337,7 +337,7 @@ def __init__(self, feed_list, place, batch_size, generator, drop_last):
             if not in_pir_mode():
                 assert var.lod_level == 0, "lod_level must be 0"
             self.converters.append(
-                DataToLoDTensorConverter(
+                DataToDenseTensorConverter(
                     place=self.place,
                     lod_level=0,
                     shape=var.shape,
@@ -509,7 +509,7 @@ def feed(self, iterable):
             self.feed_lod_level, self.feed_shapes, self.feed_dtypes
         ):
             converter.append(
-                DataToLoDTensorConverter(
+                DataToDenseTensorConverter(
                     place=self.place,
                     lod_level=lod_level,
                     shape=shape,
diff --git a/python/paddle/base/executor.py b/python/paddle/base/executor.py
@@ -351,7 +351,7 @@ def has_feed_operators(block, feed_targets, feed_holder_name):
         feed_targets: a dictionary of {feed_target_name: feed_target_data}
         feed_holder_name: the name of the variable that holds the data of
             all feed targets. The type of this feed_holder variable is
-            FEED_MINIBATCH, which is essentially vector<LoDTensor>.
+            FEED_MINIBATCH, which is essentially vector<DenseTensor>.
 
     Returns:
         A boolean value that indicates whether a block has feed operators
diff --git a/python/paddle/base/lod_tensor.py b/python/paddle/base/lod_tensor.py
@@ -15,7 +15,7 @@
 import numpy as np
 
 from . import core
-from .data_feeder import DataToLoDTensorConverter
+from .data_feeder import DataToDenseTensorConverter
 
 __all__ = []
 
@@ -71,8 +71,8 @@ def create_lod_tensor(data, recursive_seq_lens, place):
         return create_lod_tensor(np.array(data), recursive_seq_lens, place)
     elif isinstance(data, list):
         # dtype and shape are not important here,
-        # we only want to reuse code of DataToLoDTensorConverter
-        converter = DataToLoDTensorConverter(
+        # we only want to reuse code of DataToDenseTensorConverter
+        converter = DataToDenseTensorConverter(
             place=place,
             lod_level=len(recursive_seq_lens),
             shape=[],
diff --git a/python/paddle/framework/io.py b/python/paddle/framework/io.py
@@ -431,7 +431,7 @@ def reduce_varbase(self):
 
         return (tuple, ((name, data),))
 
-    def reduce_LoDTensor(self):
+    def reduce_DenseTensor(self):
         p = core.Place()
         p.set_place(paddle.CPUPlace())
         if self._place().is_custom_place():
@@ -462,7 +462,7 @@ def add_dispatch_table():
         # This is not a good method, because the pickle module has been modified.
         pickle.dispatch_table[core.eager.Tensor] = reduce_varbase
         pickle.dispatch_table[EagerParamBase] = reduce_varbase
-        pickle.dispatch_table[core.DenseTensor] = reduce_LoDTensor
+        pickle.dispatch_table[core.DenseTensor] = reduce_DenseTensor
         pickle.dispatch_table.update(dispatch_table_layer)
 
     def pop_dispatch_table():
@@ -485,7 +485,7 @@ def pop_dispatch_table():
         pickler = pickle.Pickler(f, protocol)
         pickler.dispatch_table = copyreg.dispatch_table.copy()
 
-        pickler.dispatch_table[core.DenseTensor] = reduce_LoDTensor
+        pickler.dispatch_table[core.DenseTensor] = reduce_DenseTensor
         pickler.dispatch_table[core.eager.Tensor] = reduce_varbase
         pickler.dispatch_table[EagerParamBase] = reduce_varbase
         pickler.dispatch_table.update(dispatch_table_layer)
