PaddlePaddle
diff --git a/‎python/paddle/base/executor.py
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+2-2Lines changed: 2 additions & 2 deletions b/‎python/paddle/base/executor.py
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diff --git a/‎python/paddle/base/lod_tensor.py
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+13-13Lines changed: 13 additions & 13 deletions b/‎python/paddle/base/lod_tensor.py
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+13-13Lines changed: 13 additions & 13 deletions
diff --git a/‎python/paddle/base/multiprocess_utils.py
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diff --git a/‎python/paddle/framework/io.py
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diff --git a/‎python/paddle/hapi/model.py
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+5-5Lines changed: 5 additions & 5 deletions b/‎python/paddle/hapi/model.py
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diff --git a/‎python/paddle/incubate/layers/nn.py
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+5-5Lines changed: 5 additions & 5 deletions b/‎python/paddle/incubate/layers/nn.py
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diff --git a/‎python/paddle/io/dataloader/dataloader_iter.py
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diff --git a/‎python/paddle/io/multiprocess_utils.py
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diff --git a/‎python/paddle/jit/dy2static/partial_program.py
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diff --git a/‎python/paddle/jit/dy2static/pir_partial_program.py
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+1-1Lines changed: 1 addition & 1 deletion b/‎python/paddle/jit/dy2static/pir_partial_program.py
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@@ -148,7 +148,7 @@ def scope_guard(scope: core._Scope) -> Generator[None, None, None]:
 def as_numpy(tensor, copy=False):
     """
     Convert a Tensor to a numpy.ndarray, its only support Tensor without LoD information.
-    For higher dimensional sequence data, please use LoDTensor directly.
+    For higher dimensional sequence data, please use DenseTensor directly.
 
     Examples:
         .. code-block:: python
@@ -718,7 +718,7 @@ def _get_program_cache_key(feed, fetch_list):
 def _as_lodtensor(data, place, dtype=None):
     """
     Convert numpy.ndarray to Tensor, its only support Tensor without LoD information.
-    For higher dimensional sequence data, please use LoDTensor directly.
+    For higher dimensional sequence data, please use DenseTensor directly.
 
     Examples:
 
 
@@ -22,7 +22,7 @@
 
 def create_lod_tensor(data, recursive_seq_lens, place):
     """
-    Create a LoDTensor from a numpy array, list or existing LoDTensor.
+    Create a DenseTensor from a numpy array, list or existing LoDTensor.
 
     The implementation is as follows:
 
@@ -32,13 +32,13 @@ def create_lod_tensor(data, recursive_seq_lens, place):
     2. Convert :code:`recursive_seq_lens` to a offset-based LoD.
 
     3. Based on :code:`place` , copy the :code:`data` from a numpy array, list
-       or existing LoDTensor to CPU or GPU device.
+       or existing DenseTensor to CPU or GPU device.
 
     4. Set offset-based LoD to the output LoDTensor.
 
-    Suppose we want to create a LoDTensor to hold data for word sequences,
+    Suppose we want to create a DenseTensor to hold data for word sequences,
     where each word is represented by an integer. If we want to create
-    a LoDTensor to represent two sentences, one of 2 words, and one of 3 words.
+    a DenseTensor to represent two sentences, one of 2 words, and one of 3 words.
 
     Then :code:`data` would be a numpy array of integers with shape (5, 1).
     :code:`recursive_seq_lens` would be [[2, 3]], indicating the word number
@@ -53,10 +53,10 @@ def create_lod_tensor(data, recursive_seq_lens, place):
         recursive_seq_lens (list[list[int]]): a list of lists indicating the
                 length-based LoD info.
         place (CPUPlace|CUDAPlace): CPU or GPU place indicating where the data
-                in the created LoDTensor will be stored.
+                in the created DenseTensor will be stored.
 
     Returns:
-         A LoDTensor with tensor data and recursive_seq_lens info.
+         A DenseTensor with tensor data and recursive_seq_lens info.
 
     Examples:
 
@@ -118,11 +118,11 @@ def create_random_int_lodtensor(
     """
         :api_attr: Static Graph
 
-    Create a LoDTensor containing random integers.
+    Create a DenseTensor containing random integers.
 
     The implementation is as follows:
 
-    1. Obtain the shape of output LoDTensor based on :code:`recursive_seq_lens`
+    1. Obtain the shape of output DenseTensor based on :code:`recursive_seq_lens`
        and :code:`base_shape` . The first dimension of the shape is the total
        length of sequences, while the other dimensions are the same as
        :code:`base_shape` .
@@ -131,26 +131,26 @@ def create_random_int_lodtensor(
        array as parameter :code:`data` of :ref:`api_paddle_base_create_lod_tensor` to
        create the output LoDTensor.
 
-    Suppose we want to create a LoDTensor to hold data for 2 sequences, where
+    Suppose we want to create a DenseTensor to hold data for 2 sequences, where
     the dimension of the sequences are [2, 30] and [3, 30] respectively.
     The :code:`recursive_seq_lens` would be [[2, 3]], and :code:`base_shape`
     would be [30] (the other dimensions excluding the sequence length).
-    Therefore, the shape of the output LoDTensor would be [5, 30], where
+    Therefore, the shape of the output DenseTensor would be [5, 30], where
     the first dimension 5 is the total lengths of the sequences, and the
     other dimensions are :code:`base_shape`.
 
     Args:
         recursive_seq_lens (list[list[int]]): a list of lists indicating the
                 length-based LoD info.
-        base_shape (list[int]): the shape of the output LoDTensor excluding
+        base_shape (list[int]): the shape of the output DenseTensor excluding
                 the first dimension.
         place (CPUPlace|CUDAPlace): CPU or GPU place indicating where
-                the data in the created LoDTensor will be stored.
+                the data in the created DenseTensor will be stored.
         low (int): the lower bound of the random integers.
         high (int): the upper bound of the random integers.
 
     Returns:
-        A LoDTensor with tensor data and recursive_seq_lens info, whose data
+        A DenseTensor with tensor data and recursive_seq_lens info, whose data
         is inside [low, high].
 
     Examples:
 
@@ -24,7 +24,7 @@
 MP_STATUS_CHECK_INTERVAL = 5.0
 
 # NOTE: [ mmap files clear ] If there is still data in the multiprocess queue when the main process finishes reading,
-# the data in the queue needs to be popped. Then the LoDTensor read by the main process
+# the data in the queue needs to be popped. Then the DenseTensor read by the main process
 # from the child process will automatically clear the memory-mapped file.
 multiprocess_queue_set = set()
 
 
@@ -556,7 +556,7 @@ def _transformed_from_varbase(obj):
 
 
 def _transformed_from_lodtensor(obj):
-    # In paddle2.1 version, LoDTensor is saved as np.array(tensor).
+    # In paddle2.1 version, DenseTensor is saved as np.array(tensor).
     # When executing paddle.load, use this function to determine whether to restore to Tensor/LoDTensor.
     if isinstance(obj, np.ndarray):
         return True
 
@@ -568,7 +568,7 @@ def _run(self, inputs, labels=None):
             if len(name) > 0:
                 rets.insert(i, feed[name])
 
-        # LoDTensor cannot be fetch as numpy directly
+        # DenseTensor cannot be fetch as numpy directly
         rets = [np.array(v) for v in rets]
         if self.mode == 'test':
             return rets[:]
@@ -1001,7 +1001,7 @@ def _run(self, inputs, labels=None):
             if len(name) > 0:
                 rets.insert(i, feed[name])
 
-        # LoDTensor cannot be fetch as numpy directly
+        # DenseTensor cannot be fetch as numpy directly
         rets = [np.array(v) for v in rets]
         if self.mode == 'test':
             return rets[:]
@@ -2615,8 +2615,8 @@ def predict(
                 field of a sample is in shape [X, Y], test_data contains N samples, predict
                 output field will be in shape [N, X, Y] if stack_output is True, and will
                 be a length N list in shape [[X, Y], [X, Y], ..., [X, Y]] if stack_outputs
-                is False. stack_outputs as False is used for LoDTensor output situation,
-                it is recommended set as True if outputs contains no LoDTensor. Default: False.
+                is False. stack_outputs as False is used for DenseTensor output situation,
+                it is recommended set as True if outputs contains no DenseTensor. Default: False.
             verbose (int, optional): The verbosity mode, should be 0, 1, or 2. 0 = silent,
                 1 = progress bar, 2 = one line per batch. Default: 1.
             callbacks(Sequence[Callback]|Callback|None, optional): A Callback instance, Default: None.
@@ -2790,7 +2790,7 @@ def _run_one_epoch(
             #   ([input1, input2, ...], [label1, label2, ...])
             # To handle all of these, flatten (nested) list to list.
             data = paddle.utils.flatten(data)
-            # LoDTensor.shape is callable, where LoDTensor comes from
+            # DenseTensor.shape is callable, where DenseTensor comes from
             # DataLoader in static graph
 
             batch_size = (
 
@@ -59,7 +59,7 @@ def fused_seqpool_cvm(
 
     This OP is the fusion of sequence_pool and continuous_value_model op.
 
-    **Note:** The Op only receives List of LoDTensor as input, only support SUM pooling now.
+    **Note:** The Op only receives List of DenseTensor as input, only support SUM pooling now.
 
     Args:
         input(Tensor): Input is List of LoDTensor.
@@ -181,7 +181,7 @@ def search_pyramid_hash(
         dtype (str, optional): The data type of output Tensor, float32. Default: float32.
 
     Returns:
-        Tensor: LoDTensor of pyramid hash embedding.
+        Tensor: DenseTensor of pyramid hash embedding.
     """
     helper = LayerHelper('search_pyramid_hash', **locals())
 
@@ -275,7 +275,7 @@ def shuffle_batch(x: Tensor, seed: int | Tensor | None = None) -> Tensor:
     """
     This layer shuffle input tensor :attr:`x` . Normally, :attr:`x` is 2-D LoDTensor.
 
-    :attr:`x` is a LoDTensor to be shuffled with shape :math:`[N_1, N_2, ..., N_k, D]` . Note that the last dim of input will not be shuffled.
+    :attr:`x` is a DenseTensor to be shuffled with shape :math:`[N_1, N_2, ..., N_k, D]` . Note that the last dim of input will not be shuffled.
     :math:`N_1 * N_2 * ... * N_k` numbers of elements with length :math:`D` will be shuffled randomly.
 
     Examples:
@@ -294,12 +294,12 @@ def shuffle_batch(x: Tensor, seed: int | Tensor | None = None) -> Tensor:
               Out.dims = [4, 2]
 
     Args:
-        x (Tensor): The input Tensor. The input Tensor is a N-D LoDTensor with type int, float32 or float64.
+        x (Tensor): The input Tensor. The input Tensor is a N-D DenseTensor with type int, float32 or float64.
         seed (None|int|Tensor, optional): The start up seed. If set, seed will be set as the start up seed of shuffle engine.
             If not set(Default), start up seed of shuffle engine will be generated randomly. Default: None.
 
     Returns:
-        Tensor: The shuffled LoDTensor with the same shape and lod as input.
+        Tensor: The shuffled DenseTensor with the same shape and lod as input.
 
     Examples:
 
 
@@ -632,7 +632,7 @@ def _thread_loop(self, legacy_expected_place):
                             for tensor in batch:
                                 array.append(tensor)
                         else:
-                            # LoDTensor not in shared memory is not
+                            # DenseTensor not in shared memory is not
                             # serializable, cannot be create in workers
                             for slot in batch:
                                 if isinstance(slot, paddle.Tensor):
 
@@ -24,7 +24,7 @@
 MP_STATUS_CHECK_INTERVAL = 5.0
 
 # NOTE: [ mmap files clear ] If there is still data in the multiprocess queue when the main process finishes reading,
-# the data in the queue needs to be popped. Then the LoDTensor read by the main process
+# the data in the queue needs to be popped. Then the DenseTensor read by the main process
 # from the child process will automatically clear the memory-mapped file.
 multiprocess_queue_set = set()
 
 
@@ -1077,7 +1077,7 @@ def _set_grad_type(self, params, train_program):
         # will be SelectedRows, not LoDTensor. But tracer will just
         # set param grad Tensor by forward Tensor(LoDTensor)
         # If we don't change grad_var type here, RunProgramOp need
-        # transform SelectedRows to LoDTensor forcibly, it may not
+        # transform SelectedRows to DenseTensor forcibly, it may not
         # be user wanted result.
         for param in params:
             grad_name = param.name + core.grad_var_suffix()
 
@@ -1208,7 +1208,7 @@ def _set_grad_type(self, params, train_program: RunnableProgram):
         # will be SelectedRows, not LoDTensor. But tracer will just
         # set param grad Tensor by forward Tensor(LoDTensor)
         # If we don't change grad_var type here, RunProgramOp need
-        # transform SelectedRows to LoDTensor forcibly, it may not
+        # transform SelectedRows to DenseTensor forcibly, it may not
         # be user wanted result.
         forward_params_grads = train_program.param_grad_values
         train_program = train_program.program