【fluid api clear】remove transpose (#47917)

* remove transpose

* codestyle check

* modify CI_STATIC

* modify CI_STATIC

* modify enable static()

* remove unused import

* fix conflict about stack

* fix conflict about stack

* fix conflict about stack

* endless conflict

python/paddle/fluid/contrib/layers/rnn_impl.py

@@ -326,7 +326,7 @@ def basic_gru(
             )
 
     if batch_first:
-        input = layers.transpose(input, [1, 0, 2])
+        input = paddle.transpose(input, [1, 0, 2])
 
     mask = None
     if sequence_length:
@@ -334,7 +334,7 @@ def basic_gru(
         mask = layers.sequence_mask(
             sequence_length, maxlen=max_seq_len, dtype='float32'
         )
-        mask = layers.transpose(mask, [1, 0])
+        mask = paddle.transpose(mask, [1, 0])
 
     direc_num = 1
     if bidirectional:
@@ -425,7 +425,7 @@ def basic_gru(
         )
 
         if batch_first:
-            rnn_out = layers.transpose(rnn_out, [1, 0, 2])
+            rnn_out = paddle.transpose(rnn_out, [1, 0, 2])
         return rnn_out, last_hidden
     else:
 
@@ -433,7 +433,7 @@ def basic_gru(
         last_hidden = fw_last_hidden
 
         if batch_first:
-            rnn_out = layers.transpose(rnn_out, [1, 0, 2])
+            rnn_out = paddle.transpose(rnn_out, [1, 0, 2])
 
         return rnn_out, last_hidden
 
@@ -610,7 +610,7 @@ def basic_lstm(
             )
 
     if batch_first:
-        input = layers.transpose(input, [1, 0, 2])
+        input = paddle.transpose(input, [1, 0, 2])
 
     mask = None
     if sequence_length:
@@ -619,7 +619,7 @@ def basic_lstm(
             sequence_length, maxlen=max_seq_len, dtype='float32'
         )
 
-        mask = layers.transpose(mask, [1, 0])
+        mask = paddle.transpose(mask, [1, 0])
 
     direc_num = 1
     if bidirectional:
@@ -740,7 +740,7 @@ def basic_lstm(
         )
 
         if batch_first:
-            rnn_out = layers.transpose(rnn_out, [1, 0, 2])
+            rnn_out = paddle.transpose(rnn_out, [1, 0, 2])
         return rnn_out, last_hidden, last_cell
     else:
 
@@ -749,7 +749,7 @@ def basic_lstm(
         last_cell = fw_last_cell
 
         if batch_first:
-            rnn_out = layers.transpose(rnn_out, [1, 0, 2])
+            rnn_out = paddle.transpose(rnn_out, [1, 0, 2])
 
         return rnn_out, last_hidden, last_cell
 

python/paddle/fluid/layers/control_flow.py

@@ -625,10 +625,12 @@ class StaticRNN:
     Examples:
         .. code-block:: python
 
+            import paddle
             import paddle.fluid as fluid
             import paddle.fluid.layers as layers
 
             vocab_size, hidden_size=10000, 200
+            paddle.enable_static()
             x = fluid.data(name="x", shape=[None, 1, 1], dtype='int64')
             # create word sequence
             x_emb = layers.embedding(
@@ -637,7 +639,7 @@ class StaticRNN:
                 dtype='float32',
                 is_sparse=False)
             # transform batch size to dim 1
-            x_emb = layers.transpose(x_emb, perm=[1, 0, 2])
+            x_emb = paddle.transpose(x_emb, perm=[1, 0, 2])
 
             rnn = fluid.layers.StaticRNN()
             with rnn.step():
@@ -714,10 +716,12 @@ class StaticRNN:
         Examples 1:
             .. code-block:: python
 
+                import paddle
                 import paddle.fluid as fluid
                 import paddle.fluid.layers as layers
 
                 vocab_size, hidden_size=10000, 200
+                paddle.enable_static()
                 x = fluid.data(name="x", shape=[None, 1, 1], dtype='int64')
                 # create word sequence
                 x_emb = layers.embedding(
@@ -726,7 +730,7 @@ class StaticRNN:
                         dtype='float32',
                         is_sparse=False)
                 # transform batch size to dim 1
-                x_emb = layers.transpose(x_emb, perm=[1, 0, 2])
+                x_emb = paddle.transpose(x_emb, perm=[1, 0, 2])
 
                 rnn = fluid.layers.StaticRNN()
                 with rnn.step():
@@ -742,9 +746,11 @@ class StaticRNN:
         Examples 2:
             .. code-block:: python
 
+                import paddle
                 import paddle.fluid as fluid
                 import paddle.fluid.layers as layers
                 vocab_size, hidden_size=10000, 200
+                paddle.enable_static()
                 x = fluid.data(name="x", shape=[None, 1, 1], dtype='int64')
                 # create word sequence
                 x_emb = layers.embedding(
@@ -753,7 +759,7 @@ class StaticRNN:
                         dtype='float32',
                         is_sparse=False)
                 # transform batch size to dim 1
-                x_emb = layers.transpose(x_emb, perm=[1, 0, 2])
+                x_emb = paddle.transpose(x_emb, perm=[1, 0, 2])
                 boot_memory = fluid.layers.data(name='boot', shape=[hidden_size], dtype='float32', lod_level=1)
                 rnn = fluid.layers.StaticRNN()
                 with rnn.step():
@@ -842,10 +848,12 @@ class StaticRNN:
         Examples:
             .. code-block:: python
 
+                import paddle
                 import paddle.fluid as fluid
                 import paddle.fluid.layers as layers
 
                 vocab_size, hidden_size=10000, 200
+                paddle.enable_static()
                 x = fluid.data(name="x", shape=[None, 1, 1], dtype='int64')
                 # create word sequence
                 x_emb = layers.embedding(
@@ -854,7 +862,7 @@ class StaticRNN:
                         dtype='float32',
                         is_sparse=False)
                 # transform batch size to dim 1
-                x_emb = layers.transpose(x_emb, perm=[1, 0, 2])
+                x_emb = paddle.transpose(x_emb, perm=[1, 0, 2])
 
                 rnn = fluid.layers.StaticRNN()
                 with rnn.step():
@@ -893,10 +901,12 @@ class StaticRNN:
         Examples:
             .. code-block:: python
 
+                import paddle
                 import paddle.fluid as fluid
                 import paddle.fluid.layers as layers
 
                 vocab_size, hidden_size=10000, 200
+                paddle.enable_static()
                 x = fluid.data(name="x", shape=[None, 1, 1], dtype='int64')
                 # create word sequence
                 x_emb = layers.embedding(
@@ -905,7 +915,7 @@ class StaticRNN:
                         dtype='float32',
                         is_sparse=False)
                 # transform batch size to dim 1
-                x_emb = layers.transpose(x_emb, perm=[1, 0, 2])
+                x_emb = paddle.transpose(x_emb, perm=[1, 0, 2])
 
                 rnn = fluid.layers.StaticRNN()
                 with rnn.step():
@@ -953,10 +963,12 @@ class StaticRNN:
         Examples:
             .. code-block:: python
 
+                import paddle
                 import paddle.fluid as fluid
                 import paddle.fluid.layers as layers
 
                 vocab_size, hidden_size=10000, 200
+                paddle.enable_static()
                 x = fluid.data(name="x", shape=[None, 1, 1], dtype='int64')
                 # create word sequence
                 x_emb = layers.embedding(
@@ -965,7 +977,7 @@ class StaticRNN:
                         dtype='float32',
                         is_sparse=False)
                 # transform batch size to dim 1
-                x_emb = layers.transpose(x_emb, perm=[1, 0, 2])
+                x_emb = paddle.transpose(x_emb, perm=[1, 0, 2])
 
                 rnn = fluid.layers.StaticRNN()
                 with rnn.step():

python/paddle/fluid/layers/detection.py

@@ -774,7 +774,7 @@ def detection_output(
         code_type='decode_center_size',
     )
     scores = nn.softmax(input=scores)
-    scores = nn.transpose(scores, perm=[0, 2, 1])
+    scores = paddle.transpose(scores, perm=[0, 2, 1])
     scores.stop_gradient = True
     nmsed_outs = helper.create_variable_for_type_inference(
         dtype=decoded_box.dtype
@@ -2443,7 +2443,7 @@ def multi_box_head(
             stride=stride,
         )
 
-        mbox_loc = nn.transpose(mbox_loc, perm=[0, 2, 3, 1])
+        mbox_loc = paddle.transpose(mbox_loc, perm=[0, 2, 3, 1])
         mbox_loc_flatten = nn.flatten(mbox_loc, axis=1)
         mbox_locs.append(mbox_loc_flatten)
 
@@ -2456,7 +2456,7 @@ def multi_box_head(
             padding=pad,
             stride=stride,
         )
-        conf_loc = nn.transpose(conf_loc, perm=[0, 2, 3, 1])
+        conf_loc = paddle.transpose(conf_loc, perm=[0, 2, 3, 1])
         conf_loc_flatten = nn.flatten(conf_loc, axis=1)
         mbox_confs.append(conf_loc_flatten)
 

python/paddle/fluid/layers/nn.py

@@ -89,7 +89,6 @@ __all__ = [
     'l2_normalize',
     'matmul',
     'topk',
-    'transpose',
     'im2sequence',
     'row_conv',
     'multiplex',
@@ -4875,108 +4874,6 @@ def ctc_greedy_decoder(
         return ctc_out, ctc_out_len
 
 
-def transpose(x, perm, name=None):
-    """
-    Permute the data dimensions of `input` according to `perm`.
-
-    The `i`-th dimension  of the returned tensor will correspond to the
-    perm[i]-th dimension of `input`.
-
-    Args:
-        x (Tensor): The input Tensor. It is a N-D Tensor of data types bool, float32, float64, int32.
-        perm (list|tuple): Permute the input according to the data of perm.
-        name (str): The name of this layer. It is optional.
-
-    Returns:
-        Tensor: A transposed n-D Tensor, with data type being bool, float32, float64, int32, int64.
-
-    For Example:
-
-        .. code-block:: text
-
-         x = [[[ 1  2  3  4] [ 5  6  7  8] [ 9 10 11 12]]
-             [[13 14 15 16] [17 18 19 20] [21 22 23 24]]]
-         shape(x) =  [2,3,4]
-
-         # Example 1
-         perm0 = [1,0,2]
-         y_perm0 = [[[ 1  2  3  4] [13 14 15 16]]
-                   [[ 5  6  7  8]  [17 18 19 20]]
-                   [[ 9 10 11 12]  [21 22 23 24]]]
-         shape(y_perm0) = [3,2,4]
-
-         # Example 2
-         perm1 = [2,1,0]
-         y_perm1 = [[[ 1 13] [ 5 17] [ 9 21]]
-                   [[ 2 14] [ 6 18] [10 22]]
-                   [[ 3 15]  [ 7 19]  [11 23]]
-                   [[ 4 16]  [ 8 20]  [12 24]]]
-         shape(y_perm1) = [4,3,2]
-
-    Examples:
-
-        .. code-block:: python
-
-            import paddle
-
-            x = paddle.randn([2, 3, 4])
-            x_transposed = paddle.transpose(x, perm=[1, 0, 2])
-            print(x_transposed.shape)
-            # [3L, 2L, 4L]
-
-    """
-    if in_dygraph_mode():
-        return _C_ops.transpose(x, perm)
-    else:
-        if _in_legacy_dygraph():
-            out, _ = _legacy_C_ops.transpose2(x, 'axis', perm)
-            return out
-
-    check_variable_and_dtype(
-        x,
-        'x',
-        [
-            'bool',
-            'float16',
-            'float32',
-            'float64',
-            'int32',
-            'int64',
-            'complex64',
-            'complex128',
-        ],
-        'transpose',
-    )
-    check_type(perm, 'perm', (list, tuple), 'transpose')
-    if isinstance(perm, tuple):
-        perm = list(perm)
-    if len(perm) != len(x.shape):
-        raise ValueError(
-            "Input(perm) is the permutation of dimensions of Input(x), "
-            "its length should be equal to dimensions of Input(x), "
-            "but received dimension of Input(x) is %s, "
-            "the length of Input(perm) is %s." % (len(x.shape), len(perm))
-        )
-    for idx, dim in enumerate(perm):
-        if dim >= len(x.shape):
-            raise ValueError(
-                "Each element in Input(perm) should be less than Input(x)'s dimension, "
-                "but %d-th element in Input(perm) is %d which exceeds Input(x)'s "
-                "dimension %d." % (idx, perm[idx], len(x.shape))
-            )
-
-    helper = LayerHelper('transpose', **locals())
-    out = helper.create_variable_for_type_inference(x.dtype)
-    x_shape = helper.create_variable_for_type_inference(x.dtype)
-    helper.append_op(
-        type='transpose2',
-        inputs={'X': [x]},
-        outputs={'Out': [out], 'XShape': [x_shape]},
-        attrs={'axis': perm},
-    )
-    return out
-
-
 def im2sequence(
     input,
     filter_size=1,

python/paddle/fluid/layers/rnn.py

@@ -16,6 +16,7 @@ import sys
 from functools import partial, reduce
 import warnings
 
+
 import paddle
 from paddle.utils import deprecated
 from . import nn
@@ -563,7 +564,7 @@ def _maybe_copy(state, new_state, step_mask):
 
 def _transpose_batch_time(x):
     perm = [1, 0] + list(range(2, len(x.shape)))
-    return nn.transpose(x, perm)
+    return paddle.transpose(x, perm)
 
 
 def _rnn_dynamic_graph(
@@ -591,7 +592,7 @@ def _rnn_dynamic_graph(
         mask = sequence_lod.sequence_mask(
             sequence_length, maxlen=time_steps, dtype=inputs.dtype
         )
-        mask = nn.transpose(mask, [1, 0])
+        mask = paddle.transpose(mask, [1, 0])
 
     if is_reverse:
         inputs = map_structure(lambda x: tensor.reverse(x, axis=[0]), inputs)
@@ -678,7 +679,7 @@ def _rnn_static_graph(
             maxlen=max_seq_len,
             dtype=flatten(initial_states)[0].dtype,
         )
-        mask = nn.transpose(mask, [1, 0])
+        mask = paddle.transpose(mask, [1, 0])
     if is_reverse:
         inputs = map_structure(lambda x: tensor.reverse(x, axis=[0]), inputs)
         mask = tensor.reverse(mask, axis=[0]) if sequence_length else None
@@ -1032,14 +1033,14 @@ class BeamSearchDecoder(Decoder):
         expand_times = [1] * len(x.shape)
         expand_times[1] = beam_size
         x = paddle.tile(x, expand_times)  # [batch_size, beam_size, ...]
-        x = nn.transpose(
+        x = paddle.transpose(
             x, list(range(2, len(x.shape))) + [0, 1]
         )  # [..., batch_size, beam_size]
         # use 0 to copy to avoid wrong shape
         x = paddle.reshape(
             x, shape=[0] * (len(x.shape) - 2) + [-1]
         )  # [..., batch_size * beam_size]
-        x = nn.transpose(
+        x = paddle.transpose(
             x, [len(x.shape) - 1] + list(range(0, len(x.shape) - 1))
         )  # [batch_size * beam_size, ...]
         return x
@@ -1557,7 +1558,9 @@ def _dynamic_decode_imperative(
 
     if not output_time_major:
         final_outputs = map_structure(
-            lambda x: nn.transpose(x, [1, 0] + list(range(2, len(x.shape)))),
+            lambda x: paddle.transpose(
+                x, [1, 0] + list(range(2, len(x.shape)))
+            ),
             final_outputs,
         )
 
@@ -1629,7 +1632,7 @@ def _dynamic_decode_declarative(
         return new_state
 
     def _transpose_batch_time(x):
-        return nn.transpose(x, [1, 0] + list(range(2, len(x.shape))))
+        return paddle.transpose(x, [1, 0] + list(range(2, len(x.shape))))
 
     def _create_array_out_of_while(dtype):
         current_block_idx = default_main_program().current_block_idx

python/paddle/fluid/nets.py

@@ -577,7 +577,7 @@ def scaled_dot_product_attention(
 
         # permute the dimensions into:
         # [batch_size, num_heads, max_sequence_len, hidden_size_per_head]
-        return layers.transpose(x=reshaped, perm=[0, 2, 1, 3])
+        return paddle.transpose(x=reshaped, perm=[0, 2, 1, 3])
 
     def __combine_heads(x):
         """
@@ -598,7 +598,7 @@ def scaled_dot_product_attention(
         if len(x.shape) != 4:
             raise ValueError("Input(x) should be a 4-D Tensor.")
 
-        trans_x = layers.transpose(x, perm=[0, 2, 1, 3])
+        trans_x = paddle.transpose(x, perm=[0, 2, 1, 3])
         return paddle.reshape(
             x=trans_x,
             shape=list(

python/paddle/fluid/tests/unittests/auto_parallel/test_dist_op_cost.py

@@ -16,6 +16,7 @@ import unittest
 import copy
 
 import paddle
+
 from paddle.distributed.fleet import auto
 from paddle.distributed.auto_parallel.cluster import Cluster
 from paddle.distributed.auto_parallel.operators.common import (
@@ -151,9 +152,7 @@ class TestDistOpCost(unittest.TestCase):
                             auto.ProcessMesh([0, 1], dim_names=["x"]),
                             ["x", None],
                         )
-                out = paddle.fluid.layers.transpose(
-                    out, [1, 0]
-                )  # [8, 2] [-1, 0]
+                out = paddle.transpose(out, [1, 0])  # [8, 2] [-1, 0]
 
                 # matmul
                 param1 = paddle.fluid.layers.create_parameter(
@@ -188,9 +187,7 @@ class TestDistOpCost(unittest.TestCase):
                     tmp_out, param2
                 )  # [8, 4] [-1, 0]
 
-                out8 = paddle.fluid.layers.transpose(
-                    out2, [1, 0]
-                )  # [4, 8] [0, -1]
+                out8 = paddle.transpose(out2, [1, 0])  # [4, 8] [0, -1]
 
                 # reshape
                 out9 = paddle.reshape(out8, [8, 2, 4])  # [4, 2, 4] [0, -1, -1]
@@ -266,9 +263,7 @@ class TestDistOpCost(unittest.TestCase):
                             auto.ProcessMesh([0, 1], dim_names=["x"]),
                             ["x", None],
                         )
-                out = paddle.fluid.layers.transpose(
-                    out, [1, 0]
-                )  # [8, 2] [-1, 0]
+                out = paddle.transpose(out, [1, 0])  # [8, 2] [-1, 0]
 
                 # matmul_v2
                 param1 = paddle.fluid.layers.create_parameter(
@@ -300,9 +295,7 @@ class TestDistOpCost(unittest.TestCase):
                 tmp_out = paddle.matmul(out1, tmp_param)
                 out2 = paddle.matmul(tmp_out, param2)  # [8, 4] [-1, 0]
 
-                out8 = paddle.fluid.layers.transpose(
-                    out2, [1, 0]
-                )  # [4, 8] [0, -1]
+                out8 = paddle.transpose(out2, [1, 0])  # [4, 8] [0, -1]
 
                 # reshape
                 out9 = paddle.reshape(out8, [8, 2, 4])  # [4, 2, 4] [0, -1, -1]
@@ -377,9 +370,7 @@ class TestDistOpCost(unittest.TestCase):
                             auto.ProcessMesh([0, 1], dim_names=["x"]),
                             ["x", None],
                         )
-                out = paddle.fluid.layers.transpose(
-                    out, [1, 0]
-                )  # [8, 2] [-1, 0]
+                out = paddle.transpose(out, [1, 0])  # [8, 2] [-1, 0]
 
                 # mul
                 param1 = paddle.fluid.layers.create_parameter(
@@ -414,9 +405,7 @@ class TestDistOpCost(unittest.TestCase):
                     tmp_out, param2
                 )  # [8, 4] [-1, 0]
 
-                out8 = paddle.fluid.layers.transpose(
-                    out2, [1, 0]
-                )  # [4, 8] [0, -1]
+                out8 = paddle.transpose(out2, [1, 0])  # [4, 8] [0, -1]
 
                 # reshape
                 out9 = paddle.reshape(out8, [8, 2, 4])  # [4, 2, 4] [0, -1, -1]

python/paddle/fluid/tests/unittests/collective/fleet/parallel_dygraph_transformer.py

@@ -328,15 +328,16 @@ class MultiHeadAttentionLayer(Layer):
         reshaped_q = paddle.reshape(
             x=q, shape=[0, 0, self._n_head, self._d_key]
         )
-        transpose_q = fluid.layers.transpose(x=reshaped_q, perm=[0, 2, 1, 3])
+
+        transpose_q = paddle.transpose(x=reshaped_q, perm=[0, 2, 1, 3])
         reshaped_k = paddle.reshape(
             x=k, shape=[0, 0, self._n_head, self._d_key]
         )
-        transpose_k = fluid.layers.transpose(x=reshaped_k, perm=[0, 2, 1, 3])
+        transpose_k = paddle.transpose(x=reshaped_k, perm=[0, 2, 1, 3])
         reshaped_v = paddle.reshape(
             x=v, shape=[0, 0, self._n_head, self._d_value]
         )
-        transpose_v = fluid.layers.transpose(x=reshaped_v, perm=[0, 2, 1, 3])
+        transpose_v = paddle.transpose(x=reshaped_v, perm=[0, 2, 1, 3])
 
         # scale dot product attention
         product = fluid.layers.matmul(
@@ -362,7 +363,8 @@ class MultiHeadAttentionLayer(Layer):
         # combine heads
         if len(out.shape) != 4:
             raise ValueError("Input(x) should be a 4-D Tensor.")
-        trans_x = fluid.layers.transpose(out, perm=[0, 2, 1, 3])
+
+        trans_x = paddle.transpose(out, perm=[0, 2, 1, 3])
         final_out = paddle.reshape(
             x=trans_x,
             shape=[0, 0, trans_x.shape[2] * trans_x.shape[3]],

python/paddle/fluid/tests/unittests/dist_transformer.py

@@ -23,6 +23,7 @@ import glob
 import random
 import tarfile
 
+import paddle
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 from test_dist_base import TestDistRunnerBase, runtime_main, RUN_STEP
@@ -1148,7 +1149,7 @@ def multi_head_attention(
 
         # permute the dimensions into:
         # [batch_size, n_head, max_sequence_len, hidden_size_per_head]
-        return layers.transpose(x=reshaped, perm=[0, 2, 1, 3])
+        return paddle.transpose(x=reshaped, perm=[0, 2, 1, 3])
 
     def __combine_heads(x):
         """
@@ -1160,7 +1161,7 @@ def multi_head_attention(
         if len(x.shape) != 4:
             raise ValueError("Input(x) should be a 4-D Tensor.")
 
-        trans_x = layers.transpose(x, perm=[0, 2, 1, 3])
+        trans_x = paddle.transpose(x, perm=[0, 2, 1, 3])
         # The value 0 in shape attr means copying the corresponding dimension
         # size of the input as the output dimension size.
         return paddle.reshape(

python/paddle/fluid/tests/unittests/dygraph_to_static/seq2seq_dygraph_model.py

@@ -176,7 +176,7 @@ class BaseModel(fluid.dygraph.Layer):
         )
 
     def _transpose_batch_time(self, x):
-        return fluid.layers.transpose(x, [1, 0] + list(range(2, len(x.shape))))
+        return paddle.transpose(x, [1, 0] + list(range(2, len(x.shape))))
 
     def _merge_batch_beams(self, x):
         return paddle.reshape(x, shape=(-1, x.shape[2]))
@@ -234,7 +234,7 @@ class BaseModel(fluid.dygraph.Layer):
         enc_len_mask = fluid.layers.sequence_mask(
             src_sequence_length, maxlen=max_seq_len, dtype="float32"
         )
-        enc_len_mask = fluid.layers.transpose(enc_len_mask, [1, 0])
+        enc_len_mask = paddle.transpose(enc_len_mask, [1, 0])
 
         # TODO: Because diff exits if call while_loop in static graph.
         # In while block, a Variable created in parent block participates in the calculation of gradient,
@@ -336,7 +336,7 @@ class BaseModel(fluid.dygraph.Layer):
         enc_len_mask = fluid.layers.sequence_mask(
             src_sequence_length, maxlen=max_seq_len, dtype="float32"
         )
-        enc_len_mask = fluid.layers.transpose(enc_len_mask, [1, 0])
+        enc_len_mask = paddle.transpose(enc_len_mask, [1, 0])
 
         for k in range(args.max_seq_len):
             enc_step_input = src_emb[k]
@@ -643,7 +643,7 @@ class AttentionModel(fluid.dygraph.Layer):
         )
 
     def _transpose_batch_time(self, x):
-        return fluid.layers.transpose(x, [1, 0] + list(range(2, len(x.shape))))
+        return paddle.transpose(x, [1, 0] + list(range(2, len(x.shape))))
 
     def _merge_batch_beams(self, x):
         return paddle.reshape(x, shape=(-1, x.shape[2]))
@@ -653,14 +653,14 @@ class AttentionModel(fluid.dygraph.Layer):
         expand_times = [1] * len(x.shape)
         expand_times[1] = self.beam_size
         x = fluid.layers.expand(x, expand_times)  # [batch_size, beam_size, ...]
-        x = fluid.layers.transpose(
+        x = paddle.transpose(
             x, list(range(2, len(x.shape))) + [0, 1]
         )  # [..., batch_size, beam_size]
         # use 0 to copy to avoid wrong shape
         x = paddle.reshape(
             x, shape=[0] * (len(x.shape) - 2) + [-1]
         )  # [..., batch_size * beam_size]
-        x = fluid.layers.transpose(
+        x = paddle.transpose(
             x, [len(x.shape) - 1] + list(range(0, len(x.shape) - 1))
         )  # [batch_size * beam_size, ...]
         return x
@@ -691,9 +691,9 @@ class AttentionModel(fluid.dygraph.Layer):
         attn = fluid.layers.matmul(query, memory, transpose_y=True)
 
         if mask is not None:
-            attn = fluid.layers.transpose(attn, [1, 0, 2])
+            attn = paddle.transpose(attn, [1, 0, 2])
             attn = fluid.layers.elementwise_add(attn, mask * 1000000000, -1)
-            attn = fluid.layers.transpose(attn, [1, 0, 2])
+            attn = paddle.transpose(attn, [1, 0, 2])
         weight = fluid.layers.softmax(attn)
         weight_memory = fluid.layers.matmul(weight, memory)
 
@@ -743,7 +743,7 @@ class AttentionModel(fluid.dygraph.Layer):
             src_sequence_length, maxlen=max_seq_len, dtype="float32"
         )
         enc_padding_mask = enc_len_mask - 1.0
-        enc_len_mask = fluid.layers.transpose(enc_len_mask, [1, 0])
+        enc_len_mask = paddle.transpose(enc_len_mask, [1, 0])
 
         enc_outputs = []
         # TODO: Because diff exits if call while_loop in static graph.

python/paddle/fluid/tests/unittests/dygraph_to_static/test_ptb_lm.py

@@ -122,12 +122,12 @@ class SimpleLSTMRNN(fluid.Layer):
         last_hidden = paddle.reshape(
             last_hidden, shape=[-1, self._num_layers, self._hidden_size]
         )
-        last_hidden = fluid.layers.transpose(x=last_hidden, perm=[1, 0, 2])
+        last_hidden = paddle.transpose(x=last_hidden, perm=[1, 0, 2])
         last_cell = fluid.layers.concat(cell_array, 1)
         last_cell = paddle.reshape(
             last_cell, shape=[-1, self._num_layers, self._hidden_size]
         )
-        last_cell = fluid.layers.transpose(x=last_cell, perm=[1, 0, 2])
+        last_cell = paddle.transpose(x=last_cell, perm=[1, 0, 2])
         return real_res, last_hidden, last_cell
 
 

python/paddle/fluid/tests/unittests/dygraph_to_static/transformer_dygraph_model.py

@@ -138,12 +138,13 @@ class MultiHeadAttention(Layer):
         k = self.k_fc(keys)
         v = self.v_fc(values)
         # split head
+
         q = paddle.reshape(x=q, shape=[0, 0, self.n_head, self.d_key])
-        q = layers.transpose(x=q, perm=[0, 2, 1, 3])
+        q = paddle.transpose(x=q, perm=[0, 2, 1, 3])
         k = paddle.reshape(x=k, shape=[0, 0, self.n_head, self.d_key])
-        k = layers.transpose(x=k, perm=[0, 2, 1, 3])
+        k = paddle.transpose(x=k, perm=[0, 2, 1, 3])
         v = paddle.reshape(x=v, shape=[0, 0, self.n_head, self.d_value])
-        v = layers.transpose(x=v, perm=[0, 2, 1, 3])
+        v = paddle.transpose(x=v, perm=[0, 2, 1, 3])
 
         if cache is not None:
             cache_k, cache_v = cache["k"], cache["v"]
@@ -160,8 +161,10 @@ class MultiHeadAttention(Layer):
         if self.dropout_rate:
             weights = layers.dropout(weights, dropout_prob=self.dropout_rate)
             out = layers.matmul(weights, v)
-        out = layers.transpose(out, perm=[0, 2, 1, 3])
+
+        out = paddle.transpose(out, perm=[0, 2, 1, 3])
         out = paddle.reshape(x=out, shape=[0, 0, out.shape[2] * out.shape[3]])
+
         out = self.proj_fc(out)
         return out
 
@@ -703,7 +706,7 @@ class Transformer(Layer):
 
         def merge_batch_beams(tensor):
             var_dim_in_state = 2  # count in beam dim
-            tensor = layers.transpose(
+            tensor = paddle.transpose(
                 tensor,
                 list(range(var_dim_in_state, len(tensor.shape)))
                 + list(range(0, var_dim_in_state)),
@@ -714,7 +717,7 @@ class Transformer(Layer):
                 [0] * (len(tensor.shape) - var_dim_in_state)
                 + [batch_size * beam_size],
             )
-            res = layers.transpose(
+            res = paddle.transpose(
                 tensor,
                 list(
                     range(
@@ -728,7 +731,7 @@ class Transformer(Layer):
 
         def split_batch_beams(tensor):
             var_dim_in_state = 1
-            tensor = layers.transpose(
+            tensor = paddle.transpose(
                 tensor,
                 list(range(var_dim_in_state, len(tensor.shape)))
                 + list(range(0, var_dim_in_state)),
@@ -738,7 +741,7 @@ class Transformer(Layer):
                 [0] * (len(tensor.shape) - var_dim_in_state)
                 + [batch_size, beam_size],
             )
-            res = layers.transpose(
+            res = paddle.transpose(
                 tensor,
                 list(
                     range(
@@ -878,7 +881,7 @@ class Transformer(Layer):
 
         predict_ids = paddle.stack(predict_ids, axis=0)
         parent_ids = paddle.stack(parent_ids, axis=0)
-        finished_seq = layers.transpose(
+        finished_seq = paddle.transpose(
             layers.gather_tree(predict_ids, parent_ids), [1, 2, 0]
         )
         finished_scores = topk_scores

python/paddle/fluid/tests/unittests/dygraph_to_static/yolov3.py

@@ -16,6 +16,7 @@ import os
 import sys
 
 import paddle
+
 import paddle.fluid as fluid
 from paddle.fluid.dygraph import declarative
 from paddle.fluid.param_attr import ParamAttr
@@ -345,9 +346,7 @@ class YOLOv3(fluid.dygraph.Layer):
                     name="yolo_box" + str(i),
                 )
                 self.boxes.append(boxes)
-                self.scores.append(
-                    fluid.layers.transpose(scores, perm=[0, 2, 1])
-                )
+                self.scores.append(paddle.transpose(scores, perm=[0, 2, 1]))
             self.downsample //= 2
 
         if not self.is_train:

python/paddle/fluid/tests/unittests/ipu/test_transpose_op_ipu.py

@@ -46,7 +46,7 @@ class TestBase(IPUOpTest):
         x = paddle.static.data(
             name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32'
         )
-        out = paddle.fluid.layers.transpose(x, **self.attrs)
+        out = paddle.transpose(x, **self.attrs)
         self.fetch_list = [out.name]
 
     def run_model(self, exec_mode):

python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_cpu_bfloat16_pass.py

@@ -15,6 +15,7 @@
 import unittest
 import numpy as np
 from inference_pass_test import InferencePassTest
+import paddle
 import paddle.fluid as fluid
 from paddle.fluid.core import PassVersionChecker
 import paddle
@@ -27,8 +28,10 @@ class TestMKLDNNCpuBfloat16Pass(InferencePassTest):
             x = fluid.data(
                 name='x', shape=[-1] + self.shape_x, dtype=self.d_type
             )
-            out = fluid.layers.transpose(x, perm=[0, 1, 2, 3])
+
+            out = paddle.transpose(x, perm=[0, 1, 2, 3])
             out = paddle.reshape(out, [0, 0, 0, 0])
+
             out = fluid.layers.fc(out, size=1)
 
             self.feeds = {

python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_matmul_op_output_fuse_pass.py

@@ -14,6 +14,7 @@
 
 import unittest
 import numpy as np
+
 import paddle
 import paddle.fluid as fluid
 from inference_pass_test import InferencePassTest
@@ -36,8 +37,9 @@ class TestMKLDNNMatmulFuseOp(InferencePassTest):
                 name='y', shape=[-1] + self.shape_y, dtype=self.d_type
             )
             out = fluid.layers.matmul(x, y)
-            out = fluid.layers.transpose(out, perm=[0, 2, 1, 3])
+            out = paddle.transpose(out, perm=[0, 2, 1, 3])
             out = paddle.reshape(out, [0, 0, self.shape_y[0] * self.shape_y[2]])
+
             out = fluid.layers.relu(out)
         return out
 
@@ -77,7 +79,7 @@ class TestMKLDNNMatmulOpNotFusedWrongTransposeAxis(TestMKLDNNMatmulFuseOp):
                 name='y', shape=[-1] + self.shape_y, dtype=self.d_type
             )
             out = fluid.layers.matmul(x, y)
-            out = fluid.layers.transpose(out, perm=[0, 1, 2, 3])
+            out = paddle.transpose(out, perm=[0, 1, 2, 3])
             out = paddle.reshape(out, [0, 0, 0, 0])
             out = fluid.layers.fc(out, size=1)
         return out
@@ -100,11 +102,10 @@ class TestMKLDNNMatmulOpNotFusedBreakPattern(TestMKLDNNMatmulFuseOp):
                 name='y', shape=[-1] + self.shape_y, dtype=self.d_type
             )
             out = fluid.layers.matmul(x, y)
-            out = fluid.layers.transpose(out, perm=[0, 2, 1, 3])
-            out = fluid.layers.transpose(
-                out, perm=[0, 1, 2, 3]
-            )  # breaks pattern
+            out = paddle.transpose(out, perm=[0, 2, 1, 3])
+            out = paddle.transpose(out, perm=[0, 1, 2, 3])  # breaks pattern
             out = paddle.reshape(out, [0, 0, self.shape_y[0] * self.shape_y[2]])
+
             out = fluid.layers.relu(out)
         return out
 

python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_reshape_transpose_matmul_v2_fuse_pass.py

@@ -33,8 +33,10 @@ class TestReshapeTransposeMatmulV2OneDNNFusePass(InferencePassTest):
             weight = fluid.layers.create_parameter(
                 shape=self.weight_shape, dtype="float32"
             )
+
             reshape = paddle.reshape(data, shape=self.reshape_shape)
-            transpose = fluid.layers.transpose(reshape, self.tranpose_perm)
+            transpose = paddle.transpose(reshape, self.tranpose_perm)
+
             matmul = paddle.matmul(
                 transpose,
                 weight,

python/paddle/fluid/tests/unittests/ir/inference/test_trt_anchor_generator_op.py

@@ -15,6 +15,8 @@
 import unittest
 import numpy as np
 from inference_pass_test import InferencePassTest
+
+import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.core import PassVersionChecker
@@ -64,7 +66,7 @@ class TRTAnchorGeneratorBaseTest(InferencePassTest):
                 stride=self.stride,
             )
             if self.dynamic_shape_params is not None:
-                anchor = fluid.layers.transpose(anchor, [2, 3, 0, 1])
+                anchor = paddle.transpose(anchor, [2, 3, 0, 1])
             out = fluid.layers.batch_norm(anchor, is_test=True)
 
         self.fetch_list = [out, var]

python/paddle/fluid/tests/unittests/ir/inference/test_trt_shuffle_channel_detect_pass.py

@@ -15,6 +15,8 @@
 import unittest
 import numpy as np
 from inference_pass_test import InferencePassTest
+
+import paddle
 import paddle.fluid as fluid
 from paddle.fluid.core import PassVersionChecker
 from paddle.fluid.core import AnalysisConfig
@@ -28,8 +30,9 @@ class ShuffleChannelFuseTRTPassTest(InferencePassTest):
                 name="data", shape=[-1, 6, 64, 64], dtype="float32"
             )
             reshape1 = paddle.reshape(x=data, shape=[-1, 2, 3, 64, 64])
-            trans = fluid.layers.transpose(x=reshape1, perm=[0, 2, 1, 3, 4])
+            trans = paddle.transpose(x=reshape1, perm=[0, 2, 1, 3, 4])
             reshape2 = paddle.reshape(x=trans, shape=[-1, 6, 64, 64])
+
             out = fluid.layers.batch_norm(reshape2, is_test=True)
 
         self.feeds = {

python/paddle/fluid/tests/unittests/ir/inference/test_trt_subgraph_pass.py

@@ -17,6 +17,8 @@ import shutil
 import unittest
 import numpy as np
 from inference_pass_test import InferencePassTest
+
+import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.core import PassVersionChecker
@@ -226,7 +228,7 @@ class TensorRTSubgraphPassTransposeTest(InferencePassTest):
         self.fetch_list = [out]
 
     def append_transpose(self, data):
-        return fluid.layers.transpose(data, [0, 3, 1, 2])
+        return paddle.transpose(data, [0, 3, 1, 2])
 
     def test_check_output(self):
         if core.is_compiled_with_cuda():

python/paddle/fluid/tests/unittests/ir/inference/test_trt_transpose_flatten_concat_fuse_pass.py

@@ -15,6 +15,8 @@
 import unittest
 import numpy as np
 from inference_pass_test import InferencePassTest
+
+import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.core import AnalysisConfig
@@ -30,8 +32,8 @@ class TransposeFlattenConcatFusePassTRTTest(InferencePassTest):
             data2 = fluid.data(
                 name="data2", shape=[8, 32, 128], dtype="float32"
             )
-            trans1 = fluid.layers.transpose(data1, perm=[0, 2, 1])
-            trans2 = fluid.layers.transpose(data2, perm=[0, 2, 1])
+            trans1 = paddle.transpose(data1, perm=[0, 2, 1])
+            trans2 = paddle.transpose(data2, perm=[0, 2, 1])
             flatt1 = fluid.layers.flatten(trans1)
             flatt2 = fluid.layers.flatten(trans2)
             concat_out = fluid.layers.concat([flatt1, flatt2], axis=1)

python/paddle/fluid/tests/unittests/mlu/test_transpose_op_mlu.py

@@ -192,26 +192,26 @@ class TestTransposeOpError(unittest.TestCase):
 
             def test_x_Variable_check():
                 # the Input(x)'s type must be Variable
-                fluid.layers.transpose("not_variable", perm=[1, 0, 2])
+                paddle.transpose("not_variable", perm=[1, 0, 2])
 
             self.assertRaises(TypeError, test_x_Variable_check)
 
             def test_perm_list_check():
                 # Input(perm)'s type must be list
-                fluid.layers.transpose(x, perm="[1, 0, 2]")
+                paddle.transpose(x, perm="[1, 0, 2]")
 
             self.assertRaises(TypeError, test_perm_list_check)
 
             def test_perm_length_and_x_dim_check():
                 # Input(perm) is the permutation of dimensions of Input(input)
                 # its length should be equal to dimensions of Input(input)
-                fluid.layers.transpose(x, perm=[1, 0, 2, 3, 4])
+                paddle.transpose(x, perm=[1, 0, 2, 3, 4])
 
             self.assertRaises(ValueError, test_perm_length_and_x_dim_check)
 
             def test_each_elem_value_check():
                 # Each element in Input(perm) should be less than Input(x)'s dimension
-                fluid.layers.transpose(x, perm=[3, 5, 7])
+                paddle.transpose(x, perm=[3, 5, 7])
 
             self.assertRaises(ValueError, test_each_elem_value_check)
 

python/paddle/fluid/tests/unittests/test_cholesky_op.py

@@ -74,7 +74,7 @@ class TestCholeskyOp(OpTest):
             root = layers.create_parameter(
                 dtype=root_data.dtype, shape=root_data.shape
             )
-            root_t = layers.transpose(root, self.trans_dims)
+            root_t = paddle.transpose(root, self.trans_dims)
             x = layers.matmul(x=root, y=root_t) + 1e-05
             out = paddle.cholesky(x, upper=self.attrs["upper"])
             grad_check(root, out, x_init=root_data, place=place)

python/paddle/fluid/tests/unittests/test_eager_deletion_padding_rnn.py

@@ -154,7 +154,7 @@ def lm_model(
             hidden_array.append(pre_hidden)
             cell_array.append(pre_cell)
 
-        input_embedding = layers.transpose(input_embedding, perm=[1, 0, 2])
+        input_embedding = paddle.transpose(input_embedding, perm=[1, 0, 2])
         rnn = PaddingRNN()
 
         with rnn.step():
@@ -230,7 +230,7 @@ def lm_model(
                 c, axes=[0], starts=[num_steps - 1], ends=[num_steps]
             )
             last_cell_array.append(last_c)
-        real_res = layers.transpose(x=real_res, perm=[1, 0, 2])
+        real_res = paddle.transpose(x=real_res, perm=[1, 0, 2])
         last_hidden = layers.concat(last_hidden_array, 0)
         last_cell = layers.concat(last_cell_array, 0)
 
@@ -317,17 +317,17 @@ def lm_model(
         last_hidden = paddle.reshape(
             last_hidden, shape=[-1, num_layers, hidden_size]
         )
-        last_hidden = layers.transpose(x=last_hidden, perm=[1, 0, 2])
+        last_hidden = paddle.transpose(x=last_hidden, perm=[1, 0, 2])
 
         last_cell = layers.concat(cell_array, 1)
         last_cell = paddle.reshape(
             last_cell, shape=[-1, num_layers, hidden_size]
         )
-        last_cell = layers.transpose(x=last_cell, perm=[1, 0, 2])
+        last_cell = paddle.transpose(x=last_cell, perm=[1, 0, 2])
 
         real_res = layers.concat(res, 0)
         real_res = paddle.reshape(real_res, shape=[len, -1, hidden_size])
-        real_res = layers.transpose(x=real_res, perm=[1, 0, 2])
+        real_res = paddle.transpose(x=real_res, perm=[1, 0, 2])
 
         return real_res, last_hidden, last_cell
 
@@ -404,7 +404,7 @@ def lm_model(
             init_cell=init_cell_reshape,
         )
     elif rnn_model == "cudnn":
-        x_emb = layers.transpose(x_emb, perm=[1, 0, 2])
+        x_emb = paddle.transpose(x_emb, perm=[1, 0, 2])
         rnn_out, last_hidden, last_cell = layers.lstm(
             x_emb,
             init_hidden_reshape,
@@ -417,7 +417,7 @@ def lm_model(
                 low=-init_scale, high=init_scale
             ),
         )
-        rnn_out = layers.transpose(rnn_out, perm=[1, 0, 2])
+        rnn_out = paddle.transpose(rnn_out, perm=[1, 0, 2])
     elif rnn_model == "basic_lstm":
         rnn_out, last_hidden, last_cell = basic_lstm(
             x_emb,

python/paddle/fluid/tests/unittests/test_imperative_deepcf.py

@@ -119,9 +119,10 @@ class DeepCF(fluid.Layer):
     def forward(self, users, items):
         # users_emb = self._user_emb(users)
         # items_emb = self._item_emb(items)
+
         users_emb = paddle.gather(self._rating_matrix, users)
         items_emb = paddle.gather(
-            fluid.layers.transpose(self._rating_matrix, [1, 0]), items
+            paddle.transpose(self._rating_matrix, [1, 0]), items
         )
         users_emb.stop_gradient = True
         items_emb.stop_gradient = True

python/paddle/fluid/tests/unittests/test_imperative_lod_tensor_to_selected_rows.py

@@ -63,7 +63,7 @@ class SimpleNet(fluid.Layer):
     def forward(self, input, label):
         x_emb = self.embedding(input)
         projection = fluid.layers.matmul(
-            x_emb, fluid.layers.transpose(self.embedding.weight, perm=[1, 0])
+            x_emb, paddle.transpose(self.embedding.weight, perm=[1, 0])
         )
         projection = fluid.layers.elementwise_add(projection, self.softmax_bias)
         projection = paddle.reshape(projection, shape=[-1, self.vocab_size])

python/paddle/fluid/tests/unittests/test_imperative_ocr_attention_model.py

@@ -264,7 +264,7 @@ class EncoderNet(fluid.dygraph.Layer):
         #    stride=[1, 1],
         #    filter_size=[conv_features.shape[2], 1])
 
-        transpose_conv_features = fluid.layers.transpose(
+        transpose_conv_features = paddle.transpose(
             conv_features, perm=[0, 3, 1, 2]
         )
         sliced_feature = paddle.reshape(

python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py

@@ -131,17 +131,17 @@ class SimpleLSTMRNN(fluid.Layer):
                 paddle.reshape(self._input, shape=[1, -1, self._hidden_size])
             )
         real_res = fluid.layers.concat(res, 0)
-        real_res = fluid.layers.transpose(x=real_res, perm=[1, 0, 2])
+        real_res = paddle.transpose(x=real_res, perm=[1, 0, 2])
         last_hidden = fluid.layers.concat(self.hidden_array, 1)
         last_hidden = paddle.reshape(
             last_hidden, shape=[-1, self._num_layers, self._hidden_size]
         )
-        last_hidden = fluid.layers.transpose(x=last_hidden, perm=[1, 0, 2])
+        last_hidden = paddle.transpose(x=last_hidden, perm=[1, 0, 2])
         last_cell = fluid.layers.concat(self.cell_array, 1)
         last_cell = paddle.reshape(
             last_cell, shape=[-1, self._num_layers, self._hidden_size]
         )
-        last_cell = fluid.layers.transpose(x=last_cell, perm=[1, 0, 2])
+        last_cell = paddle.transpose(x=last_cell, perm=[1, 0, 2])
         return real_res, last_hidden, last_cell
 
 

python/paddle/fluid/tests/unittests/test_imperative_save_load.py

@@ -126,17 +126,17 @@ class SimpleLSTMRNN(fluid.Layer):
                 paddle.reshape(self._input, shape=[1, -1, self._hidden_size])
             )
         real_res = fluid.layers.concat(res, 0)
-        real_res = fluid.layers.transpose(x=real_res, perm=[1, 0, 2])
+        real_res = paddle.transpose(x=real_res, perm=[1, 0, 2])
         last_hidden = fluid.layers.concat(self.hidden_array, 1)
         last_hidden = paddle.reshape(
             last_hidden, shape=[-1, self._num_layers, self._hidden_size]
         )
-        last_hidden = fluid.layers.transpose(x=last_hidden, perm=[1, 0, 2])
+        last_hidden = paddle.transpose(x=last_hidden, perm=[1, 0, 2])
         last_cell = fluid.layers.concat(self.cell_array, 1)
         last_cell = paddle.reshape(
             last_cell, shape=[-1, self._num_layers, self._hidden_size]
         )
-        last_cell = fluid.layers.transpose(x=last_cell, perm=[1, 0, 2])
+        last_cell = paddle.transpose(x=last_cell, perm=[1, 0, 2])
         return real_res, last_hidden, last_cell
 
 

python/paddle/fluid/tests/unittests/test_imperative_save_load_v2.py

@@ -14,6 +14,7 @@
 
 import os
 import unittest
+import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.dygraph.nn import Embedding
@@ -128,17 +129,17 @@ class SimpleLSTMRNN(fluid.Layer):
                 paddle.reshape(self._input, shape=[1, -1, self._hidden_size])
             )
         real_res = fluid.layers.concat(res, 0)
-        real_res = fluid.layers.transpose(x=real_res, perm=[1, 0, 2])
+        real_res = paddle.transpose(x=real_res, perm=[1, 0, 2])
         last_hidden = fluid.layers.concat(self.hidden_array, 1)
         last_hidden = paddle.reshape(
             last_hidden, shape=[-1, self._num_layers, self._hidden_size]
         )
-        last_hidden = fluid.layers.transpose(x=last_hidden, perm=[1, 0, 2])
+        last_hidden = paddle.transpose(x=last_hidden, perm=[1, 0, 2])
         last_cell = fluid.layers.concat(self.cell_array, 1)
         last_cell = paddle.reshape(
             last_cell, shape=[-1, self._num_layers, self._hidden_size]
         )
-        last_cell = fluid.layers.transpose(x=last_cell, perm=[1, 0, 2])
+        last_cell = paddle.transpose(x=last_cell, perm=[1, 0, 2])
         return real_res, last_hidden, last_cell
 
 

python/paddle/fluid/tests/unittests/test_imperative_selected_rows_to_lod_tensor.py

@@ -73,7 +73,7 @@ class SimpleNet(fluid.Layer):
         fc = fluid.layers.matmul(x_emb, self.softmax_weight)
         fc = fluid.layers.elementwise_add(fc, self.softmax_bias)
         projection = fluid.layers.matmul(
-            fc, fluid.layers.transpose(self.embedding.weight, perm=[1, 0])
+            fc, paddle.transpose(self.embedding.weight, perm=[1, 0])
         )
         projection = paddle.reshape(projection, shape=[-1, self.vocab_size])
         loss = fluid.layers.softmax_with_cross_entropy(

python/paddle/fluid/tests/unittests/test_imperative_transformer_sorted_gradient.py

@@ -479,15 +479,16 @@ class MultiHeadAttentionLayer(Layer):
         reshaped_q = paddle.reshape(
             x=q, shape=[0, 0, self._n_head, self._d_key]
         )
-        transpose_q = fluid.layers.transpose(x=reshaped_q, perm=[0, 2, 1, 3])
+
+        transpose_q = paddle.transpose(x=reshaped_q, perm=[0, 2, 1, 3])
         reshaped_k = paddle.reshape(
             x=k, shape=[0, 0, self._n_head, self._d_key]
         )
-        transpose_k = fluid.layers.transpose(x=reshaped_k, perm=[0, 2, 1, 3])
+        transpose_k = paddle.transpose(x=reshaped_k, perm=[0, 2, 1, 3])
         reshaped_v = paddle.reshape(
             x=v, shape=[0, 0, self._n_head, self._d_value]
         )
-        transpose_v = fluid.layers.transpose(x=reshaped_v, perm=[0, 2, 1, 3])
+        transpose_v = paddle.transpose(x=reshaped_v, perm=[0, 2, 1, 3])
 
         # scale dot product attention
         product = fluid.layers.matmul(
@@ -513,7 +514,7 @@ class MultiHeadAttentionLayer(Layer):
         # combine heads
         if len(out.shape) != 4:
             raise ValueError("Input(x) should be a 4-D Tensor.")
-        trans_x = fluid.layers.transpose(out, perm=[0, 2, 1, 3])
+        trans_x = paddle.transpose(out, perm=[0, 2, 1, 3])
         final_out = paddle.reshape(
             x=trans_x,
             shape=[0, 0, trans_x.shape[2] * trans_x.shape[3]],

python/paddle/fluid/tests/unittests/test_rnn_cell_api.py

@@ -15,6 +15,7 @@
 import unittest
 import numpy
 
+import paddle
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 import paddle.fluid.core as core
@@ -335,7 +336,7 @@ class TestRnnError(unittest.TestCase):
                 name="sequence_length", shape=[None], dtype='int64'
             )
 
-            inputs_dynamic_rnn = layers.transpose(
+            inputs_dynamic_rnn = paddle.transpose(
                 inputs_basic_lstm, perm=[1, 0, 2]
             )
             cell = LSTMCell(hidden_size, name="LSTMCell_for_rnn")
@@ -428,7 +429,7 @@ class TestRnn(unittest.TestCase):
             name="sequence_length", shape=[None], dtype='int64'
         )
 
-        inputs_dynamic_rnn = layers.transpose(inputs_basic_lstm, perm=[1, 0, 2])
+        inputs_dynamic_rnn = paddle.transpose(inputs_basic_lstm, perm=[1, 0, 2])
         cell = LSTMCell(self.hidden_size, name="LSTMCell_for_rnn")
         output, final_state = dynamic_rnn(
             cell=cell,
@@ -436,7 +437,7 @@ class TestRnn(unittest.TestCase):
             sequence_length=sequence_length,
             is_reverse=False,
         )
-        output_new = layers.transpose(output, perm=[1, 0, 2])
+        output_new = paddle.transpose(output, perm=[1, 0, 2])
 
         rnn_out, last_hidden, last_cell = basic_lstm(
             inputs_basic_lstm,

python/paddle/fluid/tests/unittests/test_static_save_load.py

@@ -138,17 +138,17 @@ class SimpleLSTMRNN(fluid.Layer):
                 paddle.reshape(self._input, shape=[1, -1, self._hidden_size])
             )
         real_res = fluid.layers.concat(res, 0)
-        real_res = fluid.layers.transpose(x=real_res, perm=[1, 0, 2])
+        real_res = paddle.transpose(x=real_res, perm=[1, 0, 2])
         last_hidden = fluid.layers.concat(self.hidden_array, 1)
         last_hidden = paddle.reshape(
             last_hidden, shape=[-1, self._num_layers, self._hidden_size]
         )
-        last_hidden = fluid.layers.transpose(x=last_hidden, perm=[1, 0, 2])
+        last_hidden = paddle.transpose(x=last_hidden, perm=[1, 0, 2])
         last_cell = fluid.layers.concat(self.cell_array, 1)
         last_cell = paddle.reshape(
             last_cell, shape=[-1, self._num_layers, self._hidden_size]
         )
-        last_cell = fluid.layers.transpose(x=last_cell, perm=[1, 0, 2])
+        last_cell = paddle.transpose(x=last_cell, perm=[1, 0, 2])
         return real_res, last_hidden, last_cell
 
 

python/paddle/fluid/tests/unittests/test_transpose_op.py

@@ -291,7 +291,7 @@ class TestTransposeOpError(unittest.TestCase):
 
             def test_x_Variable_check():
                 # the Input(x)'s type must be Variable
-                fluid.layers.transpose("not_variable", perm=[1, 0, 2])
+                paddle.transpose("not_variable", perm=[1, 0, 2])
 
             self.assertRaises(TypeError, test_x_Variable_check)
 
@@ -300,26 +300,26 @@ class TestTransposeOpError(unittest.TestCase):
                 x1 = fluid.layers.data(
                     name='x1', shape=[10, 5, 3], dtype='int8'
                 )
-                fluid.layers.transpose(x1, perm=[1, 0, 2])
+                paddle.transpose(x1, perm=[1, 0, 2])
 
             self.assertRaises(TypeError, test_x_dtype_check)
 
             def test_perm_list_check():
                 # Input(perm)'s type must be list
-                fluid.layers.transpose(x, perm="[1, 0, 2]")
+                paddle.transpose(x, perm="[1, 0, 2]")
 
             self.assertRaises(TypeError, test_perm_list_check)
 
             def test_perm_length_and_x_dim_check():
                 # Input(perm) is the permutation of dimensions of Input(input)
                 # its length should be equal to dimensions of Input(input)
-                fluid.layers.transpose(x, perm=[1, 0, 2, 3, 4])
+                paddle.transpose(x, perm=[1, 0, 2, 3, 4])
 
             self.assertRaises(ValueError, test_perm_length_and_x_dim_check)
 
             def test_each_elem_value_check():
                 # Each element in Input(perm) should be less than Input(x)'s dimension
-                fluid.layers.transpose(x, perm=[3, 5, 7])
+                paddle.transpose(x, perm=[3, 5, 7])
 
             self.assertRaises(ValueError, test_each_elem_value_check)
 

python/paddle/fluid/tests/unittests/transformer_model.py

@@ -121,7 +121,7 @@ def multi_head_attention(
 
         # permute the dimensions into:
         # [batch_size, n_head, max_sequence_len, hidden_size_per_head]
-        return layers.transpose(x=reshaped, perm=[0, 2, 1, 3])
+        return paddle.transpose(x=reshaped, perm=[0, 2, 1, 3])
 
     def __combine_heads(x):
         """
@@ -133,7 +133,7 @@ def multi_head_attention(
         if len(x.shape) != 4:
             raise ValueError("Input(x) should be a 4-D Tensor.")
 
-        trans_x = layers.transpose(x, perm=[0, 2, 1, 3])
+        trans_x = paddle.transpose(x, perm=[0, 2, 1, 3])
         # FIXME(guosheng): Decouple the program desc with batch_size.
         return paddle.reshape(
             x=trans_x,