PaddlePaddle · SigureMo · Feb 3, 2024 · Feb 2, 2024 · Feb 2, 2024 · Feb 3, 2024
diff --git a/test/collective/fleet/parallel_dygraph_control_flow_same.py b/test/collective/fleet/parallel_dygraph_control_flow_same.py
@@ -19,7 +19,6 @@
 )
 
 import paddle
-from paddle.base.dygraph.base import to_variable
 from paddle.nn import Linear
 
 np.random.seed(2021)
@@ -77,7 +76,7 @@ def get_model(self):
     def run_one_loop(self, model, optimizer, batch):
         x_data = np.array(list(batch))
         x_data = x_data.reshape((-1, 10))
-        x = to_variable(x_data)
+        x = paddle.to_tensor(x_data)
         out = model(x)
         loss = out.sum() / len(batch)
         return loss

diff --git a/test/collective/fleet/parallel_dygraph_se_resnext.py b/test/collective/fleet/parallel_dygraph_se_resnext.py
@@ -19,7 +19,6 @@
 
 import paddle
 from paddle import base
-from paddle.base.dygraph.base import to_variable
 from paddle.nn import Linear
 
 batch_size = 64
@@ -342,8 +341,8 @@ def run_one_loop(self, model, opt, data):
         )
         dy_x_data = dy_x_data / 255.0
         y_data = np.array([x[1] for x in data]).astype('int64').reshape(bs, 1)
-        img = to_variable(dy_x_data)
-        label = to_variable(y_data)
+        img = paddle.to_tensor(dy_x_data)
+        label = paddle.to_tensor(y_data)
         label.stop_gradient = True
 
         out = model(img)

diff --git a/test/collective/fleet/parallel_dygraph_sync_batch_norm.py b/test/collective/fleet/parallel_dygraph_sync_batch_norm.py
@@ -19,7 +19,6 @@
 )
 
 import paddle
-from paddle.base.dygraph.base import to_variable
 from paddle.nn import Conv2D, SyncBatchNorm
 
 
@@ -88,7 +87,7 @@ def run_one_loop(self, model, opt, data):
         dy_x_data = np.array([x[0].reshape(3, 224, 224) for x in data]).astype(
             'float32'
         )
-        img = to_variable(dy_x_data)
+        img = paddle.to_tensor(dy_x_data)
         img.stop_gradient = False
 
         out = model(img)

diff --git a/test/collective/fleet/parallel_dygraph_transformer.py b/test/collective/fleet/parallel_dygraph_transformer.py
@@ -21,7 +21,6 @@
 import paddle
 import paddle.nn.functional as F
 from paddle import base
-from paddle.base.dygraph import to_variable
 from paddle.nn import Layer
 from paddle.optimizer.lr import NoamDecay
 
@@ -1046,7 +1045,7 @@ def np_to_variable(data):
         + decoder_data_input_fields[:-1]
         + label_data_input_fields
     ):
-        var_inputs.append(to_variable(data_inputs[i], name=field))
+        var_inputs.append(paddle.to_tensor(data_inputs[i]))
 
     enc_inputs = var_inputs[0 : len(encoder_data_input_fields)]
     dec_inputs = var_inputs[

diff --git a/test/legacy_test/op_test.py b/test/legacy_test/op_test.py
@@ -895,11 +895,11 @@ def _create_var_from_numpy(self, value):
         if isinstance(value, tuple):
             data = value[0]
             lod = value[1]
-            v = base.dygraph.base.to_variable(value=data)
+            v = paddle.to_tensor(data)
             v.value().get_tensor().set_recursive_sequence_lengths(lod)
             return v
         else:
-            return base.dygraph.base.to_variable(value)
+            return paddle.to_tensor(value)
 
     def get_sequence_batch_size_1_input(self, lod=None, shape=None):
         """Get LoD input data whose batch size is 1.

diff --git a/test/legacy_test/parallel_dygraph_mnist.py b/test/legacy_test/parallel_dygraph_mnist.py
@@ -19,7 +19,6 @@
 )
 
 import paddle
-from paddle.base.dygraph.base import to_variable
 
 
 class SimpleImgConvPool(paddle.nn.Layer):
@@ -125,8 +124,8 @@ def run_one_loop(self, model, opt, data):
             .astype('int64')
             .reshape(batch_size, 1)
         )
-        img = to_variable(dy_x_data)
-        label = to_variable(y_data)
+        img = paddle.to_tensor(dy_x_data)
+        label = paddle.to_tensor(y_data)
         label.stop_gradient = True
 
         avg_loss = model(img, label)

diff --git a/test/legacy_test/parallel_dygraph_shared_unused_var.py b/test/legacy_test/parallel_dygraph_shared_unused_var.py
@@ -24,7 +24,6 @@
 )
 
 import paddle
-from paddle.base.dygraph.base import to_variable
 from paddle.nn import Linear
 
 np.random.seed(2021)
@@ -70,7 +69,7 @@ def get_model(self):
     def run_one_loop(self, model, optimizer, batch):
         x_data = np.array(list(batch))
         x_data = x_data.reshape((-1, 10))
-        x = to_variable(x_data)
+        x = paddle.to_tensor(x_data)
         out = model(x)
         loss = out.sum() / len(batch)
         return loss

diff --git a/test/legacy_test/parallel_dygraph_sparse_embedding.py b/test/legacy_test/parallel_dygraph_sparse_embedding.py
@@ -25,7 +25,6 @@
 
 import paddle
 from paddle import base
-from paddle.base.dygraph.base import to_variable
 from paddle.nn import Embedding
 
 
@@ -131,8 +130,8 @@ def run_one_loop(self, model, optimizer, batch):
         x_data = x_data.reshape((-1, num_steps, 1))
         y_data = y_data.reshape((-1, 1))
 
-        x = to_variable(x_data)
-        y = to_variable(y_data)
+        x = paddle.to_tensor(x_data)
+        y = paddle.to_tensor(y_data)
 
         dy_loss = model(x, y)
 

diff --git a/test/mkldnn/check_flags_mkldnn_ops_on_off.py b/test/mkldnn/check_flags_mkldnn_ops_on_off.py
@@ -45,8 +45,8 @@ def check():
     helper = LayerHelper(base.unique_name.generate("test"), act="relu")
     func = helper.append_activation
     with base.dygraph.guard(base.core.CPUPlace()):
-        a = base.dygraph.to_variable(a_np)
-        b = base.dygraph.to_variable(b_np)
+        a = paddle.to_tensor(a_np)
+        b = paddle.to_tensor(b_np)
         y = paddle.add(x=a, y=b)
         y = paddle.matmul(x=y, y=b, transpose_y=True)
         res1 = func(y)

diff --git a/test/mkldnn/check_flags_use_mkldnn.py b/test/mkldnn/check_flags_use_mkldnn.py
@@ -16,6 +16,7 @@
 
 import numpy as np
 
+import paddle
 from paddle import base
 from paddle.base.framework import _global_flags
 from paddle.base.layer_helper import LayerHelper
@@ -35,7 +36,7 @@ def check():
     helper = LayerHelper(base.unique_name.generate("test"), act="relu")
     func = helper.append_activation
     with base.dygraph.guard(base.core.CPUPlace()):
-        a = base.dygraph.to_variable(a_np)
+        a = paddle.to_tensor(a_np)
         res1 = func(a)
         res2 = np.maximum(a_np, 0)
     np.testing.assert_array_equal(res1.numpy(), res2)