remove to_variable from 2.0 (#27528)

python/paddle/__init__.py

@@ -230,7 +230,6 @@ from .framework import CPUPlace  #DEFINE_ALIAS
 from .framework import CUDAPlace  #DEFINE_ALIAS
 from .framework import CUDAPinnedPlace  #DEFINE_ALIAS
 
-from .framework import to_variable  #DEFINE_ALIAS
 from .framework import grad  #DEFINE_ALIAS
 from .framework import no_grad  #DEFINE_ALIAS
 from .framework import save  #DEFINE_ALIAS

python/paddle/fluid/dygraph/nn.py

@@ -3230,14 +3230,11 @@ class Flatten(layers.Layer):
         .. code-block:: python
 
           import paddle
-          from paddle import to_variable
           import numpy as np
-
-          inp_np = np.ones([5, 2, 3, 4]).astype('float32')
-          
           paddle.disable_static()
 
-          inp_np = to_variable(inp_np)
+          inp_np = np.ones([5, 2, 3, 4]).astype('float32')
+          inp_np = paddle.to_tensor(inp_np)
           flatten = paddle.nn.Flatten(start_axis=1, stop_axis=2)
           flatten_res = flatten(inp_np)
 

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

@@ -228,7 +228,7 @@ class TestTanhAPI(unittest.TestCase):
 
     def test_dygraph_api(self):
         paddle.disable_static(self.place)
-        x = paddle.to_variable(self.x_np)
+        x = paddle.to_tensor(self.x_np)
         out1 = F.tanh(x)
         out2 = paddle.tanh(x)
         th = paddle.nn.Tanh()
@@ -573,7 +573,7 @@ class TestHardShrinkAPI(unittest.TestCase):
 
     def test_dygraph_api(self):
         paddle.disable_static(self.place)
-        x = paddle.to_variable(self.x_np)
+        x = paddle.to_tensor(self.x_np)
         out1 = F.hardshrink(x)
         hd = paddle.nn.Hardshrink()
         out2 = hd(x)
@@ -639,7 +639,7 @@ class TestHardtanhAPI(unittest.TestCase):
 
     def test_dygraph_api(self):
         paddle.disable_static(self.place)
-        x = paddle.to_variable(self.x_np)
+        x = paddle.to_tensor(self.x_np)
         out1 = F.hardtanh(x)
         m = paddle.nn.Hardtanh()
         out2 = m(x)
@@ -1063,7 +1063,7 @@ class TestLeakyReluAPI(unittest.TestCase):
 
     def test_dygraph_api(self):
         paddle.disable_static(self.place)
-        x = paddle.to_variable(self.x_np)
+        x = paddle.to_tensor(self.x_np)
         out1 = F.leaky_relu(x)
         m = paddle.nn.LeakyReLU()
         out2 = m(x)

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

@@ -25,7 +25,7 @@ class TestAdamaxAPI(unittest.TestCase):
     def test_adamax_api_dygraph(self):
         paddle.disable_static()
         value = np.arange(26).reshape(2, 13).astype("float32")
-        a = paddle.to_variable(value)
+        a = paddle.to_tensor(value)
         linear = paddle.nn.Linear(13, 5)
         adam = paddle.optimizer.Adamax(
             learning_rate=0.01,

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

@@ -22,7 +22,7 @@ class TestAdamWOp(unittest.TestCase):
     def test_adamw_op_dygraph(self):
         paddle.disable_static()
         value = np.arange(26).reshape(2, 13).astype("float32")
-        a = paddle.to_variable(value)
+        a = paddle.to_tensor(value)
         linear = paddle.nn.Linear(13, 5)
         adam = paddle.optimizer.AdamW(
             learning_rate=0.01,
@@ -37,7 +37,7 @@ class TestAdamWOp(unittest.TestCase):
     def test_adamw_op_coverage(self):
         paddle.disable_static()
         value = np.arange(26).reshape(2, 13).astype("float32")
-        a = paddle.to_variable(value)
+        a = paddle.to_tensor(value)
         linear = paddle.nn.Linear(13, 5)
         adam = paddle.optimizer.AdamW(
             learning_rate=0.0,

python/paddle/fluid/tests/unittests/test_adaptive_avg_pool2d.py

@@ -147,7 +147,7 @@ class TestAdaptiveAvgPool2dAPI(unittest.TestCase):
                          if core.is_compiled_with_cuda() else [False]):
             place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
             paddle.disable_static(place=place)
-            x = paddle.to_variable(self.x_np)
+            x = paddle.to_tensor(self.x_np)
 
             out_1 = paddle.nn.functional.adaptive_avg_pool2d(
                 x=x, output_size=[3, 3])
@@ -245,7 +245,7 @@ class TestAdaptiveAvgPool2dClassAPI(unittest.TestCase):
                          if core.is_compiled_with_cuda() else [False]):
             place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
             paddle.disable_static(place=place)
-            x = paddle.to_variable(self.x_np)
+            x = paddle.to_tensor(self.x_np)
 
             adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(output_size=[3, 3])
             out_1 = adaptive_avg_pool(x=x)

python/paddle/fluid/tests/unittests/test_adaptive_avg_pool3d.py

@@ -162,7 +162,7 @@ class TestAdaptiveAvgPool3dAPI(unittest.TestCase):
                          if core.is_compiled_with_cuda() else [False]):
             place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
             paddle.disable_static(place=place)
-            x = paddle.to_variable(self.x_np)
+            x = paddle.to_tensor(self.x_np)
 
             out_1 = paddle.nn.functional.adaptive_avg_pool3d(
                 x=x, output_size=[3, 3, 3])
@@ -262,7 +262,7 @@ class TestAdaptiveAvgPool3dClassAPI(unittest.TestCase):
                          if core.is_compiled_with_cuda() else [False]):
             place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
             paddle.disable_static(place=place)
-            x = paddle.to_variable(self.x_np)
+            x = paddle.to_tensor(self.x_np)
 
             adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d(
                 output_size=[3, 3, 3])

python/paddle/fluid/tests/unittests/test_adaptive_max_pool2d.py

@@ -147,7 +147,7 @@ class TestAdaptiveMaxPool2dAPI(unittest.TestCase):
                          if core.is_compiled_with_cuda() else [False]):
             place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
             paddle.disable_static(place=place)
-            x = paddle.to_variable(self.x_np)
+            x = paddle.to_tensor(self.x_np)
 
             out_1 = paddle.nn.functional.adaptive_max_pool2d(
                 x=x, return_indices=False, output_size=[3, 3])
@@ -240,7 +240,7 @@ class TestAdaptiveMaxPool2dClassAPI(unittest.TestCase):
                          if core.is_compiled_with_cuda() else [False]):
             place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
             paddle.disable_static(place=place)
-            x = paddle.to_variable(self.x_np)
+            x = paddle.to_tensor(self.x_np)
 
             adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d(output_size=[3, 3])
             out_1 = adaptive_max_pool(x=x)

python/paddle/fluid/tests/unittests/test_adaptive_max_pool3d.py

@@ -162,7 +162,7 @@ class TestAdaptiveMaxPool3dAPI(unittest.TestCase):
                          if core.is_compiled_with_cuda() else [False]):
             place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
             paddle.disable_static(place=place)
-            x = paddle.to_variable(self.x_np)
+            x = paddle.to_tensor(self.x_np)
 
             out_1 = paddle.nn.functional.adaptive_max_pool3d(
                 x=x, output_size=[3, 3, 3])
@@ -257,7 +257,7 @@ class TestAdaptiveMaxPool3dClassAPI(unittest.TestCase):
                          if core.is_compiled_with_cuda() else [False]):
             place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
             paddle.disable_static(place=place)
-            x = paddle.to_variable(self.x_np)
+            x = paddle.to_tensor(self.x_np)
 
             adaptive_max_pool = paddle.nn.AdaptiveMaxPool3d(
                 output_size=[3, 3, 3])

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

@@ -244,9 +244,9 @@ class TestAddMMAPI(unittest.TestCase):
 
         def test_error1():
             data_x_wrong = np.ones((2, 3)).astype(np.float32)
-            x = paddle.to_variable(data_x_wrong)
-            y = paddle.to_variable(data_y)
-            input = paddle.to_variable(data_input)
+            x = paddle.to_tensor(data_x_wrong)
+            y = paddle.to_tensor(data_y)
+            input = paddle.to_tensor(data_input)
             out = paddle.tensor.addmm( input=input, x=x, y=y, beta=0.5, alpha=5.0 )
         self.assertRaises(ValueError, test_error1)
 '''

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

@@ -98,9 +98,9 @@ class TestArangeImperative(unittest.TestCase):
         x2 = paddle.tensor.arange(5)
         x3 = paddle.tensor.creation.arange(5)
 
-        start = paddle.to_variable(np.array([0], 'float32'))
-        end = paddle.to_variable(np.array([5], 'float32'))
-        step = paddle.to_variable(np.array([1], 'float32'))
+        start = paddle.to_tensor(np.array([0], 'float32'))
+        end = paddle.to_tensor(np.array([5], 'float32'))
+        step = paddle.to_tensor(np.array([1], 'float32'))
         x4 = paddle.arange(start, end, step, 'int64')
         paddle.enable_static()
 

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

@@ -96,7 +96,7 @@ class TestDygraph(unittest.TestCase):
         a = np.random.rand(3, 3)
         a_t = np.transpose(a, [1, 0])
         x_data = np.matmul(a, a_t) + 1e-03
-        x = paddle.to_variable(x_data)
+        x = paddle.to_tensor(x_data)
         out = paddle.cholesky(x, upper=False)
 
 

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

@@ -168,9 +168,9 @@ class TestClipAPI(unittest.TestCase):
         paddle.disable_static(place)
         data_shape = [1, 9, 9, 4]
         data = np.random.random(data_shape).astype('float32')
-        images = paddle.to_variable(data, dtype='float32')
-        v_min = paddle.to_variable(np.array([0.2], dtype=np.float32))
-        v_max = paddle.to_variable(np.array([0.8], dtype=np.float32))
+        images = paddle.to_tensor(data, dtype='float32')
+        v_min = paddle.to_tensor(np.array([0.2], dtype=np.float32))
+        v_max = paddle.to_tensor(np.array([0.8], dtype=np.float32))
 
         out_1 = paddle.clip(images, min=0.2, max=0.8)
         out_2 = paddle.clip(images, min=0.2, max=0.9)

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

@@ -285,9 +285,9 @@ class TestConcatAPI(unittest.TestCase):
         in2 = np.array([[11, 12, 13], [14, 15, 16]])
         in3 = np.array([[21, 22], [23, 24]])
         paddle.disable_static()
-        x1 = paddle.to_variable(in1)
-        x2 = paddle.to_variable(in2)
-        x3 = paddle.to_variable(in3)
+        x1 = paddle.to_tensor(in1)
+        x2 = paddle.to_tensor(in2)
+        x3 = paddle.to_tensor(in3)
         out1 = fluid.layers.concat(input=[x1, x2, x3], axis=-1)
         out2 = paddle.concat(x=[x1, x2], axis=0)
         np_out1 = np.concatenate([in1, in2, in3], axis=-1)

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

@@ -75,8 +75,8 @@ class TestCosineSimilarityAPI(unittest.TestCase):
         np_x2 = np.random.rand(*shape).astype(np.float32)
         np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
 
-        tesnor_x1 = paddle.to_variable(np_x1)
-        tesnor_x2 = paddle.to_variable(np_x2)
+        tesnor_x1 = paddle.to_tensor(np_x1)
+        tesnor_x2 = paddle.to_tensor(np_x2)
         y = F.cosine_similarity(tesnor_x1, tesnor_x2, axis=axis, eps=eps)
 
         self.assertTrue(np.allclose(y.numpy(), np_out))
@@ -92,8 +92,8 @@ class TestCosineSimilarityAPI(unittest.TestCase):
         np_x2 = np.random.rand(*shape).astype(np.float32)
         np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
 
-        tesnor_x1 = paddle.to_variable(np_x1)
-        tesnor_x2 = paddle.to_variable(np_x2)
+        tesnor_x1 = paddle.to_tensor(np_x1)
+        tesnor_x2 = paddle.to_tensor(np_x2)
         y = F.cosine_similarity(tesnor_x1, tesnor_x2, axis=axis, eps=eps)
 
         self.assertTrue(np.allclose(y.numpy(), np_out))
@@ -110,8 +110,8 @@ class TestCosineSimilarityAPI(unittest.TestCase):
         np_x2 = np.random.rand(*shape2).astype(np.float32)
         np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
 
-        tesnor_x1 = paddle.to_variable(np_x1)
-        tesnor_x2 = paddle.to_variable(np_x2)
+        tesnor_x1 = paddle.to_tensor(np_x1)
+        tesnor_x2 = paddle.to_tensor(np_x2)
         y = F.cosine_similarity(tesnor_x1, tesnor_x2, axis=axis, eps=eps)
 
         self.assertTrue(np.allclose(y.numpy(), np_out))
@@ -129,8 +129,8 @@ class TestCosineSimilarityAPI(unittest.TestCase):
         np_out = self._get_numpy_out(np_x1, np_x2, axis=axis, eps=eps)
 
         cos_sim_func = nn.CosineSimilarity(axis=axis, eps=eps)
-        tesnor_x1 = paddle.to_variable(np_x1)
-        tesnor_x2 = paddle.to_variable(np_x2)
+        tesnor_x1 = paddle.to_tensor(np_x1)
+        tesnor_x2 = paddle.to_tensor(np_x2)
         y = cos_sim_func(tesnor_x1, tesnor_x2)
 
         self.assertTrue(np.allclose(y.numpy(), np_out))

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

@@ -21,13 +21,12 @@ import paddle
 import paddle.fluid.core as core
 import paddle.fluid as fluid
 from paddle.fluid import compiler, Program, program_guard
-from paddle import to_variable
 
 
 class TestCumsumOp(unittest.TestCase):
     def run_cases(self):
         data_np = np.arange(12).reshape(3, 4)
-        data = to_variable(data_np)
+        data = paddle.to_tensor(data_np)
 
         y = paddle.cumsum(data)
         z = np.cumsum(data_np)

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

@@ -20,7 +20,6 @@ import paddle
 import paddle.fluid as fluid
 from paddle.fluid.dygraph import Linear
 import paddle.fluid.core as core
-from paddle import to_variable
 
 
 class TestDefaultType(unittest.TestCase):

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

@@ -36,7 +36,7 @@ class TestDirectory(unittest.TestCase):
     def test_new_directory(self):
         new_directory = [
             'paddle.enable_static', 'paddle.disable_static',
-            'paddle.in_dynamic_mode', 'paddle.to_variable', 'paddle.grad',
+            'paddle.in_dynamic_mode', 'paddle.to_tensor', 'paddle.grad',
             'paddle.no_grad', 'paddle.save', 'paddle.load',
             'paddle.static.save', 'paddle.static.load',
             'paddle.distributed.ParallelEnv',

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

@@ -195,7 +195,7 @@ class TestFlattenPython(unittest.TestCase):
 
         def test_Negative():
             paddle.disable_static()
-            img = paddle.to_variable(x)
+            img = paddle.to_tensor(x)
             out = paddle.flatten(img, start_axis=-2, stop_axis=-1)
             return out.numpy().shape
 

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

@@ -211,7 +211,7 @@ class TestImperative(unittest.TestCase):
         paddle.disable_static()
         self.assertTrue(paddle.in_dynamic_mode())
         np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
-        var_inp = paddle.to_variable(np_inp)
+        var_inp = paddle.to_tensor(np_inp)
         mlp = MLP(input_size=2)
         out = mlp(var_inp)
         dy_out1 = out.numpy()
@@ -221,7 +221,7 @@ class TestImperative(unittest.TestCase):
         self.assertFalse(paddle.in_dynamic_mode())
         paddle.disable_static()
         self.assertTrue(paddle.in_dynamic_mode())
-        var_inp = paddle.to_variable(np_inp)
+        var_inp = paddle.to_tensor(np_inp)
         mlp = MLP(input_size=2)
         out = mlp(var_inp)
         dy_out2 = out.numpy()

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

@@ -54,7 +54,7 @@ class TestSimpleNet(unittest.TestCase):
                     # grad_clip = fluid.clip.GradientClipByGlobalNorm(5.0)
 
                     input_word = np.array([[1, 2], [2, 1]]).astype('int64')
-                    input = paddle.to_variable(input_word)
+                    input = paddle.to_tensor(input_word)
 
                     simplenet = SimpleNet(20, 32, dtype)
                     adam = SGDOptimizer(

python/paddle/fluid/tests/unittests/test_isfinite_v2_op.py

@@ -41,7 +41,7 @@ def run_dygraph(x_np, op_str, use_gpu=True):
     if use_gpu and fluid.core.is_compiled_with_cuda():
         place = paddle.CUDAPlace(0)
     paddle.disable_static(place)
-    x = paddle.to_variable(x_np)
+    x = paddle.to_tensor(x_np)
     dygraph_result = getattr(paddle.tensor, op_str)(x)
     return dygraph_result
 

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

@@ -543,9 +543,9 @@ class TestJitSaveMultiCases(unittest.TestCase):
         loaded_layer = paddle.jit.load(model_path)
         loaded_layer.eval()
         # inference & compare
-        x = paddle.to_variable(np.random.random((1, 784)).astype('float32'))
+        x = paddle.to_tensor(np.random.random((1, 784)).astype('float32'))
         if with_label:
-            y = paddle.to_variable(np.random.random((1, 1)).astype('int64'))
+            y = paddle.to_tensor(np.random.random((1, 1)).astype('int64'))
             pred, _ = layer(x, y)
             pred = pred.numpy()
         else:
@@ -677,7 +677,7 @@ class TestJitSaveMultiCases(unittest.TestCase):
 
         model_path = "test_not_prune_output_spec_name_warning"
         configs = paddle.SaveLoadConfig()
-        out = paddle.to_variable(np.random.random((1, 1)).astype('float'))
+        out = paddle.to_tensor(np.random.random((1, 1)).astype('float'))
         configs.output_spec = [out]
         paddle.jit.save(layer, model_path, configs=configs)
 
@@ -709,7 +709,7 @@ class TestJitSaveMultiCases(unittest.TestCase):
 
         model_path = "test_prune_to_static_after_train"
         configs = paddle.SaveLoadConfig()
-        out = paddle.to_variable(np.random.random((1, 1)).astype('float'))
+        out = paddle.to_tensor(np.random.random((1, 1)).astype('float'))
         configs.output_spec = [out]
         with self.assertRaises(ValueError):
             paddle.jit.save(
@@ -730,7 +730,7 @@ class TestJitSaveLoadEmptyLayer(unittest.TestCase):
 
     def test_save_load_empty_layer(self):
         layer = EmptyLayer()
-        x = paddle.to_variable(np.random.random((10)).astype('float32'))
+        x = paddle.to_tensor(np.random.random((10)).astype('float32'))
         out = layer(x)
         paddle.jit.save(layer, self.model_path)
         load_layer = paddle.jit.load(self.model_path)
@@ -746,8 +746,8 @@ class TestJitSaveLoadNoParamLayer(unittest.TestCase):
 
     def test_save_load_no_param_layer(self):
         layer = NoParamLayer()
-        x = paddle.to_variable(np.random.random((5)).astype('float32'))
-        y = paddle.to_variable(np.random.random((5)).astype('float32'))
+        x = paddle.to_tensor(np.random.random((5)).astype('float32'))
+        y = paddle.to_tensor(np.random.random((5)).astype('float32'))
         out = layer(x, y)
         paddle.jit.save(layer, self.model_path)
         load_layer = paddle.jit.load(self.model_path)

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

@@ -90,7 +90,7 @@ class TestKLDivLossDygraph(unittest.TestCase):
         with paddle.fluid.dygraph.guard():
             kldiv_criterion = paddle.nn.KLDivLoss(reduction)
             pred_loss = kldiv_criterion(
-                paddle.to_variable(x), paddle.to_variable(target))
+                paddle.to_tensor(x), paddle.to_tensor(target))
             self.assertTrue(np.allclose(pred_loss.numpy(), gt_loss))
 
     def test_kl_loss_batchmean(self):

python/paddle/fluid/tests/unittests/test_l1_loss.py

@@ -26,8 +26,8 @@ class TestFunctionalL1Loss(unittest.TestCase):
         self.label_np = np.random.random(size=(10, 10, 5)).astype(np.float32)
 
     def run_imperative(self):
-        input = paddle.to_variable(self.input_np)
-        label = paddle.to_variable(self.label_np)
+        input = paddle.to_tensor(self.input_np)
+        label = paddle.to_tensor(self.label_np)
         dy_result = paddle.nn.functional.l1_loss(input, label)
         expected = np.mean(np.abs(self.input_np - self.label_np))
         self.assertTrue(np.allclose(dy_result.numpy(), expected))
@@ -106,8 +106,8 @@ class TestClassL1Loss(unittest.TestCase):
         self.label_np = np.random.random(size=(10, 10, 5)).astype(np.float32)
 
     def run_imperative(self):
-        input = paddle.to_variable(self.input_np)
-        label = paddle.to_variable(self.label_np)
+        input = paddle.to_tensor(self.input_np)
+        label = paddle.to_tensor(self.label_np)
         l1_loss = paddle.nn.loss.L1Loss()
         dy_result = l1_loss(input, label)
         expected = np.mean(np.abs(self.input_np - self.label_np))

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

@@ -96,7 +96,7 @@ class TestNNLogSoftmaxAPI(unittest.TestCase):
 
         # test dygrapg api
         paddle.disable_static()
-        x = paddle.to_variable(self.x)
+        x = paddle.to_tensor(self.x)
         y = logsoftmax(x)
         self.assertTrue(np.allclose(y.numpy(), ref_out))
         paddle.enable_static()
@@ -127,7 +127,7 @@ class TestNNFunctionalLogSoftmaxAPI(unittest.TestCase):
         self.assertTrue(np.allclose(out[0], ref_out))
 
         paddle.disable_static()
-        x = paddle.to_variable(self.x)
+        x = paddle.to_tensor(self.x)
         y = F.log_softmax(x, axis, dtype)
         self.assertTrue(np.allclose(y.numpy(), ref_out), True)
         paddle.enable_static()

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

@@ -111,7 +111,7 @@ class TestLogsumexpAPI(unittest.TestCase):
         self.assertTrue(np.allclose(res[0], out_ref))
 
         paddle.disable_static(self.place)
-        x = paddle.to_variable(self.x)
+        x = paddle.to_tensor(self.x)
         out = paddle.logsumexp(x, axis, keepdim)
         self.assertTrue(np.allclose(out.numpy(), out_ref))
         paddle.enable_static()
@@ -126,7 +126,7 @@ class TestLogsumexpAPI(unittest.TestCase):
 
     def test_alias(self):
         paddle.disable_static(self.place)
-        x = paddle.to_variable(self.x)
+        x = paddle.to_tensor(self.x)
         out1 = paddle.logsumexp(x)
         out2 = paddle.tensor.logsumexp(x)
         out3 = paddle.tensor.math.logsumexp(x)

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

@@ -80,7 +80,7 @@ class ApiMaxTest(unittest.TestCase):
     def test_imperative_api(self):
         paddle.disable_static()
         np_x = np.array([10, 10]).astype('float64')
-        x = paddle.to_variable(np_x)
+        x = paddle.to_tensor(np_x)
         z = paddle.max(x, axis=0)
         np_z = z.numpy()
         z_expected = np.array(np.max(np_x, axis=0))

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

@@ -61,8 +61,8 @@ class ApiMaximumTest(unittest.TestCase):
     def test_dynamic_api(self):
         paddle.disable_static()
         np_x = np.array([10, 10]).astype('float64')
-        x = paddle.to_variable(self.input_x)
-        y = paddle.to_variable(self.input_y)
+        x = paddle.to_tensor(self.input_x)
+        y = paddle.to_tensor(self.input_y)
         z = paddle.maximum(x, y)
         np_z = z.numpy()
         z_expected = np.array(np.maximum(self.input_x, self.input_y))
@@ -73,8 +73,8 @@ class ApiMaximumTest(unittest.TestCase):
         np_x = np.random.rand(5, 4, 3, 2).astype("float64")
         np_y = np.random.rand(4, 3).astype("float64")
 
-        x = paddle.to_variable(self.input_x)
-        y = paddle.to_variable(self.input_y)
+        x = paddle.to_tensor(self.input_x)
+        y = paddle.to_tensor(self.input_y)
         result_1 = paddle.maximum(x, y, axis=1)
         result_2 = paddle.maximum(x, y, axis=-2)
         self.assertEqual((result_1.numpy() == result_2.numpy()).all(), True)

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

@@ -204,7 +204,7 @@ class TestMeanAPI(unittest.TestCase):
         paddle.disable_static(self.place)
 
         def test_case(x, axis=None, keepdim=False):
-            x_tensor = paddle.to_variable(x)
+            x_tensor = paddle.to_tensor(x)
             out = paddle.mean(x_tensor, axis, keepdim)
             if isinstance(axis, list):
                 axis = tuple(axis)

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

@@ -80,7 +80,7 @@ class ApiMinTest(unittest.TestCase):
     def test_imperative_api(self):
         paddle.disable_static()
         np_x = np.array([10, 10]).astype('float64')
-        x = paddle.to_variable(np_x)
+        x = paddle.to_tensor(np_x)
         z = paddle.min(x, axis=0)
         np_z = z.numpy()
         z_expected = np.array(np.min(np_x, axis=0))

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

@@ -63,7 +63,7 @@ class TestRandnOpForDygraph(unittest.TestCase):
         dim_2 = paddle.fill_constant([1], "int32", 50)
         x3 = paddle.randn(shape=[dim_1, dim_2, 784])
 
-        var_shape = paddle.to_variable(np.array(shape))
+        var_shape = paddle.to_tensor(np.array(shape))
         x4 = paddle.randn(var_shape)
 
         for out in [x1, x2, x3, x4]:

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

@@ -105,8 +105,8 @@ class TestRetainGraph(unittest.TestCase):
         A = np.random.rand(2, 3, 32, 32).astype('float32')
         B = np.random.rand(2, 3, 32, 32).astype('float32')
 
-        realA = paddle.to_variable(A)
-        realB = paddle.to_variable(B)
+        realA = paddle.to_tensor(A)
+        realB = paddle.to_tensor(B)
         fakeB = g(realA)
 
         optim_d.clear_gradients()

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

@@ -487,24 +487,24 @@ class TestTransformer(unittest.TestCase):
                 dropout=dropout,
                 weight_attr=[None],
                 bias_attr=[False])
-            src = paddle.to_variable(
+            src = paddle.to_tensor(
                 np.random.rand(batch_size, source_length, d_model).astype(
                     "float32"))
-            tgt = paddle.to_variable(
+            tgt = paddle.to_tensor(
                 np.random.rand(batch_size, target_length, d_model).astype(
                     "float32"))
             src_mask = np.zeros((batch_size, n_head, source_length,
                                  source_length)).astype("float32")
             src_mask[0][0][0][0] = -np.inf
-            src_mask = paddle.to_variable(src_mask)
+            src_mask = paddle.to_tensor(src_mask)
             tgt_mask = np.zeros((batch_size, n_head, target_length,
                                  target_length)).astype("float32")
             tgt_mask[0][0][0][0] = -1e9
             memory_mask = np.zeros((batch_size, n_head, target_length,
                                     source_length)).astype("float32")
             memory_mask[0][0][0][0] = -1e9
-            tgt_mask, memory_mask = paddle.to_variable(
-                tgt_mask), paddle.to_variable(memory_mask)
+            tgt_mask, memory_mask = paddle.to_tensor(
+                tgt_mask), paddle.to_tensor(memory_mask)
             trans_output = transformer(src, tgt, src_mask, tgt_mask,
                                        memory_mask)
 
@@ -521,24 +521,24 @@ class TestTransformer(unittest.TestCase):
                 dropout=dropout,
                 weight_attr=[None, None],
                 bias_attr=[False, False])
-            src = paddle.to_variable(
+            src = paddle.to_tensor(
                 np.random.rand(batch_size, source_length, d_model).astype(
                     "float32"))
-            tgt = paddle.to_variable(
+            tgt = paddle.to_tensor(
                 np.random.rand(batch_size, target_length, d_model).astype(
                     "float32"))
             src_mask = np.zeros((batch_size, n_head, source_length,
                                  source_length)).astype("float32")
             src_mask[0][0][0][0] = -np.inf
-            src_mask = paddle.to_variable(src_mask)
+            src_mask = paddle.to_tensor(src_mask)
             tgt_mask = np.zeros((batch_size, n_head, target_length,
                                  target_length)).astype("float32")
             tgt_mask[0][0][0][0] = -1e9
             memory_mask = np.zeros((batch_size, n_head, target_length,
                                     source_length)).astype("float32")
             memory_mask[0][0][0][0] = -1e9
-            tgt_mask, memory_mask = paddle.to_variable(
-                tgt_mask), paddle.to_variable(memory_mask)
+            tgt_mask, memory_mask = paddle.to_tensor(
+                tgt_mask), paddle.to_tensor(memory_mask)
             trans_output = transformer(src, tgt, src_mask, tgt_mask,
                                        memory_mask)
 
@@ -555,24 +555,24 @@ class TestTransformer(unittest.TestCase):
                 dropout=dropout,
                 weight_attr=[None, None, None],
                 bias_attr=[False, False, True])
-            src = paddle.to_variable(
+            src = paddle.to_tensor(
                 np.random.rand(batch_size, source_length, d_model).astype(
                     "float32"))
-            tgt = paddle.to_variable(
+            tgt = paddle.to_tensor(
                 np.random.rand(batch_size, target_length, d_model).astype(
                     "float32"))
             src_mask = np.zeros((batch_size, n_head, source_length,
                                  source_length)).astype("float32")
             src_mask[0][0][0][0] = -np.inf
-            src_mask = paddle.to_variable(src_mask)
+            src_mask = paddle.to_tensor(src_mask)
             tgt_mask = np.zeros((batch_size, n_head, target_length,
                                  target_length)).astype("float32")
             tgt_mask[0][0][0][0] = -1e9
             memory_mask = np.zeros((batch_size, n_head, target_length,
                                     source_length)).astype("float32")
             memory_mask[0][0][0][0] = -1e9
-            tgt_mask, memory_mask = paddle.to_variable(
-                tgt_mask), paddle.to_variable(memory_mask)
+            tgt_mask, memory_mask = paddle.to_tensor(
+                tgt_mask), paddle.to_tensor(memory_mask)
             trans_output = transformer(src, tgt, src_mask, tgt_mask,
                                        memory_mask)
 
@@ -588,24 +588,24 @@ class TestTransformer(unittest.TestCase):
                 dim_feedforward=dim_feedforward,
                 dropout=dropout,
                 bias_attr=False)
-            src = paddle.to_variable(
+            src = paddle.to_tensor(
                 np.random.rand(batch_size, source_length, d_model).astype(
                     "float32"))
-            tgt = paddle.to_variable(
+            tgt = paddle.to_tensor(
                 np.random.rand(batch_size, target_length, d_model).astype(
                     "float32"))
             src_mask = np.zeros((batch_size, n_head, source_length,
                                  source_length)).astype("float32")
             src_mask[0][0][0][0] = -np.inf
-            src_mask = paddle.to_variable(src_mask)
+            src_mask = paddle.to_tensor(src_mask)
             tgt_mask = np.zeros((batch_size, n_head, target_length,
                                  target_length)).astype("float32")
             tgt_mask[0][0][0][0] = -1e9
             memory_mask = np.zeros((batch_size, n_head, target_length,
                                     source_length)).astype("float32")
             memory_mask[0][0][0][0] = -1e9
-            tgt_mask, memory_mask = paddle.to_variable(
-                tgt_mask), paddle.to_variable(memory_mask)
+            tgt_mask, memory_mask = paddle.to_tensor(
+                tgt_mask), paddle.to_tensor(memory_mask)
             trans_output = transformer(src, tgt, src_mask, tgt_mask,
                                        memory_mask)
 

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

@@ -63,7 +63,7 @@ class TestZerosLikeImpeartive(unittest.TestCase):
         place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
         ) else fluid.CPUPlace()
         paddle.disable_static(place)
-        x = paddle.to_variable(np.ones(shape))
+        x = paddle.to_tensor(np.ones(shape))
         for dtype in [np.bool, np.float32, np.float64, np.int32, np.int64]:
             out = zeros_like(x, dtype)
             self.assertEqual((out.numpy() == np.zeros(shape, dtype)).all(),

python/paddle/tensor/linalg.py

@@ -707,20 +707,14 @@ def cross(x, y, axis=None, name=None):
     Examples:
         .. code-block:: python
             import paddle
-            from paddle import to_variable
-            import numpy as np
-
             paddle.disable_static()
 
-            data_x = np.array([[1.0, 1.0, 1.0],
+            x = paddle.to_tensor([[1.0, 1.0, 1.0],
                                   [2.0, 2.0, 2.0],
                                   [3.0, 3.0, 3.0]])
-            data_y = np.array([[1.0, 1.0, 1.0],
+            y = paddle.to_tensor([[1.0, 1.0, 1.0],
                                   [1.0, 1.0, 1.0],
                                   [1.0, 1.0, 1.0]])
-            x = to_variable(data_x)
-            y = to_variable(data_y)
-
             z1 = paddle.cross(x, y)
             print(z1.numpy())
             # [[-1. -1. -1.]

python/paddle/tensor/math.py

@@ -1650,12 +1650,11 @@ def cumsum(x, axis=None, dtype=None, name=None):
         .. code-block:: python
             
             import paddle
-            from paddle import to_variable
             import numpy as np
 
             paddle.disable_static()
             data_np = np.arange(12).reshape(3, 4)
-            data = to_variable(data_np)
+            data = paddle.to_tensor(data_np)
 
             y = paddle.cumsum(data)
             print(y.numpy())

tools/wlist.json

@@ -251,9 +251,10 @@
         "BilinearTensorProduct",
         "GroupNorm",
         "SpectralNorm",
-        "TreeConv",
+        "TreeConv"
+    ],
+    "wlist_temp":[
         "prroi_pool",
-        "to_tensor",
         "ChunkEvaluator",
         "EditDistance",
         "ErrorClipByValue",