PaddlePaddle
diff --git a/‎test/dygraph_to_static/seq2seq_dygraph_model.py
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+49-58Lines changed: 49 additions & 58 deletions b/‎test/dygraph_to_static/seq2seq_dygraph_model.py
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+49-58Lines changed: 49 additions & 58 deletions
diff --git a/‎test/dygraph_to_static/simnet_dygraph_model.py
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+11-10Lines changed: 11 additions & 10 deletions b/‎test/dygraph_to_static/simnet_dygraph_model.py
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+11-10Lines changed: 11 additions & 10 deletions
diff --git a/‎test/dygraph_to_static/test_basic_api_transformation.py
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+1-26Lines changed: 1 addition & 26 deletions b/‎test/dygraph_to_static/test_basic_api_transformation.py
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+1-26Lines changed: 1 addition & 26 deletions
diff --git a/‎test/dygraph_to_static/test_declarative.py
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+6-9Lines changed: 6 additions & 9 deletions b/‎test/dygraph_to_static/test_declarative.py
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+6-9Lines changed: 6 additions & 9 deletions
@@ -12,13 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import numpy as np
 from seq2seq_utils import Seq2SeqModelHyperParams as args
 
 import paddle
-from paddle import base
-from paddle.base import ParamAttr
-from paddle.base.dygraph.base import to_variable
 from paddle.nn import Embedding, Layer
 
 INF = 1.0 * 1e5
@@ -112,14 +108,16 @@ def __init__(
         self.mode = mode
         self.kinf = 1e9
 
-        param_attr = ParamAttr(initializer=uniform_initializer(self.init_scale))
-        bias_attr = ParamAttr(initializer=zero_constant)
+        param_attr = paddle.ParamAttr(
+            initializer=uniform_initializer(self.init_scale)
+        )
+        bias_attr = paddle.ParamAttr(initializer=zero_constant)
         forget_bias = 1.0
 
         self.src_embeder = Embedding(
             self.src_vocab_size,
             self.hidden_size,
-            weight_attr=base.ParamAttr(
+            weight_attr=paddle.ParamAttr(
                 initializer=uniform_initializer(init_scale)
             ),
         )
@@ -128,7 +126,7 @@ def __init__(
             self.tar_vocab_size,
             self.hidden_size,
             sparse=False,
-            weight_attr=base.ParamAttr(
+            weight_attr=paddle.ParamAttr(
                 initializer=uniform_initializer(init_scale)
             ),
         )
@@ -137,7 +135,7 @@ def __init__(
         for i in range(num_layers):
             self.enc_units.append(
                 self.add_sublayer(
-                    "enc_units_%d" % i,
+                    f"enc_units_{i}",
                     BasicLSTMUnit(
                         hidden_size=self.hidden_size,
                         input_size=self.hidden_size,
@@ -152,7 +150,7 @@ def __init__(
         for i in range(num_layers):
             self.dec_units.append(
                 self.add_sublayer(
-                    "dec_units_%d" % i,
+                    f"dec_units_{i}",
                     BasicLSTMUnit(
                         hidden_size=self.hidden_size,
                         input_size=self.hidden_size,
@@ -211,11 +209,11 @@ def forward(self, inputs):
 
         # NOTE: modify model code about `enc_hidden` and `enc_cell` to transforme dygraph code successfully.
         # Because nested list can't be transformed now.
-        enc_hidden_0 = to_variable(
-            np.zeros((self.batch_size, self.hidden_size), dtype='float32')
+        enc_hidden_0 = paddle.zeros(
+            shape=[self.batch_size, self.hidden_size], dtype='float32'
         )
-        enc_cell_0 = to_variable(
-            np.zeros((self.batch_size, self.hidden_size), dtype='float32')
+        enc_cell_0 = paddle.zeros(
+            shape=[self.batch_size, self.hidden_size], dtype='float32'
         )
         zero = paddle.zeros(shape=[1], dtype="int64")
         enc_hidden = paddle.tensor.create_array(dtype="float32")
@@ -292,8 +290,8 @@ def forward(self, inputs):
 
         dec_output = paddle.stack(dec_output)
         dec_output = self.fc(self._transpose_batch_time(dec_output))
-        loss = paddle.nn.functional.softmax_with_cross_entropy(
-            logits=dec_output, label=label, soft_label=False
+        loss = paddle.nn.functional.cross_entropy(
+            input=dec_output, label=label, soft_label=False, reduction="none"
         )
         loss = paddle.squeeze(loss, axis=[2])
         max_tar_seq_len = paddle.shape(tar)[1]
@@ -312,11 +310,11 @@ def beam_search(self, inputs):
             self.batch_size = src.shape[0]
 
         src_emb = self.src_embeder(self._transpose_batch_time(src))
-        enc_hidden_0 = to_variable(
-            np.zeros((self.batch_size, self.hidden_size), dtype='float32')
+        enc_hidden_0 = paddle.zeros(
+            shape=[self.batch_size, self.hidden_size], dtype='float32'
         )
-        enc_cell_0 = to_variable(
-            np.zeros((self.batch_size, self.hidden_size), dtype='float32')
+        enc_cell_0 = paddle.zeros(
+            shape=[self.batch_size, self.hidden_size], dtype='float32'
         )
         zero = paddle.zeros(shape=[1], dtype="int64")
         enc_hidden = paddle.tensor.create_array(dtype="float32")
@@ -367,23 +365,17 @@ def beam_search(self, inputs):
 
         # beam search
         batch_beam_shape = (self.batch_size, self.beam_size)
-        vocab_size_tensor = to_variable(
-            np.full((1), self.tar_vocab_size)
-        ).astype("int64")
-        start_token_tensor = to_variable(
-            np.full(batch_beam_shape, self.beam_start_token, dtype='int64')
+        vocab_size_tensor = paddle.full([1], self.tar_vocab_size, dtype="int64")
+        start_token_tensor = paddle.full(
+            batch_beam_shape, self.beam_start_token, dtype="int64"
         )
-        end_token_tensor = to_variable(
-            np.full(batch_beam_shape, self.beam_end_token, dtype='int64')
+        end_token_tensor = paddle.full(
+            batch_beam_shape, self.beam_end_token, dtype="int64"
         )
         step_input = self.tar_embeder(start_token_tensor)
-        beam_finished = to_variable(
-            np.full(batch_beam_shape, 0, dtype='float32')
-        )
-        beam_state_log_probs = to_variable(
-            np.array(
-                [[0.0] + [-self.kinf] * (self.beam_size - 1)], dtype="float32"
-            )
+        beam_finished = paddle.full(batch_beam_shape, 0, dtype="float32")
+        beam_state_log_probs = paddle.to_tensor(
+            [[0.0] + [-self.kinf] * (self.beam_size - 1)], dtype="float32"
         )
         beam_state_log_probs = paddle.expand(
             beam_state_log_probs,
@@ -395,8 +387,7 @@ def beam_search(self, inputs):
 
         batch_pos = paddle.expand(
             paddle.unsqueeze(
-                to_variable(np.arange(0, self.batch_size, 1, dtype="int64")),
-                [1],
+                paddle.arange(0, self.batch_size, 1, dtype="int64"), [1]
             ),
             [-1, self.beam_size],
         )
@@ -437,9 +428,7 @@ def beam_search(self, inputs):
             )
             noend_array = [-self.kinf] * self.tar_vocab_size
             noend_array[self.beam_end_token] = 0
-            noend_mask_tensor = to_variable(
-                np.array(noend_array, dtype='float32')
-            )
+            noend_mask_tensor = paddle.to_tensor(noend_array, dtype="float32")
 
             step_log_probs = paddle.multiply(
                 paddle.expand(
@@ -537,14 +526,16 @@ def __init__(
         self.mode = mode
         self.kinf = 1e9
 
-        param_attr = ParamAttr(initializer=uniform_initializer(self.init_scale))
-        bias_attr = ParamAttr(initializer=zero_constant)
+        param_attr = paddle.ParamAttr(
+            initializer=uniform_initializer(self.init_scale)
+        )
+        bias_attr = paddle.ParamAttr(initializer=zero_constant)
         forget_bias = 1.0
 
         self.src_embeder = Embedding(
             self.src_vocab_size,
             self.hidden_size,
-            weight_attr=base.ParamAttr(
+            weight_attr=paddle.ParamAttr(
                 name='source_embedding',
                 initializer=uniform_initializer(init_scale),
             ),
@@ -554,7 +545,7 @@ def __init__(
             self.tar_vocab_size,
             self.hidden_size,
             sparse=False,
-            weight_attr=base.ParamAttr(
+            weight_attr=paddle.ParamAttr(
                 name='target_embedding',
                 initializer=uniform_initializer(init_scale),
             ),
@@ -564,7 +555,7 @@ def __init__(
         for i in range(num_layers):
             self.enc_units.append(
                 self.add_sublayer(
-                    "enc_units_%d" % i,
+                    f"enc_units_{i}",
                     BasicLSTMUnit(
                         hidden_size=self.hidden_size,
                         input_size=self.hidden_size,
@@ -580,12 +571,12 @@ def __init__(
             if i == 0:
                 self.dec_units.append(
                     self.add_sublayer(
-                        "dec_units_%d" % i,
+                        f"dec_units_{i}",
                         BasicLSTMUnit(
                             hidden_size=self.hidden_size,
                             input_size=self.hidden_size * 2,
-                            param_attr=ParamAttr(
-                                name="dec_units_%d" % i,
+                            param_attr=paddle.ParamAttr(
+                                name=f"dec_units_{i}",
                                 initializer=uniform_initializer(
                                     self.init_scale
                                 ),
@@ -598,12 +589,12 @@ def __init__(
             else:
                 self.dec_units.append(
                     self.add_sublayer(
-                        "dec_units_%d" % i,
+                        f"dec_units_{i}",
                         BasicLSTMUnit(
                             hidden_size=self.hidden_size,
                             input_size=self.hidden_size,
-                            param_attr=ParamAttr(
-                                name="dec_units_%d" % i,
+                            param_attr=paddle.ParamAttr(
+                                name=f"dec_units_{i}",
                                 initializer=uniform_initializer(
                                     self.init_scale
                                 ),
@@ -726,12 +717,12 @@ def forward(self, inputs):
 
         # NOTE: modify model code about `enc_hidden` and `enc_cell` to transform dygraph code successfully.
         # Because nested list can't be transformed now.
-        enc_hidden_0 = to_variable(
-            np.zeros((self.batch_size, self.hidden_size), dtype='float32')
+        enc_hidden_0 = paddle.zeros(
+            shape=[self.batch_size, self.hidden_size], dtype='float32'
         )
         enc_hidden_0.stop_gradient = True
-        enc_cell_0 = to_variable(
-            np.zeros((self.batch_size, self.hidden_size), dtype='float32')
+        enc_cell_0 = paddle.zeros(
+            shape=[self.batch_size, self.hidden_size], dtype='float32'
         )
         enc_hidden_0.stop_gradient = True
         zero = paddle.zeros(shape=[1], dtype="int64")
@@ -789,8 +780,8 @@ def forward(self, inputs):
         enc_outputs = self._transpose_batch_time(enc_outputs)
 
         # train
-        input_feed = to_variable(
-            np.zeros((self.batch_size, self.hidden_size), dtype='float32')
+        input_feed = paddle.zeros(
+            shape=[self.batch_size, self.hidden_size], dtype='float32'
         )
         # NOTE: set stop_gradient here, otherwise grad var is null
         input_feed.stop_gradient = True
@@ -828,8 +819,8 @@ def forward(self, inputs):
 
         dec_output = paddle.stack(dec_output)
         dec_output = self.fc(self._transpose_batch_time(dec_output))
-        loss = paddle.nn.functional.softmax_with_cross_entropy(
-            logits=dec_output, label=label, soft_label=False
+        loss = paddle.nn.functional.cross_entropy(
+            input=dec_output, label=label, soft_label=False, reduction="none"
         )
         loss = paddle.squeeze(loss, axis=[2])
         max_tar_seq_len = paddle.shape(tar)[1]
 
@@ -1,4 +1,4 @@
-#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -298,15 +298,16 @@ class FC(paddle.nn.Layer):
 
     Examples:
         .. code-block:: python
-          from paddle.base.dygraph.base import to_variable
-          import paddle.base as base
-          from paddle.base.dygraph import FC
-          import numpy as np
-          data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32')
-          with base.dygraph.guard():
-              fc = FC("fc", 64, num_flatten_dims=2)
-              data = to_variable(data)
-              conv = fc(data)
+
+            import paddle
+            import paddle.base as base
+            from paddle.base.dygraph import FC
+            import numpy as np
+            data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32')
+            with base.dygraph.guard():
+                fc = FC("fc", 64, num_flatten_dims=2)
+                data = paddle.to_tensor(data)
+                conv = fc(data)
     """
 
     def __init__(
 
@@ -21,30 +21,13 @@
 )
 
 import paddle
-from paddle import base, to_tensor
-from paddle.base import dygraph
-from paddle.base.dygraph import to_variable
+from paddle import to_tensor
 from paddle.jit.api import to_static
 
 SEED = 2020
 np.random.seed(SEED)
 
 
-def dyfunc_to_variable(x):
-    res = base.dygraph.to_variable(x, name=None, zero_copy=None)
-    return res
-
-
-def dyfunc_to_variable_2(x):
-    res = dygraph.to_variable(value=np.zeros(shape=(1), dtype=np.int32))
-    return res
-
-
-def dyfunc_to_variable_3(x):
-    res = to_variable(x, name=None, zero_copy=None)
-    return res
-
-
 def dyfunc_to_tensor(x):
     res1 = paddle.to_tensor(x, dtype=None, place=None, stop_gradient=True)
     res2 = paddle.tensor.to_tensor(data=res1)
@@ -73,15 +56,7 @@ def setUp(self):
             dyfunc_bool_to_tensor,
             dyfunc_int_to_tensor,
             dyfunc_float_to_tensor,
-            dyfunc_to_variable,
-            dyfunc_to_variable_2,
-            dyfunc_to_variable_3,
         ]
-        self.place = (
-            paddle.CUDAPlace(0)
-            if paddle.is_compiled_with_cuda()
-            else paddle.CPUPlace()
-        )
 
     def get_dygraph_output(self):
         res = self.dygraph_func(self.input).numpy()
 
@@ -19,11 +19,9 @@
 import numpy as np
 from dygraph_to_static_utils import (
     Dy2StTestBase,
-    enable_to_static_guard,
     test_ast_only,
     test_legacy_and_pt_and_pir,
 )
-from test_basic_api_transformation import dyfunc_to_variable
 
 import paddle
 from paddle.framework import use_pir_api
@@ -35,6 +33,11 @@
 from paddle.static import InputSpec
 
 
+def call_to_tensor(x):
+    res = paddle.to_tensor(x)
+    return res
+
+
 def create_simple_net():
     class SimpleNet(Layer):
         def __init__(self):
@@ -370,7 +373,7 @@ class TestDeclarativeAPI(Dy2StTestBase):
     @test_ast_only
     @test_legacy_and_pt_and_pir
     def test_error(self):
-        func = paddle.jit.to_static(dyfunc_to_variable)
+        func = paddle.jit.to_static(call_to_tensor)
 
         paddle.enable_static()
 
@@ -379,12 +382,6 @@ def test_error(self):
         with self.assertRaises(RuntimeError):
             func(np.ones(5).astype("int32"))
 
-        with enable_to_static_guard(False):
-            with self.assertRaises(AssertionError):
-                # AssertionError: We Only support to_variable in imperative mode,
-                #  please use base.dygraph.guard() as context to run it in imperative Mode
-                func(np.ones(5).astype("int32"))
-
         paddle.disable_static()