[Dy2St] 移除 declarative 接口

python/paddle/fluid/data_feeder.py

@@ -116,7 +116,7 @@ def check_type(input, input_name, expected_type, op_name, extra_message=''):
         return
 
     # NOTE: `in_declarative_mode` is used to determined whether this op is called under
-    # @declarative in transformation from dygrah to static layer. We add VarBase in
+    # @to_static in transformation from dygrah to static layer. We add VarBase in
     # expected_type to skip checking because varBase may be created and used in unusual way.
     from .dygraph.base import in_declarative_mode
 

python/paddle/fluid/dygraph/base.py

@@ -44,13 +44,13 @@
     'to_variable',
 ]
 
-# Flag that indicates whether running code under `@declarative`
+# Flag that indicates whether running code under `@to_static`
 _in_declarative_mode_ = False
 
 
 def in_declarative_mode():
     """
-    Return a bool value that indicates whether running code under `@declarative`
+    Return a bool value that indicates whether running code under `@to_static`
 
     """
     return _in_declarative_mode_

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

@@ -17,7 +17,7 @@
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.dygraph import Layer
-from paddle.jit.api import declarative
+from paddle.jit.api import to_static
 from paddle.nn import Linear
 
 
@@ -373,7 +373,7 @@ def __init__(
             bias_attr="next_sent_fc.b_0",
         )
 
-    @declarative
+    @to_static
     def forward(
         self,
         src_ids,

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

@@ -21,7 +21,7 @@
 from paddle.fluid import ParamAttr, layers
 from paddle.fluid.dygraph import Layer
 from paddle.fluid.dygraph.base import to_variable
-from paddle.jit.api import declarative
+from paddle.jit.api import to_static
 from paddle.nn import Embedding
 
 INF = 1.0 * 1e5
@@ -207,7 +207,7 @@ def _gather(self, x, indices, batch_pos):
         topk_coordinates = paddle.stack([batch_pos, indices], axis=2)
         return paddle.gather_nd(x, topk_coordinates)
 
-    @declarative
+    @to_static
     def forward(self, inputs):
         src, tar, label, src_sequence_length, tar_sequence_length = inputs
         if src.shape[0] < self.batch_size:
@@ -312,7 +312,7 @@ def forward(self, inputs):
 
         return loss
 
-    @declarative
+    @to_static
     def beam_search(self, inputs):
         src, tar, label, src_sequence_length, tar_sequence_length = inputs
         if src.shape[0] < self.batch_size:
@@ -724,7 +724,7 @@ def _change_size_for_array(self, func, array):
 
         return array
 
-    @declarative
+    @to_static
     def forward(self, inputs):
         src, tar, label, src_sequence_length, tar_sequence_length = inputs
         if src.shape[0] < self.batch_size:

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

@@ -18,7 +18,7 @@
 import paddle.fluid as fluid
 import paddle.fluid.param_attr as attr
 from paddle.fluid.dygraph import Layer
-from paddle.jit.api import declarative
+from paddle.jit.api import to_static
 from paddle.static import Variable
 
 
@@ -495,7 +495,7 @@ def __init__(self, conf_dict):
         self.bow_layer_po = FCLayer(self.bow_dim, None, "fc").ops()
         self.softmax_layer = FCLayer(2, "softmax", "cos_sim").ops()
 
-    @declarative
+    @to_static
     def forward(self, left, right):
         """
         Forward network

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

@@ -19,7 +19,7 @@
 import paddle
 import paddle.fluid as fluid
 from paddle.jit import ProgramTranslator
-from paddle.jit.api import declarative
+from paddle.jit.api import to_static
 
 
 @paddle.jit.to_static
@@ -28,7 +28,7 @@ def dyfunc_assert_variable(x):
     assert x_v
 
 
-@declarative
+@to_static
 def dyfunc_assert_non_variable(x=True):
     assert x
 

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

@@ -18,7 +18,7 @@
 
 import paddle
 import paddle.fluid as fluid
-from paddle.jit.api import declarative
+from paddle.jit.api import to_static
 from paddle.jit.dy2static.program_translator import ProgramTranslator
 from paddle.jit.dy2static.utils import Dygraph2StaticException
 
@@ -36,7 +36,7 @@ def test_error(self):
         if self.dyfunc:
             with self.assertRaisesRegex(Dygraph2StaticException, self.error):
                 ProgramTranslator().enable(True)
-                self.assertTrue(declarative(self.dyfunc)(self.x))
+                self.assertTrue(to_static(self.dyfunc)(self.x))
         paddle.fluid.dygraph.base._in_declarative_mode_ = False
         ProgramTranslator().enable(False)
 
@@ -223,7 +223,7 @@ def run_dygraph_mode(self):
 
     def run_static_mode(self):
         with fluid.dygraph.guard():
-            res = declarative(self.dygraph_func)(self.input)
+            res = to_static(self.dygraph_func)(self.input)
             return res.numpy()
 
     def test_transformed_static_result(self):

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

@@ -21,7 +21,7 @@
 import paddle
 import paddle.fluid as fluid
 from paddle.jit import ProgramTranslator
-from paddle.jit.api import declarative
+from paddle.jit.api import to_static
 from paddle.jit.dy2static import convert_to_static
 
 
@@ -138,7 +138,7 @@ def test_cache(self):
         self.assertTrue(id(static_func), id(cached_func))
 
 
-@declarative
+@to_static
 def sum_even_until_limit(max_len, limit):
     ret_sum = fluid.dygraph.to_variable(np.zeros((1)).astype('int32'))
     for i in range(max_len):
@@ -166,7 +166,7 @@ def test_output(self):
             ret = sum_even_until_limit(80, 10)
             self.assertEqual(ret.numpy(), 30)
 
-            ret = declarative(sum_under_while)(100)
+            ret = to_static(sum_under_while)(100)
             self.assertEqual(ret.numpy(), 5050)
 
 

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

@@ -17,40 +17,40 @@
 import numpy as np
 
 import paddle.fluid as fluid
-from paddle.jit.api import declarative
+from paddle.jit.api import to_static
 
 SEED = 2020
 np.random.seed(SEED)
 
 
-@declarative
+@to_static
 def test_bool_cast(x):
     x = fluid.dygraph.to_variable(x)
     x = bool(x)
     return x
 
 
-@declarative
+@to_static
 def test_int_cast(x):
     x = fluid.dygraph.to_variable(x)
     x = int(x)
     return x
 
 
-@declarative
+@to_static
 def test_float_cast(x):
     x = fluid.dygraph.to_variable(x)
     x = float(x)
     return x
 
 
-@declarative
+@to_static
 def test_not_var_cast(x):
     x = int(x)
     return x
 
 
-@declarative
+@to_static
 def test_mix_cast(x):
     x = fluid.dygraph.to_variable(x)
     x = int(x)

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

@@ -39,7 +39,7 @@
 import paddle
 from paddle.fluid.dygraph import to_variable
 from paddle.jit import ProgramTranslator
-from paddle.jit.api import declarative
+from paddle.jit.api import to_static
 from paddle.nn import BatchNorm
 
 # Note: Set True to eliminate randomness.
@@ -82,7 +82,7 @@ def __init__(self, input_channel, istrain=True):
                 input_channel
             )
 
-    @declarative
+    @to_static
     def forward(self, input_A, input_B):
         """
         Generator of GAN model.
@@ -133,7 +133,7 @@ def forward(self, input_A, input_B):
             g_loss,
         )
 
-    @declarative
+    @to_static
     def discriminatorA(self, input_A, input_B):
         """
         Discriminator A of GAN model.
@@ -143,7 +143,7 @@ def discriminatorA(self, input_A, input_B):
 
         return rec_B, fake_pool_rec_B
 
-    @declarative
+    @to_static
     def discriminatorB(self, input_A, input_B):
         """
         Discriminator B of GAN model.

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

@@ -23,7 +23,7 @@
 import paddle.fluid as fluid
 from paddle.fluid.dygraph import Layer, to_variable
 from paddle.jit import ProgramTranslator
-from paddle.jit.api import declarative
+from paddle.jit.api import to_static
 from paddle.jit.dy2static.program_translator import (
     ConcreteProgram,
     StaticFunction,
@@ -38,13 +38,12 @@ def __init__(self):
         super().__init__()
         self.linear = paddle.nn.Linear(10, 3)
 
-    @declarative(input_spec=[InputSpec(shape=[None, 10], dtype='float32')])
+    @to_static(input_spec=[InputSpec(shape=[None, 10], dtype='float32')])
     def forward(self, x, a=1, b=2):
         y = self.inner_function(x)
         return y
 
-    # `declarative` is not essential, add it to test for robustness.
-    @declarative
+    @to_static
     def inner_function(self, x):
         y = self.linear(x)
         return y
@@ -53,14 +52,14 @@ def add_func(self, x, y):
         z = x + y
         return z
 
-    @declarative(input_spec=[[InputSpec([None, 10]), InputSpec([None, 10])]])
+    @to_static(input_spec=[[InputSpec([None, 10]), InputSpec([None, 10])]])
     def func_with_list(self, l, int_val=1):
         x, y = l
         z = x + y
         z = z + int_val
         return z
 
-    @declarative(
+    @to_static(
         input_spec=[{'x': InputSpec([None, 10]), 'y': InputSpec([None, 10])}]
     )
     def func_with_dict(self, d):
@@ -70,7 +69,7 @@ def func_with_dict(self, d):
 
         return z
 
-    @declarative(
+    @to_static(
         input_spec=[
             [
                 InputSpec([None]),
@@ -135,8 +134,8 @@ def test_with_input_spec(self):
 
             # 3. we can decorate any method
             x_2 = to_variable(np.ones([4, 20]).astype('float32'))
-            # uses `declarative(func)` instead of `@declarative`
-            net.add_func = declarative(net.add_func)
+            # uses `to_static(func)` instead of `@to_static`
+            net.add_func = to_static(net.add_func)
             out = net.add_func(x_2, np.ones([20]).astype('float32'))
             self.assertTrue(len(net.add_func.program_cache) == 1)
 
@@ -164,7 +163,7 @@ def test_with_error(self):
 
             # 2. requires len(input_spec) <= len(args)
             with self.assertRaises(ValueError):
-                net.add_func = declarative(
+                net.add_func = to_static(
                     net.add_func,
                     input_spec=[
                         InputSpec([-1, 10]),
@@ -182,7 +181,7 @@ def test_concrete_program(self):
 
             net = SimpleNet()
             # We can get concrete_program by specificing InputSpec information. Faking input is no need.
-            net.add_func = declarative(
+            net.add_func = to_static(
                 net.add_func,
                 input_spec=[InputSpec([-1, 10]), InputSpec([-1, 10], name='y')],
             )
@@ -191,14 +190,14 @@ def test_concrete_program(self):
             self.assertTrue(cp1.inputs[-1].name == 'y')
 
             # generate another program
-            net.add_func = declarative(
+            net.add_func = to_static(
                 net.add_func,
                 input_spec=[InputSpec([10]), InputSpec([10], name='label')],
             )
             cp2 = net.add_func.concrete_program
             self.assertTrue(cp2.inputs[-1].shape == (10,))
             self.assertTrue(cp2.inputs[-1].name == 'label')
-            # Note(Aurelius84): New instance will be returned if we use `declarative(foo)` every time.
+            # Note(Aurelius84): New instance will be returned if we use `to_static(foo)` every time.
             # So number of cache program is 1.
             self.assertTrue(len(net.add_func.program_cache) == 1)
             self.assertTrue(cp1 != cp2)
@@ -219,7 +218,7 @@ def test_with_different_input(self):
             y_data = np.ones([10]).astype('float32') * 2
             z_data = np.ones([10]).astype('float32') * 2.2
 
-            foo = declarative(foo_func)
+            foo = to_static(foo_func)
 
             # [16, 10] + [10] (varbase)
             out_1 = foo(to_variable(x_data), to_variable(y_data))
@@ -260,7 +259,7 @@ def test_with_different_input(self):
 
     def test_get_concrete_program(self):
 
-        foo = declarative(foo_func)
+        foo = to_static(foo_func)
 
         # 1. specific InputSpec for `x`/`y`
         concrete_program_1 = foo.get_concrete_program(
@@ -349,7 +348,7 @@ def test_nest_input(self):
 
 class TestDeclarativeAPI(unittest.TestCase):
     def test_error(self):
-        func = declarative(dyfunc_to_variable)
+        func = to_static(dyfunc_to_variable)
 
         paddle.enable_static()
 
@@ -373,20 +372,20 @@ def setUp(self):
 
     def test_fake_input(self):
         net = SimpleNet()
-        net = declarative(net)
+        net = to_static(net)
         y = net(self.x)
         self.assertTrue(len(net.forward.program_cache) == 1)
 
     def test_input_spec(self):
         net = SimpleNet()
-        net = declarative(net, input_spec=[InputSpec([None, 8, 10])])
+        net = to_static(net, input_spec=[InputSpec([None, 8, 10])])
         self.assertTrue(len(net.forward.inputs) == 1)
         self.assertTrue(len(net.forward.program_cache) == 1)
         input_shape = net.forward.inputs[0].shape
         self.assertListEqual(list(input_shape), [-1, 8, 10])
 
         # redecorate
-        net = declarative(net, input_spec=[InputSpec([None, 16, 10])])
+        net = to_static(net, input_spec=[InputSpec([None, 16, 10])])
         input_shape = net.forward.inputs[0].shape
         self.assertListEqual(list(input_shape), [-1, 16, 10])