Merge pull request #16523 from junjun315/tensor_api

move imperative to dygraph

python/paddle/fluid/__init__.py

@@ -34,7 +34,7 @@ from . import io
 from . import evaluator
 from . import initializer
 from . import layers
-from . import imperative
+from . import dygraph
 from . import contrib
 from . import nets
 from . import optimizer
@@ -71,7 +71,7 @@ __all__ = framework.__all__ + executor.__all__ + \
         'initializer',
         'layers',
         'contrib',
-        'imperative',
+        'dygraph',
         'transpiler',
         'nets',
         'optimizer',

python/paddle/fluid/imperative/base.py → python/paddle/fluid/dygraph/base.py

@@ -22,7 +22,7 @@ __all__ = ['enabled', 'guard', 'to_variable']
 
 
 def enabled():
-    return framework._in_imperative_mode()
+    return framework._in_dygraph_mode()
 
 
 @signature_safe_contextmanager
@@ -39,14 +39,14 @@ def guard(place=None):
 
     with framework.program_guard(train, startup):
         with framework.unique_name.guard():
-            with framework._imperative_guard(tracer):
-                with framework._imperative_place_guard(place):
+            with framework._dygraph_guard(tracer):
+                with framework._dygraph_place_guard(place):
                     yield
 
 
 def to_variable(value, block=None, name=None):
     if isinstance(value, np.ndarray):
-        assert enabled(), "to_variable could only be called in imperative mode"
+        assert enabled(), "to_variable could only be called in dygraph mode"
 
         if not block:
             block = framework.default_main_program().current_block()

python/paddle/fluid/imperative/checkpoint.py → python/paddle/fluid/dygraph/checkpoint.py

@@ -68,7 +68,7 @@ def save_persistables(vardict, dirname, filename=None):
             dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden,
                                                         init_cell)
             param_path = "./my_paddle_model"
-            fluid.imperative.checkpoint.save_persistables(ptb_model.state_dict(), dirname=param_path,
+            fluid.dygraph.save_persistables(ptb_model.state_dict(), dirname=param_path,
                                        layer=ptb_model)
     """
     if isinstance(vardict, collections.OrderedDict):
@@ -97,17 +97,17 @@ def load_persistables(vardict, dirname, filename=None):
 
     Examples:
         .. code-block:: python
-            my_layer = layer(fluid.imperative.Layer)
+            my_layer = layer(fluid.dygraph.Layer)
             param_path = "./my_paddle_model"
 
-            param_dict = fluid.imperative.checkpoint.load_persistables(my_layer.parameters(), param_path)
+            param_dict = fluid.dygraph.load_persistables(my_layer.parameters(), param_path)
             param_1 = param_dict['PtbModel_0.w_1']
 
             or:
-            my_layer = layer(fluid.imperative.Layer)
+            my_layer = layer(fluid.dygraph.Layer)
             param_path = "./my_paddle_model"
             filename = "model.file"
-            param_dict = fluid.imperative.checkpoint.load_persistables(my_layer.state_dict(), param_path,
+            param_dict = fluid.dygraph.load_persistables(my_layer.state_dict(), param_path,
                                                                        filename=filename)
             param_1 = param_dict['PtbModel_0.w_1']
 

python/paddle/fluid/imperative/layer_object_helper.py → python/paddle/fluid/dygraph/layer_object_helper.py

@@ -16,7 +16,7 @@ from __future__ import print_function
 
 import copy
 import six
-from ..framework import Parameter, _in_imperative_mode
+from ..framework import Parameter, _in_dygraph_mode
 from ..param_attr import ParamAttr
 from .. import core
 from six.moves import zip

python/paddle/fluid/imperative/layers.py → python/paddle/fluid/dygraph/layers.py

@@ -283,7 +283,7 @@ class PyLayer(core.PyLayer):
 
     @classmethod
     def __call__(cls, *inputs):
-        tracer = framework._imperative_tracer()
+        tracer = framework._dygraph_tracer()
         block = framework.default_main_program().current_block()
         ivar_inputs = [x._ivar for x in inputs]
 

python/paddle/fluid/imperative/nn.py → python/paddle/fluid/dygraph/nn.py

@@ -133,7 +133,7 @@ class Conv2D(layers.Layer):
             outputs={'Out': [pre_act]},
             attrs={'axis': 1})
 
-        # Currently, we don't support inplace in imperative mode
+        # Currently, we don't support inplace in dygraph mode
         return self._helper.append_activation(pre_act, act=self._act)
 
 
@@ -265,7 +265,7 @@ class FC(layers.Layer):
                 attrs={'axis': self._num_flatten_dims})
         else:
             pre_activation = pre_bias
-        # Currently, we don't support inplace in imperative mode
+        # Currently, we don't support inplace in dygraph mode
         return self._helper.append_activation(pre_activation, act=self._act)
 
 
@@ -387,7 +387,7 @@ class BatchNorm(layers.Layer):
                 "use_global_stats": self._use_global_stats
             })
 
-        # Currently, we don't support inplace in imperative mode
+        # Currently, we don't support inplace in dygraph mode
         return self._helper.append_activation(batch_norm_out, self._act)
 
 
@@ -426,7 +426,7 @@ class Embedding(layers.Layer):
 
           dict_size = len(dataset.ids)
           input = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32')
-          embedding = fluid.imperative.Embedding(size=[dict_size, 16])
+          embedding = fluid.dygraph.Embedding(size=[dict_size, 16])
           fc = embedding(input)
     """
 

python/paddle/fluid/imperative/tracer.py → python/paddle/fluid/dygraph/tracer.py

@@ -24,12 +24,12 @@ __all__ = ['Tracer']
 
 
 def release_op(op):
-    del framework._imperative_tracer()._ops[op._trace_id]
+    del framework._dygraph_tracer()._ops[op._trace_id]
 
 
 class Tracer(core.Tracer):
     """
-    Python wrapper of imperative tracer
+    Python wrapper of dygraph tracer
     """
 
     def __init__(self, block):

python/paddle/fluid/framework.py

@@ -75,20 +75,20 @@ GRAD_VAR_SUFFIX = core.kGradVarSuffix()
 ZERO_VAR_SUFFIX = core.kZeroVarSuffix()
 CONTROL_DEP_VAR_PREFIX = core.kControlDepVarName()
 
-_imperative_tracer_ = None
-_imperative_current_expected_place_ = None
+_dygraph_tracer_ = None
+_dygraph_current_expected_place_ = None
 
 
-def _in_imperative_mode():
-    return _imperative_tracer_ is not None
+def _in_dygraph_mode():
+    return _dygraph_tracer_ is not None
 
 
-def _imperative_tracer():
-    return _imperative_tracer_
+def _dygraph_tracer():
+    return _dygraph_tracer_
 
 
 def _current_expected_place():
-    return _imperative_current_expected_place_
+    return _dygraph_current_expected_place_
 
 
 def _cpu_num():
@@ -396,7 +396,7 @@ class Variable(object):
             if not isinstance(dtype, core.VarDesc.VarType):
                 dtype = convert_np_dtype_to_dtype_(dtype)
 
-        if _in_imperative_mode():
+        if _in_dygraph_mode():
             # record vars in tracer rather than blocks
             self._ivar = kwargs.get("ivar", None)
             if not self._ivar:
@@ -406,7 +406,7 @@ class Variable(object):
                     _current_expected_place(), stop_gradient, True
                     if persistable else False)
             if persistable:
-                _imperative_tracer().trace_var(name, self)
+                _dygraph_tracer().trace_var(name, self)
         else:
             self.error_clip = error_clip
 
@@ -515,8 +515,8 @@ class Variable(object):
         Returns:
             str: The debug string.
         """
-        if _in_imperative_mode():
-            # TODO(panyx0718): add more imperative debug info.
+        if _in_dygraph_mode():
+            # TODO(panyx0718): add more dygraph debug info.
             return 'name %s, dtype: %s shape: %s' % (self.name, self.dtype,
                                                      self.shape)
 
@@ -548,42 +548,42 @@ class Variable(object):
 
     @property
     def _stop_gradient(self):
-        if _in_imperative_mode():
+        if _in_dygraph_mode():
             return self._ivar.stop_gradient
         else:
             return self.stop_gradient
 
     @_stop_gradient.setter
     def _stop_gradient(self, s):
-        if _in_imperative_mode():
+        if _in_dygraph_mode():
             self._ivar.stop_gradient = s
         else:
             self.stop_gradient = s
 
     @property
     def persistable(self):
-        if _in_imperative_mode():
+        if _in_dygraph_mode():
             return self._ivar.persistable
         else:
             return self.desc.persistable()
 
     @persistable.setter
     def persistable(self, p):
-        if _in_imperative_mode():
+        if _in_dygraph_mode():
             return self._ivar.persistable
         else:
             self.desc.set_persistable(p)
 
     @property
     def name(self):
-        if _in_imperative_mode():
+        if _in_dygraph_mode():
             return self._ivar.name
         else:
             return cpt.to_text(self.desc.name())
 
     @name.setter
     def name(self, new_name):
-        if _in_imperative_mode():
+        if _in_dygraph_mode():
             self._ivar.name = new_name
         else:
             self.desc.set_name(new_name)
@@ -591,26 +591,26 @@ class Variable(object):
     @property
     def shape(self):
         # convert to tuple, make it as same as numpy API.
-        if _in_imperative_mode():
+        if _in_dygraph_mode():
             return self._ivar.shape
         else:
             return tuple(self.desc.shape())
 
     @property
     def dtype(self):
-        if _in_imperative_mode():
+        if _in_dygraph_mode():
             return self._ivar.dtype
         else:
             return self.desc.dtype()
 
     @property
     def lod_level(self):
-        # TODO(minqiyang): Support lod_level in imperative mode
+        # TODO(minqiyang): Support lod_level in dygraph mode
         return self.desc.lod_level()
 
     @property
     def type(self):
-        if _in_imperative_mode():
+        if _in_dygraph_mode():
             return self._ivar.dtype
         else:
             return self.desc.type()
@@ -918,7 +918,7 @@ class Operator(object):
                  inputs=None,
                  outputs=None,
                  attrs=None):
-        if _in_imperative_mode():
+        if _in_dygraph_mode():
             if type is None:
                 raise ValueError(
                     "`type` to initialized an Operator can not be None.")
@@ -1037,7 +1037,7 @@ class Operator(object):
                     for arg in out_args:
                         out_arg_names.append(cpt.to_text(arg.name))
                         # TODO(minqiyang): could we remove variable's op in static mode?
-                        if not _in_imperative_mode():
+                        if not _in_dygraph_mode():
                             arg.op = self
                     self.desc.set_output(out_proto.name, out_arg_names)
 
@@ -1083,7 +1083,7 @@ class Operator(object):
 
     @property
     def type(self):
-        if _in_imperative_mode():
+        if _in_dygraph_mode():
             return self.iop.type
         else:
             return self.desc.type()
@@ -1626,7 +1626,7 @@ class Block(object):
         Returns:
             Operator: the append Operator.
         """
-        if _in_imperative_mode():
+        if _in_dygraph_mode():
             op = Operator(
                 block=self,
                 desc=None,
@@ -1638,9 +1638,8 @@ class Block(object):
             # record ops in tracer rather than blocks
             #
             # TODO(minqiyang): add op stop_gradient support in static mode too.
-            # currently, we only support stop_gradient in imperative mode.
-            _imperative_tracer().trace_op(op,
-                                          kwargs.get("stop_gradient", False))
+            # currently, we only support stop_gradient in dygraph mode.
+            _dygraph_tracer().trace_op(op, kwargs.get("stop_gradient", False))
         else:
             op_desc = self.desc.append_op()
             op = Operator(
@@ -1699,7 +1698,7 @@ class Block(object):
         return self.ops[start:end]
 
     def _prepend_op(self, *args, **kwargs):
-        if _in_imperative_mode():
+        if _in_dygraph_mode():
             op = Operator(
                 self,
                 None,
@@ -1707,8 +1706,7 @@ class Block(object):
                 inputs=kwargs.get("inputs", None),
                 outputs=kwargs.get("outputs", None),
                 attrs=kwargs.get("attrs", None))
-            _imperative_tracer().trace_op(op,
-                                          kwargs.get("stop_gradient", False))
+            _dygraph_tracer().trace_op(op, kwargs.get("stop_gradient", False))
         else:
             op_desc = self.desc._prepend_op()
             op = Operator(
@@ -3511,22 +3509,22 @@ def _get_var(name, program=None):
 
 
 @signature_safe_contextmanager
-def _imperative_guard(tracer):
-    global _imperative_tracer_
-    tmp_trace = _imperative_tracer_
-    _imperative_tracer_ = tracer
+def _dygraph_guard(tracer):
+    global _dygraph_tracer_
+    tmp_trace = _dygraph_tracer_
+    _dygraph_tracer_ = tracer
 
     yield
 
-    _imperative_tracer_ = tmp_trace
+    _dygraph_tracer_ = tmp_trace
 
 
 @signature_safe_contextmanager
-def _imperative_place_guard(place):
-    global _imperative_current_expected_place_
-    tmp_place = _imperative_current_expected_place_
-    _imperative_current_expected_place_ = place
+def _dygraph_place_guard(place):
+    global _dygraph_current_expected_place_
+    tmp_place = _dygraph_current_expected_place_
+    _dygraph_current_expected_place_ = place
 
     yield
 
-    _imperative_current_expected_place_ = tmp_place
+    _dygraph_current_expected_place_ = tmp_place

python/paddle/fluid/initializer.py

@@ -165,7 +165,7 @@ class ConstantInitializer(Initializer):
                 'force_cpu': self._force_cpu or force_init_on_cpu()
             },
             stop_gradient=True)
-        if not framework._in_imperative_mode():
+        if not framework._in_dygraph_mode():
             var.op = op
         return op
 
@@ -245,7 +245,7 @@ class UniformInitializer(Initializer):
                 attrs={"in_dtype": out_var.dtype,
                        "out_dtype": var.dtype})
 
-        if not framework._in_imperative_mode():
+        if not framework._in_dygraph_mode():
             var.op = op
         return op
 
@@ -324,7 +324,7 @@ class NormalInitializer(Initializer):
                 outputs={"Out": var},
                 attrs={"in_dtype": out_var.dtype,
                        "out_dtype": var.dtype})
-        if not framework._in_imperative_mode():
+        if not framework._in_dygraph_mode():
             var.op = op
         return op
 
@@ -403,7 +403,7 @@ class TruncatedNormalInitializer(Initializer):
                 outputs={"Out": var},
                 attrs={"in_dtype": out_var.dtype,
                        "out_dtype": var.dtype})
-        if not framework._in_imperative_mode():
+        if not framework._in_dygraph_mode():
             var.op = op
         return op
 
@@ -509,7 +509,7 @@ class XavierInitializer(Initializer):
                     "seed": self._seed
                 },
                 stop_gradient=True)
-        if not framework._in_imperative_mode():
+        if not framework._in_dygraph_mode():
             var.op = op
         return op
 
@@ -610,7 +610,7 @@ class MSRAInitializer(Initializer):
                     "seed": self._seed
                 },
                 stop_gradient=True)
-        if not framework._in_imperative_mode():
+        if not framework._in_dygraph_mode():
             var.op = op
         return op
 
@@ -709,7 +709,7 @@ class BilinearInitializer(Initializer):
                 'shape': list(shape),
                 value_name: values
             })
-        if not framework._in_imperative_mode():
+        if not framework._in_dygraph_mode():
             var.op = op
         return op
 
@@ -768,7 +768,7 @@ class NumpyArrayInitializer(Initializer):
                 value_name: values
             },
             stop_gradient=True)
-        if not framework._in_imperative_mode():
+        if not framework._in_dygraph_mode():
             var.op = op
         return op
 

python/paddle/fluid/install_check.py

@@ -17,7 +17,7 @@ from .param_attr import ParamAttr
 from .initializer import Constant
 from . import layers
 from . import backward
-from .imperative import Layer, nn
+from .dygraph import Layer, nn
 from . import executor
 
 from . import core

python/paddle/fluid/layer_helper.py

@@ -17,7 +17,7 @@ from __future__ import print_function
 import copy
 import six
 
-from .framework import Parameter, dtype_is_floating, _in_imperative_mode
+from .framework import Parameter, dtype_is_floating, _in_dygraph_mode
 from . import unique_name
 from paddle.fluid.initializer import Constant, Xavier
 from .param_attr import ParamAttr
@@ -30,9 +30,9 @@ class LayerHelper(LayerHelperBase):
     def __init__(self, layer_type, **kwargs):
         self.kwargs = kwargs
         name = self.kwargs.get('name', None)
-        # TODO(panyx0718, minqiyang): imperative mode
+        # TODO(panyx0718, minqiyang): dygraph mode
         # can not use both `layer_type` and `name`. Deprecate LayerHelper
-        # and write a Helper for imperative mode.
+        # and write a Helper for dygraph mode.
         if name is None:
             self.kwargs['name'] = unique_name.generate(layer_type)
 

python/paddle/fluid/layer_helper_base.py

@@ -17,7 +17,7 @@ from __future__ import print_function
 import copy
 import numpy as np
 
-from .framework import Variable, default_main_program, default_startup_program, _in_imperative_mode, _current_expected_place
+from .framework import Variable, default_main_program, default_startup_program, _in_dygraph_mode, _current_expected_place
 from . import unique_name
 from .param_attr import ParamAttr, WeightNormParamAttr
 from . import core
@@ -54,8 +54,8 @@ class LayerHelperBase(object):
         Return Variable construct from value
         """
         if isinstance(value, np.ndarray):
-            assert _in_imperative_mode(
-            ), "to_variable could only be called in imperative mode"
+            assert _in_dygraph_mode(
+            ), "to_variable could only be called in dygraph mode"
 
             if not block:
                 block = default_main_program().current_block()
@@ -302,8 +302,8 @@ class LayerHelperBase(object):
             param = self._create_weight_normalize(attr, shape, dtype)
             WeightNormParamAttr.params_with_weight_norm.append(param)
             return param
-        if _in_imperative_mode():
-            # In imperative mode, we want the returned parameter to be
+        if _in_dygraph_mode():
+            # In dygraph mode, we want the returned parameter to be
             # initialized so that it can be used imperatively.
             return self.main_program.global_block().create_parameter(
                 dtype=dtype,
@@ -370,7 +370,7 @@ class LayerHelperBase(object):
                initializer: initializer to use
         """
         assert isinstance(var, Variable)
-        if _in_imperative_mode():
+        if _in_dygraph_mode():
             initializer(var, var.block)
         else:
             self.startup_program.global_block().create_var(

python/paddle/fluid/layers/nn.py

@@ -23,8 +23,8 @@ import os
 import inspect
 from ..layer_helper import LayerHelper
 from ..initializer import Normal, Constant, NumpyArrayInitializer
-from ..framework import Variable, OpProtoHolder, _in_imperative_mode
-from ..imperative import base
+from ..framework import Variable, OpProtoHolder, _in_dygraph_mode
+from ..dygraph import base
 from ..param_attr import ParamAttr
 from .layer_function_generator import autodoc, templatedoc, _generate_doc_string_
 from .tensor import concat, assign
@@ -32,7 +32,7 @@ from . import utils
 from .. import unique_name
 from functools import reduce
 from .. import core
-from ..imperative import layers
+from ..dygraph import layers
 
 __all__ = [
     'fc',
@@ -296,7 +296,6 @@ def fc(input,
           data_2 = fluid.layers.data(name="data_2", shape=[24, 36], dtype="float32")
           fc = fluid.layers.fc(input=[data_1, data_2], size=1000, act="tanh")
     """
-
     helper = LayerHelper("fc", **locals())
 
     dtype = helper.input_dtype()
@@ -3279,6 +3278,8 @@ def layer_norm(input,
         >>>                          dtype='float32')
         >>> x = fluid.layers.layer_norm(input=data, begin_norm_axis=1)
     """
+    assert _in_dygraph_mode(
+    ) is not True, "please use FC instead of fc in dygraph mode!"
     helper = LayerHelper('layer_norm', **locals())
     dtype = helper.input_dtype()
 
@@ -6405,8 +6406,8 @@ def squeeze(input, axes, name=None):
             x = layers.data(name='x', shape=[5, 1, 10])
             y = layers.sequeeze(input=x, axes=[1])
     """
-    assert not _in_imperative_mode(), (
-        "squeeze layer is not supported in imperative mode yet.")
+    assert not _in_dygraph_mode(), (
+        "squeeze layer is not supported in dygraph mode yet.")
     helper = LayerHelper("squeeze", **locals())
     out = helper.create_variable_for_type_inference(dtype=input.dtype)
     x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
@@ -9144,7 +9145,7 @@ def _elementwise_op(helper):
     op_type = helper.layer_type
     x = helper.kwargs.get('x', None)
     y = helper.kwargs.get('y', None)
-    if _in_imperative_mode():
+    if _in_dygraph_mode():
         x = base.to_variable(x)
         y = base.to_variable(y)
 

python/paddle/fluid/layers/tensor.py

@@ -20,7 +20,6 @@ from ..framework import convert_np_dtype_to_dtype_
 from ..framework import Variable
 from ..initializer import Constant, force_init_on_cpu
 from ..core import VarDesc
-from ..imperative import base as imperative_base
 from .layer_function_generator import templatedoc
 import numpy
 

python/paddle/fluid/optimizer.py

@@ -30,7 +30,6 @@ from .initializer import Constant
 from .layer_helper import LayerHelper
 from .layers import ops
 from .regularizer import append_regularization_ops
-from .imperative import base as imperative_base
 from paddle.fluid import core
 from paddle.fluid.layers import tensor
 from functools import reduce
@@ -169,7 +168,7 @@ class Optimizer(object):
             name = self._name + "_" + name
         if (name in self._accumulators and
                 param.name in self._accumulators[name]):
-            if framework._in_imperative_mode():
+            if framework._in_dygraph_mode():
                 return self._accumulators[name][param.name]
             raise Exception("Accumulator {} already exists for parameter {}".
                             format(name, param.name))
@@ -396,11 +395,11 @@ class Optimizer(object):
         """
         self._dtype = loss.dtype
         optimize_ops = []
-        if framework._in_imperative_mode():
+        if framework._in_dygraph_mode():
             if parameter_list is not None:
                 parameters = parameter_list
             else:
-                parameters = framework._imperative_tracer().all_parameters()
+                parameters = framework._dygraph_tracer().all_parameters()
 
             params_grads = []
             for param in parameters:

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

@@ -262,14 +262,14 @@ class OpTest(unittest.TestCase):
         if isinstance(value, tuple):
             data = value[0]
             lod = value[1]
-            v = fluid.imperative.base.to_variable(value=data)
+            v = fluid.dygraph.base.to_variable(value=data)
             v._ivar.value().get_tensor().set_recursive_sequence_lengths(lod)
             return v
         else:
-            return fluid.imperative.base.to_variable(value)
+            return fluid.dygraph.base.to_variable(value)
 
-    def _calc_imperative_output(self, place, parallel=False, no_check_set=None):
-        with fluid.imperative.base.guard(place=place):
+    def _calc_dygraph_output(self, place, parallel=False, no_check_set=None):
+        with fluid.dygraph.base.guard(place=place):
             block = fluid.default_main_program().global_block()
 
             # prepare input variable
@@ -316,7 +316,7 @@ class OpTest(unittest.TestCase):
 
             return outputs
 
-    def _calc_output(self, place, parallel=False, no_check_set=None):
+    def _calc_output(self, place, parallel=False, no_check_set=None, loss=None):
         program = Program()
         block = program.global_block()
         self._append_ops(block)
@@ -329,8 +329,14 @@ class OpTest(unittest.TestCase):
             use_cuda = False
             if isinstance(place, fluid.CUDAPlace(0)):
                 use_cuda = True
-            executor = fluid.ParallelExecutor(
-                use_cuda=use_cuda, loss_name=loss.name, main_program=program)
+            if loss:
+                executor = fluid.ParallelExecutor(
+                    use_cuda=use_cuda,
+                    loss_name=loss.name,
+                    main_program=program)
+            else:
+                executor = fluid.ParallelExecutor(
+                    use_cuda=use_cuda, main_program=program)
         else:
             executor = Executor(place)
 
@@ -364,9 +370,9 @@ class OpTest(unittest.TestCase):
                                 atol,
                                 no_check_set=None,
                                 equal_nan=False,
-                                check_imperative=False):
-        if check_imperative:
-            imperative_outs = self._calc_imperative_output(
+                                check_dygraph=False):
+        if check_dygraph:
+            dygraph_outs = self._calc_dygraph_output(
                 place, no_check_set=no_check_set)
         outs, fetch_list = self._calc_output(place, no_check_set=no_check_set)
 
@@ -393,8 +399,8 @@ class OpTest(unittest.TestCase):
                                          type(sub_out))
                 for item in sub_out:
                     sub_out_name, expect = item[0], item[1]
-                    if check_imperative:
-                        imperative_actual = imperative_outs[sub_out_name][0]
+                    if check_dygraph:
+                        imperative_actual = dygraph_outs[sub_out_name][0]
                         imperative_actual_t = np.array(
                             imperative_actual._ivar.value().get_tensor())
                     idx = find_actual(sub_out_name, fetch_list)
@@ -407,7 +413,7 @@ class OpTest(unittest.TestCase):
                             actual_t, expect_t, atol=atol, equal_nan=equal_nan),
                         "Output (" + sub_out_name + ") has diff at " +
                         str(place))
-                    if check_imperative:
+                    if check_dygraph:
                         self.assertTrue(
                             np.allclose(
                                 imperative_actual_t,
@@ -415,21 +421,21 @@ class OpTest(unittest.TestCase):
                                 atol=atol,
                                 equal_nan=equal_nan),
                             "Output (" + sub_out_name + ") has diff at " +
-                            str(place) + " in imperative mode")
+                            str(place) + " in dygraph mode")
                     if isinstance(expect, tuple):
                         self.assertListEqual(
                             actual.recursive_sequence_lengths(), expect[1],
                             "Output (" + sub_out_name +
                             ") has different lod at " + str(place))
-                    if check_imperative:
+                    if check_dygraph:
                         self.assertListEqual(
                             imperative_actual._ivar.value().get_tensor()
                             .recursive_sequence_lengths(), expect[1],
                             "Output (" + out_name + ") has different lod at " +
-                            str(place) + " in imperative mode")
+                            str(place) + " in dygraph mode")
             else:
-                if check_imperative:
-                    imperative_actual = imperative_outs[out_name][0]
+                if check_dygraph:
+                    imperative_actual = dygraph_outs[out_name][0]
                     imperative_actual_t = np.array(
                         imperative_actual._ivar.value().get_tensor())
                 idx = find_actual(out_name, fetch_list)
@@ -443,7 +449,7 @@ class OpTest(unittest.TestCase):
                     "Output (" + out_name + ") has diff at " + str(place) +
                     "\nExpect " + str(expect_t) + "\n" + "But Got" +
                     str(actual_t) + " in class " + self.__class__.__name__)
-                if check_imperative:
+                if check_dygraph:
                     self.assertTrue(
                         np.allclose(
                             imperative_actual_t,
@@ -458,12 +464,12 @@ class OpTest(unittest.TestCase):
                     self.assertListEqual(actual.recursive_sequence_lengths(),
                                          expect[1], "Output (" + out_name +
                                          ") has different lod at " + str(place))
-                    if check_imperative:
+                    if check_dygraph:
                         self.assertListEqual(
                             imperative_actual._ivar.value().get_tensor()
                             .recursive_sequence_lengths(), expect[1],
                             "Output (" + out_name + ") has different lod at " +
-                            str(place) + " in imperative mode")
+                            str(place) + " in dygraph mode")
 
     def _get_places(self):
         if self.dtype == np.float16:
@@ -490,11 +496,11 @@ class OpTest(unittest.TestCase):
                      atol=1e-5,
                      no_check_set=None,
                      equal_nan=False,
-                     check_imperative=False):
+                     check_dygraph=False):
         places = self._get_places()
         for place in places:
             self.check_output_with_place(place, atol, no_check_set, equal_nan,
-                                         check_imperative)
+                                         check_dygraph)
 
     def check_output_customized(self, checker):
         places = self._get_places()

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

@@ -18,7 +18,7 @@ import numpy as np
 import paddle.fluid as fluid
 
 
-class L1(fluid.imperative.Layer):
+class L1(fluid.dygraph.Layer):
     def __init__(self, prefix):
         super(L1, self).__init__(prefix)
         self._param_attr = fluid.ParamAttr(
@@ -32,7 +32,7 @@ class L1(fluid.imperative.Layer):
         return self.w1 + self.w2
 
 
-class L2(fluid.imperative.Layer):
+class L2(fluid.dygraph.Layer):
     def __init__(self, prefix):
         super(L2, self).__init__(prefix)
         self.layer1 = L1(self.full_name())
@@ -42,7 +42,7 @@ class L2(fluid.imperative.Layer):
         return self.layer1() + self.layer2()
 
 
-class L3(fluid.imperative.Layer):
+class L3(fluid.dygraph.Layer):
     def __init__(self, prefix):
         super(L3, self).__init__(prefix)
         self.layer1 = L2(self.full_name())
@@ -54,7 +54,7 @@ class L3(fluid.imperative.Layer):
 
 class TestBaseLayer(unittest.TestCase):
     def test_one_level(self):
-        with fluid.imperative.guard():
+        with fluid.dygraph.guard():
             l = L1('test_one_level')
             ret = l()
             self.assertEqual(l.w1.name, "test_one_level/L1_0.w_0")
@@ -62,7 +62,7 @@ class TestBaseLayer(unittest.TestCase):
             self.assertTrue(np.allclose(ret._numpy(), 0.2 * np.ones([2, 2])))
 
     def test_three_level(self):
-        with fluid.imperative.guard():
+        with fluid.dygraph.guard():
             l = L3('test_three_level')
             names = [p.name for p in l.parameters()]
             ret = l()

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

@@ -156,7 +156,7 @@ class TestGRUOp(OpTest):
         }
 
     def test_check_output(self):
-        self.check_output(atol=1e-8, check_imperative=True)
+        self.check_output(atol=1e-8, check_dygraph=True)
 
     def test_check_grad(self):
         self.check_grad(['Input', 'H0', 'Weight', 'Bias'], ['Hidden'])

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

@@ -18,11 +18,11 @@ import numpy as np
 
 import paddle.fluid as fluid
 from paddle.fluid import core
-from paddle.fluid.imperative.nn import FC
+from paddle.fluid.dygraph.nn import FC
 from test_imperative_base import new_program_scope
 
 
-class MyLayer(fluid.imperative.Layer):
+class MyLayer(fluid.dygraph.Layer):
     def __init__(self, name_scope):
         super(MyLayer, self).__init__(name_scope)
 
@@ -34,7 +34,7 @@ class MyLayer(fluid.imperative.Layer):
         return [x]
 
 
-class MyPyLayer(fluid.imperative.PyLayer):
+class MyPyLayer(fluid.dygraph.PyLayer):
     def __init__(self):
         super(MyPyLayer, self).__init__()
 
@@ -48,7 +48,7 @@ class MyPyLayer(fluid.imperative.PyLayer):
         return np.array(dout) * (1 - np.square(np.array(out)))
 
 
-class MLP(fluid.imperative.Layer):
+class MLP(fluid.dygraph.Layer):
     def __init__(self, name_scope):
         super(MLP, self).__init__(name_scope)
         self._fc1 = FC(self.full_name(),
@@ -71,7 +71,7 @@ class MLP(fluid.imperative.Layer):
         return x
 
 
-class SimpleRNNCell(fluid.imperative.Layer):
+class SimpleRNNCell(fluid.dygraph.Layer):
     def __init__(self, name_scope, step_input_size, hidden_size, output_size,
                  param_attr):
         super(SimpleRNNCell, self).__init__(name_scope)
@@ -159,7 +159,7 @@ class SimpleRNNCell(fluid.imperative.Layer):
         return reduce_out, hidden
 
 
-class SimpleRNN(fluid.imperative.Layer):
+class SimpleRNN(fluid.dygraph.Layer):
     def __init__(self, name_scope):
         super(SimpleRNN, self).__init__(name_scope)
         self.seq_len = 4
@@ -194,10 +194,10 @@ class SimpleRNN(fluid.imperative.Layer):
 class TestImperative(unittest.TestCase):
     def test_sum_op(self):
         x = np.ones([2, 2], np.float32)
-        with fluid.imperative.guard():
+        with fluid.dygraph.guard():
             inputs = []
             for _ in range(10):
-                inputs.append(fluid.imperative.base.to_variable(x))
+                inputs.append(fluid.dygraph.base.to_variable(x))
             ret = fluid.layers.sums(inputs)
             loss = fluid.layers.reduce_sum(ret)
             loss._backward()
@@ -205,17 +205,17 @@ class TestImperative(unittest.TestCase):
             self.assertTrue(np.allclose(inputs[0]._gradient(), x))
 
     def test_layer(self):
-        with fluid.imperative.guard():
+        with fluid.dygraph.guard():
             cl = core.Layer()
             cl.forward([])
-            l = fluid.imperative.Layer("l")
+            l = fluid.dygraph.Layer("l")
             self.assertRaises(NotImplementedError, l.forward, [])
 
     def test_pylayer_func_id(self):
 
-        with fluid.imperative.guard():
+        with fluid.dygraph.guard():
 
-            class PyLayer1(fluid.imperative.PyLayer):
+            class PyLayer1(fluid.dygraph.PyLayer):
                 def __init__(self):
                     super(PyLayer1, self).__init__()
 
@@ -227,7 +227,7 @@ class TestImperative(unittest.TestCase):
                 def backward(input):
                     return input
 
-            class PyLayer2(fluid.imperative.PyLayer):
+            class PyLayer2(fluid.dygraph.PyLayer):
                 def __init__(self):
                     super(PyLayer2, self).__init__()
 
@@ -241,21 +241,21 @@ class TestImperative(unittest.TestCase):
 
             py_layer_1 = PyLayer1()
             py_layer_2 = PyLayer2()
-            py_layer_1(fluid.imperative.base.to_variable(np.ones([2, 2])))
-            py_layer_2(fluid.imperative.base.to_variable(np.ones([2, 2])))
+            py_layer_1(fluid.dygraph.base.to_variable(np.ones([2, 2])))
+            py_layer_2(fluid.dygraph.base.to_variable(np.ones([2, 2])))
             id = py_layer_1.forward_id
             self.assertGreater(id, 0)
             self.assertEqual(py_layer_1.backward_id, id + 1)
             self.assertEqual(py_layer_2.forward_id, id + 2)
             self.assertEqual(py_layer_2.backward_id, id + 3)
-            py_layer_1(fluid.imperative.base.to_variable(np.ones([2, 2])))
+            py_layer_1(fluid.dygraph.base.to_variable(np.ones([2, 2])))
             self.assertEqual(py_layer_1.forward_id, id)
 
     def test_pylayer(self):
         np_inp = np.ones([2, 2], np.float32)
-        with fluid.imperative.guard():
+        with fluid.dygraph.guard():
             my_py_layer = MyPyLayer()
-            var_inp = fluid.imperative.base.to_variable(np_inp)
+            var_inp = fluid.dygraph.base.to_variable(np_inp)
             outs = my_py_layer(var_inp)
             dy_out = np.sum(outs[0]._numpy())
             outs[0]._backward()
@@ -282,8 +282,8 @@ class TestImperative(unittest.TestCase):
 
     def test_layer_in_out(self):
         np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32)
-        with fluid.imperative.guard():
-            var_inp = fluid.imperative.base.to_variable(np_inp)
+        with fluid.dygraph.guard():
+            var_inp = fluid.dygraph.base.to_variable(np_inp)
             l = MyLayer("my_layer")
             x = l(var_inp)[0]
             self.assertIsNotNone(x)
@@ -310,8 +310,8 @@ class TestImperative(unittest.TestCase):
 
     def test_mlp(self):
         np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
-        with fluid.imperative.guard():
-            var_inp = fluid.imperative.base.to_variable(np_inp)
+        with fluid.dygraph.guard():
+            var_inp = fluid.dygraph.base.to_variable(np_inp)
             mlp = MLP("mlp")
             out = mlp(var_inp)
             dy_out = out._numpy()
@@ -353,8 +353,8 @@ class TestImperative(unittest.TestCase):
                            [10.0, 11.0, 12.0]])
         np_inp = np_inp.reshape((1, 4, 3))
         np_inp = np_inp.astype(np.float32)
-        with fluid.imperative.guard():
-            var_inp = fluid.imperative.base.to_variable(np_inp)
+        with fluid.dygraph.guard():
+            var_inp = fluid.dygraph.base.to_variable(np_inp)
             var_inp = fluid.layers.reshape(var_inp, shape=[1, 4, 3])
             simple_rnn = SimpleRNN("simple_rnn")
             outs, pre_hiddens = simple_rnn.forward(var_inp)

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

@@ -18,11 +18,11 @@ import numpy as np
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.optimizer import SGDOptimizer
-from paddle.fluid.imperative.nn import Conv2D, Pool2D, FC
-from paddle.fluid.imperative.base import to_variable
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC
+from paddle.fluid.dygraph.base import to_variable
 
 
-class SimpleImgConvPool(fluid.imperative.Layer):
+class SimpleImgConvPool(fluid.dygraph.Layer):
     def __init__(self,
                  name_scope,
                  num_channels,
@@ -71,7 +71,7 @@ class SimpleImgConvPool(fluid.imperative.Layer):
         return x
 
 
-class MNIST(fluid.imperative.Layer):
+class MNIST(fluid.dygraph.Layer):
     def __init__(self, name_scope):
         super(MNIST, self).__init__(name_scope)
 
@@ -98,12 +98,12 @@ class MNIST(fluid.imperative.Layer):
         return x
 
 
-class TestImperativeCheckpoint(unittest.TestCase):
+class TestDygraphCheckpoint(unittest.TestCase):
     def save_load_persistables(self):
         seed = 90
         epoch_num = 1
 
-        with fluid.imperative.guard():
+        with fluid.dygraph.guard():
             fluid.default_startup_program().random_seed = seed
             fluid.default_main_program().random_seed = seed
 
@@ -135,14 +135,14 @@ class TestImperativeCheckpoint(unittest.TestCase):
 
                     avg_loss._backward()
                     sgd.minimize(avg_loss)
-                    fluid.imperative.save_persistables(mnist, "save_dir")
+                    fluid.dygraph.save_persistables(mnist, "save_dir")
                     mnist.clear_gradients()
 
                     for param in mnist.parameters():
                         dy_param_init_value[param.name] = param._numpy()
 
                     mnist.load_dict(
-                        fluid.imperative.load_persistables(mnist, "save_dir"))
+                        fluid.dygraph.load_persistables(mnist, "save_dir"))
 
                     restore = mnist.parameters()
 

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

@@ -22,7 +22,7 @@ import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from test_imperative_base import new_program_scope
-from paddle.fluid.imperative.base import to_variable
+from paddle.fluid.dygraph.base import to_variable
 
 # Can use Amusic dataset as the DeepCF describes.
 DATA_PATH = os.environ.get('DATA_PATH', '')
@@ -32,11 +32,11 @@ NUM_BATCHES = int(os.environ.get('NUM_BATCHES', 5))
 NUM_EPOCHES = int(os.environ.get('NUM_EPOCHES', 1))
 
 
-class DMF(fluid.imperative.Layer):
+class DMF(fluid.dygraph.Layer):
     def __init__(self, name_scope):
         super(DMF, self).__init__(name_scope)
-        self._user_latent = fluid.imperative.FC(self.full_name(), 256)
-        self._item_latent = fluid.imperative.FC(self.full_name(), 256)
+        self._user_latent = fluid.dygraph.FC(self.full_name(), 256)
+        self._item_latent = fluid.dygraph.FC(self.full_name(), 256)
 
         self._user_layers = []
         self._item_layers = []
@@ -45,12 +45,12 @@ class DMF(fluid.imperative.Layer):
             self._user_layers.append(
                 self.add_sublayer(
                     'user_layer_%d' % i,
-                    fluid.imperative.FC(
+                    fluid.dygraph.FC(
                         self.full_name(), self._hid_sizes[i], act='relu')))
             self._item_layers.append(
                 self.add_sublayer(
                     'item_layer_%d' % i,
-                    fluid.imperative.FC(
+                    fluid.dygraph.FC(
                         self.full_name(), self._hid_sizes[i], act='relu')))
 
     def forward(self, users, items):
@@ -63,18 +63,18 @@ class DMF(fluid.imperative.Layer):
         return fluid.layers.elementwise_mul(users, items)
 
 
-class MLP(fluid.imperative.Layer):
+class MLP(fluid.dygraph.Layer):
     def __init__(self, name_scope):
         super(MLP, self).__init__(name_scope)
-        self._user_latent = fluid.imperative.FC(self.full_name(), 256)
-        self._item_latent = fluid.imperative.FC(self.full_name(), 256)
+        self._user_latent = fluid.dygraph.FC(self.full_name(), 256)
+        self._item_latent = fluid.dygraph.FC(self.full_name(), 256)
         self._match_layers = []
         self._hid_sizes = [128, 64]
         for i in range(len(self._hid_sizes)):
             self._match_layers.append(
                 self.add_sublayer(
                     'match_layer_%d' % i,
-                    fluid.imperative.FC(
+                    fluid.dygraph.FC(
                         self.full_name(), self._hid_sizes[i], act='relu')))
         self._mat
 
@@ -88,7 +88,7 @@ class MLP(fluid.imperative.Layer):
         return match_vec
 
 
-class DeepCF(fluid.imperative.Layer):
+class DeepCF(fluid.dygraph.Layer):
     def __init__(self, name_scope, num_users, num_items, matrix):
         super(DeepCF, self).__init__(name_scope)
         self._num_users = num_users
@@ -103,7 +103,7 @@ class DeepCF(fluid.imperative.Layer):
 
         self._mlp = MLP(self.full_name())
         self._dmf = DMF(self.full_name())
-        self._match_fc = fluid.imperative.FC(self.full_name(), 1, act='sigmoid')
+        self._match_fc = fluid.dygraph.FC(self.full_name(), 1, act='sigmoid')
 
     def forward(self, users, items):
         # users_emb = self._user_emb(users)
@@ -191,7 +191,7 @@ def load_data(DATA_PATH):
            np.expand_dims(labels_np, -1), num_users, num_items, matrix
 
 
-class TestImperativeDeepCF(unittest.TestCase):
+class TestDygraphDeepCF(unittest.TestCase):
     def test_deefcf(self):
         seed = 90
         if DATA_PATH:
@@ -237,7 +237,7 @@ class TestImperativeDeepCF(unittest.TestCase):
                         fetch_list=[loss])[0]
                     sys.stderr.write('static loss %s\n' % static_loss)
 
-        with fluid.imperative.guard():
+        with fluid.dygraph.guard():
             fluid.default_startup_program().random_seed = seed
             fluid.default_main_program().random_seed = seed
 

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

@@ -22,12 +22,12 @@ import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.optimizer import SGDOptimizer
-from paddle.fluid.imperative.nn import Conv2D, Pool2D, FC
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC
 from test_imperative_base import new_program_scope
-from paddle.fluid.imperative.base import to_variable
+from paddle.fluid.dygraph.base import to_variable
 
 
-class Discriminator(fluid.imperative.Layer):
+class Discriminator(fluid.dygraph.Layer):
     def __init__(self, name_scope):
         super(Discriminator, self).__init__(name_scope)
         self._fc1 = FC(self.full_name(), size=32, act='elu')
@@ -38,7 +38,7 @@ class Discriminator(fluid.imperative.Layer):
         return self._fc2(x)
 
 
-class Generator(fluid.imperative.Layer):
+class Generator(fluid.dygraph.Layer):
     def __init__(self, name_scope):
         super(Generator, self).__init__(name_scope)
         self._fc1 = FC(self.full_name(), size=64, act='elu')
@@ -51,7 +51,7 @@ class Generator(fluid.imperative.Layer):
         return self._fc3(x)
 
 
-class TestImperativeGAN(unittest.TestCase):
+class TestDygraphGAN(unittest.TestCase):
     def test_gan_float32(self):
         seed = 90
 
@@ -130,7 +130,7 @@ class TestImperativeGAN(unittest.TestCase):
                     scope.find_var(param.name).get_tensor())
 
         dy_params = dict()
-        with fluid.imperative.guard():
+        with fluid.dygraph.guard():
             fluid.default_startup_program().random_seed = seed
             fluid.default_main_program().random_seed = seed
 

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

@@ -22,16 +22,16 @@ import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.optimizer import AdamOptimizer
-from paddle.fluid.imperative.nn import Conv2D, Pool2D, FC
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC
 from test_imperative_base import new_program_scope
-from paddle.fluid.imperative.base import to_variable
+from paddle.fluid.dygraph.base import to_variable
 
 
 def gen_data():
     pass
 
 
-class GraphConv(fluid.imperative.Layer):
+class GraphConv(fluid.dygraph.Layer):
     def __init__(self, name_scope, in_features, out_features):
         super(GraphConv, self).__init__(name_scope)
 
@@ -50,7 +50,7 @@ class GraphConv(fluid.imperative.Layer):
         return fluid.layers.matmul(adj, support) + self.bias
 
 
-class GCN(fluid.imperative.Layer):
+class GCN(fluid.dygraph.Layer):
     def __init__(self, name_scope, num_hidden):
         super(GCN, self).__init__(name_scope)
         self.gc = GraphConv(self.full_name(), num_hidden, 32)
@@ -61,7 +61,7 @@ class GCN(fluid.imperative.Layer):
         return self.gc2(x, adj)
 
 
-class TestImperativeGNN(unittest.TestCase):
+class TestDygraphGNN(unittest.TestCase):
     def test_gnn_float32(self):
         seed = 90
 
@@ -115,7 +115,7 @@ class TestImperativeGNN(unittest.TestCase):
             static_weight = np.array(
                 scope.find_var(model.gc.weight.name).get_tensor())
 
-        with fluid.imperative.guard():
+        with fluid.dygraph.guard():
             fluid.default_startup_program().random_seed = seed
             fluid.default_main_program().random_seed = seed
 

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

@@ -23,12 +23,12 @@ import paddle
 import paddle.fluid as fluid
 from paddle.fluid import core
 from paddle.fluid.optimizer import SGDOptimizer
-from paddle.fluid.imperative.nn import Conv2D, Pool2D, FC
-from paddle.fluid.imperative.base import to_variable
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC
+from paddle.fluid.dygraph.base import to_variable
 from test_imperative_base import new_program_scope
 
 
-class SimpleImgConvPool(fluid.imperative.Layer):
+class SimpleImgConvPool(fluid.dygraph.Layer):
     def __init__(self,
                  name_scope,
                  num_channels,
@@ -77,7 +77,7 @@ class SimpleImgConvPool(fluid.imperative.Layer):
         return x
 
 
-class MNIST(fluid.imperative.Layer):
+class MNIST(fluid.dygraph.Layer):
     def __init__(self, name_scope):
         super(MNIST, self).__init__(name_scope)
 
@@ -104,11 +104,11 @@ class MNIST(fluid.imperative.Layer):
         return x
 
 
-class TestImperativeMnist(unittest.TestCase):
+class TestDygraphMnist(unittest.TestCase):
     def test_mnist_float32(self):
         seed = 90
         epoch_num = 1
-        with fluid.imperative.guard():
+        with fluid.dygraph.guard():
             fluid.default_startup_program().random_seed = seed
             fluid.default_main_program().random_seed = seed
 

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

@@ -16,17 +16,17 @@ from __future__ import print_function
 
 import unittest
 import paddle.fluid as fluid
-from paddle.fluid.imperative.nn import Embedding
+from paddle.fluid.dygraph.nn import Embedding
 import paddle.fluid.framework as framework
 from paddle.fluid.optimizer import SGDOptimizer
-from paddle.fluid.imperative.base import to_variable
+from paddle.fluid.dygraph.base import to_variable
 from test_imperative_base import new_program_scope
 import numpy as np
 import six
 from paddle.fluid.backward import append_backward
 
 
-class SimpleLSTMRNN(fluid.imperative.Layer):
+class SimpleLSTMRNN(fluid.dygraph.Layer):
     def __init__(self,
                  name_scope,
                  hidden_size,
@@ -131,7 +131,7 @@ class SimpleLSTMRNN(fluid.imperative.Layer):
         return real_res, last_hidden, last_cell
 
 
-class PtbModel(fluid.imperative.Layer):
+class PtbModel(fluid.dygraph.Layer):
     def __init__(self,
                  name_scope,
                  hidden_size,
@@ -214,7 +214,7 @@ class PtbModel(fluid.imperative.Layer):
         return loss, last_hidden, last_cell
 
 
-class TestImperativePtbRnn(unittest.TestCase):
+class TestDygraphPtbRnn(unittest.TestCase):
     def test_ptb_rnn_cpu_float32(self):
         seed = 90
         hidden_size = 10
@@ -224,7 +224,7 @@ class TestImperativePtbRnn(unittest.TestCase):
         init_scale = 0.1
         batch_size = 4
 
-        with fluid.imperative.guard():
+        with fluid.dygraph.guard():
             fluid.default_startup_program().random_seed = seed
             fluid.default_main_program().random_seed = seed
             # TODO: marsyang1993 Change seed to

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

@@ -21,8 +21,8 @@ import paddle
 import paddle.fluid as fluid
 from paddle.fluid import core
 from paddle.fluid.layer_helper import LayerHelper
-from paddle.fluid.imperative.nn import Conv2D, Pool2D, BatchNorm, FC
-from paddle.fluid.imperative.base import to_variable
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, FC
+from paddle.fluid.dygraph.base import to_variable
 from test_imperative_base import new_program_scope
 
 batch_size = 8
@@ -57,7 +57,7 @@ def optimizer_setting(params):
         lr = []
         lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
         optimizer = fluid.optimizer.SGD(learning_rate=0.01)
-        # TODO(minqiyang): Add learning rate scheduler support to imperative mode
+        # TODO(minqiyang): Add learning rate scheduler support to dygraph mode
         #  optimizer = fluid.optimizer.Momentum(
     #  learning_rate=params["lr"],
     #  learning_rate=fluid.layers.piecewise_decay(
@@ -68,7 +68,7 @@ def optimizer_setting(params):
     return optimizer
 
 
-class ConvBNLayer(fluid.imperative.Layer):
+class ConvBNLayer(fluid.dygraph.Layer):
     def __init__(self,
                  name_scope,
                  num_channels,
@@ -99,7 +99,7 @@ class ConvBNLayer(fluid.imperative.Layer):
         return y
 
 
-class BottleneckBlock(fluid.imperative.Layer):
+class BottleneckBlock(fluid.dygraph.Layer):
     def __init__(self,
                  name_scope,
                  num_channels,
@@ -156,7 +156,7 @@ class BottleneckBlock(fluid.imperative.Layer):
         return layer_helper.append_activation(y)
 
 
-class ResNet(fluid.imperative.Layer):
+class ResNet(fluid.dygraph.Layer):
     def __init__(self, name_scope, layers=50, class_dim=102):
         super(ResNet, self).__init__(name_scope)
 
@@ -226,13 +226,13 @@ class ResNet(fluid.imperative.Layer):
         return y
 
 
-class TestImperativeResnet(unittest.TestCase):
+class TestDygraphResnet(unittest.TestCase):
     def test_resnet_float32(self):
         seed = 90
 
         batch_size = train_parameters["batch_size"]
         batch_num = 20
-        with fluid.imperative.guard():
+        with fluid.dygraph.guard():
             fluid.default_startup_program().random_seed = seed
             fluid.default_main_program().random_seed = seed
 

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

@@ -16,7 +16,7 @@ from __future__ import print_function
 
 import unittest
 import paddle.fluid as fluid
-from paddle.fluid.imperative import Embedding, LayerNorm, FC, to_variable, Layer, guard
+from paddle.fluid.dygraph import Embedding, LayerNorm, FC, to_variable, Layer, guard
 from test_imperative_base import new_program_scope
 from paddle.fluid import core
 import numpy as np
@@ -623,7 +623,7 @@ class PrepareEncoderDecoderLayer(Layer):
                 initializer=fluid.initializer.NumpyArrayInitializer(pos_inp),
                 trainable=False))
 
-        # use in imperative_mode to fit different length batch
+        # use in dygraph_mode to fit different length batch
         # self._pos_emb._w = to_variable(
         #     position_encoding_init(self._src_max_len, self._src_emb_dim))
 
@@ -946,7 +946,7 @@ class TransFormer(Layer):
         return sum_cost, avg_cost, predict, token_num
 
 
-class TestImperativeTransformer(unittest.TestCase):
+class TestDygraphTransformer(unittest.TestCase):
     def test_transformer_float32(self):
         seed = 90
         with guard():

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

@@ -29,8 +29,8 @@ from paddle.fluid import core
 from paddle.fluid.initializer import Constant
 import paddle.fluid.layers as layers
 from test_imperative_base import new_program_scope
-from paddle.fluid.imperative import nn
-from paddle.fluid.imperative import base
+from paddle.fluid.dygraph import nn
+from paddle.fluid.dygraph import base
 
 
 class LayerTest(unittest.TestCase):
@@ -68,7 +68,7 @@ class LayerTest(unittest.TestCase):
 
     @contextlib.contextmanager
     def dynamic_graph(self, force_to_use_cpu=False):
-        with fluid.imperative.guard(
+        with fluid.dygraph.guard(
                 self._get_place(force_to_use_cpu=force_to_use_cpu)):
             fluid.default_startup_program().random_seed = self.seed
             fluid.default_main_program().random_seed = self.seed

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

@@ -19,7 +19,6 @@ from paddle.fluid.framework import default_main_program, Program, convert_np_dty
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 import numpy as np
-from test_imperative_base import new_program_scope
 
 
 class TestVariable(unittest.TestCase):
@@ -153,7 +152,7 @@ class TestVariableImperative(unittest.TestCase):
         self.assertEqual([1, 1, 100], nw.shape)
 
     def test_slice(self):
-        with fluid.imperative.guard():
+        with fluid.dygraph.guard():
             self._test_slice()
 
 

python/setup.py.in

@@ -102,7 +102,7 @@ packages=['paddle',
           'paddle.reader',
           'paddle.distributed',
           'paddle.fluid',
-          'paddle.fluid.imperative',
+          'paddle.fluid.dygraph',
           'paddle.fluid.proto',
           'paddle.fluid.proto.profiler',
           'paddle.fluid.distributed',

tools/print_signatures.py

@@ -28,7 +28,7 @@ import hashlib
 
 member_dict = collections.OrderedDict()
 
-experimental_namespace = {"paddle.fluid.imperative"}
+experimental_namespace = {"paddle.fluid.dygraph"}
 
 
 def md5(doc):