merge confict, test=develop

paddle/fluid/API.spec

@@ -13,6 +13,7 @@ paddle.fluid.name_scope (ArgSpec(args=['prefix'], varargs=None, keywords=None, d
 paddle.fluid.cuda_places (ArgSpec(args=['device_ids'], varargs=None, keywords=None, defaults=(None,)), ('document', '7d9a51fc9cf3c5245b5227080a8064c3'))
 paddle.fluid.cpu_places (ArgSpec(args=['device_count'], varargs=None, keywords=None, defaults=(None,)), ('document', '4c0cd83f0b401fc2ff84c70974e5d210'))
 paddle.fluid.cuda_pinned_places (ArgSpec(args=['device_count'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd0c3ebd813c39958c92b78e3eef7e912'))
+paddle.fluid.in_dygraph_mode (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'f06314a1cb30c96b5808dde2219c2dae'))
 paddle.fluid.Executor.__init__ (ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.Executor.close (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'f5369953dd0c443961cf79f7a00e1a03'))
 paddle.fluid.Executor.infer_from_dataset (ArgSpec(args=['self', 'program', 'dataset', 'scope', 'thread', 'debug', 'fetch_list', 'fetch_info', 'print_period'], varargs=None, keywords=None, defaults=(None, None, None, 0, False, None, None, 100)), ('document', '9c7decb955b9c4f718114179c8985581'))

python/paddle/fluid/dygraph/base.py

@@ -22,7 +22,7 @@ __all__ = ['enabled', 'guard', 'to_variable']
 
 
 def enabled():
-    return framework._in_dygraph_mode()
+    return framework.in_dygraph_mode()
 
 
 @signature_safe_contextmanager

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_dygraph_mode
+from ..framework import Parameter, in_dygraph_mode
 from ..param_attr import ParamAttr
 from .. import core
 from six.moves import zip

python/paddle/fluid/dygraph/nn.py

@@ -19,7 +19,7 @@ from six.moves import reduce
 from .. import core
 from ..layers import utils
 from . import layers
-from ..framework import Variable, _in_dygraph_mode, OpProtoHolder, Parameter
+from ..framework import Variable, in_dygraph_mode, OpProtoHolder, Parameter
 from ..param_attr import ParamAttr
 from ..initializer import Normal, Constant, NumpyArrayInitializer
 import numpy as np
@@ -2128,7 +2128,7 @@ class SequenceConv(layers.Layer):
                  bias_attr=None,
                  param_attr=None,
                  act=None):
-        assert not _in_dygraph_mode(
+        assert not in_dygraph_mode(
         ), "SequenceConv is not supported by dynamic graph mode yet!"
         super(SequenceConv, self).__init__(name_scope)
         self._num_filters = num_filters
@@ -2168,7 +2168,7 @@ class RowConv(layers.Layer):
                  future_context_size,
                  param_attr=None,
                  act=None):
-        assert not _in_dygraph_mode(
+        assert not in_dygraph_mode(
         ), "RowConv is not supported by dynamic graph mode yet!"
         super(RowConv, self).__init__(name_scope)
         self._act = act

python/paddle/fluid/framework.py

@@ -67,6 +67,7 @@ __all__ = [
     'cuda_places',
     'cpu_places',
     'cuda_pinned_places',
+    'in_dygraph_mode',
 ]
 
 EMPTY_VAR_NAME = core.kEmptyVarName()
@@ -79,7 +80,10 @@ _dygraph_tracer_ = None
 _dygraph_current_expected_place_ = None
 
 
-def _in_dygraph_mode():
+def in_dygraph_mode():
+    '''
+    Returns(bool): True if the program is running in dynamic graph mode
+    '''
     return _dygraph_tracer_ is not None
 
 
@@ -396,7 +400,7 @@ class Variable(object):
             if not isinstance(dtype, core.VarDesc.VarType):
                 dtype = convert_np_dtype_to_dtype_(dtype)
 
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             # record vars in tracer rather than blocks
             self._ivar = kwargs.get("ivar", None)
             if not self._ivar:
@@ -516,7 +520,7 @@ class Variable(object):
         Returns:
             str: The debug string.
         """
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             # TODO(panyx0718): add more dygraph debug info.
             return 'name %s, dtype: %s shape: %s' % (self.name, self.dtype,
                                                      self.shape)
@@ -549,42 +553,42 @@ class Variable(object):
 
     @property
     def stop_gradient(self):
-        if _in_dygraph_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_dygraph_mode():
+        if in_dygraph_mode():
             self._ivar.stop_gradient = s
         else:
             self._stop_gradient = s
 
     @property
     def persistable(self):
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             return self._ivar.persistable
         else:
             return self.desc.persistable()
 
     @persistable.setter
     def persistable(self, p):
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             return self._ivar.persistable
         else:
             self.desc.set_persistable(p)
 
     @property
     def name(self):
-        if _in_dygraph_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_dygraph_mode():
+        if in_dygraph_mode():
             self._ivar.name = new_name
         else:
             self.desc.set_name(new_name)
@@ -592,14 +596,14 @@ class Variable(object):
     @property
     def shape(self):
         # convert to tuple, make it as same as numpy API.
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             return self._ivar.shape
         else:
             return tuple(self.desc.shape())
 
     @property
     def dtype(self):
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             return self._ivar.dtype
         else:
             return self.desc.dtype()
@@ -611,7 +615,7 @@ class Variable(object):
 
     @property
     def type(self):
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             return self._ivar.dtype
         else:
             return self.desc.type()
@@ -930,7 +934,7 @@ class Operator(object):
                  inputs=None,
                  outputs=None,
                  attrs=None):
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             if type is None:
                 raise ValueError(
                     "`type` to initialized an Operator can not be None.")
@@ -1049,7 +1053,7 @@ 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_dygraph_mode():
+                        if not in_dygraph_mode():
                             arg.op = self
                     self.desc.set_output(out_proto.name, out_arg_names)
 
@@ -1095,7 +1099,7 @@ class Operator(object):
 
     @property
     def type(self):
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             return self.iop.type
         else:
             return self.desc.type()
@@ -1638,7 +1642,7 @@ class Block(object):
         Returns:
             Operator: the append Operator.
         """
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             op = Operator(
                 block=self,
                 desc=None,
@@ -1710,7 +1714,7 @@ class Block(object):
         return self.ops[start:end]
 
     def _prepend_op(self, *args, **kwargs):
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             op = Operator(
                 self,
                 None,

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_dygraph_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_dygraph_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_dygraph_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_dygraph_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_dygraph_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_dygraph_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_dygraph_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_dygraph_mode():
+        if not framework.in_dygraph_mode():
             var.op = op
         return op
 

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_dygraph_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

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_dygraph_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
@@ -54,7 +54,7 @@ class LayerHelperBase(object):
         Return Variable construct from value
         """
         if isinstance(value, np.ndarray):
-            assert _in_dygraph_mode(
+            assert in_dygraph_mode(
             ), "to_variable could only be called in dygraph mode"
 
             if not block:
@@ -302,7 +302,7 @@ class LayerHelperBase(object):
             param = self._create_weight_normalize(attr, shape, dtype)
             WeightNormParamAttr.params_with_weight_norm.append(param)
             return param
-        if _in_dygraph_mode():
+        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(
@@ -370,7 +370,7 @@ class LayerHelperBase(object):
                initializer: initializer to use
         """
         assert isinstance(var, Variable)
-        if _in_dygraph_mode():
+        if in_dygraph_mode():
             initializer(var, var.block)
         else:
             self.startup_program.global_block().create_var(

python/paddle/fluid/layers/nn.py

@@ -23,7 +23,7 @@ import os
 import inspect
 from ..layer_helper import LayerHelper
 from ..initializer import Normal, Constant, NumpyArrayInitializer
-from ..framework import Variable, OpProtoHolder, _in_dygraph_mode
+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_
@@ -3288,7 +3288,7 @@ def layer_norm(input,
         >>>                          dtype='float32')
         >>> x = fluid.layers.layer_norm(input=data, begin_norm_axis=1)
     """
-    assert _in_dygraph_mode(
+    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()
@@ -6454,7 +6454,7 @@ 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_dygraph_mode(), (
+    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)
@@ -9193,7 +9193,7 @@ def _elementwise_op(helper):
     op_type = helper.layer_type
     x = helper.kwargs.get('x', None)
     y = helper.kwargs.get('y', None)
-    if _in_dygraph_mode():
+    if in_dygraph_mode():
         x = base.to_variable(x)
         y = base.to_variable(y)
 

python/paddle/fluid/optimizer.py

@@ -55,7 +55,7 @@ class Optimizer(object):
     """
 
     def __init__(self, learning_rate, regularization=None, name=None):
-        if framework._in_dygraph_mode():
+        if framework.in_dygraph_mode():
             if not isinstance(learning_rate, float) and \
                     not isinstance(learning_rate, LearningRateDecay):
                 raise TypeError(
@@ -205,7 +205,7 @@ class Optimizer(object):
             name = self._name + "_" + name
         if (name in self._accumulators and
                 param.name in self._accumulators[name]):
-            if framework._in_dygraph_mode():
+            if framework.in_dygraph_mode():
                 return self._accumulators[name][param.name]
             raise Exception("Accumulator {} already exists for parameter {}".
                             format(name, param.name))
@@ -363,7 +363,7 @@ class Optimizer(object):
             See examples in `apply_gradients`.
         """
         self._dtype = loss.dtype
-        if framework._in_dygraph_mode():
+        if framework.in_dygraph_mode():
             if parameter_list is not None:
                 parameters = parameter_list
             else:
@@ -448,7 +448,7 @@ class Optimizer(object):
         Returns:
             list: A list of operators appended to the current program.
         """
-        if framework._in_dygraph_mode():
+        if framework.in_dygraph_mode():
             with program_guard(framework.default_main_program(),
                                framework.default_startup_program()):
                 optimize_ops = self._create_optimization_pass(params_grads)

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

@@ -16,7 +16,8 @@ from __future__ import print_function
 
 import unittest
 import paddle.fluid as fluid
-from paddle.fluid import Embedding
+import paddle.fluid.core as core
+from paddle.fluid.dygraph.nn import Embedding
 import paddle.fluid.framework as framework
 from paddle.fluid.optimizer import SGDOptimizer
 from paddle.fluid.dygraph.base import to_variable
@@ -43,7 +44,7 @@ class SimpleLSTMRNN(fluid.Layer):
         self.cell_array = []
         self.hidden_array = []
 
-    def _build_once(self, input_embedding, init_hidden=None, init_cell=None):
+    def build_once(self, input_embedding, init_hidden=None, init_cell=None):
         self.weight_1_arr = []
         self.weight_2_arr = []
         self.bias_arr = []
@@ -175,7 +176,7 @@ class PtbModel(fluid.Layer):
             default_initializer=fluid.initializer.UniformInitializer(
                 low=-self.init_scale, high=self.init_scale))
 
-    def _build_once(self, input, label, init_hidden, init_cell):
+    def build_once(self, input, label, init_hidden, init_cell):
         pass
 
     def forward(self, input, label, init_hidden, init_cell):
@@ -277,7 +278,8 @@ class TestDygraphPtbRnn(unittest.TestCase):
                 num_steps=num_steps,
                 init_scale=init_scale)
 
-            exe = fluid.Executor(fluid.CPUPlace())
+            exe = fluid.Executor(fluid.CPUPlace(
+            ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
             sgd = SGDOptimizer(learning_rate=1e-3)
             x = fluid.layers.data(
                 name="x", shape=[-1, num_steps, 1], dtype='int64')
@@ -331,18 +333,15 @@ class TestDygraphPtbRnn(unittest.TestCase):
                         static_param_updated[static_param_name_list[k -
                                                                     3]] = out[k]
 
-        self.assertTrue(np.allclose(static_loss_value, dy_loss.numpy()))
-        self.assertTrue(np.allclose(static_last_cell_value, last_cell.numpy()))
+        self.assertTrue(np.array_equal(static_loss_value, dy_loss.numpy()))
         self.assertTrue(
-            np.allclose(static_last_hidden_value, last_hidden.numpy()))
+            np.array_equal(static_last_cell_value, last_cell.numpy()))
+        self.assertTrue(
+            np.array_equal(static_last_hidden_value, last_hidden.numpy()))
         for key, value in six.iteritems(static_param_init):
-            # print("static_init name: {}, value {}".format(key, value))
-            # print("dy_init name: {}, value {}".format(key, dy_param_init[key]))
-            self.assertTrue(np.allclose(value, dy_param_init[key]))
+            self.assertTrue(np.array_equal(value, dy_param_init[key]))
         for key, value in six.iteritems(static_param_updated):
-            # print("static name: {}, value {}".format(key, value))
-            # print("dy name: {}, value {}".format(key, dy_param_updated[key]))
-            self.assertTrue(np.allclose(value, dy_param_updated[key]))
+            self.assertTrue(np.array_equal(value, dy_param_updated[key]))
 
 
 if __name__ == '__main__':