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Merge pull request #16561 from junjun315/move-api-to-root

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Move dygraph api to root
revert-16734-refine/test_imperative_transformer
lujun 6 years ago committed by GitHub
parent 95410652e8 14db0680c0
commit 9bd44b94da
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GPG Key ID: 4AEE18F83AFDEB23
26 changed files with 731 additions and 295 deletions
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1
paddle/fluid/API.spec
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@ -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.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.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.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.__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.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')) 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'))

2
python/paddle/fluid/__init__.py
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@ -66,6 +66,8 @@ from . import compiler
from .compiler import * from .compiler import *
from paddle.fluid.layers.math_op_patch import monkey_patch_variable from paddle.fluid.layers.math_op_patch import monkey_patch_variable
from . import install_check from . import install_check
from .dygraph.nn import *
from .dygraph.layers import *
Tensor = LoDTensor Tensor = LoDTensor

2
python/paddle/fluid/dygraph/base.py
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@ -22,7 +22,7 @@ __all__ = ['enabled', 'guard', 'to_variable']
def enabled(): def enabled():
return framework._in_dygraph_mode() return framework.in_dygraph_mode()
@signature_safe_contextmanager @signature_safe_contextmanager

10
python/paddle/fluid/dygraph/checkpoint.py
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@ -97,20 +97,12 @@ def load_persistables(vardict, dirname, filename=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
my_layer = layer(fluid.dygraph.Layer) my_layer = layer(fluid.Layer)
param_path = "./my_paddle_model" param_path = "./my_paddle_model"
param_dict = fluid.dygraph.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'] param_1 = param_dict['PtbModel_0.w_1']
or:
my_layer = layer(fluid.dygraph.Layer)
param_path = "./my_paddle_model"
filename = "model.file"
param_dict = fluid.dygraph.load_persistables(my_layer.state_dict(), param_path,
filename=filename)
param_1 = param_dict['PtbModel_0.w_1']
""" """
if isinstance(vardict, collections.OrderedDict): if isinstance(vardict, collections.OrderedDict):
return _load_var_from_file(vardict, dirname, filename) return _load_var_from_file(vardict, dirname, filename)

2
python/paddle/fluid/dygraph/layer_object_helper.py
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@ -16,7 +16,7 @@ from __future__ import print_function
import copy import copy
import six import six
from ..framework import Parameter, _in_dygraph_mode from ..framework import Parameter, in_dygraph_mode
from ..param_attr import ParamAttr from ..param_attr import ParamAttr
from .. import core from .. import core
from six.moves import zip from six.moves import zip

6
python/paddle/fluid/dygraph/layers.py
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@ -139,14 +139,14 @@ class Layer(core.Layer):
def clear_gradients(self): def clear_gradients(self):
for p in self.parameters(): for p in self.parameters():
p._clear_gradient() p.clear_gradient()
def _build_once(self, *args): def build_once(self, *args):
pass pass
def __call__(self, *inputs): def __call__(self, *inputs):
if not self._built: if not self._built:
self._build_once(*inputs) self.build_once(*inputs)
outputs = self.forward(*inputs) outputs = self.forward(*inputs)
self._built = True self._built = True

588
python/paddle/fluid/dygraph/nn.py
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62
python/paddle/fluid/framework.py
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@ -67,6 +67,7 @@ __all__ = [
'cuda_places', 'cuda_places',
'cpu_places', 'cpu_places',
'cuda_pinned_places', 'cuda_pinned_places',
'in_dygraph_mode',
] ]
EMPTY_VAR_NAME = core.kEmptyVarName() EMPTY_VAR_NAME = core.kEmptyVarName()
@ -79,7 +80,10 @@ _dygraph_tracer_ = None
_dygraph_current_expected_place_ = 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 return _dygraph_tracer_ is not None
@ -396,7 +400,7 @@ class Variable(object):
if not isinstance(dtype, core.VarDesc.VarType): if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype) dtype = convert_np_dtype_to_dtype_(dtype)
if _in_dygraph_mode(): if in_dygraph_mode():
# record vars in tracer rather than blocks # record vars in tracer rather than blocks
self._ivar = kwargs.get("ivar", None) self._ivar = kwargs.get("ivar", None)
if not self._ivar: if not self._ivar:
@ -482,21 +486,21 @@ class Variable(object):
self.block.vars[name] = self self.block.vars[name] = self
self.op = None self.op = None
self.stop_gradient = stop_gradient self._stop_gradient = stop_gradient
self.is_data = is_data self.is_data = is_data
def _numpy(self): def numpy(self):
new_ivar = self._ivar._copy_to(core.CPUPlace(), True) new_ivar = self._ivar._copy_to(core.CPUPlace(), True)
return np.array(new_ivar.value().get_tensor()) return np.array(new_ivar.value().get_tensor())
def _backward(self): def backward(self):
self._ivar._run_backward() self._ivar._run_backward()
def _gradient(self): def gradient(self):
new_ivar = self._ivar._grad_ivar()._copy_to(core.CPUPlace(), True) new_ivar = self._ivar._grad_ivar()._copy_to(core.CPUPlace(), True)
return np.array(new_ivar.value().get_tensor()) return np.array(new_ivar.value().get_tensor())
def _clear_gradient(self): def clear_gradient(self):
self._ivar._clear_gradient() self._ivar._clear_gradient()
def __str__(self): def __str__(self):
@ -516,7 +520,7 @@ class Variable(object):
Returns: Returns:
str: The debug string. str: The debug string.
""" """
if _in_dygraph_mode(): if in_dygraph_mode():
# TODO(panyx0718): add more dygraph debug info. # TODO(panyx0718): add more dygraph debug info.
return 'name %s, dtype: %s shape: %s' % (self.name, self.dtype, return 'name %s, dtype: %s shape: %s' % (self.name, self.dtype,
self.shape) self.shape)
@ -535,7 +539,7 @@ class Variable(object):
__repr__ = __str__ __repr__ = __str__
def _set_desc(self, input): def set_desc(self, input):
""" """
Set the variable description. Set the variable description.
@ -548,43 +552,43 @@ class Variable(object):
self.desc = input self.desc = input
@property @property
def _stop_gradient(self): def stop_gradient(self):
if _in_dygraph_mode(): if in_dygraph_mode():
return self._ivar.stop_gradient return self._ivar.stop_gradient
else: else:
return self.stop_gradient return self._stop_gradient
@_stop_gradient.setter @stop_gradient.setter
def _stop_gradient(self, s): def stop_gradient(self, s):
if _in_dygraph_mode(): if in_dygraph_mode():
self._ivar.stop_gradient = s self._ivar.stop_gradient = s
else: else:
self.stop_gradient = s self._stop_gradient = s
@property @property
def persistable(self): def persistable(self):
if _in_dygraph_mode(): if in_dygraph_mode():
return self._ivar.persistable return self._ivar.persistable
else: else:
return self.desc.persistable() return self.desc.persistable()
@persistable.setter @persistable.setter
def persistable(self, p): def persistable(self, p):
if _in_dygraph_mode(): if in_dygraph_mode():
return self._ivar.persistable return self._ivar.persistable
else: else:
self.desc.set_persistable(p) self.desc.set_persistable(p)
@property @property
def name(self): def name(self):
if _in_dygraph_mode(): if in_dygraph_mode():
return self._ivar.name return self._ivar.name
else: else:
return cpt.to_text(self.desc.name()) return cpt.to_text(self.desc.name())
@name.setter @name.setter
def name(self, new_name): def name(self, new_name):
if _in_dygraph_mode(): if in_dygraph_mode():
self._ivar.name = new_name self._ivar.name = new_name
else: else:
self.desc.set_name(new_name) self.desc.set_name(new_name)
@ -592,14 +596,14 @@ class Variable(object):
@property @property
def shape(self): def shape(self):
# convert to tuple, make it as same as numpy API. # convert to tuple, make it as same as numpy API.
if _in_dygraph_mode(): if in_dygraph_mode():
return self._ivar.shape return self._ivar.shape
else: else:
return tuple(self.desc.shape()) return tuple(self.desc.shape())
@property @property
def dtype(self): def dtype(self):
if _in_dygraph_mode(): if in_dygraph_mode():
return self._ivar.dtype return self._ivar.dtype
else: else:
return self.desc.dtype() return self.desc.dtype()
@ -611,7 +615,7 @@ class Variable(object):
@property @property
def type(self): def type(self):
if _in_dygraph_mode(): if in_dygraph_mode():
return self._ivar.dtype return self._ivar.dtype
else: else:
return self.desc.type() return self.desc.type()
@ -721,7 +725,7 @@ class Variable(object):
name=unique_name.generate(".".join(self.name)), name=unique_name.generate(".".join(self.name)),
dtype=self.dtype, dtype=self.dtype,
persistable=self.persistable, persistable=self.persistable,
stop_gradient=self._stop_gradient, ) stop_gradient=self.stop_gradient, )
else: else:
return self return self
@ -930,7 +934,7 @@ class Operator(object):
inputs=None, inputs=None,
outputs=None, outputs=None,
attrs=None): attrs=None):
if _in_dygraph_mode(): if in_dygraph_mode():
if type is None: if type is None:
raise ValueError( raise ValueError(
"`type` to initialized an Operator can not be None.") "`type` to initialized an Operator can not be None.")
@ -1049,7 +1053,7 @@ class Operator(object):
for arg in out_args: for arg in out_args:
out_arg_names.append(cpt.to_text(arg.name)) out_arg_names.append(cpt.to_text(arg.name))
# TODO(minqiyang): could we remove variable's op in static mode? # TODO(minqiyang): could we remove variable's op in static mode?
if not _in_dygraph_mode(): if not in_dygraph_mode():
arg.op = self arg.op = self
self.desc.set_output(out_proto.name, out_arg_names) self.desc.set_output(out_proto.name, out_arg_names)
@ -1095,7 +1099,7 @@ class Operator(object):
@property @property
def type(self): def type(self):
if _in_dygraph_mode(): if in_dygraph_mode():
return self.iop.type return self.iop.type
else: else:
return self.desc.type() return self.desc.type()
@ -1638,7 +1642,7 @@ class Block(object):
Returns: Returns:
Operator: the append Operator. Operator: the append Operator.
""" """
if _in_dygraph_mode(): if in_dygraph_mode():
op = Operator( op = Operator(
block=self, block=self,
desc=None, desc=None,
@ -1710,7 +1714,7 @@ class Block(object):
return self.ops[start:end] return self.ops[start:end]
def _prepend_op(self, *args, **kwargs): def _prepend_op(self, *args, **kwargs):
if _in_dygraph_mode(): if in_dygraph_mode():
op = Operator( op = Operator(
self, self,
None, None,

16
python/paddle/fluid/initializer.py
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@ -165,7 +165,7 @@ class ConstantInitializer(Initializer):
'force_cpu': self._force_cpu or force_init_on_cpu() 'force_cpu': self._force_cpu or force_init_on_cpu()
}, },
stop_gradient=True) stop_gradient=True)
if not framework._in_dygraph_mode(): if not framework.in_dygraph_mode():
var.op = op var.op = op
return op return op
@ -245,7 +245,7 @@ class UniformInitializer(Initializer):
attrs={"in_dtype": out_var.dtype, attrs={"in_dtype": out_var.dtype,
"out_dtype": var.dtype}) "out_dtype": var.dtype})
if not framework._in_dygraph_mode(): if not framework.in_dygraph_mode():
var.op = op var.op = op
return op return op
@ -324,7 +324,7 @@ class NormalInitializer(Initializer):
outputs={"Out": var}, outputs={"Out": var},
attrs={"in_dtype": out_var.dtype, attrs={"in_dtype": out_var.dtype,
"out_dtype": var.dtype}) "out_dtype": var.dtype})
if not framework._in_dygraph_mode(): if not framework.in_dygraph_mode():
var.op = op var.op = op
return op return op
@ -403,7 +403,7 @@ class TruncatedNormalInitializer(Initializer):
outputs={"Out": var}, outputs={"Out": var},
attrs={"in_dtype": out_var.dtype, attrs={"in_dtype": out_var.dtype,
"out_dtype": var.dtype}) "out_dtype": var.dtype})
if not framework._in_dygraph_mode(): if not framework.in_dygraph_mode():
var.op = op var.op = op
return op return op
@ -509,7 +509,7 @@ class XavierInitializer(Initializer):
"seed": self._seed "seed": self._seed
}, },
stop_gradient=True) stop_gradient=True)
if not framework._in_dygraph_mode(): if not framework.in_dygraph_mode():
var.op = op var.op = op
return op return op
@ -610,7 +610,7 @@ class MSRAInitializer(Initializer):
"seed": self._seed "seed": self._seed
}, },
stop_gradient=True) stop_gradient=True)
if not framework._in_dygraph_mode(): if not framework.in_dygraph_mode():
var.op = op var.op = op
return op return op
@ -709,7 +709,7 @@ class BilinearInitializer(Initializer):
'shape': list(shape), 'shape': list(shape),
value_name: values value_name: values
}) })
if not framework._in_dygraph_mode(): if not framework.in_dygraph_mode():
var.op = op var.op = op
return op return op
@ -768,7 +768,7 @@ class NumpyArrayInitializer(Initializer):
value_name: values value_name: values
}, },
stop_gradient=True) stop_gradient=True)
if not framework._in_dygraph_mode(): if not framework.in_dygraph_mode():
var.op = op var.op = op
return op return op

2
python/paddle/fluid/layer_helper.py
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@ -17,7 +17,7 @@ from __future__ import print_function
import copy import copy
import six 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 . import unique_name
from paddle.fluid.initializer import Constant, Xavier from paddle.fluid.initializer import Constant, Xavier
from .param_attr import ParamAttr from .param_attr import ParamAttr

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

44
python/paddle/fluid/layers/nn.py
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@ -23,7 +23,7 @@ import os
import inspect import inspect
from ..layer_helper import LayerHelper from ..layer_helper import LayerHelper
from ..initializer import Normal, Constant, NumpyArrayInitializer 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 ..dygraph import base
from ..param_attr import ParamAttr from ..param_attr import ParamAttr
from .layer_function_generator import autodoc, templatedoc, _generate_doc_string_ from .layer_function_generator import autodoc, templatedoc, _generate_doc_string_
@ -481,7 +481,7 @@ def dynamic_lstm(input,
forward, _ = fluid.layers.dynamic_lstm( forward, _ = fluid.layers.dynamic_lstm(
input=forward_proj, size=hidden_dim * 4, use_peepholes=False) input=forward_proj, size=hidden_dim * 4, use_peepholes=False)
""" """
assert _in_dygraph_mode( assert in_dygraph_mode(
) is not True, "please use lstm instead of dynamic_lstm in dygraph mode!" ) is not True, "please use lstm instead of dynamic_lstm in dygraph mode!"
assert bias_attr is not False, "bias_attr should not be False in dynamic_lstmp." assert bias_attr is not False, "bias_attr should not be False in dynamic_lstmp."
helper = LayerHelper('lstm', **locals()) helper = LayerHelper('lstm', **locals())
@ -867,7 +867,7 @@ def dynamic_lstmp(input,
proj_activation="tanh") proj_activation="tanh")
""" """
assert _in_dygraph_mode( assert in_dygraph_mode(
) is not True, "please use lstm instead of dynamic_lstmp in dygraph mode!" ) is not True, "please use lstm instead of dynamic_lstmp in dygraph mode!"
assert bias_attr is not False, "bias_attr should not be False in dynamic_lstmp." assert bias_attr is not False, "bias_attr should not be False in dynamic_lstmp."
@ -1041,7 +1041,7 @@ def dynamic_gru(input,
hidden = fluid.layers.dynamic_gru(input=x, size=hidden_dim) hidden = fluid.layers.dynamic_gru(input=x, size=hidden_dim)
""" """
assert _in_dygraph_mode( assert in_dygraph_mode(
) is not True, "please use gru instead of dynamic_gru in dygraph mode!" ) is not True, "please use gru instead of dynamic_gru in dygraph mode!"
helper = LayerHelper('gru', **locals()) helper = LayerHelper('gru', **locals())
@ -1760,7 +1760,7 @@ def sequence_conv(input,
Variable: output of sequence_conv Variable: output of sequence_conv
""" """
assert not _in_dygraph_mode(), ( assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.") "sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_conv', **locals()) helper = LayerHelper('sequence_conv', **locals())
dtype = helper.input_dtype() dtype = helper.input_dtype()
@ -1821,7 +1821,7 @@ def sequence_softmax(input, use_cudnn=False, name=None):
dtype='float32', lod_level=1) dtype='float32', lod_level=1)
x_sequence_softmax = fluid.layers.sequence_softmax(input=x) x_sequence_softmax = fluid.layers.sequence_softmax(input=x)
""" """
assert not _in_dygraph_mode(), ( assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.") "sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_softmax', **locals()) helper = LayerHelper('sequence_softmax', **locals())
dtype = helper.input_dtype() dtype = helper.input_dtype()
@ -2315,7 +2315,7 @@ def sequence_pool(input, pool_type, is_test=False):
last_x = fluid.layers.sequence_pool(input=x, pool_type='last') last_x = fluid.layers.sequence_pool(input=x, pool_type='last')
first_x = fluid.layers.sequence_pool(input=x, pool_type='first') first_x = fluid.layers.sequence_pool(input=x, pool_type='first')
""" """
assert not _in_dygraph_mode(), ( assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.") "sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_pool', **locals()) helper = LayerHelper('sequence_pool', **locals())
dtype = helper.input_dtype() dtype = helper.input_dtype()
@ -2356,7 +2356,7 @@ def sequence_concat(input, name=None):
out = fluid.layers.sequence_concat(input=[seq1, seq2, seq3]) out = fluid.layers.sequence_concat(input=[seq1, seq2, seq3])
""" """
assert not _in_dygraph_mode(), ( assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.") "sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_concat', **locals()) helper = LayerHelper('sequence_concat', **locals())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype()) out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
@ -2485,7 +2485,7 @@ def sequence_slice(input, offset, length, name=None):
subseqs = fluid.layers.sequence_slice(input=seqs, offset=offset, subseqs = fluid.layers.sequence_slice(input=seqs, offset=offset,
length=length) length=length)
""" """
assert not _in_dygraph_mode(), ( assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.") "sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper("sequence_slice", **locals()) helper = LayerHelper("sequence_slice", **locals())
dtype = helper.input_dtype() dtype = helper.input_dtype()
@ -3307,7 +3307,7 @@ def layer_norm(input,
>>> dtype='float32') >>> dtype='float32')
>>> x = fluid.layers.layer_norm(input=data, begin_norm_axis=1) >>> 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!" ) is not True, "please use FC instead of fc in dygraph mode!"
helper = LayerHelper('layer_norm', **locals()) helper = LayerHelper('layer_norm', **locals())
dtype = helper.input_dtype() dtype = helper.input_dtype()
@ -3946,7 +3946,7 @@ def sequence_expand(x, y, ref_level=-1, name=None):
dtype='float32', lod_level=1) dtype='float32', lod_level=1)
out = layers.sequence_expand(x=x, y=y, ref_level=0) out = layers.sequence_expand(x=x, y=y, ref_level=0)
""" """
assert not _in_dygraph_mode(), ( assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.") "sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_expand', input=x, **locals()) helper = LayerHelper('sequence_expand', input=x, **locals())
dtype = helper.input_dtype() dtype = helper.input_dtype()
@ -4014,7 +4014,7 @@ def sequence_expand_as(x, y, name=None):
dtype='float32', lod_level=1) dtype='float32', lod_level=1)
out = layers.sequence_expand_as(x=x, y=y) out = layers.sequence_expand_as(x=x, y=y)
""" """
assert not _in_dygraph_mode(), ( assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.") "sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_expand_as', input=x, **locals()) helper = LayerHelper('sequence_expand_as', input=x, **locals())
dtype = helper.input_dtype() dtype = helper.input_dtype()
@ -4062,7 +4062,7 @@ def sequence_pad(x, pad_value, maxlen=None, name=None):
out = fluid.layers.sequence_pad(x=x, pad_value=pad_value) out = fluid.layers.sequence_pad(x=x, pad_value=pad_value)
""" """
assert not _in_dygraph_mode(), ( assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.") "sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_pad', input=x, **locals()) helper = LayerHelper('sequence_pad', input=x, **locals())
dtype = helper.input_dtype() dtype = helper.input_dtype()
@ -4130,7 +4130,7 @@ def sequence_unpad(x, length, name=None):
out = fluid.layers.sequence_unpad(x=x, length=len) out = fluid.layers.sequence_unpad(x=x, length=len)
""" """
assert not _in_dygraph_mode(), ( assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.") "sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_unpad', input=x, **locals()) helper = LayerHelper('sequence_unpad', input=x, **locals())
dtype = helper.input_dtype() dtype = helper.input_dtype()
@ -5305,7 +5305,7 @@ def sequence_reshape(input, new_dim):
x = fluid.layers.data(shape=[5, 20], dtype='float32', lod_level=1) x = fluid.layers.data(shape=[5, 20], dtype='float32', lod_level=1)
x_reshaped = fluid.layers.sequence_reshape(input=x, new_dim=10) x_reshaped = fluid.layers.sequence_reshape(input=x, new_dim=10)
""" """
assert not _in_dygraph_mode(), ( assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.") "sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_reshape', **locals()) helper = LayerHelper('sequence_reshape', **locals())
out = helper.create_variable_for_type_inference(helper.input_dtype()) out = helper.create_variable_for_type_inference(helper.input_dtype())
@ -5841,7 +5841,7 @@ def im2sequence(input,
input=layer, stride=[1, 1], filter_size=[2, 2]) input=layer, stride=[1, 1], filter_size=[2, 2])
""" """
assert not _in_dygraph_mode(), ( assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.") "sequence layer is not supported in dygraph mode yet.")
if isinstance(filter_size, int): if isinstance(filter_size, int):
@ -6485,7 +6485,7 @@ def squeeze(input, axes, name=None):
x = layers.data(name='x', shape=[5, 1, 10]) x = layers.data(name='x', shape=[5, 1, 10])
y = layers.sequeeze(input=x, axes=[1]) 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.") "squeeze layer is not supported in dygraph mode yet.")
helper = LayerHelper("squeeze", **locals()) helper = LayerHelper("squeeze", **locals())
out = helper.create_variable_for_type_inference(dtype=input.dtype) out = helper.create_variable_for_type_inference(dtype=input.dtype)
@ -7631,7 +7631,7 @@ def sequence_scatter(input, index, updates, name=None):
output = fluid.layers.sequence_scatter(input, index, updates) output = fluid.layers.sequence_scatter(input, index, updates)
""" """
assert not _in_dygraph_mode(), ( assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.") "sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_scatter', **locals()) helper = LayerHelper('sequence_scatter', **locals())
dtype = helper.input_dtype() dtype = helper.input_dtype()
@ -8721,7 +8721,7 @@ def sequence_enumerate(input, win_size, pad_value=0, name=None):
x = fluid.layers.data(shape[30, 1], dtype='int32', lod_level=1) x = fluid.layers.data(shape[30, 1], dtype='int32', lod_level=1)
out = fluid.layers.sequence_enumerate(input=x, win_size=3, pad_value=0) out = fluid.layers.sequence_enumerate(input=x, win_size=3, pad_value=0)
""" """
assert not _in_dygraph_mode(), ( assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.") "sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_enumerate', **locals()) helper = LayerHelper('sequence_enumerate', **locals())
out = helper.create_variable_for_type_inference( out = helper.create_variable_for_type_inference(
@ -8762,7 +8762,7 @@ def sequence_mask(x, maxlen=None, dtype='int64', name=None):
Variable: The output sequence mask. Variable: The output sequence mask.
""" """
assert not _in_dygraph_mode(), ( assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.") "sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper('sequence_mask', **locals()) helper = LayerHelper('sequence_mask', **locals())
@ -9241,7 +9241,7 @@ def _elementwise_op(helper):
op_type = helper.layer_type op_type = helper.layer_type
x = helper.kwargs.get('x', None) x = helper.kwargs.get('x', None)
y = helper.kwargs.get('y', None) y = helper.kwargs.get('y', None)
if _in_dygraph_mode(): if in_dygraph_mode():
x = base.to_variable(x) x = base.to_variable(x)
y = base.to_variable(y) y = base.to_variable(y)
@ -9814,7 +9814,7 @@ def sequence_reverse(x, name=None):
Returns: Returns:
out(${y_type}): ${y_comment} out(${y_type}): ${y_comment}
""" """
assert not _in_dygraph_mode(), ( assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.") "sequence layer is not supported in dygraph mode yet.")
helper = LayerHelper("sequence_reverse", **locals()) helper = LayerHelper("sequence_reverse", **locals())
if name is None: if name is None:

8
python/paddle/fluid/optimizer.py
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@ -55,7 +55,7 @@ class Optimizer(object):
""" """
def __init__(self, learning_rate, regularization=None, name=None): 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 \ if not isinstance(learning_rate, float) and \
not isinstance(learning_rate, LearningRateDecay): not isinstance(learning_rate, LearningRateDecay):
raise TypeError( raise TypeError(
@ -205,7 +205,7 @@ class Optimizer(object):
name = self._name + "_" + name name = self._name + "_" + name
if (name in self._accumulators and if (name in self._accumulators and
param.name in self._accumulators[name]): param.name in self._accumulators[name]):
if framework._in_dygraph_mode(): if framework.in_dygraph_mode():
return self._accumulators[name][param.name] return self._accumulators[name][param.name]
raise Exception("Accumulator {} already exists for parameter {}". raise Exception("Accumulator {} already exists for parameter {}".
format(name, param.name)) format(name, param.name))
@ -363,7 +363,7 @@ class Optimizer(object):
See examples in `apply_gradients`. See examples in `apply_gradients`.
""" """
self._dtype = loss.dtype self._dtype = loss.dtype
if framework._in_dygraph_mode(): if framework.in_dygraph_mode():
if parameter_list is not None: if parameter_list is not None:
parameters = parameter_list parameters = parameter_list
else: else:
@ -448,7 +448,7 @@ class Optimizer(object):
Returns: Returns:
list: A list of operators appended to the current program. 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(), with program_guard(framework.default_main_program(),
framework.default_startup_program()): framework.default_startup_program()):
optimize_ops = self._create_optimization_pass(params_grads) optimize_ops = self._create_optimization_pass(params_grads)

10
python/paddle/fluid/tests/unittests/test_base_layer.py
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@ -18,7 +18,7 @@ import numpy as np
import paddle.fluid as fluid import paddle.fluid as fluid
class L1(fluid.dygraph.Layer): class L1(fluid.Layer):
def __init__(self, prefix): def __init__(self, prefix):
super(L1, self).__init__(prefix) super(L1, self).__init__(prefix)
self._param_attr = fluid.ParamAttr( self._param_attr = fluid.ParamAttr(
@ -32,7 +32,7 @@ class L1(fluid.dygraph.Layer):
return self.w1 + self.w2 return self.w1 + self.w2
class L2(fluid.dygraph.Layer): class L2(fluid.Layer):
def __init__(self, prefix): def __init__(self, prefix):
super(L2, self).__init__(prefix) super(L2, self).__init__(prefix)
self.layer1 = L1(self.full_name()) self.layer1 = L1(self.full_name())
@ -42,7 +42,7 @@ class L2(fluid.dygraph.Layer):
return self.layer1() + self.layer2() return self.layer1() + self.layer2()
class L3(fluid.dygraph.Layer): class L3(fluid.Layer):
def __init__(self, prefix): def __init__(self, prefix):
super(L3, self).__init__(prefix) super(L3, self).__init__(prefix)
self.layer1 = L2(self.full_name()) self.layer1 = L2(self.full_name())
@ -59,7 +59,7 @@ class TestBaseLayer(unittest.TestCase):
ret = l() ret = l()
self.assertEqual(l.w1.name, "test_one_level/L1_0.w_0") self.assertEqual(l.w1.name, "test_one_level/L1_0.w_0")
self.assertEqual(l.w2.name, "test_one_level/L1_0.w_1") self.assertEqual(l.w2.name, "test_one_level/L1_0.w_1")
self.assertTrue(np.allclose(ret._numpy(), 0.2 * np.ones([2, 2]))) self.assertTrue(np.allclose(ret.numpy(), 0.2 * np.ones([2, 2])))
def test_three_level(self): def test_three_level(self):
with fluid.dygraph.guard(): with fluid.dygraph.guard():
@ -72,7 +72,7 @@ class TestBaseLayer(unittest.TestCase):
self.assertEqual(names[3], "test_three_level/L3_0/L2_0/L1_1.w_1") self.assertEqual(names[3], "test_three_level/L3_0/L2_0/L1_1.w_1")
self.assertEqual(names[4], "test_three_level/L3_0/L2_1/L1_0.w_0") self.assertEqual(names[4], "test_three_level/L3_0/L2_1/L1_0.w_0")
self.assertEqual(names[5], "test_three_level/L3_0/L2_1/L1_0.w_1") self.assertEqual(names[5], "test_three_level/L3_0/L2_1/L1_0.w_1")
self.assertTrue(np.allclose(ret._numpy(), 0.8 * np.ones([2, 2]))) self.assertTrue(np.allclose(ret.numpy(), 0.8 * np.ones([2, 2])))
if __name__ == '__main__': if __name__ == '__main__':

56
python/paddle/fluid/tests/unittests/test_imperative_basic.py
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@ -18,11 +18,11 @@ import numpy as np
import paddle.fluid as fluid import paddle.fluid as fluid
from paddle.fluid import core from paddle.fluid import core
from paddle.fluid.dygraph.nn import FC from paddle.fluid import FC
from test_imperative_base import new_program_scope from test_imperative_base import new_program_scope
class MyLayer(fluid.dygraph.Layer): class MyLayer(fluid.Layer):
def __init__(self, name_scope): def __init__(self, name_scope):
super(MyLayer, self).__init__(name_scope) super(MyLayer, self).__init__(name_scope)
@ -34,7 +34,7 @@ class MyLayer(fluid.dygraph.Layer):
return [x] return [x]
class MyPyLayer(fluid.dygraph.PyLayer): class MyPyLayer(fluid.PyLayer):
def __init__(self): def __init__(self):
super(MyPyLayer, self).__init__() super(MyPyLayer, self).__init__()
@ -48,7 +48,7 @@ class MyPyLayer(fluid.dygraph.PyLayer):
return np.array(dout) * (1 - np.square(np.array(out))) return np.array(dout) * (1 - np.square(np.array(out)))
class MLP(fluid.dygraph.Layer): class MLP(fluid.Layer):
def __init__(self, name_scope): def __init__(self, name_scope):
super(MLP, self).__init__(name_scope) super(MLP, self).__init__(name_scope)
self._fc1 = FC(self.full_name(), self._fc1 = FC(self.full_name(),
@ -71,7 +71,7 @@ class MLP(fluid.dygraph.Layer):
return x return x
class SimpleRNNCell(fluid.dygraph.Layer): class SimpleRNNCell(fluid.Layer):
def __init__(self, name_scope, step_input_size, hidden_size, output_size, def __init__(self, name_scope, step_input_size, hidden_size, output_size,
param_attr): param_attr):
super(SimpleRNNCell, self).__init__(name_scope) super(SimpleRNNCell, self).__init__(name_scope)
@ -81,7 +81,7 @@ class SimpleRNNCell(fluid.dygraph.Layer):
self._dtype = core.VarDesc.VarType.FP32 self._dtype = core.VarDesc.VarType.FP32
self.param_attr = param_attr self.param_attr = param_attr
def _build_once(self, inputs, pre_hidden): def build_once(self, inputs, pre_hidden):
i2h_param_shape = [self.step_input_size, self.hidden_size] i2h_param_shape = [self.step_input_size, self.hidden_size]
h2h_param_shape = [self.hidden_size, self.hidden_size] h2h_param_shape = [self.hidden_size, self.hidden_size]
h2o_param_shape = [self.output_size, self.hidden_size] h2o_param_shape = [self.output_size, self.hidden_size]
@ -159,7 +159,7 @@ class SimpleRNNCell(fluid.dygraph.Layer):
return reduce_out, hidden return reduce_out, hidden
class SimpleRNN(fluid.dygraph.Layer): class SimpleRNN(fluid.Layer):
def __init__(self, name_scope): def __init__(self, name_scope):
super(SimpleRNN, self).__init__(name_scope) super(SimpleRNN, self).__init__(name_scope)
self.seq_len = 4 self.seq_len = 4
@ -200,22 +200,22 @@ class TestImperative(unittest.TestCase):
inputs.append(fluid.dygraph.base.to_variable(x)) inputs.append(fluid.dygraph.base.to_variable(x))
ret = fluid.layers.sums(inputs) ret = fluid.layers.sums(inputs)
loss = fluid.layers.reduce_sum(ret) loss = fluid.layers.reduce_sum(ret)
loss._backward() loss.backward()
self.assertTrue(np.allclose(ret._numpy(), x * 10)) self.assertTrue(np.allclose(ret.numpy(), x * 10))
self.assertTrue(np.allclose(inputs[0]._gradient(), x)) self.assertTrue(np.allclose(inputs[0].gradient(), x))
def test_layer(self): def test_layer(self):
with fluid.dygraph.guard(): with fluid.dygraph.guard():
cl = core.Layer() cl = core.Layer()
cl.forward([]) cl.forward([])
l = fluid.dygraph.Layer("l") l = fluid.Layer("l")
self.assertRaises(NotImplementedError, l.forward, []) self.assertRaises(NotImplementedError, l.forward, [])
def test_pylayer_func_id(self): def test_pylayer_func_id(self):
with fluid.dygraph.guard(): with fluid.dygraph.guard():
class PyLayer1(fluid.dygraph.PyLayer): class PyLayer1(fluid.PyLayer):
def __init__(self): def __init__(self):
super(PyLayer1, self).__init__() super(PyLayer1, self).__init__()
@ -227,7 +227,7 @@ class TestImperative(unittest.TestCase):
def backward(input): def backward(input):
return input return input
class PyLayer2(fluid.dygraph.PyLayer): class PyLayer2(fluid.PyLayer):
def __init__(self): def __init__(self):
super(PyLayer2, self).__init__() super(PyLayer2, self).__init__()
@ -257,9 +257,9 @@ class TestImperative(unittest.TestCase):
my_py_layer = MyPyLayer() my_py_layer = MyPyLayer()
var_inp = fluid.dygraph.base.to_variable(np_inp) var_inp = fluid.dygraph.base.to_variable(np_inp)
outs = my_py_layer(var_inp) outs = my_py_layer(var_inp)
dy_out = np.sum(outs[0]._numpy()) dy_out = np.sum(outs[0].numpy())
outs[0]._backward() outs[0].backward()
dy_grad = var_inp._gradient() dy_grad = var_inp.gradient()
with new_program_scope(): with new_program_scope():
inp = fluid.layers.data( inp = fluid.layers.data(
@ -287,9 +287,9 @@ class TestImperative(unittest.TestCase):
l = MyLayer("my_layer") l = MyLayer("my_layer")
x = l(var_inp)[0] x = l(var_inp)[0]
self.assertIsNotNone(x) self.assertIsNotNone(x)
dy_out = x._numpy() dy_out = x.numpy()
x._backward() x.backward()
dy_grad = l._x_for_debug._gradient() dy_grad = l._x_for_debug.gradient()
with new_program_scope(): with new_program_scope():
inp = fluid.layers.data( inp = fluid.layers.data(
@ -314,9 +314,9 @@ class TestImperative(unittest.TestCase):
var_inp = fluid.dygraph.base.to_variable(np_inp) var_inp = fluid.dygraph.base.to_variable(np_inp)
mlp = MLP("mlp") mlp = MLP("mlp")
out = mlp(var_inp) out = mlp(var_inp)
dy_out = out._numpy() dy_out = out.numpy()
out._backward() out.backward()
dy_grad = mlp._fc1._w._gradient() dy_grad = mlp._fc1._w.gradient()
with new_program_scope(): with new_program_scope():
inp = fluid.layers.data( inp = fluid.layers.data(
@ -358,7 +358,7 @@ class TestImperative(unittest.TestCase):
x = fluid.layers.elementwise_add(inp1, inp2) x = fluid.layers.elementwise_add(inp1, inp2)
else: else:
x = fluid.layers.elementwise_sub(inp1, inp2) x = fluid.layers.elementwise_sub(inp1, inp2)
dygraph_result = x._numpy() dygraph_result = x.numpy()
# static graph # static graph
with new_program_scope(): with new_program_scope():
@ -407,11 +407,11 @@ class TestImperative(unittest.TestCase):
var_inp = fluid.layers.reshape(var_inp, shape=[1, 4, 3]) var_inp = fluid.layers.reshape(var_inp, shape=[1, 4, 3])
simple_rnn = SimpleRNN("simple_rnn") simple_rnn = SimpleRNN("simple_rnn")
outs, pre_hiddens = simple_rnn.forward(var_inp) outs, pre_hiddens = simple_rnn.forward(var_inp)
dy_out = outs[3]._numpy() dy_out = outs[3].numpy()
outs[3]._backward() outs[3].backward()
dy_grad_h2o = simple_rnn._cell._h2o_w._gradient() dy_grad_h2o = simple_rnn._cell._h2o_w.gradient()
dy_grad_h2h = simple_rnn._cell._h2h_w._gradient() dy_grad_h2h = simple_rnn._cell._h2h_w.gradient()
dy_grad_i2h = simple_rnn._cell._i2h_w._gradient() dy_grad_i2h = simple_rnn._cell._i2h_w.gradient()
with new_program_scope(): with new_program_scope():
inp = fluid.layers.data( inp = fluid.layers.data(

14
python/paddle/fluid/tests/unittests/test_imperative_checkpoint.py
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@ -18,11 +18,11 @@ import numpy as np
import paddle import paddle
import paddle.fluid as fluid import paddle.fluid as fluid
from paddle.fluid.optimizer import SGDOptimizer from paddle.fluid.optimizer import SGDOptimizer
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC from paddle.fluid import Conv2D, Pool2D, FC
from paddle.fluid.dygraph.base import to_variable from paddle.fluid.dygraph.base import to_variable
class SimpleImgConvPool(fluid.dygraph.Layer): class SimpleImgConvPool(fluid.Layer):
def __init__(self, def __init__(self,
name_scope, name_scope,
num_channels, num_channels,
@ -71,7 +71,7 @@ class SimpleImgConvPool(fluid.dygraph.Layer):
return x return x
class MNIST(fluid.dygraph.Layer): class MNIST(fluid.Layer):
def __init__(self, name_scope): def __init__(self, name_scope):
super(MNIST, self).__init__(name_scope) super(MNIST, self).__init__(name_scope)
@ -125,21 +125,21 @@ class TestDygraphCheckpoint(unittest.TestCase):
img = to_variable(dy_x_data) img = to_variable(dy_x_data)
label = to_variable(y_data) label = to_variable(y_data)
label._stop_gradient = True label.stop_gradient = True
cost = mnist(img) cost = mnist(img)
loss = fluid.layers.cross_entropy(cost, label) loss = fluid.layers.cross_entropy(cost, label)
avg_loss = fluid.layers.mean(loss) avg_loss = fluid.layers.mean(loss)
dy_out = avg_loss._numpy() dy_out = avg_loss.numpy()
avg_loss._backward() avg_loss.backward()
sgd.minimize(avg_loss) sgd.minimize(avg_loss)
fluid.dygraph.save_persistables(mnist, "save_dir") fluid.dygraph.save_persistables(mnist, "save_dir")
mnist.clear_gradients() mnist.clear_gradients()
for param in mnist.parameters(): for param in mnist.parameters():
dy_param_init_value[param.name] = param._numpy() dy_param_init_value[param.name] = param.numpy()
mnist.load_dict( mnist.load_dict(
fluid.dygraph.load_persistables(mnist, "save_dir")) fluid.dygraph.load_persistables(mnist, "save_dir"))

31
python/paddle/fluid/tests/unittests/test_imperative_deepcf.py
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@ -32,11 +32,11 @@ NUM_BATCHES = int(os.environ.get('NUM_BATCHES', 5))
NUM_EPOCHES = int(os.environ.get('NUM_EPOCHES', 1)) NUM_EPOCHES = int(os.environ.get('NUM_EPOCHES', 1))
class DMF(fluid.dygraph.Layer): class DMF(fluid.Layer):
def __init__(self, name_scope): def __init__(self, name_scope):
super(DMF, self).__init__(name_scope) super(DMF, self).__init__(name_scope)
self._user_latent = fluid.dygraph.FC(self.full_name(), 256) self._user_latent = fluid.FC(self.full_name(), 256)
self._item_latent = fluid.dygraph.FC(self.full_name(), 256) self._item_latent = fluid.FC(self.full_name(), 256)
self._user_layers = [] self._user_layers = []
self._item_layers = [] self._item_layers = []
@ -45,13 +45,11 @@ class DMF(fluid.dygraph.Layer):
self._user_layers.append( self._user_layers.append(
self.add_sublayer( self.add_sublayer(
'user_layer_%d' % i, 'user_layer_%d' % i,
fluid.dygraph.FC( fluid.FC(self.full_name(), self._hid_sizes[i], act='relu')))
self.full_name(), self._hid_sizes[i], act='relu')))
self._item_layers.append( self._item_layers.append(
self.add_sublayer( self.add_sublayer(
'item_layer_%d' % i, 'item_layer_%d' % i,
fluid.dygraph.FC( fluid.FC(self.full_name(), self._hid_sizes[i], act='relu')))
self.full_name(), self._hid_sizes[i], act='relu')))
def forward(self, users, items): def forward(self, users, items):
users = self._user_latent(users) users = self._user_latent(users)
@ -63,19 +61,18 @@ class DMF(fluid.dygraph.Layer):
return fluid.layers.elementwise_mul(users, items) return fluid.layers.elementwise_mul(users, items)
class MLP(fluid.dygraph.Layer): class MLP(fluid.Layer):
def __init__(self, name_scope): def __init__(self, name_scope):
super(MLP, self).__init__(name_scope) super(MLP, self).__init__(name_scope)
self._user_latent = fluid.dygraph.FC(self.full_name(), 256) self._user_latent = fluid.FC(self.full_name(), 256)
self._item_latent = fluid.dygraph.FC(self.full_name(), 256) self._item_latent = fluid.FC(self.full_name(), 256)
self._match_layers = [] self._match_layers = []
self._hid_sizes = [128, 64] self._hid_sizes = [128, 64]
for i in range(len(self._hid_sizes)): for i in range(len(self._hid_sizes)):
self._match_layers.append( self._match_layers.append(
self.add_sublayer( self.add_sublayer(
'match_layer_%d' % i, 'match_layer_%d' % i,
fluid.dygraph.FC( fluid.FC(self.full_name(), self._hid_sizes[i], act='relu')))
self.full_name(), self._hid_sizes[i], act='relu')))
self._mat self._mat
def forward(self, users, items): def forward(self, users, items):
@ -88,7 +85,7 @@ class MLP(fluid.dygraph.Layer):
return match_vec return match_vec
class DeepCF(fluid.dygraph.Layer): class DeepCF(fluid.Layer):
def __init__(self, name_scope, num_users, num_items, matrix): def __init__(self, name_scope, num_users, num_items, matrix):
super(DeepCF, self).__init__(name_scope) super(DeepCF, self).__init__(name_scope)
self._num_users = num_users self._num_users = num_users
@ -99,11 +96,11 @@ class DeepCF(fluid.dygraph.Layer):
matrix.dtype, matrix.dtype,
is_bias=False, is_bias=False,
default_initializer=fluid.initializer.NumpyArrayInitializer(matrix)) default_initializer=fluid.initializer.NumpyArrayInitializer(matrix))
self._rating_matrix._stop_gradient = True self._rating_matrix.stop_gradient = True
self._mlp = MLP(self.full_name()) self._mlp = MLP(self.full_name())
self._dmf = DMF(self.full_name()) self._dmf = DMF(self.full_name())
self._match_fc = fluid.dygraph.FC(self.full_name(), 1, act='sigmoid') self._match_fc = fluid.FC(self.full_name(), 1, act='sigmoid')
def forward(self, users, items): def forward(self, users, items):
# users_emb = self._user_emb(users) # users_emb = self._user_emb(users)
@ -255,10 +252,10 @@ class TestDygraphDeepCF(unittest.TestCase):
fluid.layers.log_loss(prediction, fluid.layers.log_loss(prediction,
to_variable(labels_np[ to_variable(labels_np[
slice:slice + BATCH_SIZE]))) slice:slice + BATCH_SIZE])))
loss._backward() loss.backward()
adam.minimize(loss) adam.minimize(loss)
deepcf.clear_gradients() deepcf.clear_gradients()
dy_loss = loss._numpy() dy_loss = loss.numpy()
sys.stderr.write('dynamic loss: %s %s\n' % (slice, dy_loss)) sys.stderr.write('dynamic loss: %s %s\n' % (slice, dy_loss))
self.assertEqual(static_loss, dy_loss) self.assertEqual(static_loss, dy_loss)

18
python/paddle/fluid/tests/unittests/test_imperative_gan.py
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@ -22,12 +22,12 @@ import paddle
import paddle.fluid as fluid import paddle.fluid as fluid
import paddle.fluid.core as core import paddle.fluid.core as core
from paddle.fluid.optimizer import SGDOptimizer from paddle.fluid.optimizer import SGDOptimizer
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC from paddle.fluid import Conv2D, Pool2D, FC
from test_imperative_base import new_program_scope from test_imperative_base import new_program_scope
from paddle.fluid.dygraph.base import to_variable from paddle.fluid.dygraph.base import to_variable
class Discriminator(fluid.dygraph.Layer): class Discriminator(fluid.Layer):
def __init__(self, name_scope): def __init__(self, name_scope):
super(Discriminator, self).__init__(name_scope) super(Discriminator, self).__init__(name_scope)
self._fc1 = FC(self.full_name(), size=32, act='elu') self._fc1 = FC(self.full_name(), size=32, act='elu')
@ -38,7 +38,7 @@ class Discriminator(fluid.dygraph.Layer):
return self._fc2(x) return self._fc2(x)
class Generator(fluid.dygraph.Layer): class Generator(fluid.Layer):
def __init__(self, name_scope): def __init__(self, name_scope):
super(Generator, self).__init__(name_scope) super(Generator, self).__init__(name_scope)
self._fc1 = FC(self.full_name(), size=64, act='elu') self._fc1 = FC(self.full_name(), size=64, act='elu')
@ -150,7 +150,7 @@ class TestDygraphGAN(unittest.TestCase):
x=d_fake, label=to_variable(np.zeros([2, 1], np.float32)))) x=d_fake, label=to_variable(np.zeros([2, 1], np.float32))))
d_loss = d_loss_real + d_loss_fake d_loss = d_loss_real + d_loss_fake
d_loss._backward() d_loss.backward()
sgd.minimize(d_loss) sgd.minimize(d_loss)
discriminator.clear_gradients() discriminator.clear_gradients()
generator.clear_gradients() generator.clear_gradients()
@ -160,15 +160,15 @@ class TestDygraphGAN(unittest.TestCase):
g_loss = fluid.layers.reduce_mean( g_loss = fluid.layers.reduce_mean(
fluid.layers.sigmoid_cross_entropy_with_logits( fluid.layers.sigmoid_cross_entropy_with_logits(
x=d_fake, label=to_variable(np.ones([2, 1], np.float32)))) x=d_fake, label=to_variable(np.ones([2, 1], np.float32))))
g_loss._backward() g_loss.backward()
sgd.minimize(g_loss) sgd.minimize(g_loss)
for p in discriminator.parameters(): for p in discriminator.parameters():
dy_params[p.name] = p._numpy() dy_params[p.name] = p.numpy()
for p in generator.parameters(): for p in generator.parameters():
dy_params[p.name] = p._numpy() dy_params[p.name] = p.numpy()
dy_g_loss = g_loss._numpy() dy_g_loss = g_loss.numpy()
dy_d_loss = d_loss._numpy() dy_d_loss = d_loss.numpy()
self.assertEqual(dy_g_loss, static_g_loss) self.assertEqual(dy_g_loss, static_g_loss)
self.assertEqual(dy_d_loss, static_d_loss) self.assertEqual(dy_d_loss, static_d_loss)

13
python/paddle/fluid/tests/unittests/test_imperative_gnn.py
Unescape View File

@ -15,14 +15,12 @@
import contextlib import contextlib
import unittest import unittest
import numpy as np import numpy as np
import six
import sys import sys
import paddle import paddle
import paddle.fluid as fluid import paddle.fluid as fluid
import paddle.fluid.core as core import paddle.fluid.core as core
from paddle.fluid.optimizer import AdamOptimizer from paddle.fluid.optimizer import AdamOptimizer
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC
from test_imperative_base import new_program_scope from test_imperative_base import new_program_scope
from paddle.fluid.dygraph.base import to_variable from paddle.fluid.dygraph.base import to_variable
@ -31,7 +29,7 @@ def gen_data():
pass pass
class GraphConv(fluid.dygraph.Layer): class GraphConv(fluid.Layer):
def __init__(self, name_scope, in_features, out_features): def __init__(self, name_scope, in_features, out_features):
super(GraphConv, self).__init__(name_scope) super(GraphConv, self).__init__(name_scope)
@ -50,7 +48,7 @@ class GraphConv(fluid.dygraph.Layer):
return fluid.layers.matmul(adj, support) + self.bias return fluid.layers.matmul(adj, support) + self.bias
class GCN(fluid.dygraph.Layer): class GCN(fluid.Layer):
def __init__(self, name_scope, num_hidden): def __init__(self, name_scope, num_hidden):
super(GCN, self).__init__(name_scope) super(GCN, self).__init__(name_scope)
self.gc = GraphConv(self.full_name(), num_hidden, 32) self.gc = GraphConv(self.full_name(), num_hidden, 32)
@ -134,10 +132,9 @@ class TestDygraphGNN(unittest.TestCase):
loss = fluid.layers.reduce_sum(loss) loss = fluid.layers.reduce_sum(loss)
adam = AdamOptimizer(learning_rate=1e-3) adam = AdamOptimizer(learning_rate=1e-3)
adam.minimize(loss) adam.minimize(loss)
self.assertEqual(static_loss, loss._numpy()) self.assertEqual(static_loss, loss.numpy())
self.assertTrue( self.assertTrue(np.allclose(static_weight, model.gc.weight.numpy()))
np.allclose(static_weight, model.gc.weight._numpy())) sys.stderr.write('%s %s\n' % (static_loss, loss.numpy()))
sys.stderr.write('%s %s\n' % (static_loss, loss._numpy()))
if __name__ == '__main__': if __name__ == '__main__':

10
python/paddle/fluid/tests/unittests/test_imperative_mnist.py
Unescape View File

@ -128,25 +128,25 @@ class TestImperativeMnist(unittest.TestCase):
img = to_variable(dy_x_data) img = to_variable(dy_x_data)
label = to_variable(y_data) label = to_variable(y_data)
label._stop_gradient = True label.stop_gradient = True
cost = mnist(img) cost = mnist(img)
loss = fluid.layers.cross_entropy(cost, label) loss = fluid.layers.cross_entropy(cost, label)
avg_loss = fluid.layers.mean(loss) avg_loss = fluid.layers.mean(loss)
dy_out = avg_loss._numpy() dy_out = avg_loss.numpy()
if epoch == 0 and batch_id == 0: if epoch == 0 and batch_id == 0:
for param in mnist.parameters(): for param in mnist.parameters():
dy_param_init_value[param.name] = param._numpy() dy_param_init_value[param.name] = param.numpy()
avg_loss._backward() avg_loss.backward()
sgd.minimize(avg_loss) sgd.minimize(avg_loss)
mnist.clear_gradients() mnist.clear_gradients()
dy_param_value = {} dy_param_value = {}
for param in mnist.parameters(): for param in mnist.parameters():
dy_param_value[param.name] = param._numpy() dy_param_value[param.name] = param.numpy()
with new_program_scope(): with new_program_scope():
fluid.default_startup_program().random_seed = seed fluid.default_startup_program().random_seed = seed

10
python/paddle/fluid/tests/unittests/test_imperative_optimizer.py
Unescape View File

@ -28,7 +28,7 @@ from paddle.fluid.dygraph.base import to_variable
from test_imperative_base import new_program_scope from test_imperative_base import new_program_scope
class MLP(fluid.dygraph.Layer): class MLP(fluid.Layer):
def __init__(self, name_scope, param_attr=None, bias_attr=None): def __init__(self, name_scope, param_attr=None, bias_attr=None):
super(MLP, self).__init__(name_scope) super(MLP, self).__init__(name_scope)
@ -75,18 +75,18 @@ class TestImperativeOptimizerBase(unittest.TestCase):
cost = mlp(img) cost = mlp(img)
avg_loss = fluid.layers.reduce_mean(cost) avg_loss = fluid.layers.reduce_mean(cost)
dy_out = avg_loss._numpy() dy_out = avg_loss.numpy()
if batch_id == 0: if batch_id == 0:
for param in mlp.parameters(): for param in mlp.parameters():
dy_param_init_value[param.name] = param._numpy() dy_param_init_value[param.name] = param.numpy()
avg_loss._backward() avg_loss.backward()
optimizer.minimize(avg_loss) optimizer.minimize(avg_loss)
mlp.clear_gradients() mlp.clear_gradients()
dy_param_value = {} dy_param_value = {}
for param in mlp.parameters(): for param in mlp.parameters():
dy_param_value[param.name] = param._numpy() dy_param_value[param.name] = param.numpy()
with new_program_scope(): with new_program_scope():
fluid.default_startup_program().random_seed = seed fluid.default_startup_program().random_seed = seed

21
python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py
Unescape View File

@ -24,10 +24,9 @@ from paddle.fluid.dygraph.base import to_variable
from test_imperative_base import new_program_scope from test_imperative_base import new_program_scope
import numpy as np import numpy as np
import six import six
from paddle.fluid.backward import append_backward
class SimpleLSTMRNN(fluid.dygraph.Layer): class SimpleLSTMRNN(fluid.Layer):
def __init__(self, def __init__(self,
name_scope, name_scope,
hidden_size, hidden_size,
@ -45,7 +44,7 @@ class SimpleLSTMRNN(fluid.dygraph.Layer):
self.cell_array = [] self.cell_array = []
self.hidden_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_1_arr = []
self.weight_2_arr = [] self.weight_2_arr = []
self.bias_arr = [] self.bias_arr = []
@ -132,7 +131,7 @@ class SimpleLSTMRNN(fluid.dygraph.Layer):
return real_res, last_hidden, last_cell return real_res, last_hidden, last_cell
class PtbModel(fluid.dygraph.Layer): class PtbModel(fluid.Layer):
def __init__(self, def __init__(self,
name_scope, name_scope,
hidden_size, hidden_size,
@ -177,7 +176,7 @@ class PtbModel(fluid.dygraph.Layer):
default_initializer=fluid.initializer.UniformInitializer( default_initializer=fluid.initializer.UniformInitializer(
low=-self.init_scale, high=self.init_scale)) 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 pass
def forward(self, input, label, init_hidden, init_cell): def forward(self, input, label, init_hidden, init_cell):
@ -260,13 +259,13 @@ class TestDygraphPtbRnn(unittest.TestCase):
init_cell) init_cell)
if i == 0: if i == 0:
for param in ptb_model.parameters(): for param in ptb_model.parameters():
dy_param_init[param.name] = param._numpy() dy_param_init[param.name] = param.numpy()
dy_loss._backward() dy_loss.backward()
sgd.minimize(dy_loss) sgd.minimize(dy_loss)
ptb_model.clear_gradients() ptb_model.clear_gradients()
if i == batch_num - 1: if i == batch_num - 1:
for param in ptb_model.parameters(): for param in ptb_model.parameters():
dy_param_updated[param.name] = param._numpy() dy_param_updated[param.name] = param.numpy()
with new_program_scope(): with new_program_scope():
fluid.default_startup_program().random_seed = seed fluid.default_startup_program().random_seed = seed
@ -334,11 +333,11 @@ class TestDygraphPtbRnn(unittest.TestCase):
static_param_updated[static_param_name_list[k - static_param_updated[static_param_name_list[k -
3]] = out[k] 3]] = out[k]
self.assertTrue(np.array_equal(static_loss_value, dy_loss._numpy())) self.assertTrue(np.array_equal(static_loss_value, dy_loss.numpy()))
self.assertTrue( self.assertTrue(
np.array_equal(static_last_cell_value, last_cell._numpy())) np.array_equal(static_last_cell_value, last_cell.numpy()))
self.assertTrue( self.assertTrue(
np.array_equal(static_last_hidden_value, last_hidden._numpy())) np.array_equal(static_last_hidden_value, last_hidden.numpy()))
for key, value in six.iteritems(static_param_init): for key, value in six.iteritems(static_param_init):
self.assertTrue(np.array_equal(value, dy_param_init[key])) self.assertTrue(np.array_equal(value, dy_param_init[key]))
for key, value in six.iteritems(static_param_updated): for key, value in six.iteritems(static_param_updated):

20
python/paddle/fluid/tests/unittests/test_imperative_resnet.py
Unescape View File

@ -21,7 +21,7 @@ import paddle
import paddle.fluid as fluid import paddle.fluid as fluid
from paddle.fluid import core from paddle.fluid import core
from paddle.fluid.layer_helper import LayerHelper from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, FC from paddle.fluid import Conv2D, Pool2D, BatchNorm, FC
from paddle.fluid.dygraph.base import to_variable from paddle.fluid.dygraph.base import to_variable
from test_imperative_base import new_program_scope from test_imperative_base import new_program_scope
@ -68,7 +68,7 @@ def optimizer_setting(params):
return optimizer return optimizer
class ConvBNLayer(fluid.dygraph.Layer): class ConvBNLayer(fluid.Layer):
def __init__(self, def __init__(self,
name_scope, name_scope,
num_channels, num_channels,
@ -99,7 +99,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
return y return y
class BottleneckBlock(fluid.dygraph.Layer): class BottleneckBlock(fluid.Layer):
def __init__(self, def __init__(self,
name_scope, name_scope,
num_channels, num_channels,
@ -156,7 +156,7 @@ class BottleneckBlock(fluid.dygraph.Layer):
return layer_helper.append_activation(y) return layer_helper.append_activation(y)
class ResNet(fluid.dygraph.Layer): class ResNet(fluid.Layer):
def __init__(self, name_scope, layers=50, class_dim=102): def __init__(self, name_scope, layers=50, class_dim=102):
super(ResNet, self).__init__(name_scope) super(ResNet, self).__init__(name_scope)
@ -247,7 +247,7 @@ class TestDygraphResnet(unittest.TestCase):
dy_param_init_value = {} dy_param_init_value = {}
for param in resnet.parameters(): for param in resnet.parameters():
dy_param_init_value[param.name] = param._numpy() dy_param_init_value[param.name] = param.numpy()
for batch_id, data in enumerate(train_reader()): for batch_id, data in enumerate(train_reader()):
if batch_id >= batch_num: if batch_id >= batch_num:
@ -260,20 +260,20 @@ class TestDygraphResnet(unittest.TestCase):
img = to_variable(dy_x_data) img = to_variable(dy_x_data)
label = to_variable(y_data) label = to_variable(y_data)
label._stop_gradient = True label.stop_gradient = True
out = resnet(img) out = resnet(img)
loss = fluid.layers.cross_entropy(input=out, label=label) loss = fluid.layers.cross_entropy(input=out, label=label)
avg_loss = fluid.layers.mean(x=loss) avg_loss = fluid.layers.mean(x=loss)
dy_out = avg_loss._numpy() dy_out = avg_loss.numpy()
if batch_id == 0: if batch_id == 0:
for param in resnet.parameters(): for param in resnet.parameters():
if param.name not in dy_param_init_value: if param.name not in dy_param_init_value:
dy_param_init_value[param.name] = param._numpy() dy_param_init_value[param.name] = param.numpy()
avg_loss._backward() avg_loss.backward()
dy_grad_value = {} dy_grad_value = {}
for param in resnet.parameters(): for param in resnet.parameters():
@ -288,7 +288,7 @@ class TestDygraphResnet(unittest.TestCase):
dy_param_value = {} dy_param_value = {}
for param in resnet.parameters(): for param in resnet.parameters():
dy_param_value[param.name] = param._numpy() dy_param_value[param.name] = param.numpy()
with new_program_scope(): with new_program_scope():
fluid.default_startup_program().random_seed = seed fluid.default_startup_program().random_seed = seed

12
python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py
Unescape View File

@ -333,7 +333,7 @@ class TestImperativeResneXt(unittest.TestCase):
dy_param_init_value = {} dy_param_init_value = {}
for param in se_resnext.parameters(): for param in se_resnext.parameters():
dy_param_init_value[param.name] = param._numpy() dy_param_init_value[param.name] = param.numpy()
for epoch_id in range(epoch_num): for epoch_id in range(epoch_num):
for batch_id, data in enumerate(train_reader()): for batch_id, data in enumerate(train_reader()):
@ -349,19 +349,19 @@ class TestImperativeResneXt(unittest.TestCase):
img = to_variable(dy_x_data) img = to_variable(dy_x_data)
label = to_variable(y_data) label = to_variable(y_data)
label._stop_gradient = True label.stop_gradient = True
out = se_resnext(img) out = se_resnext(img)
loss = fluid.layers.cross_entropy(input=out, label=label) loss = fluid.layers.cross_entropy(input=out, label=label)
avg_loss = fluid.layers.mean(x=loss) avg_loss = fluid.layers.mean(x=loss)
dy_out = avg_loss._numpy() dy_out = avg_loss.numpy()
if batch_id == 0: if batch_id == 0:
for param in se_resnext.parameters(): for param in se_resnext.parameters():
if param.name not in dy_param_init_value: if param.name not in dy_param_init_value:
dy_param_init_value[param.name] = param._numpy() dy_param_init_value[param.name] = param.numpy()
avg_loss._backward() avg_loss.backward()
#dy_grad_value = {} #dy_grad_value = {}
#for param in se_resnext.parameters(): #for param in se_resnext.parameters():
@ -375,7 +375,7 @@ class TestImperativeResneXt(unittest.TestCase):
dy_param_value = {} dy_param_value = {}
for param in se_resnext.parameters(): for param in se_resnext.parameters():
dy_param_value[param.name] = param._numpy() dy_param_value[param.name] = param.numpy()
with new_program_scope(): with new_program_scope():
fluid.default_startup_program().random_seed = seed fluid.default_startup_program().random_seed = seed

18
python/paddle/fluid/tests/unittests/test_imperative_transformer.py
Unescape View File

@ -16,7 +16,8 @@ from __future__ import print_function
import unittest import unittest
import paddle.fluid as fluid import paddle.fluid as fluid
from paddle.fluid.dygraph import Embedding, LayerNorm, FC, to_variable, Layer, guard from paddle.fluid import Embedding, LayerNorm, FC, Layer
from paddle.fluid.dygraph import to_variable, guard
from test_imperative_base import new_program_scope from test_imperative_base import new_program_scope
from paddle.fluid import core from paddle.fluid import core
import numpy as np import numpy as np
@ -992,14 +993,14 @@ class TestDygraphTransformer(unittest.TestCase):
enc_inputs, dec_inputs, label, weights) enc_inputs, dec_inputs, label, weights)
if i == 0: if i == 0:
for param in transformer.parameters(): for param in transformer.parameters():
dy_param_init[param.name] = param._numpy() dy_param_init[param.name] = param.numpy()
dy_avg_cost._backward() dy_avg_cost.backward()
optimizer.minimize(dy_avg_cost) optimizer.minimize(dy_avg_cost)
transformer.clear_gradients() transformer.clear_gradients()
if i == batch_num - 1: if i == batch_num - 1:
for param in transformer.parameters(): for param in transformer.parameters():
dy_param_updated[param.name] = param._numpy() dy_param_updated[param.name] = param.numpy()
with new_program_scope(): with new_program_scope():
fluid.default_startup_program().random_seed = seed fluid.default_startup_program().random_seed = seed
@ -1076,13 +1077,14 @@ class TestDygraphTransformer(unittest.TestCase):
4]] = out[k] 4]] = out[k]
self.assertTrue( self.assertTrue(
np.array_equal(static_avg_cost_value, dy_avg_cost._numpy())) np.array_equal(static_avg_cost_value, dy_avg_cost.numpy()))
self.assertTrue( self.assertTrue(
np.array_equal(static_sum_cost_value, dy_sum_cost._numpy())) np.array_equal(static_sum_cost_value, dy_sum_cost.numpy()))
self.assertTrue( self.assertTrue(
np.array_equal(static_predict_value, dy_predict._numpy())) np.array_equal(static_predict_value, dy_predict.numpy()))
self.assertTrue( self.assertTrue(
np.array_equal(static_token_num_value, dy_token_num._numpy())) np.array_equal(static_token_num_value, dy_token_num.numpy()))
for key, value in six.iteritems(static_param_init): for key, value in six.iteritems(static_param_init):
self.assertTrue(np.array_equal(value, dy_param_init[key])) self.assertTrue(np.array_equal(value, dy_param_init[key]))
for key, value in six.iteritems(static_param_updated): for key, value in six.iteritems(static_param_updated):

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