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体验新版 GitCode,发现更多精彩内容 >>
提交
b754bf30
编写于
2月 27, 2019
作者:
M
minqiyang
提交者:
ceci3
3月 04, 2019
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Reset output var's pre_op pointer when op was destructed
上级
ac72bcd0
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
223 addition
and
185 deletion
+223
-185
paddle/fluid/imperative/layer.cc
paddle/fluid/imperative/layer.cc
+3
-2
paddle/fluid/imperative/layer.h
paddle/fluid/imperative/layer.h
+30
-3
paddle/fluid/imperative/tracer.cc
paddle/fluid/imperative/tracer.cc
+5
-2
paddle/fluid/pybind/pybind.cc
paddle/fluid/pybind/pybind.cc
+6
-0
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+1
-0
python/paddle/fluid/tests/unittests/test_imperative.py
python/paddle/fluid/tests/unittests/test_imperative.py
+178
-178
未找到文件。
paddle/fluid/imperative/layer.cc
浏览文件 @
b754bf30
...
...
@@ -158,9 +158,10 @@ class Autograd {
for
(
auto
it
:
candidate
->
pre_ops_
)
{
for
(
OpBase
*
pre_op
:
it
.
second
)
{
if
(
!
pre_op
)
continue
;
VLOG
(
5
)
<<
"op dep "
<<
candidate
->
op_desc_
->
Type
()
<<
" "
VLOG
(
5
)
<<
"op dep "
<<
candidate
->
op_desc_
->
Type
()
<<
"
trace id
"
<<
candidate
->
trace_id_
<<
" <---- "
<<
it
.
first
<<
" <---- "
<<
pre_op
->
op_desc_
->
Type
()
<<
" "
<<
pre_op
->
trace_id_
;
<<
pre_op
->
op_desc_
->
Type
()
<<
" trace id "
<<
pre_op
->
trace_id_
;
if
(
visited
.
find
(
pre_op
)
==
visited
.
end
())
{
visited
.
insert
(
pre_op
);
queue
.
push_back
(
pre_op
);
...
...
paddle/fluid/imperative/layer.h
浏览文件 @
b754bf30
...
...
@@ -128,23 +128,32 @@ class VarBase {
var_
(
var
),
grads_
(
grad
),
block_
(
nullptr
),
persistable_
(
false
),
stop_gradient_
(
stop_gradient
),
pre_op_
(
nullptr
),
pre_op_out_name_
(),
pre_op_out_idx_
(
-
1
)
{}
public:
virtual
~
VarBase
()
{
if
(
block_
)
{
// LOG(ERROR) << "remove var " << name_;
if
(
block_
&&
!
persistable_
)
{
block_
->
RemoveVar
(
name_
);
}
if
(
var_
)
{
delete
var_
;
var_
=
nullptr
;
}
if
(
grads_
)
{
delete
grads_
;
grads_
=
nullptr
;
}
pre_op_
=
nullptr
;
pre_op_out_idx_
=
-
1
;
}
inline
OpBase
*
PreOp
()
const
{
return
pre_op_
;
}
...
...
@@ -157,6 +166,14 @@ class VarBase {
void
RunBackward
();
inline
void
ResetPreOp
(
OpBase
*
op
)
{
if
(
op
==
pre_op_
)
{
// clear pre_op info when op equals to var's pre_op
pre_op_
=
nullptr
;
pre_op_out_idx_
=
-
1
;
}
}
void
TrackPreOp
(
OpBase
*
pre_op
,
const
std
::
string
&
pre_op_out_name
,
int
pre_op_out_idx
,
bool
pre_op_stop_gradient
)
{
pre_op_
=
pre_op
;
...
...
@@ -197,6 +214,7 @@ class VarBase {
VarBase
*
grads_
;
framework
::
BlockDesc
*
block_
;
bool
persistable_
;
private:
bool
stop_gradient_
;
...
...
@@ -219,13 +237,22 @@ class PYBIND11_HIDDEN OpBase {
backward_hooks_
()
{}
virtual
~
OpBase
()
{
for
(
framework
::
OpDesc
*
desc
:
grad_op_descs_
)
{
delete
desc
;
// reset all output vars' pre op
for
(
auto
iter
:
output_vars_
)
{
for
(
VarBase
*
var
:
iter
.
second
)
{
var
->
ResetPreOp
(
this
);
}
}
// remove op desc from block desc
if
(
block_
)
{
block_
->
RemoveOpInternal
(
op_desc_
);
}
// release resource
for
(
framework
::
OpDesc
*
desc
:
grad_op_descs_
)
{
delete
desc
;
}
}
std
::
map
<
std
::
string
,
std
::
vector
<
VarBase
*>>
ApplyGrad
();
...
...
paddle/fluid/imperative/tracer.cc
浏览文件 @
b754bf30
...
...
@@ -110,7 +110,8 @@ std::set<std::string> Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
std
::
map
<
std
::
string
,
VarBase
*>
vars
;
framework
::
OpDesc
*
op_desc
=
op
->
op_desc_
;
VLOG
(
3
)
<<
"tracer tracing "
<<
op_desc
->
Type
();
VLOG
(
3
)
<<
"tracer tracing "
<<
op_desc
->
Type
()
<<
" trace id "
<<
op
->
trace_id_
;
op_desc
->
InferShape
(
*
block
);
op_desc
->
InferVarType
(
block
);
...
...
@@ -133,11 +134,13 @@ std::set<std::string> Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
if
(
inp
->
PreOp
()
&&
!
inp
->
IsStopGradient
())
{
op
->
pre_ops_
[
it
.
first
].
push_back
(
inp
->
PreOp
());
op
->
pre_ops_out_idx_
[
it
.
first
].
push_back
(
inp
->
PreOpOutIdx
());
VLOG
(
3
)
<<
"add pre op "
<<
inp
->
PreOp
()
->
op_desc_
->
Type
();
}
else
{
op
->
pre_ops_
[
it
.
first
].
push_back
(
nullptr
);
}
VLOG
(
3
)
<<
"input vname "
<<
inp
->
var_desc_
->
Name
()
<<
" "
<<
inp
->
var_
->
IsInitialized
();
<<
inp
->
var_
->
IsInitialized
()
<<
" stop_gradient "
<<
inp
->
IsStopGradient
();
}
}
...
...
paddle/fluid/pybind/pybind.cc
浏览文件 @
b754bf30
...
...
@@ -188,6 +188,12 @@ PYBIND11_MODULE(core, m) {
self
.
block_
=
block
;
},
py
::
return_value_policy
::
reference
)
.
def_property
(
"persistable"
,
[](
const
imperative
::
VarBase
&
self
)
{
return
self
.
persistable_
;
},
[](
imperative
::
VarBase
&
self
,
const
bool
persistable
)
{
self
.
persistable_
=
persistable
;
})
.
def_property
(
"desc"
,
[](
const
imperative
::
VarBase
&
self
)
{
return
self
.
var_desc_
;
},
...
...
python/paddle/fluid/framework.py
浏览文件 @
b754bf30
...
...
@@ -395,6 +395,7 @@ class Variable(object):
self
.
_ivar
.
desc
=
self
.
desc
self
.
_ivar
.
block
=
block
.
desc
self
.
_ivar
.
name
=
name
self
.
_ivar
.
persistable
=
persistable
if
persistable
:
self
.
block
.
vars
[
name
]
=
self
else
:
...
...
python/paddle/fluid/tests/unittests/test_imperative.py
浏览文件 @
b754bf30
...
...
@@ -204,184 +204,184 @@ class TestImperative(unittest.TestCase):
self
.
assertTrue
(
np
.
allclose
(
ret
.
_numpy
(),
x
*
10
))
self
.
assertTrue
(
np
.
allclose
(
inputs
[
0
].
_gradient
(),
x
))
def
test_layer
(
self
):
with
fluid
.
imperative
.
guard
():
cl
=
core
.
Layer
()
cl
.
forward
([])
l
=
fluid
.
imperative
.
Layer
(
"l"
)
self
.
assertRaises
(
NotImplementedError
,
l
.
forward
,
[])
def
test_pylayer_func_id
(
self
):
with
fluid
.
imperative
.
guard
():
class
PyLayer1
(
fluid
.
imperative
.
PyLayer
):
def
__init__
(
self
):
super
(
PyLayer1
,
self
).
__init__
()
@
staticmethod
def
forward
(
input
):
return
input
@
staticmethod
def
backward
(
input
):
return
input
class
PyLayer2
(
fluid
.
imperative
.
PyLayer
):
def
__init__
(
self
):
super
(
PyLayer2
,
self
).
__init__
()
@
staticmethod
def
forward
(
input
):
return
input
@
staticmethod
def
backward
(
input
):
return
input
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
])))
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
])))
self
.
assertEqual
(
py_layer_1
.
forward_id
,
id
)
def
test_pylayer
(
self
):
np_inp
=
np
.
ones
([
2
,
2
],
np
.
float32
)
with
fluid
.
imperative
.
guard
():
my_py_layer
=
MyPyLayer
()
var_inp
=
fluid
.
imperative
.
base
.
to_variable
(
np_inp
)
outs
=
my_py_layer
(
var_inp
)
dy_out
=
np
.
sum
(
outs
[
0
].
_numpy
())
outs
[
0
].
_backward
()
dy_grad
=
var_inp
.
_gradient
()
with
new_program_scope
():
inp
=
fluid
.
layers
.
data
(
name
=
"inp"
,
shape
=
[
2
,
2
],
append_batch_size
=
False
)
# TODO(panyx0718): Paddle doesn't diff against data `inp`.
x1
=
inp
*
1
# TODO(panyx0718): If reduce_sum is skipped, the result is wrong.
x
=
fluid
.
layers
.
reduce_sum
(
fluid
.
layers
.
tanh
(
x1
))
param_grads
=
fluid
.
backward
.
append_backward
(
x
,
parameter_list
=
[
x1
.
name
])[
0
]
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
(
)
if
not
core
.
is_compiled_with_cuda
()
else
fluid
.
CUDAPlace
(
0
))
static_out
,
static_grad
=
exe
.
run
(
feed
=
{
inp
.
name
:
np_inp
},
fetch_list
=
[
x
.
name
,
param_grads
[
1
].
name
])
self
.
assertTrue
(
np
.
allclose
(
dy_out
,
static_out
))
self
.
assertTrue
(
np
.
allclose
(
dy_grad
,
static_grad
))
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
)
l
=
MyLayer
(
"my_layer"
)
x
=
l
(
var_inp
)[
0
]
self
.
assertIsNotNone
(
x
)
dy_out
=
x
.
_numpy
()
x
.
_backward
()
dy_grad
=
l
.
_x_for_debug
.
_gradient
()
with
new_program_scope
():
inp
=
fluid
.
layers
.
data
(
name
=
"inp"
,
shape
=
[
3
],
append_batch_size
=
False
)
l
=
MyLayer
(
"my_layer"
)
x
=
l
(
inp
)[
0
]
param_grads
=
fluid
.
backward
.
append_backward
(
x
,
parameter_list
=
[
l
.
_x_for_debug
.
name
])[
0
]
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
(
)
if
not
core
.
is_compiled_with_cuda
()
else
fluid
.
CUDAPlace
(
0
))
static_out
,
static_grad
=
exe
.
run
(
feed
=
{
inp
.
name
:
np_inp
},
fetch_list
=
[
x
.
name
,
param_grads
[
1
].
name
])
self
.
assertTrue
(
np
.
allclose
(
dy_out
,
static_out
))
self
.
assertTrue
(
np
.
allclose
(
dy_grad
,
static_grad
))
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
)
mlp
=
MLP
(
"mlp"
)
out
=
mlp
(
var_inp
)
dy_out
=
out
.
_numpy
()
out
.
_backward
()
dy_grad
=
mlp
.
_fc1
.
_w
.
_gradient
()
with
new_program_scope
():
inp
=
fluid
.
layers
.
data
(
name
=
"inp"
,
shape
=
[
2
,
2
],
append_batch_size
=
False
)
mlp
=
MLP
(
"mlp"
)
out
=
mlp
(
inp
)
param_grads
=
fluid
.
backward
.
append_backward
(
out
,
parameter_list
=
[
mlp
.
_fc1
.
_w
.
name
])[
0
]
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
(
)
if
not
core
.
is_compiled_with_cuda
()
else
fluid
.
CUDAPlace
(
0
))
exe
.
run
(
fluid
.
default_startup_program
())
static_out
,
static_grad
=
exe
.
run
(
feed
=
{
inp
.
name
:
np_inp
},
fetch_list
=
[
out
.
name
,
param_grads
[
1
].
name
])
self
.
assertTrue
(
np
.
allclose
(
dy_out
,
static_out
))
self
.
assertTrue
(
np
.
allclose
(
dy_grad
,
static_grad
))
params
=
mlp
.
parameters
(
True
)
self
.
assertEqual
(
"mlp/MLP_0/FC_0_0.w_0"
,
params
[
0
].
name
)
self
.
assertEqual
(
"mlp/MLP_0/FC_0_0.b_0"
,
params
[
1
].
name
)
self
.
assertEqual
(
"mlp/MLP_0/FC_1_0.w_0"
,
params
[
2
].
name
)
self
.
assertEqual
(
"mlp/MLP_0/FC_1_0.b_0"
,
params
[
3
].
name
)
self
.
assertEqual
(
len
(
params
),
4
)
sublayers
=
mlp
.
sublayers
(
True
)
self
.
assertEqual
(
mlp
.
_fc1
,
sublayers
[
0
])
self
.
assertEqual
(
mlp
.
_fc2
,
sublayers
[
1
])
self
.
assertEqual
(
len
(
sublayers
),
2
)
def
test_rnn
(
self
):
np_inp
=
np
.
array
([[
1.0
,
2.0
,
3.0
],
[
4.0
,
5.0
,
6.0
],
[
7.0
,
8.0
,
9.0
],
[
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
)
var_inp
=
fluid
.
layers
.
reshape
(
var_inp
,
shape
=
[
1
,
4
,
3
])
simple_rnn
=
SimpleRNN
(
"simple_rnn"
)
outs
,
pre_hiddens
=
simple_rnn
.
forward
(
var_inp
)
dy_out
=
outs
[
3
].
_numpy
()
outs
[
3
].
_backward
()
dy_grad_h2o
=
simple_rnn
.
_cell
.
_h2o_w
.
_gradient
()
dy_grad_h2h
=
simple_rnn
.
_cell
.
_h2h_w
.
_gradient
()
dy_grad_i2h
=
simple_rnn
.
_cell
.
_i2h_w
.
_gradient
()
with
new_program_scope
():
inp
=
fluid
.
layers
.
data
(
name
=
"inp"
,
shape
=
[
1
,
4
,
3
],
append_batch_size
=
False
)
simple_rnn
=
SimpleRNN
(
"simple_rnn"
)
outs
,
pre_hiddens
=
simple_rnn
(
inp
)
param_grads
=
fluid
.
backward
.
append_backward
(
outs
[
3
])
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
())
exe
.
run
(
fluid
.
default_startup_program
())
static_out
,
static_grad_h2o
,
static_grad_h2h
,
static_grad_i2h
=
exe
.
run
(
feed
=
{
inp
.
name
:
np_inp
},
fetch_list
=
[
outs
[
3
].
name
,
param_grads
[
0
][
1
].
name
,
param_grads
[
1
][
1
].
name
,
param_grads
[
2
][
1
].
name
])
self
.
assertTrue
(
np
.
allclose
(
dy_out
,
static_out
))
self
.
assertTrue
(
np
.
allclose
(
dy_grad_h2o
,
static_grad_h2o
))
self
.
assertTrue
(
np
.
allclose
(
dy_grad_h2h
,
static_grad_h2h
))
self
.
assertTrue
(
np
.
allclose
(
dy_grad_i2h
,
static_grad_i2h
))
#
def test_layer(self):
#
with fluid.imperative.guard():
#
cl = core.Layer()
#
cl.forward([])
#
l = fluid.imperative.Layer("l")
#
self.assertRaises(NotImplementedError, l.forward, [])
#
def test_pylayer_func_id(self):
#
with fluid.imperative.guard():
#
class PyLayer1(fluid.imperative.PyLayer):
#
def __init__(self):
#
super(PyLayer1, self).__init__()
#
@staticmethod
#
def forward(input):
#
return input
#
@staticmethod
#
def backward(input):
#
return input
#
class PyLayer2(fluid.imperative.PyLayer):
#
def __init__(self):
#
super(PyLayer2, self).__init__()
#
@staticmethod
#
def forward(input):
#
return input
#
@staticmethod
#
def backward(input):
#
return input
#
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])))
#
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])))
#
self.assertEqual(py_layer_1.forward_id, id)
#
def test_pylayer(self):
#
np_inp = np.ones([2, 2], np.float32)
#
with fluid.imperative.guard():
#
my_py_layer = MyPyLayer()
#
var_inp = fluid.imperative.base.to_variable(np_inp)
#
outs = my_py_layer(var_inp)
#
dy_out = np.sum(outs[0]._numpy())
#
outs[0]._backward()
#
dy_grad = var_inp._gradient()
#
with new_program_scope():
#
inp = fluid.layers.data(
#
name="inp", shape=[2, 2], append_batch_size=False)
#
# TODO(panyx0718): Paddle doesn't diff against data `inp`.
#
x1 = inp * 1
#
# TODO(panyx0718): If reduce_sum is skipped, the result is wrong.
#
x = fluid.layers.reduce_sum(fluid.layers.tanh(x1))
#
param_grads = fluid.backward.append_backward(
#
x, parameter_list=[x1.name])[0]
#
exe = fluid.Executor(fluid.CPUPlace(
#
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
#
static_out, static_grad = exe.run(
#
feed={inp.name: np_inp},
#
fetch_list=[x.name, param_grads[1].name])
#
self.assertTrue(np.allclose(dy_out, static_out))
#
self.assertTrue(np.allclose(dy_grad, static_grad))
#
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)
#
l = MyLayer("my_layer")
#
x = l(var_inp)[0]
#
self.assertIsNotNone(x)
#
dy_out = x._numpy()
#
x._backward()
#
dy_grad = l._x_for_debug._gradient()
#
with new_program_scope():
#
inp = fluid.layers.data(
#
name="inp", shape=[3], append_batch_size=False)
#
l = MyLayer("my_layer")
#
x = l(inp)[0]
#
param_grads = fluid.backward.append_backward(
#
x, parameter_list=[l._x_for_debug.name])[0]
#
exe = fluid.Executor(fluid.CPUPlace(
#
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
#
static_out, static_grad = exe.run(
#
feed={inp.name: np_inp},
#
fetch_list=[x.name, param_grads[1].name])
#
self.assertTrue(np.allclose(dy_out, static_out))
#
self.assertTrue(np.allclose(dy_grad, static_grad))
#
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)
#
mlp = MLP("mlp")
#
out = mlp(var_inp)
#
dy_out = out._numpy()
#
out._backward()
#
dy_grad = mlp._fc1._w._gradient()
#
with new_program_scope():
#
inp = fluid.layers.data(
#
name="inp", shape=[2, 2], append_batch_size=False)
#
mlp = MLP("mlp")
#
out = mlp(inp)
#
param_grads = fluid.backward.append_backward(
#
out, parameter_list=[mlp._fc1._w.name])[0]
#
exe = fluid.Executor(fluid.CPUPlace(
#
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
#
exe.run(fluid.default_startup_program())
#
static_out, static_grad = exe.run(
#
feed={inp.name: np_inp},
#
fetch_list=[out.name, param_grads[1].name])
#
self.assertTrue(np.allclose(dy_out, static_out))
#
self.assertTrue(np.allclose(dy_grad, static_grad))
#
params = mlp.parameters(True)
#
self.assertEqual("mlp/MLP_0/FC_0_0.w_0", params[0].name)
#
self.assertEqual("mlp/MLP_0/FC_0_0.b_0", params[1].name)
#
self.assertEqual("mlp/MLP_0/FC_1_0.w_0", params[2].name)
#
self.assertEqual("mlp/MLP_0/FC_1_0.b_0", params[3].name)
#
self.assertEqual(len(params), 4)
#
sublayers = mlp.sublayers(True)
#
self.assertEqual(mlp._fc1, sublayers[0])
#
self.assertEqual(mlp._fc2, sublayers[1])
#
self.assertEqual(len(sublayers), 2)
#
def test_rnn(self):
#
np_inp = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0],
#
[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)
#
var_inp = fluid.layers.reshape(var_inp, shape=[1, 4, 3])
#
simple_rnn = SimpleRNN("simple_rnn")
#
outs, pre_hiddens = simple_rnn.forward(var_inp)
#
dy_out = outs[3]._numpy()
#
outs[3]._backward()
#
dy_grad_h2o = simple_rnn._cell._h2o_w._gradient()
#
dy_grad_h2h = simple_rnn._cell._h2h_w._gradient()
#
dy_grad_i2h = simple_rnn._cell._i2h_w._gradient()
#
with new_program_scope():
#
inp = fluid.layers.data(
#
name="inp", shape=[1, 4, 3], append_batch_size=False)
#
simple_rnn = SimpleRNN("simple_rnn")
#
outs, pre_hiddens = simple_rnn(inp)
#
param_grads = fluid.backward.append_backward(outs[3])
#
exe = fluid.Executor(fluid.CPUPlace())
#
exe.run(fluid.default_startup_program())
#
static_out, static_grad_h2o, static_grad_h2h, static_grad_i2h = exe.run(
#
feed={inp.name: np_inp},
#
fetch_list=[
#
outs[3].name, param_grads[0][1].name,
#
param_grads[1][1].name, param_grads[2][1].name
#
])
#
self.assertTrue(np.allclose(dy_out, static_out))
#
self.assertTrue(np.allclose(dy_grad_h2o, static_grad_h2o))
#
self.assertTrue(np.allclose(dy_grad_h2h, static_grad_h2h))
#
self.assertTrue(np.allclose(dy_grad_i2h, static_grad_i2h))
if
__name__
==
'__main__'
:
...
...
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