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体验新版 GitCode,发现更多精彩内容 >>
提交
e686818a
编写于
1月 22, 2019
作者:
J
JiabinYang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
simple RNN
上级
af1cee5a
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
107 addition
and
25 deletion
+107
-25
paddle/fluid/imperative/tracer.cc
paddle/fluid/imperative/tracer.cc
+2
-0
python/paddle/fluid/imperative/nn.py
python/paddle/fluid/imperative/nn.py
+42
-18
python/paddle/fluid/tests/unittests/test_imperative.py
python/paddle/fluid/tests/unittests/test_imperative.py
+63
-7
未找到文件。
paddle/fluid/imperative/tracer.cc
浏览文件 @
e686818a
...
...
@@ -28,6 +28,8 @@ void CreateGradOp(const framework::OpDesc& op_desc,
.
GradOpMaker
()(
op_desc
,
no_grad_set
,
grad_to_var
,
grad_sub_block
);
PADDLE_ENFORCE
(
grad_op_descs
.
size
()
==
1
,
"Only support 1 grad op now."
);
// TODO(panyx0718): Leak?
// TODO(marsyang1993): Change grad_op_desc pointer to
// vector<framework::OpDesc*> to allow multi grad_op
*
grad_op_desc
=
grad_op_descs
[
0
].
release
();
}
...
...
python/paddle/fluid/imperative/nn.py
浏览文件 @
e686818a
...
...
@@ -23,11 +23,7 @@ from ..framework import Variable, OpProtoHolder
from
..param_attr
import
ParamAttr
from
..initializer
import
Normal
,
Constant
__all__
=
[
'Conv2D'
,
'Pool2D'
,
'FC'
,
]
__all__
=
[
'Conv2D'
,
'Pool2D'
,
'FC'
,
'SimpleRNNCell'
]
class
Conv2D
(
layers
.
Layer
):
...
...
@@ -251,14 +247,9 @@ class FC(layers.Layer):
class
SimpleRNNCell
(
layers
.
Layer
):
def
__init__
(
self
,
step_input_size
,
hidden_size
,
output_size
,
param_attr
,
dtype
=
core
.
VarDesc
.
VarType
.
FP32
):
def
__init__
(
self
,
step_input_size
,
hidden_size
,
output_size
,
param_attr
):
super
(
SimpleRNNCell
,
self
).
__init__
()
self
.
input_size
=
step_input_size
self
.
step_
input_size
=
step_input_size
self
.
hidden_size
=
hidden_size
self
.
output_size
=
output_size
self
.
_dype
=
core
.
VarDesc
.
VarType
.
FP32
...
...
@@ -266,7 +257,7 @@ class SimpleRNNCell(layers.Layer):
self
.
_helper
=
LayerHelper
(
'SimpleRNNCell'
,
act
=
"tanh"
,
param_attr
=
param_attr
)
def
_build_once
(
self
,
inputs
):
def
_build_once
(
self
,
inputs
,
pre_hidden
):
i2h_param_shape
=
[
self
.
step_input_size
,
self
.
hidden_size
]
h2h_param_shape
=
[
self
.
hidden_size
,
self
.
hidden_size
]
h2o_param_shape
=
[
self
.
output_size
,
self
.
hidden_size
]
...
...
@@ -294,6 +285,7 @@ class SimpleRNNCell(layers.Layer):
out
=
self
.
_helper
.
create_variable_for_type_inference
(
self
.
_dype
)
softmax_out
=
self
.
_helper
.
create_variable_for_type_inference
(
self
.
_dtype
)
self
.
_helper
.
append_op
(
type
=
"mul"
,
inputs
=
{
"X"
:
input
,
...
...
@@ -301,7 +293,7 @@ class SimpleRNNCell(layers.Layer):
outputs
=
{
"Out"
:
tmp_i2h
},
attrs
=
{
"x_num_col_dims"
:
1
,
"y_num_col_dims"
:
1
})
print
(
"mul op 1"
)
self
.
_helper
.
append_op
(
type
=
"mul"
,
inputs
=
{
"X"
:
pre_hidden
,
...
...
@@ -309,15 +301,45 @@ class SimpleRNNCell(layers.Layer):
outputs
=
{
"Out"
:
tmp_h2h
},
attrs
=
{
"x_num_col_dims"
:
1
,
"y_num_col_dims"
:
1
})
print
(
"mul op 2"
)
self
.
_helper
.
append_op
(
type
=
'sum'
,
inputs
=
{
'X'
:
[
tmp_i2h
,
tmp_h2h
]},
type
=
"elementwise_add"
,
inputs
=
{
'X'
:
tmp_h2h
,
'Y'
:
tmp_i2h
},
outputs
=
{
'Out'
:
hidden
},
attrs
=
{
'use_mkldnn'
:
False
})
attrs
=
{
'axis'
:
-
1
,
'use_mkldnn'
:
False
})
print
(
"elementwise op 1"
)
self
.
_helper
.
append_op
(
type
=
'print'
,
inputs
=
{
'In'
:
hidden
},
attrs
=
{
'first_n'
:
-
1
,
'summarize'
:
-
1
,
'message'
:
None
or
""
,
'print_tensor_name'
:
True
,
'print_tensor_type'
:
True
,
'print_tensor_shape'
:
True
,
'print_tensor_lod'
:
True
,
'print_phase'
:
'BOTH'
})
hidden
=
self
.
_helper
.
append_activation
(
hidden
)
self
.
_helper
.
append_op
(
type
=
'print'
,
inputs
=
{
'In'
:
hidden
},
attrs
=
{
'first_n'
:
-
1
,
'summarize'
:
-
1
,
'message'
:
None
or
""
,
'print_tensor_name'
:
True
,
'print_tensor_type'
:
True
,
'print_tensor_shape'
:
True
,
'print_tensor_lod'
:
True
,
'print_phase'
:
'BOTH'
})
self
.
_helper
.
append_op
(
type
=
"mul"
,
inputs
=
{
"X"
:
hidden
,
...
...
@@ -325,11 +347,13 @@ class SimpleRNNCell(layers.Layer):
outputs
=
{
"Out"
:
out
},
attrs
=
{
"x_num_col_dims"
:
1
,
"y_num_col_dims"
:
1
})
print
(
"mul op 3"
)
self
.
_helper
.
append_op
(
type
=
"softmax"
,
inputs
=
{
"X"
:
out
},
outputs
=
{
"Out"
:
softmax_out
},
attrs
=
{
"use_cudnn"
:
False
})
print
(
"softmax op 1"
)
return
softmax_out
,
hidden
python/paddle/fluid/tests/unittests/test_imperative.py
浏览文件 @
e686818a
...
...
@@ -19,7 +19,10 @@ import sys
import
paddle.fluid
as
fluid
from
paddle.fluid
import
core
from
paddle.fluid.imperative.nn
import
FC
,
SimpleRNNCell
from
paddle.fluid.imperative.nn
import
FC
from
paddle.fluid.imperative.nn
import
SimpleRNNCell
from
typing
import
List
,
Any
,
Tuple
from
test_imperative_base
import
new_program_scope
...
...
@@ -67,14 +70,34 @@ class MLP(fluid.imperative.Layer):
class
SimpleRNN
(
fluid
.
imperative
.
Layer
):
def
__init__
(
self
,
inputs
):
def
__init__
(
self
):
super
(
SimpleRNN
,
self
).
__init__
()
self
.
seq_len
=
input
.
shape
[
0
]
self
.
cell
=
SimpleRNNCell
(
input
.
shape
[
1
],
out
)
self
.
seq_len
=
4
self
.
_cell
=
SimpleRNNCell
(
3
,
3
,
3
,
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Constant
(
value
=
0.1
)))
def
forward
(
self
,
inputs
):
out
=
list
()
pre_hiddens
=
list
()
init_hidden
=
fluid
.
layers
.
tensor
.
create_parameter
(
attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Constant
(
value
=
0.1
)),
shape
=
[
1
,
3
],
dtype
=
'float32'
,
is_bias
=
False
)
pre_hidden
=
init_hidden
for
i
in
range
(
self
.
seq_len
):
x
=
self
.
_fc1
(
inputs
[
i
])
input
=
fluid
.
layers
.
slice
(
inputs
,
axes
=
[
1
],
starts
=
[
i
],
ends
=
[
i
+
1
])
input
=
fluid
.
layers
.
reshape
(
input
,
shape
=
[
1
,
3
])
pre_hidden
,
out_softmax
=
self
.
_cell
(
input
,
pre_hidden
)
out
.
append
(
out_softmax
)
return
out
,
pre_hiddens
class
TestImperative
(
unittest
.
TestCase
):
...
...
@@ -207,8 +230,41 @@ class TestImperative(unittest.TestCase):
self
.
assertTrue
(
np
.
allclose
(
dy_out
,
static_out
))
self
.
assertTrue
(
np
.
allclose
(
dy_grad
,
static_grad
))
def
test_rnn_ptb
(
self
):
np_inp
=
np
.
arrary
([])
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()
# outs, pre_hiddens = simple_rnn.forward(var_inp)
# dy_out = outs[3]._numpy()
# outs[3]._backward()
# dy_grad = simple_rnn._cell._i2h_w._gradient()
# print("dy_grad is {}".format(dy_grad))
with
new_program_scope
():
print
(
"im here"
)
inp
=
fluid
.
layers
.
data
(
name
=
"inp"
,
shape
=
[
1
,
4
,
3
],
append_batch_size
=
False
)
simple_rnn
=
SimpleRNN
()
outs
,
pre_hiddens
=
simple_rnn
(
inp
)
param_grads
=
fluid
.
backward
.
append_backward
(
outs
[
3
],
parameter_list
=
[
simple_rnn
.
_cell
.
_i2h_w
.
name
,
simple_rnn
.
_cell
.
_h2h_w
.
name
,
simple_rnn
.
_cell
.
_h2o_w
.
name
])
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
())
exe
.
run
(
fluid
.
default_startup_program
())
# print("param_grads is : {} ".format(param_grads))
static_out
,
static_grad
=
exe
.
run
(
feed
=
{
inp
.
name
:
np_inp
},
fetch_list
=
[
outs
[
3
].
name
,
param_grads
[
2
][
1
].
name
])
# self.assertTrue(np.allclose(dy_out, static_out))
# self.assertTrue(np.allclose(dy_grad, static_grad))
if
__name__
==
'__main__'
:
...
...
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