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f23691db
编写于
1月 15, 2018
作者:
Y
Yang yaming
提交者:
GitHub
1月 15, 2018
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差异文件
Merge pull request #7434 from pkuyym/fix-7195
Add static_input for DynamicRNN
上级
535fefb7
25fee871
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
212 addition
and
0 deletion
+212
-0
python/paddle/v2/fluid/layers/control_flow.py
python/paddle/v2/fluid/layers/control_flow.py
+20
-0
python/paddle/v2/fluid/tests/test_dynrnn_static_input.py
python/paddle/v2/fluid/tests/test_dynrnn_static_input.py
+192
-0
未找到文件。
python/paddle/v2/fluid/layers/control_flow.py
浏览文件 @
f23691db
...
...
@@ -1291,6 +1291,26 @@ class DynamicRNN(object):
outputs
=
{
'Out'
:
input_array
})
return
array_read
(
array
=
input_array
,
i
=
self
.
step_idx
)
def
static_input
(
self
,
x
):
self
.
_assert_in_rnn_block_
(
"static_input"
)
if
not
isinstance
(
x
,
Variable
):
raise
TypeError
(
"static_input() can only take a Variable as its input"
)
if
self
.
lod_rank_table
is
None
:
raise
RuntimeError
(
"static_input() must be called after step_input()."
)
parent_block
=
self
.
_parent_block_
()
x_reordered
=
parent_block
.
create_var
(
name
=
unique_name
(
"dynamic_rnn_static_input_reordered"
),
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
dtype
=
x
.
dtype
)
parent_block
.
append_op
(
type
=
'reorder_lod_tensor_by_rank'
,
inputs
=
{
'X'
:
[
x
],
'RankTable'
:
[
self
.
lod_rank_table
]},
outputs
=
{
'Out'
:
[
x_reordered
]})
return
shrink_memory
(
x_reordered
,
self
.
step_idx
,
self
.
lod_rank_table
)
@
contextlib
.
contextmanager
def
block
(
self
):
if
self
.
status
!=
DynamicRNN
.
BEFORE_RNN
:
...
...
python/paddle/v2/fluid/tests/test_dynrnn_static_input.py
0 → 100644
浏览文件 @
f23691db
import
unittest
import
paddle.v2
as
paddle
import
paddle.v2.fluid.core
as
core
import
paddle.v2.fluid
as
fluid
from
paddle.v2.fluid.backward
import
append_backward
import
paddle.v2.fluid.framework
as
framework
from
paddle.v2.fluid.framework
import
Program
,
switch_main_program
import
bisect
import
numpy
as
np
fluid
.
default_startup_program
().
random_seed
=
1
class
TestDyRnnStaticInput
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
_delta
=
0.005
self
.
_max_sequence_len
=
3
self
.
_program
=
Program
()
switch_main_program
(
self
.
_program
)
self
.
output_dim
=
10
self
.
place
=
core
.
CPUPlace
()
self
.
prepare_x_tensor
()
self
.
prepare_static_input_tensor
()
self
.
exe
=
fluid
.
Executor
(
self
.
place
)
def
prepare_x_tensor
(
self
):
self
.
x_tensor_dim
=
10
lod
=
[[
0
,
2
,
3
,
6
]]
shape
=
[
lod
[
0
][
-
1
],
self
.
x_tensor_dim
]
self
.
x_tensor_data
=
np
.
random
.
random
(
shape
).
astype
(
'float32'
)
self
.
x_tensor
=
core
.
LoDTensor
()
self
.
x_tensor
.
set_lod
(
lod
)
self
.
x_tensor
.
set
(
self
.
x_tensor_data
,
self
.
place
)
def
prepare_static_input_tensor
(
self
):
self
.
static_input_tensor_dim
=
4
lod
=
[[
0
,
1
,
3
,
6
]]
shape
=
[
lod
[
0
][
-
1
],
self
.
static_input_tensor_dim
]
self
.
static_input_data
=
np
.
random
.
random
(
shape
).
astype
(
'float32'
)
self
.
static_input_tensor
=
core
.
LoDTensor
()
self
.
static_input_tensor
.
set_lod
(
lod
)
self
.
static_input_tensor
.
set
(
self
.
static_input_data
,
self
.
place
)
def
fetch_value
(
self
,
var
):
fetch_outs
=
self
.
exe
.
run
(
feed
=
{
'x_tensor'
:
self
.
x_tensor
,
'static_input_tensor'
:
self
.
static_input_tensor
},
fetch_list
=
[
var
],
return_numpy
=
False
)
return
self
.
_lodtensor_to_ndarray
(
fetch_outs
[
0
])
def
_lodtensor_to_ndarray
(
self
,
lod_tensor
):
dims
=
lod_tensor
.
get_dims
()
ndarray
=
np
.
zeros
(
shape
=
dims
).
astype
(
'float32'
)
for
i
in
xrange
(
np
.
product
(
dims
)):
ndarray
.
ravel
()[
i
]
=
lod_tensor
.
get_float_element
(
i
)
return
ndarray
,
lod_tensor
.
lod
()
def
build_graph
(
self
,
only_forward
=
False
):
x_tensor
=
fluid
.
layers
.
data
(
name
=
'x_tensor'
,
shape
=
[
self
.
x_tensor_dim
],
dtype
=
'float32'
,
lod_level
=
1
)
x_tensor
.
stop_gradient
=
False
static_input_tensor
=
fluid
.
layers
.
data
(
name
=
'static_input_tensor'
,
shape
=
[
self
.
static_input_tensor_dim
],
dtype
=
'float32'
,
lod_level
=
1
)
static_input_tensor
.
stop_gradient
=
False
if
only_forward
:
static_input_out_array
=
self
.
_program
.
global_block
().
create_var
(
name
=
'static_input_out_array'
,
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR_ARRAY
,
dtype
=
'float32'
)
static_input_out_array
.
stop_gradient
=
True
rnn
=
fluid
.
layers
.
DynamicRNN
()
with
rnn
.
block
():
step_x
=
rnn
.
step_input
(
x_tensor
)
step_static_input
=
rnn
.
static_input
(
static_input_tensor
)
if
only_forward
:
fluid
.
layers
.
array_write
(
x
=
step_static_input
,
i
=
rnn
.
step_idx
,
array
=
static_input_out_array
)
last
=
fluid
.
layers
.
sequence_pool
(
input
=
step_static_input
,
pool_type
=
'last'
)
projected
=
fluid
.
layers
.
fc
(
input
=
[
step_x
,
last
],
size
=
self
.
output_dim
)
rnn
.
output
(
projected
)
if
only_forward
:
static_input_step_outs
=
[]
step_idx
=
fluid
.
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
'int64'
,
value
=
0
)
step_idx
.
stop_gradient
=
True
for
i
in
xrange
(
self
.
_max_sequence_len
):
step_out
=
fluid
.
layers
.
array_read
(
static_input_out_array
,
step_idx
)
step_out
.
stop_gradient
=
True
static_input_step_outs
.
append
(
step_out
)
fluid
.
layers
.
increment
(
x
=
step_idx
,
value
=
1.0
,
in_place
=
True
)
if
only_forward
:
return
static_input_step_outs
last
=
fluid
.
layers
.
sequence_pool
(
input
=
rnn
(),
pool_type
=
'last'
)
loss
=
fluid
.
layers
.
mean
(
x
=
last
)
append_backward
(
loss
)
static_input_grad
=
self
.
_program
.
global_block
().
var
(
framework
.
grad_var_name
(
'static_input_tensor'
))
return
static_input_grad
,
loss
def
get_seq_len_from_lod
(
self
,
lod
):
return
[
lod
[
0
][
i
+
1
]
-
lod
[
0
][
i
]
for
i
in
xrange
(
len
(
lod
[
0
])
-
1
)]
def
get_expected_static_step_outs
(
self
):
x_lod
=
self
.
x_tensor
.
lod
()
x_seq_len
=
self
.
get_seq_len_from_lod
(
x_lod
)
x_seq_len_sorted
=
sorted
(
x_seq_len
)
x_sorted_indices
=
np
.
argsort
(
x_seq_len
)[::
-
1
]
static_lod
=
self
.
static_input_tensor
.
lod
()
static_sliced
=
[
self
.
static_input_data
[
static_lod
[
0
][
i
]:
static_lod
[
0
][
i
+
1
]]
for
i
in
xrange
(
len
(
static_lod
[
0
])
-
1
)
]
static_seq_len
=
self
.
get_seq_len_from_lod
(
static_lod
)
static_reordered
=
[]
for
i
in
xrange
(
len
(
x_sorted_indices
)):
static_reordered
.
extend
(
static_sliced
[
x_sorted_indices
[
i
]].
tolist
())
static_seq_len_reordered
=
[
static_seq_len
[
x_sorted_indices
[
i
]]
for
i
in
xrange
(
len
(
x_sorted_indices
))
]
static_step_outs
=
[]
static_step_lods
=
[]
for
i
in
xrange
(
self
.
_max_sequence_len
):
end
=
len
(
x_seq_len
)
-
bisect
.
bisect_left
(
x_seq_len_sorted
,
i
+
1
)
lod
=
[
0
]
for
i
in
xrange
(
end
):
lod
.
append
(
static_seq_len_reordered
[
i
]
+
lod
[
-
1
])
static_step_lods
.
append
([
lod
])
end
=
lod
[
-
1
]
static_step_outs
.
append
(
np
.
array
(
static_reordered
[:
end
]).
astype
(
'float32'
))
return
static_step_outs
,
static_step_lods
def
test_step_out
(
self
):
static_step_outs
=
self
.
build_graph
(
only_forward
=
True
)
self
.
exe
.
run
(
framework
.
default_startup_program
())
expected_outs
,
expected_lods
=
self
.
get_expected_static_step_outs
()
for
i
in
xrange
(
self
.
_max_sequence_len
):
step_out
,
lod
=
self
.
fetch_value
(
static_step_outs
[
i
])
self
.
assertTrue
(
np
.
allclose
(
step_out
,
expected_outs
[
i
]))
self
.
assertTrue
(
np
.
allclose
(
lod
,
expected_lods
[
i
]))
def
test_network_gradient
(
self
):
static_input_grad
,
loss
=
self
.
build_graph
()
self
.
exe
.
run
(
framework
.
default_startup_program
())
actual_gradients
,
actual_lod
=
self
.
fetch_value
(
static_input_grad
)
static_input_shape
=
self
.
static_input_tensor
.
get_dims
()
numeric_gradients
=
np
.
zeros
(
shape
=
static_input_shape
).
astype
(
'float32'
)
# calculate numeric gradients
tensor_size
=
np
.
product
(
static_input_shape
)
for
i
in
xrange
(
tensor_size
):
origin
=
self
.
static_input_tensor
.
get_float_element
(
i
)
x_pos
=
origin
+
self
.
_delta
self
.
static_input_tensor
.
set_float_element
(
i
,
x_pos
)
y_pos
=
self
.
fetch_value
(
loss
)[
0
][
0
]
x_neg
=
origin
-
self
.
_delta
self
.
static_input_tensor
.
set_float_element
(
i
,
x_neg
)
y_neg
=
self
.
fetch_value
(
loss
)[
0
][
0
]
self
.
static_input_tensor
.
set_float_element
(
i
,
origin
)
numeric_gradients
.
ravel
()[
i
]
=
(
y_pos
-
y_neg
)
/
self
.
_delta
/
2
self
.
assertTrue
(
np
.
allclose
(
actual_gradients
,
numeric_gradients
,
0.001
))
self
.
assertTrue
(
np
.
allclose
(
actual_lod
,
self
.
static_input_tensor
.
lod
()))
if
__name__
==
'__main__'
:
unittest
.
main
()
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