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体验新版 GitCode,发现更多精彩内容 >>
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b6f8ccd2
编写于
4月 23, 2021
作者:
S
shanliang1992
提交者:
GitHub
4月 23, 2021
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差异文件
add lstm support on xpu test=kunlun (#32436)
上级
51bcd97d
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
488 addition
and
1 deletion
+488
-1
paddle/fluid/operators/rnn_op_xpu.cc
paddle/fluid/operators/rnn_op_xpu.cc
+314
-0
python/paddle/fluid/tests/unittests/op_test_xpu.py
python/paddle/fluid/tests/unittests/op_test_xpu.py
+1
-1
python/paddle/fluid/tests/unittests/xpu/test_rnn_op_xpu.py
python/paddle/fluid/tests/unittests/xpu/test_rnn_op_xpu.py
+173
-0
未找到文件。
paddle/fluid/operators/rnn_op_xpu.cc
0 → 100644
浏览文件 @
b6f8ccd2
/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/utils.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/xpu_header.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
DDim
=
framework
::
DDim
;
using
TensorList
=
std
::
vector
<
framework
::
Tensor
>
;
template
<
typename
TensorType
,
typename
T
>
void
reset_parameter_vector
(
const
std
::
vector
<
TensorType
>&
raw_params_vec
,
const
int
&
num_layers
,
const
bool
&
is_bidirec
,
std
::
vector
<
std
::
vector
<
T
*>>*
params_vec
)
{
// the parameter raw seuquence is [FWhi, FWhh, BWhi, BWhh] * num_layers
// + [FBhi, FBhh, BBhi, BBhh] * num_layers, we will reset the parameter to
// ([FWhi, FWhh, FBhi, FBhh] + [BWhi, BWhh, BBhi, BBhh]) * num_layers
const
int
&
direction_num
=
is_bidirec
?
2
:
1
;
const
int
&
layer_weight_size
=
4
*
direction_num
;
const
int
&
all_weight_size
=
num_layers
*
layer_weight_size
;
const
int
&
bias_start_idx
=
all_weight_size
/
2
;
for
(
int
i
=
0
;
i
<
num_layers
;
i
++
)
{
params_vec
->
at
(
i
).
resize
(
layer_weight_size
);
for
(
int
j
=
0
;
j
<
layer_weight_size
;
j
++
)
{
int
k
=
j
%
4
;
const
int
&
section
=
j
/
4
;
int
tensor_idx
=
i
*
2
*
direction_num
+
section
*
2
+
k
%
2
;
if
(
k
>=
2
)
{
tensor_idx
+=
bias_start_idx
;
}
using
remove_cv_t
=
typename
std
::
remove_cv
<
T
>::
type
;
params_vec
->
at
(
i
)[
j
]
=
raw_params_vec
[
tensor_idx
]
->
template
data
<
remove_cv_t
>();
}
}
}
template
<
typename
DeviceContext
,
typename
T
>
class
RnnXPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input
=
ctx
.
Input
<
Tensor
>
(
"Input"
);
auto
pre_state
=
ctx
.
MultiInput
<
Tensor
>
(
"PreState"
);
auto
weight_list
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"WeightList"
);
auto
state
=
ctx
.
MultiOutput
<
Tensor
>
(
"State"
);
auto
*
output
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
auto
*
reserve_data
=
ctx
.
Output
<
Tensor
>
(
"Reserve"
);
const
int
&
num_layers
=
ctx
.
Attr
<
int
>
(
"num_layers"
);
const
bool
&
is_bidirec
=
ctx
.
Attr
<
bool
>
(
"is_bidirec"
);
const
int
&
hidden_size
=
ctx
.
Attr
<
int
>
(
"hidden_size"
);
const
std
::
string
&
mode
=
ctx
.
Attr
<
std
::
string
>
(
"mode"
);
bool
has_seq_length
=
ctx
.
HasInput
(
"SequenceLength"
);
const
Tensor
*
sequence_length
=
nullptr
;
if
(
has_seq_length
)
{
sequence_length
=
ctx
.
Input
<
Tensor
>
(
"SequenceLength"
);
}
PADDLE_ENFORCE_EQ
(
mode
,
"LSTM"
,
platform
::
errors
::
InvalidArgument
(
"XPU only support LSTM mode now, current mode is %s"
,
mode
));
PADDLE_ENFORCE_EQ
(
is_bidirec
,
false
,
platform
::
errors
::
InvalidArgument
(
"XPU only support unidirectional LSTM now"
));
PADDLE_ENFORCE_EQ
(
num_layers
,
1
,
platform
::
errors
::
InvalidArgument
(
"XPU only support 1 layer LSTM now, current layer num is %s"
,
num_layers
));
auto
init_h
=
pre_state
[
0
];
auto
init_c
=
pre_state
[
1
];
auto
last_h
=
state
[
0
];
auto
last_c
=
state
[
1
];
// check shape
int
seq_len
=
input
->
dims
()[
0
];
int
batch_size
=
input
->
dims
()[
1
];
int
input_dim
=
input
->
dims
()[
2
];
PADDLE_ENFORCE_EQ
(
init_h
->
dims
()[
0
],
num_layers
,
platform
::
errors
::
InvalidArgument
(
"The num_layers of in RNN layer must"
" be the same as first dim of init "
"hidden, but received num_layers:%d,"
" dim:%d"
,
num_layers
,
init_h
->
dims
()[
0
]));
PADDLE_ENFORCE_EQ
(
init_c
->
dims
()[
0
],
num_layers
,
platform
::
errors
::
InvalidArgument
(
"The num_layers of in RNN layer must"
" be the same as first dim of cell state hidden, but received"
" num_layers:%d, dim:%d"
,
num_layers
,
init_c
->
dims
()[
0
]));
std
::
vector
<
std
::
vector
<
const
T
*>>
parameter_lists
;
parameter_lists
.
resize
(
num_layers
);
reset_parameter_vector
(
weight_list
,
num_layers
,
is_bidirec
,
&
parameter_lists
);
// init the output and allocate the memory
output
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
last_h
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
last_c
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
reserve_data
->
Resize
({
seq_len
*
batch_size
*
hidden_size
*
5
});
reserve_data
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
// get ptr from tensor
auto
x
=
input
->
data
<
T
>
();
auto
h_0
=
init_h
->
data
<
T
>
();
auto
c_0
=
init_c
->
data
<
T
>
();
auto
w_x
=
parameter_lists
[
0
][
0
];
auto
w_h
=
parameter_lists
[
0
][
1
];
auto
b_x
=
parameter_lists
[
0
][
2
];
auto
b_h
=
parameter_lists
[
0
][
3
];
auto
y
=
output
->
data
<
T
>
();
auto
last_h_ptr
=
last_h
->
data
<
T
>
();
auto
last_c_ptr
=
last_c
->
data
<
T
>
();
auto
i_f_g_o
=
reserve_data
->
data
<
T
>
();
auto
c
=
i_f_g_o
+
seq_len
*
batch_size
*
hidden_size
*
4
;
std
::
vector
<
int
>
seq_len_tensor
(
batch_size
,
seq_len
);
if
(
has_seq_length
)
{
seq_len_tensor
=
operators
::
GetDataFromTensor
(
sequence_length
);
}
// run kernel
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
int
r
=
xpu
::
lstm_train
<
T
,
T
,
int16_t
>
(
dev_ctx
.
x_context
(),
(
const
T
*
)
x
,
(
const
T
*
)
h_0
,
(
const
T
*
)
c_0
,
(
const
T
*
)
w_x
,
(
const
T
*
)
w_h
,
(
const
T
*
)
b_x
,
(
const
T
*
)
b_h
,
reinterpret_cast
<
T
*>
(
y
),
reinterpret_cast
<
T
*>
(
last_h_ptr
),
reinterpret_cast
<
T
*>
(
last_c_ptr
),
batch_size
,
input_dim
,
hidden_size
,
seq_len
,
seq_len_tensor
,
nullptr
,
nullptr
,
nullptr
,
nullptr
,
reinterpret_cast
<
T
*>
(
i_f_g_o
),
reinterpret_cast
<
T
*>
(
c
));
PADDLE_ENFORCE_EQ
(
r
,
xpu
::
Error_t
::
SUCCESS
,
platform
::
errors
::
External
(
"RnnXPU(lstm) return wrong "
"value[%d %s]"
,
r
,
XPUAPIErrorMsg
[
r
]));
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
RnnXPUGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
// get the tensor pointer for the input
auto
*
input
=
ctx
.
Input
<
Tensor
>
(
"Input"
);
auto
pre_state
=
ctx
.
MultiInput
<
Tensor
>
(
"PreState"
);
auto
weight_list
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"WeightList"
);
auto
*
output
=
ctx
.
Input
<
Tensor
>
(
"Out"
);
auto
*
reserve_data
=
ctx
.
Input
<
Tensor
>
(
"Reserve"
);
const
int
&
num_layers
=
ctx
.
Attr
<
int
>
(
"num_layers"
);
const
bool
&
is_bidirec
=
ctx
.
Attr
<
bool
>
(
"is_bidirec"
);
const
int
&
hidden_size
=
ctx
.
Attr
<
int
>
(
"hidden_size"
);
const
std
::
string
&
mode
=
ctx
.
Attr
<
std
::
string
>
(
"mode"
);
bool
has_seq_length
=
ctx
.
HasInput
(
"SequenceLength"
);
const
Tensor
*
sequence_length
=
nullptr
;
if
(
has_seq_length
)
{
sequence_length
=
ctx
.
Input
<
Tensor
>
(
"SequenceLength"
);
}
PADDLE_ENFORCE_EQ
(
mode
,
"LSTM"
,
platform
::
errors
::
InvalidArgument
(
"XPU only support LSTM mode now, current mode is %s"
,
mode
));
PADDLE_ENFORCE_EQ
(
is_bidirec
,
false
,
platform
::
errors
::
InvalidArgument
(
"XPU only support unidirectional LSTM now"
));
PADDLE_ENFORCE_EQ
(
num_layers
,
1
,
platform
::
errors
::
InvalidArgument
(
"XPU only support 1 layer LSTM now, current layer num is %s"
,
num_layers
));
auto
init_h
=
pre_state
[
0
];
auto
init_c
=
pre_state
[
1
];
auto
output_grad
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
state_grad
=
ctx
.
MultiInput
<
Tensor
>
(
framework
::
GradVarName
(
"State"
));
auto
last_h_grad
=
state_grad
[
0
];
auto
last_c_grad
=
state_grad
[
1
];
// get the tensor pointer for the output
auto
*
input_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Input"
));
auto
weight_grad_list
=
ctx
.
MultiOutput
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"WeightList"
));
auto
pre_state_grad
=
ctx
.
MultiOutput
<
Tensor
>
(
framework
::
GradVarName
(
"PreState"
));
Tensor
*
init_h_grad
=
nullptr
;
Tensor
*
init_c_grad
=
nullptr
;
if
(
pre_state_grad
.
size
()
>
0
)
{
// has gradient
init_h_grad
=
pre_state_grad
[
0
];
init_c_grad
=
pre_state_grad
[
1
];
}
// check shape
int
seq_len
=
input
->
dims
()[
0
];
int
batch_size
=
input
->
dims
()[
1
];
int
input_dim
=
input
->
dims
()[
2
];
PADDLE_ENFORCE_EQ
(
init_h
->
dims
()[
0
],
num_layers
,
platform
::
errors
::
InvalidArgument
(
"The num_layers of in RNN layer must"
" be the same as first dim of init "
"hidden, but received num_layers:%d,"
" dim:%d"
,
num_layers
,
init_h
->
dims
()[
0
]));
PADDLE_ENFORCE_EQ
(
init_c
->
dims
()[
0
],
num_layers
,
platform
::
errors
::
InvalidArgument
(
"The num_layers of in RNN layer must"
" be the same as first dim of cell state hidden, but received"
" num_layers:%d, dim:%d"
,
num_layers
,
init_c
->
dims
()[
0
]));
std
::
vector
<
std
::
vector
<
const
T
*>>
parameter_lists
;
parameter_lists
.
resize
(
num_layers
);
reset_parameter_vector
(
weight_list
,
num_layers
,
is_bidirec
,
&
parameter_lists
);
for
(
unsigned
int
i
=
0
;
i
<
weight_grad_list
.
size
();
++
i
)
{
weight_grad_list
[
i
]
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
}
std
::
vector
<
std
::
vector
<
T
*>>
parameter_lists_grad
;
parameter_lists_grad
.
resize
(
num_layers
);
reset_parameter_vector
(
weight_grad_list
,
num_layers
,
is_bidirec
,
&
parameter_lists_grad
);
// allocate the memory and initization the input_grad
input_grad
->
mutable_data
<
T
>
(
input
->
dims
(),
ctx
.
GetPlace
());
if
(
init_h_grad
)
{
init_h_grad
->
mutable_data
<
T
>
(
init_h
->
dims
(),
ctx
.
GetPlace
());
}
if
(
init_c_grad
)
{
init_c_grad
->
mutable_data
<
T
>
(
init_c
->
dims
(),
ctx
.
GetPlace
());
}
// get ptr from tensor
auto
x
=
input
->
data
<
T
>
();
auto
h_0
=
init_h
->
data
<
T
>
();
auto
c_0
=
init_c
->
data
<
T
>
();
auto
w_x
=
parameter_lists
[
0
][
0
];
auto
w_h
=
parameter_lists
[
0
][
1
];
auto
y
=
output
->
data
<
T
>
();
auto
y_grad
=
output_grad
->
data
<
T
>
();
auto
last_h_grad_ptr
=
last_h_grad
->
data
<
T
>
();
auto
last_c_grad_ptr
=
last_c_grad
->
data
<
T
>
();
auto
x_grad
=
input_grad
->
data
<
T
>
();
auto
h_0_grad
=
init_h_grad
?
init_h_grad
->
data
<
T
>
()
:
nullptr
;
auto
c_0_grad
=
init_c_grad
?
init_c_grad
->
data
<
T
>
()
:
nullptr
;
auto
w_x_grad
=
parameter_lists_grad
[
0
][
0
];
auto
w_h_grad
=
parameter_lists_grad
[
0
][
1
];
auto
b_x_grad
=
parameter_lists_grad
[
0
][
2
];
auto
b_h_grad
=
parameter_lists_grad
[
0
][
3
];
auto
i_f_g_o
=
reserve_data
->
data
<
T
>
();
auto
c
=
i_f_g_o
+
seq_len
*
batch_size
*
hidden_size
*
4
;
std
::
vector
<
int
>
seq_len_tensor
(
batch_size
,
seq_len
);
if
(
has_seq_length
)
{
seq_len_tensor
=
operators
::
GetDataFromTensor
(
sequence_length
);
}
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
int
r
=
xpu
::
lstm_grad
<
T
,
T
,
int16_t
>
(
dev_ctx
.
x_context
(),
(
const
T
*
)
x
,
(
const
T
*
)
h_0
,
(
const
T
*
)
c_0
,
(
const
T
*
)
w_x
,
(
const
T
*
)
w_h
,
(
const
T
*
)
y
,
(
const
T
*
)
y_grad
,
(
const
T
*
)
last_h_grad_ptr
,
(
const
T
*
)
last_c_grad_ptr
,
reinterpret_cast
<
T
*>
(
x_grad
),
reinterpret_cast
<
T
*>
(
h_0_grad
),
reinterpret_cast
<
T
*>
(
c_0_grad
),
w_x_grad
,
w_h_grad
,
b_x_grad
,
b_h_grad
,
batch_size
,
input_dim
,
hidden_size
,
seq_len
,
seq_len_tensor
,
nullptr
,
nullptr
,
nullptr
,
nullptr
,
i_f_g_o
,
c
);
PADDLE_ENFORCE_EQ
(
r
,
xpu
::
Error_t
::
SUCCESS
,
platform
::
errors
::
External
(
"RnnXPUGrad(lstm) return wrong "
"value[%d %s]"
,
r
,
XPUAPIErrorMsg
[
r
]));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_XPU_KERNEL
(
rnn
,
ops
::
RnnXPUKernel
<
paddle
::
platform
::
XPUDeviceContext
,
float
>
);
REGISTER_OP_XPU_KERNEL
(
rnn_grad
,
ops
::
RnnXPUGradKernel
<
paddle
::
platform
::
XPUDeviceContext
,
float
>
);
#endif // PADDLE_WITH_XPU
python/paddle/fluid/tests/unittests/op_test_xpu.py
浏览文件 @
b6f8ccd2
...
...
@@ -296,7 +296,7 @@ class XPUOpTest(OpTest):
no_grad_set
=
no_grad_set
)
self
.
_assert_is_close
(
a1
,
a2
,
inputs_to_check
,
0.00000001
,
"Gradient Check On two xpu"
)
self
.
_assert_is_close
(
a1
,
a3
,
inputs_to_check
,
0.001
,
self
.
_assert_is_close
(
a1
,
a3
,
inputs_to_check
,
max_relative_error
,
"Gradient Check On cpu & xpu"
)
def
get_grad_with_place
(
self
,
...
...
python/paddle/fluid/tests/unittests/xpu/test_rnn_op_xpu.py
0 → 100755
浏览文件 @
b6f8ccd2
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from
__future__
import
print_function
import
unittest
import
numpy
as
np
import
math
import
paddle.fluid.core
as
core
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.layers
as
layers
import
random
import
sys
sys
.
path
.
append
(
".."
)
from
op_test_xpu
import
XPUOpTest
sys
.
path
.
append
(
"../rnn"
)
from
rnn_numpy
import
SimpleRNN
,
LSTM
,
GRU
from
convert
import
get_params_for_net
random
.
seed
(
2
)
np
.
set_printoptions
(
threshold
=
np
.
inf
)
paddle
.
enable_static
()
class
TestRNNOp
(
XPUOpTest
):
def
init_size
(
self
):
self
.
seq_length
=
1
self
.
batch_size
=
1
self
.
input_size
=
5
self
.
hidden_size
=
16
def
get_weight_names
(
self
):
weight_names
=
[]
for
i
in
range
(
self
.
num_layers
):
for
j
in
range
(
0
,
2
*
self
.
direction_num
):
weight_names
.
append
(
"{}.weight_{}"
.
format
(
i
,
j
))
for
i
in
range
(
self
.
num_layers
):
for
j
in
range
(
0
,
2
*
self
.
direction_num
):
weight_names
.
append
(
"{}.bias_{}"
.
format
(
i
,
j
))
return
weight_names
def
setUp
(
self
):
self
.
init_size
()
self
.
op_type
=
"rnn"
self
.
dtype
=
np
.
float32
self
.
sequence_length
=
np
.
ones
(
(
self
.
batch_size
,
),
dtype
=
np
.
int32
)
*
self
.
seq_length
self
.
num_layers
=
1
self
.
is_bidirec
=
False
self
.
mode
=
"LSTM"
self
.
is_test
=
False
self
.
dropout
=
0.0
self
.
set_attrs
()
self
.
direction_num
=
2
if
self
.
is_bidirec
else
1
direction
=
"bidirectional"
if
self
.
is_bidirec
else
"forward"
input
=
np
.
random
.
uniform
(
low
=-
0.1
,
high
=
0.1
,
size
=
(
self
.
seq_length
,
self
.
batch_size
,
self
.
input_size
)).
astype
(
self
.
dtype
)
rnn1
=
LSTM
(
self
.
input_size
,
self
.
hidden_size
,
num_layers
=
self
.
num_layers
,
time_major
=
True
,
direction
=
direction
,
dropout
=
self
.
dropout
,
dtype
=
"float32"
)
flat_w
=
get_params_for_net
(
rnn1
)
output
,
(
last_hidden
,
last_cell
)
=
rnn1
(
input
,
sequence_length
=
self
.
sequence_length
)
init_h
=
np
.
zeros
(
(
self
.
num_layers
*
self
.
direction_num
,
self
.
batch_size
,
self
.
hidden_size
)).
astype
(
self
.
dtype
)
init_c
=
np
.
zeros
(
(
self
.
num_layers
*
self
.
direction_num
,
self
.
batch_size
,
self
.
hidden_size
)).
astype
(
self
.
dtype
)
state_out
=
np
.
ndarray
((
300
)).
astype
(
"uint8"
)
self
.
inputs
=
{
'Input'
:
input
,
'WeightList'
:
flat_w
,
'PreState'
:
[(
'init_h'
,
init_h
),
(
'init_c'
,
init_c
)],
'SequenceLength'
:
self
.
sequence_length
}
if
self
.
sequence_length
is
None
:
self
.
inputs
=
{
'Input'
:
input
,
'WeightList'
:
flat_w
,
'PreState'
:
[(
'init_h'
,
init_h
),
(
'init_c'
,
init_c
)],
}
self
.
attrs
=
{
'dropout_prob'
:
self
.
dropout
,
'is_bidirec'
:
self
.
is_bidirec
,
'input_size'
:
self
.
input_size
,
'hidden_size'
:
self
.
hidden_size
,
'num_layers'
:
self
.
num_layers
,
'mode'
:
self
.
mode
,
'is_test'
:
self
.
is_test
}
self
.
outputs
=
{
'Out'
:
output
,
"State"
:
[(
'last_hidden'
,
last_hidden
),
(
'last_cell'
,
last_cell
)],
'Reserve'
:
np
.
ndarray
((
400
)).
astype
(
"uint8"
),
'DropoutState'
:
state_out
}
def
test_output
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
,
atol
=
0.01
,
no_check_set
=
[
'Reserve'
,
'DropoutState'
])
def
set_attrs
(
self
):
pass
def
test_grad
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
if
not
self
.
is_test
:
var_name_list
=
self
.
get_weight_names
()
grad_check_list
=
[
'Input'
,
'init_h'
,
'init_c'
]
grad_check_list
.
extend
(
var_name_list
)
self
.
check_grad_with_place
(
place
,
set
(
grad_check_list
),
[
'Out'
,
'last_hidden'
,
'last_cell'
],
max_relative_error
=
0.1
)
class
TestRNNOpCase0
(
TestRNNOp
):
def
init_size
(
self
):
self
.
seq_length
=
2
self
.
batch_size
=
4
self
.
input_size
=
10
self
.
hidden_size
=
32
class
TestRNNOpCase1
(
TestRNNOp
):
def
init_size
(
self
):
self
.
seq_length
=
5
self
.
batch_size
=
16
self
.
input_size
=
30
self
.
hidden_size
=
64
class
TestRNNOpCase2
(
TestRNNOp
):
def
init_size
(
self
):
self
.
seq_length
=
10
self
.
batch_size
=
64
self
.
input_size
=
50
self
.
hidden_size
=
64
if
__name__
==
'__main__'
:
unittest
.
main
()
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