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体验新版 GitCode,发现更多精彩内容 >>
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提交
adba4384
编写于
1月 17, 2019
作者:
乔
乔龙飞 Qiao Longfei
提交者:
GitHub
1月 17, 2019
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差异文件
Merge pull request #15161 from jacquesqiao/gru-add-mode
gru add origin mode
上级
7cd4dd7c
4d15515c
变更
15
隐藏空白更改
内联
并排
Showing
15 changed file
with
251 addition
and
91 deletion
+251
-91
paddle/fluid/API.spec
paddle/fluid/API.spec
+2
-2
paddle/fluid/operators/gru_op.cc
paddle/fluid/operators/gru_op.cc
+7
-2
paddle/fluid/operators/gru_op.cu.cc
paddle/fluid/operators/gru_op.cu.cc
+2
-1
paddle/fluid/operators/gru_op.h
paddle/fluid/operators/gru_op.h
+2
-1
paddle/fluid/operators/gru_unit_op.cc
paddle/fluid/operators/gru_unit_op.cc
+7
-0
paddle/fluid/operators/gru_unit_op.h
paddle/fluid/operators/gru_unit_op.h
+23
-7
paddle/fluid/operators/math/detail/gru_cpu_kernel.h
paddle/fluid/operators/math/detail/gru_cpu_kernel.h
+26
-20
paddle/fluid/operators/math/detail/gru_gpu_kernel.h
paddle/fluid/operators/math/detail/gru_gpu_kernel.h
+6
-4
paddle/fluid/operators/math/detail/gru_kernel.h
paddle/fluid/operators/math/detail/gru_kernel.h
+60
-25
paddle/fluid/operators/math/gru_compute.cc
paddle/fluid/operators/math/gru_compute.cc
+8
-4
paddle/fluid/operators/math/gru_compute.cu
paddle/fluid/operators/math/gru_compute.cu
+8
-6
paddle/fluid/operators/math/gru_compute.h
paddle/fluid/operators/math/gru_compute.h
+4
-2
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+44
-7
python/paddle/fluid/tests/unittests/test_gru_op.py
python/paddle/fluid/tests/unittests/test_gru_op.py
+29
-4
python/paddle/fluid/tests/unittests/test_gru_unit_op.py
python/paddle/fluid/tests/unittests/test_gru_unit_op.py
+23
-6
未找到文件。
paddle/fluid/API.spec
浏览文件 @
adba4384
...
...
@@ -70,8 +70,8 @@ paddle.fluid.layers.fc ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param
paddle.fluid.layers.embedding ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32'))
paddle.fluid.layers.dynamic_lstm ArgSpec(args=['input', 'size', 'h_0', 'c_0', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'float32', None))
paddle.fluid.layers.dynamic_lstmp ArgSpec(args=['input', 'size', 'proj_size', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'proj_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'tanh', 'float32', None))
paddle.fluid.layers.dynamic_gru ArgSpec(args=['input', 'size', 'param_attr', 'bias_attr', 'is_reverse', 'gate_activation', 'candidate_activation', 'h_0'
], varargs=None, keywords=None, defaults=(None, None, False, 'sigmoid', 'tanh', Non
e))
paddle.fluid.layers.gru_unit ArgSpec(args=['input', 'hidden', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation'
], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid'
))
paddle.fluid.layers.dynamic_gru ArgSpec(args=['input', 'size', 'param_attr', 'bias_attr', 'is_reverse', 'gate_activation', 'candidate_activation', 'h_0'
, 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, False, 'sigmoid', 'tanh', None, Fals
e))
paddle.fluid.layers.gru_unit ArgSpec(args=['input', 'hidden', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation'
, 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid', False
))
paddle.fluid.layers.linear_chain_crf ArgSpec(args=['input', 'label', 'param_attr'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.crf_decoding ArgSpec(args=['input', 'param_attr', 'label'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.cos_sim ArgSpec(args=['X', 'Y'], varargs=None, keywords=None, defaults=None)
...
...
paddle/fluid/operators/gru_op.cc
浏览文件 @
adba4384
...
...
@@ -137,6 +137,10 @@ class GRUOpMaker : public framework::OpProtoAndCheckerMaker {
"(bool, defalut: False) "
"whether to compute reversed GRU."
)
.
SetDefault
(
false
);
AddAttr
<
bool
>
(
"origin_mode"
,
"bool"
"use origin mode in article https://arxiv.org/abs/1412.3555"
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(
GRU Operator implements part calculations of the complete GRU as following:
...
...
@@ -221,6 +225,7 @@ class GRUCPUKernel : public framework::OpKernel<T> {
public:
void
BatchCompute
(
const
framework
::
ExecutionContext
&
context
)
const
{
using
DeviceContext
=
paddle
::
platform
::
CPUDeviceContext
;
bool
origin_mode
=
context
.
Attr
<
bool
>
(
"origin_mode"
);
auto
*
input
=
context
.
Input
<
LoDTensor
>
(
"Input"
);
auto
*
h0
=
context
.
Input
<
Tensor
>
(
"H0"
);
auto
*
weight
=
context
.
Input
<
Tensor
>
(
"Weight"
);
...
...
@@ -327,7 +332,7 @@ class GRUCPUKernel : public framework::OpKernel<T> {
math
::
detail
::
forward_final_output
(
math
::
detail
::
forward
::
gru_finalOutput
<
T
>
(),
gru_value
,
frame_size
,
cur_batch_size
,
active_node
);
cur_batch_size
,
active_node
,
origin_mode
);
gru_value
.
prev_out_value
=
gru_value
.
output_value
;
}
...
...
@@ -351,7 +356,7 @@ class GRUCPUKernel : public framework::OpKernel<T> {
math
::
GRUUnitFunctor
<
DeviceContext
,
T
>::
compute
(
dev_ctx
,
gru_value
,
frame_size
,
cur_batch_size
,
active_node
,
active_gate
);
active_gate
,
origin_mode
);
gru_value
.
prev_out_value
=
gru_value
.
output_value
;
}
...
...
paddle/fluid/operators/gru_op.cu.cc
浏览文件 @
adba4384
...
...
@@ -21,6 +21,7 @@ template <typename DeviceContext, typename T>
class
GRUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
BatchCompute
(
const
framework
::
ExecutionContext
&
context
)
const
{
bool
origin_mode
=
context
.
Attr
<
bool
>
(
"origin_mode"
);
auto
*
input
=
context
.
Input
<
LoDTensor
>
(
"Input"
);
auto
*
h0
=
context
.
Input
<
Tensor
>
(
"H0"
);
auto
*
weight
=
context
.
Input
<
Tensor
>
(
"Weight"
);
...
...
@@ -87,7 +88,7 @@ class GRUKernel : public framework::OpKernel<T> {
gru_value
.
reset_output_value
=
reset_hidden_prev_t
.
data
<
T
>
();
math
::
GRUUnitFunctor
<
DeviceContext
,
T
>::
compute
(
dev_ctx
,
gru_value
,
frame_size
,
cur_batch_size
,
active_node
,
active_gate
);
active_gate
,
origin_mode
);
gru_value
.
prev_out_value
=
gru_value
.
output_value
;
}
...
...
paddle/fluid/operators/gru_op.h
浏览文件 @
adba4384
...
...
@@ -41,6 +41,7 @@ template <typename DeviceContext, typename T>
class
GRUGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
BatchCompute
(
const
framework
::
ExecutionContext
&
context
)
const
{
bool
origin_mode
=
context
.
Attr
<
bool
>
(
"origin_mode"
);
auto
*
h0
=
context
.
Input
<
Tensor
>
(
"H0"
);
auto
*
weight
=
context
.
Input
<
Tensor
>
(
"Weight"
);
const
T
*
weight_data
=
weight
->
data
<
T
>
();
...
...
@@ -146,7 +147,7 @@ class GRUGradKernel : public framework::OpKernel<T> {
math
::
GRUUnitGradFunctor
<
DeviceContext
,
T
>::
compute
(
dev_ctx
,
gru_value
,
gru_grad
,
frame_size
,
cur_batch_size
,
active_node
,
active_gate
);
active_gate
,
origin_mode
);
}
if
(
input_grad
)
{
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
...
...
paddle/fluid/operators/gru_unit_op.cc
浏览文件 @
adba4384
...
...
@@ -111,6 +111,13 @@ class GRUUnitOpMaker : public framework::OpProtoAndCheckerMaker {
"The activation type used in update gate and reset gate."
)
.
SetDefault
(
sigmoid
)
.
InEnum
({
identity
,
sigmoid
,
tanh
,
relu
});
AddAttr
<
bool
>
(
"origin_mode"
,
"bool"
"use origin mode in article <Learning Phrase Representations "
"using RNN Encoder–Decoder
\n
"
"for Statistical Machine "
"Translation>(https://arxiv.org/pdf/1406.1078.pdf)"
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(
GRUUnit Operator implements partial calculations of the GRU unit as following:
...
...
paddle/fluid/operators/gru_unit_op.h
浏览文件 @
adba4384
...
...
@@ -113,7 +113,11 @@ class GRUUnitKernel : public framework::OpKernel<T> {
auto
c
=
g
.
slice
(
c_offsets
,
extents
);
// output candidate
// calculate final output
h
.
device
(
place
)
=
u
*
(
c
-
h_p
)
+
h_p
;
if
(
context
.
Attr
<
bool
>
(
"origin_mode"
))
{
h
.
device
(
place
)
=
c
+
u
*
(
h_p
-
c
);
// (1 - u) * c + u * h_p
}
else
{
h
.
device
(
place
)
=
u
*
(
c
-
h_p
)
+
h_p
;
// u * c + (1 - u) * h_p
}
}
};
...
...
@@ -180,11 +184,19 @@ class GRUUnitGradKernel : public framework::OpKernel<T> {
auto
c
=
g
.
slice
(
c_offsets
,
extents
);
// output candidate
// backward for unactivated update gate
ActGradCompute
(
context
.
Attr
<
int
>
(
"gate_activation"
),
place
,
u
,
u
,
d_g
.
slice
(
u_offsets
,
extents
),
d_h
*
(
c
-
h_p
));
// backward for unactivated output candidate
ActGradCompute
(
context
.
Attr
<
int
>
(
"activation"
),
place
,
c
,
c
,
d_g
.
slice
(
c_offsets
,
extents
),
d_h
*
u
);
if
(
context
.
Attr
<
bool
>
(
"origin_mode"
))
{
ActGradCompute
(
context
.
Attr
<
int
>
(
"gate_activation"
),
place
,
u
,
u
,
d_g
.
slice
(
u_offsets
,
extents
),
d_h
*
(
h_p
-
c
));
// backward for unactivated output candidate
ActGradCompute
(
context
.
Attr
<
int
>
(
"activation"
),
place
,
c
,
c
,
d_g
.
slice
(
c_offsets
,
extents
),
d_h
*
(
1
-
u
));
}
else
{
ActGradCompute
(
context
.
Attr
<
int
>
(
"gate_activation"
),
place
,
u
,
u
,
d_g
.
slice
(
u_offsets
,
extents
),
d_h
*
(
c
-
h_p
));
// backward for unactivated output candidate
ActGradCompute
(
context
.
Attr
<
int
>
(
"activation"
),
place
,
c
,
c
,
d_g
.
slice
(
c_offsets
,
extents
),
d_h
*
u
);
}
// backward for reset_hidden_prev
auto
blas
=
math
::
GetBlas
<
DeviceContext
,
T
>
(
context
);
blas
.
GEMM
(
false
,
true
,
batch_size
,
frame_size
,
frame_size
,
1
,
...
...
@@ -213,7 +225,11 @@ class GRUUnitGradKernel : public framework::OpKernel<T> {
T
*
hidden_prev_grad_data
=
hidden_prev_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
d_h_p
=
EigenMatrix
<
T
>::
From
(
*
hidden_prev_grad
);
d_h_p
.
device
(
place
)
=
d_r_h_p
*
r
+
d_h
*
(
u
.
constant
(
T
(
1
))
-
u
);
if
(
context
.
Attr
<
bool
>
(
"origin_mode"
))
{
d_h_p
.
device
(
place
)
=
d_r_h_p
*
r
+
d_h
*
u
;
}
else
{
d_h_p
.
device
(
place
)
=
d_r_h_p
*
r
+
d_h
*
(
1
-
u
);
}
blas
.
GEMM
(
false
,
true
,
batch_size
,
frame_size
,
frame_size
*
2
,
1
,
gate_grad_data
,
frame_size
*
3
,
weight_data
,
frame_size
*
2
,
1
,
hidden_prev_grad_data
,
frame_size
);
...
...
paddle/fluid/operators/math/detail/gru_cpu_kernel.h
浏览文件 @
adba4384
...
...
@@ -56,7 +56,8 @@ template <class OpFinalOutput, typename T>
void
hl_naive_gru_forward_final_output
(
OpFinalOutput
op_final_output
,
T
*
gate_value
,
T
*
prev_output_value
,
T
*
output_value
,
int
frame_size
,
ActivationType
active_node
)
{
ActivationType
active_node
,
bool
origin_mode
)
{
T
r_value_update_gate
;
T
r_value_frame_state
;
T
r_prev_out
=
0
;
...
...
@@ -72,7 +73,7 @@ void hl_naive_gru_forward_final_output(OpFinalOutput op_final_output,
}
op_final_output
(
&
r_value_update_gate
,
&
r_value_frame_state
,
&
r_prev_out
,
&
r_output
,
active_node
);
&
r_output
,
active_node
,
origin_mode
);
frame_state
[
i
]
=
r_value_frame_state
;
output_value
[
i
]
=
r_output
;
...
...
@@ -146,7 +147,8 @@ template <class OpFinalOutput, typename T>
void
hl_avx_gru_forward_final_output
(
OpFinalOutput
op_final_output
,
T
*
gate_value
,
T
*
prev_output_value
,
T
*
output_value
,
int
frame_size
,
ActivationType
active_node
)
{
ActivationType
active_node
,
bool
origin_mode
)
{
#ifdef __AVX__
__m256
r_value_update_gate
,
r_value_update_gate_last
=
_mm256_set1_ps
(
0.0
f
);
__m256
r_value_frame_state
,
r_value_frame_state_last
=
_mm256_set1_ps
(
0.0
f
);
...
...
@@ -180,7 +182,7 @@ void hl_avx_gru_forward_final_output(OpFinalOutput op_final_output,
}
op_final_output
(
&
r_value_update_gate
,
&
r_value_frame_state
,
&
r_prev_out
,
&
r_output
,
active_node
);
&
r_output
,
active_node
,
origin_mode
);
_mm256_storeu_ps
(
reinterpret_cast
<
float
*>
(
frame_state
+
i
),
r_value_frame_state
);
...
...
@@ -190,7 +192,7 @@ void hl_avx_gru_forward_final_output(OpFinalOutput op_final_output,
if
(
rest
>
0
)
{
i
=
n
-
block
;
op_final_output
(
&
r_value_update_gate_last
,
&
r_value_frame_state_last
,
&
r_prev_out_last
,
&
r_output
,
active_node
);
&
r_prev_out_last
,
&
r_output
,
active_node
,
origin_mode
);
_mm256_storeu_ps
(
reinterpret_cast
<
float
*>
(
frame_state
+
i
),
r_value_frame_state_last
);
...
...
@@ -227,17 +229,18 @@ inline void forward_reset_output(OpResetOutput op_reset_output,
template
<
class
OpFinalOutput
,
typename
T
>
inline
void
forward_final_output
(
OpFinalOutput
op_final_output
,
GRUMetaValue
<
T
>
value
,
int
frame_size
,
int
batch_size
,
ActivationType
active_node
)
{
int
batch_size
,
ActivationType
active_node
,
bool
origin_mode
)
{
for
(
int
b
=
0
;
b
<
batch_size
;
b
++
)
{
if
(
OpFinalOutput
::
avx
&&
(
frame_size
>
static_cast
<
int
>
(
8
-
1
))
&&
(
sizeof
(
T
)
==
4
))
{
hl_avx_gru_forward_final_output
(
op_final_output
,
value
.
gate_value
,
value
.
prev_out_value
,
value
.
output_value
,
frame_size
,
active_node
);
frame_size
,
active_node
,
origin_mode
);
}
else
{
hl_naive_gru_forward_final_output
(
op_final_output
,
value
.
gate_value
,
value
.
prev_out_value
,
value
.
output_value
,
frame_size
,
active_node
);
value
.
output_value
,
frame_size
,
active_node
,
origin_mode
);
}
value
.
gate_value
+=
frame_size
*
3
;
...
...
@@ -253,7 +256,8 @@ void hl_naive_gru_backward_state_grad(OpStateGrad op_state_grad, T *gate_value,
T
*
gate_grad
,
T
*
prev_out_value
,
T
*
prev_out_grad
,
T
*
output_grad
,
int
frame_size
,
ActivationType
active_node
)
{
ActivationType
active_node
,
bool
origin_mode
)
{
T
r_update_gate_value
;
T
r_update_gate_grad
;
T
r_frame_state_value
;
...
...
@@ -279,7 +283,7 @@ void hl_naive_gru_backward_state_grad(OpStateGrad op_state_grad, T *gate_value,
op_state_grad
(
&
r_update_gate_value
,
&
r_update_gate_grad
,
&
r_frame_state_value
,
&
r_frame_state_grad
,
&
r_prev_out_value
,
&
r_prev_out_grad
,
&
r_out_grad
,
active_node
);
&
r_prev_out_grad
,
&
r_out_grad
,
active_node
,
origin_mode
);
update_gate_grad
[
i
]
=
r_update_gate_grad
;
frame_state_grad
[
i
]
=
r_frame_state_grad
;
...
...
@@ -338,8 +342,8 @@ template <class OpStateGrad, typename T>
void
hl_avx_gru_backward_state_grad
(
OpStateGrad
op_state_grad
,
T
*
gate_value
,
T
*
gate_grad
,
T
*
prev_out_value
,
T
*
prev_out_grad
,
T
*
output_grad
,
int
frame_size
,
ActivationType
active_n
ode
)
{
int
frame_size
,
ActivationType
active_node
,
bool
origin_m
ode
)
{
#ifdef __AVX__
__m256
r_update_gate_value
;
__m256
r_update_gate_grad
;
...
...
@@ -368,7 +372,7 @@ void hl_avx_gru_backward_state_grad(OpStateGrad op_state_grad, T *gate_value,
op_state_grad
(
&
r_update_gate_value
,
&
r_update_gate_grad
,
&
r_frame_state_value
,
&
r_frame_state_grad
,
&
r_prev_out_value
,
&
r_prev_out_grad
,
&
r_out_grad
,
active_node
);
&
r_prev_out_grad
,
&
r_out_grad
,
active_node
,
origin_mode
);
update_gate_grad
[
i
]
=
r_update_gate_grad
;
frame_state_grad
[
i
]
=
r_frame_state_grad
;
...
...
@@ -431,16 +435,18 @@ template <class OpStateGrad, typename T>
inline
void
backward_state_grad
(
OpStateGrad
op_state_grad
,
GRUMetaValue
<
T
>
value
,
GRUMetaGrad
<
T
>
grad
,
int
frame_size
,
int
batch_size
,
ActivationType
active_node
)
{
ActivationType
active_node
,
bool
origin_mode
)
{
for
(
int
b
=
0
;
b
<
batch_size
;
b
++
)
{
if
(
OpStateGrad
::
avx
&&
!
(
frame_size
&
(
8
-
1
))
&&
(
sizeof
(
T
)
==
4
))
{
hl_avx_gru_backward_state_grad
(
op_state_grad
,
value
.
gate_value
,
grad
.
gate_grad
,
value
.
prev_out_value
,
grad
.
prev_out_grad
,
grad
.
output_grad
,
frame_size
,
active_node
);
hl_avx_gru_backward_state_grad
(
op_state_grad
,
value
.
gate_value
,
grad
.
gate_grad
,
value
.
prev_out_value
,
grad
.
prev_out_grad
,
grad
.
output_grad
,
frame_size
,
active_node
,
origin_mode
);
}
else
{
hl_naive_gru_backward_state_grad
(
op_state_grad
,
value
.
gate_value
,
grad
.
gate_grad
,
value
.
prev_out_value
,
grad
.
prev_out_grad
,
grad
.
output_grad
,
frame_size
,
active_node
);
hl_naive_gru_backward_state_grad
(
op_state_grad
,
value
.
gate_value
,
grad
.
gate_grad
,
value
.
prev_out_value
,
grad
.
prev_out_grad
,
grad
.
output_grad
,
frame_size
,
active_node
,
origin_mode
);
}
value
.
gate_value
+=
frame_size
*
3
;
...
...
paddle/fluid/operators/math/detail/gru_gpu_kernel.h
浏览文件 @
adba4384
...
...
@@ -72,7 +72,8 @@ __global__ void KeGruForwardFinalOutput(OpFinalOutput op_final_output,
T
*
gate_value
,
T
*
prev_output_value
,
T
*
output_value
,
int
frame_size
,
int
batch_size
,
ActivationType
active_node
)
{
ActivationType
active_node
,
bool
origin_mode
)
{
const
int
frame_idx
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
frame_idx
>=
frame_size
)
return
;
int
batch_idx
=
0
;
...
...
@@ -94,7 +95,7 @@ __global__ void KeGruForwardFinalOutput(OpFinalOutput op_final_output,
}
op_final_output
(
&
r_value_update_gate
,
&
r_value_frame_state
,
&
r_prev_out
,
&
r_output
,
active_node
);
&
r_output
,
active_node
,
origin_mode
);
gate_value
[
frame_idx
+
frame_size
*
2
]
=
r_value_frame_state
;
output_value
[
frame_idx
]
=
r_output
;
...
...
@@ -109,7 +110,8 @@ __global__ void KeGruBackwardStateGrad(OpStateGrad op_state_grad, T *gate_value,
T
*
gate_grad
,
T
*
prev_out_value
,
T
*
prev_out_grad
,
T
*
output_grad
,
int
frame_size
,
int
batch_size
,
ActivationType
active_node
)
{
ActivationType
active_node
,
bool
origin_mode
)
{
const
int
frame_idx
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
frame_idx
>=
frame_size
)
return
;
int
batch_idx
=
0
;
...
...
@@ -139,7 +141,7 @@ __global__ void KeGruBackwardStateGrad(OpStateGrad op_state_grad, T *gate_value,
op_state_grad
(
&
r_update_gate_value
,
&
r_update_gate_grad
,
&
r_frame_state_value
,
&
r_frame_state_grad
,
&
r_prev_out_value
,
&
r_prev_out_grad
,
&
r_out_grad
,
active_node
);
&
r_out_grad
,
active_node
,
origin_mode
);
gate_grad
[
frame_idx
+
frame_size
*
0
]
=
r_update_gate_grad
;
gate_grad
[
frame_idx
+
frame_size
*
2
]
=
r_frame_state_grad
;
...
...
paddle/fluid/operators/math/detail/gru_kernel.h
浏览文件 @
adba4384
...
...
@@ -57,10 +57,16 @@ class gru_finalOutput {
public:
HOSTDEVICE
void
operator
()(
T
*
value_update_gate
,
T
*
value_frame_state
,
T
*
prev_out
,
T
*
value_output
,
ActivationType
act_input
)
{
ActivationType
act_input
,
bool
origin_mode
)
{
*
value_frame_state
=
activation
(
*
value_frame_state
,
act_input
);
*
value_output
=
*
prev_out
-
((
*
value_update_gate
)
*
(
*
prev_out
))
+
((
*
value_update_gate
)
*
(
*
value_frame_state
));
if
(
origin_mode
)
{
*
value_output
=
((
*
value_update_gate
)
*
(
*
prev_out
))
+
*
value_frame_state
-
((
*
value_update_gate
)
*
(
*
value_frame_state
));
}
else
{
*
value_output
=
*
prev_out
-
((
*
value_update_gate
)
*
(
*
prev_out
))
+
((
*
value_update_gate
)
*
(
*
value_frame_state
));
}
}
#ifndef __NVCC__
#ifndef __AVX__
...
...
@@ -69,11 +75,20 @@ class gru_finalOutput {
static
const
bool
avx
=
true
;
HOSTDEVICE
void
operator
()(
__m256
*
value_update_gate
,
__m256
*
value_frame_state
,
__m256
*
prev_out
,
__m256
*
value_output
,
ActivationType
act_input
)
{
__m256
*
value_output
,
ActivationType
act_input
,
bool
origin_mode
)
{
*
value_frame_state
=
activation
(
*
value_frame_state
,
act_input
);
*
value_output
=
_mm256_add_ps
(
_mm256_sub_ps
(
*
prev_out
,
_mm256_mul_ps
(
*
value_update_gate
,
*
prev_out
)),
_mm256_mul_ps
(
*
value_update_gate
,
*
value_frame_state
));
if
(
origin_mode
)
{
*
value_output
=
_mm256_sub_ps
(
_mm256_add_ps
(
_mm256_mul_ps
(
*
value_update_gate
,
*
prev_out
),
*
value_frame_state
),
_mm256_mul_ps
(
*
value_update_gate
,
*
value_frame_state
));
}
else
{
*
value_output
=
_mm256_add_ps
(
_mm256_sub_ps
(
*
prev_out
,
_mm256_mul_ps
(
*
value_update_gate
,
*
prev_out
)),
_mm256_mul_ps
(
*
value_update_gate
,
*
value_frame_state
));
}
}
#endif
#endif
...
...
@@ -88,13 +103,23 @@ class gru_stateGrad {
HOSTDEVICE
void
operator
()(
T
*
value_update_gate
,
T
*
grad_update_gate
,
T
*
value_frame_state
,
T
*
grad_frame_state
,
T
*
value_prev_out
,
T
*
grad_prev_out
,
T
*
grad_output
,
ActivationType
act_input
)
{
*
grad_update_gate
=
(
*
grad_output
*
(
*
value_frame_state
));
*
grad_update_gate
-=
(
*
grad_output
*
(
*
value_prev_out
));
*
grad_prev_out
-=
(
*
grad_output
*
(
*
value_update_gate
));
*
grad_prev_out
+=
*
grad_output
;
*
grad_frame_state
=
activation
(
*
grad_output
*
(
*
value_update_gate
),
*
value_frame_state
,
act_input
);
T
*
grad_output
,
ActivationType
act_input
,
bool
origin_mode
)
{
if
(
origin_mode
)
{
*
grad_update_gate
=
(
*
grad_output
)
*
((
*
value_prev_out
)
-
(
*
value_frame_state
));
*
grad_prev_out
+=
(
*
grad_output
*
(
*
value_update_gate
));
*
grad_frame_state
=
activation
(
*
grad_output
*
(
static_cast
<
T
>
(
1.0
)
-
(
*
value_update_gate
)),
*
value_frame_state
,
act_input
);
}
else
{
*
grad_update_gate
=
(
*
grad_output
)
*
((
*
value_frame_state
)
-
(
*
value_prev_out
));
*
grad_prev_out
+=
(
*
grad_output
*
(
static_cast
<
T
>
(
1.0
)
-
*
value_update_gate
));
*
grad_frame_state
=
activation
(
*
grad_output
*
(
*
value_update_gate
),
*
value_frame_state
,
act_input
);
}
}
#ifndef __NVCC__
#ifndef __AVX__
...
...
@@ -106,17 +131,27 @@ class gru_stateGrad {
__m256
*
value_frame_state
,
__m256
*
grad_frame_state
,
__m256
*
value_prev_out
,
__m256
*
grad_prev_out
,
__m256
*
grad_output
,
ActivationType
act_input
)
{
*
grad_update_gate
=
_mm256_mul_ps
(
*
grad_output
,
*
value_frame_state
);
*
grad_update_gate
=
_mm256_sub_ps
(
*
grad_update_gate
,
_mm256_mul_ps
(
*
grad_output
,
*
value_prev_out
));
*
grad_prev_out
=
_mm256_add_ps
(
_mm256_sub_ps
(
*
grad_prev_out
,
_mm256_mul_ps
(
*
grad_output
,
*
value_update_gate
)),
*
grad_output
);
*
grad_frame_state
=
activation
(
_mm256_mul_ps
(
*
grad_output
,
*
value_update_gate
),
*
value_frame_state
,
act_input
);
ActivationType
act_input
,
bool
origin_mode
)
{
if
(
origin_mode
)
{
*
grad_update_gate
=
_mm256_mul_ps
(
*
grad_output
,
_mm256_sub_ps
(
*
value_prev_out
,
*
value_frame_state
));
*
grad_prev_out
=
_mm256_add_ps
(
*
grad_prev_out
,
_mm256_mul_ps
(
*
grad_output
,
*
value_update_gate
));
*
grad_frame_state
=
activation
(
_mm256_mul_ps
(
*
grad_output
,
_mm256_sub_ps
(
_mm256_set1_ps
(
1.0
f
),
*
value_update_gate
)),
*
value_frame_state
,
act_input
);
}
else
{
*
grad_update_gate
=
_mm256_mul_ps
(
*
grad_output
,
_mm256_sub_ps
(
*
value_frame_state
,
*
value_prev_out
));
*
grad_prev_out
=
_mm256_add_ps
(
*
grad_prev_out
,
_mm256_mul_ps
(
*
grad_output
,
_mm256_sub_ps
(
_mm256_set1_ps
(
1.0
f
),
*
value_update_gate
)));
*
grad_frame_state
=
activation
(
_mm256_mul_ps
(
*
grad_output
,
*
value_update_gate
),
*
value_frame_state
,
act_input
);
}
}
#endif
#endif
...
...
paddle/fluid/operators/math/gru_compute.cc
浏览文件 @
adba4384
...
...
@@ -23,7 +23,8 @@ struct GRUUnitFunctor<platform::CPUDeviceContext, T> {
static
void
compute
(
const
platform
::
CPUDeviceContext
&
context
,
GRUMetaValue
<
T
>
value
,
int
frame_size
,
int
batch_size
,
const
detail
::
ActivationType
active_node
,
const
detail
::
ActivationType
active_gate
)
{
const
detail
::
ActivationType
active_gate
,
bool
origin_mode
)
{
#ifndef __NVCC__
auto
blas
=
math
::
GetBlas
<
platform
::
CPUDeviceContext
,
T
>
(
context
);
if
(
value
.
prev_out_value
)
{
...
...
@@ -43,7 +44,8 @@ struct GRUUnitFunctor<platform::CPUDeviceContext, T> {
}
detail
::
forward_final_output
(
detail
::
forward
::
gru_finalOutput
<
T
>
(),
value
,
frame_size
,
batch_size
,
active_node
);
frame_size
,
batch_size
,
active_node
,
origin_mode
);
#endif
}
};
...
...
@@ -54,10 +56,12 @@ struct GRUUnitGradFunctor<platform::CPUDeviceContext, T> {
GRUMetaValue
<
T
>
value
,
GRUMetaGrad
<
T
>
grad
,
int
frame_size
,
int
batch_size
,
const
detail
::
ActivationType
active_node
,
const
detail
::
ActivationType
active_gate
)
{
const
detail
::
ActivationType
active_gate
,
bool
origin_mode
)
{
#ifndef __NVCC__
detail
::
backward_state_grad
(
detail
::
backward
::
gru_stateGrad
<
T
>
(),
value
,
grad
,
frame_size
,
batch_size
,
active_node
);
grad
,
frame_size
,
batch_size
,
active_node
,
origin_mode
);
auto
blas
=
math
::
GetBlas
<
platform
::
CPUDeviceContext
,
T
>
(
context
);
if
(
value
.
prev_out_value
&&
grad
.
prev_out_grad
)
{
blas
.
GEMM
(
false
,
true
,
batch_size
,
frame_size
,
frame_size
,
1
,
...
...
paddle/fluid/operators/math/gru_compute.cu
浏览文件 @
adba4384
...
...
@@ -24,7 +24,8 @@ struct GRUUnitFunctor<platform::CUDADeviceContext, T> {
static
void
compute
(
const
platform
::
CUDADeviceContext
&
context
,
GRUMetaValue
<
T
>
value
,
int
frame_size
,
int
batch_size
,
const
detail
::
ActivationType
active_node
,
const
detail
::
ActivationType
active_gate
)
{
const
detail
::
ActivationType
active_gate
,
bool
origin_mode
)
{
auto
stream
=
context
.
stream
();
dim3
threads
;
dim3
grid
;
...
...
@@ -73,14 +74,14 @@ struct GRUUnitFunctor<platform::CUDADeviceContext, T> {
T
><<<
grid
,
threads
,
0
,
stream
>>>
(
detail
::
forward
::
gru_finalOutput
<
T
>
(),
value
.
gate_value
,
value
.
prev_out_value
,
value
.
output_value
,
frame_size
,
batch_size
,
active_node
);
active_node
,
origin_mode
);
}
else
{
detail
::
KeGruForwardFinalOutput
<
detail
::
forward
::
gru_finalOutput
<
T
>
,
/* is_batch= */
true
,
T
><<<
grid
,
threads
,
0
,
stream
>>>
(
detail
::
forward
::
gru_finalOutput
<
T
>
(),
value
.
gate_value
,
value
.
prev_out_value
,
value
.
output_value
,
frame_size
,
batch_size
,
active_node
);
active_node
,
origin_mode
);
}
}
};
...
...
@@ -91,7 +92,8 @@ struct GRUUnitGradFunctor<platform::CUDADeviceContext, T> {
GRUMetaValue
<
T
>
value
,
GRUMetaGrad
<
T
>
grad
,
int
frame_size
,
int
batch_size
,
const
detail
::
ActivationType
active_node
,
const
detail
::
ActivationType
active_gate
)
{
const
detail
::
ActivationType
active_gate
,
bool
origin_mode
)
{
auto
stream
=
context
.
stream
();
dim3
threads
;
dim3
grid
;
...
...
@@ -111,14 +113,14 @@ struct GRUUnitGradFunctor<platform::CUDADeviceContext, T> {
/* is_batch= */
false
><<<
grid
,
threads
,
0
,
stream
>>>
(
detail
::
backward
::
gru_stateGrad
<
T
>
(),
value
.
gate_value
,
grad
.
gate_grad
,
value
.
prev_out_value
,
grad
.
prev_out_grad
,
grad
.
output_grad
,
frame_size
,
batch_size
,
active_node
);
grad
.
output_grad
,
frame_size
,
batch_size
,
active_node
,
origin_mode
);
}
else
{
detail
::
KeGruBackwardStateGrad
<
detail
::
backward
::
gru_stateGrad
<
T
>
,
/* is_batch= */
true
><<<
grid
,
threads
,
0
,
stream
>>>
(
detail
::
backward
::
gru_stateGrad
<
T
>
(),
value
.
gate_value
,
grad
.
gate_grad
,
value
.
prev_out_value
,
grad
.
prev_out_grad
,
grad
.
output_grad
,
frame_size
,
batch_size
,
active_node
);
grad
.
output_grad
,
frame_size
,
batch_size
,
active_node
,
origin_mode
);
}
auto
blas
=
math
::
GetBlas
<
platform
::
CUDADeviceContext
,
T
>
(
context
);
...
...
paddle/fluid/operators/math/gru_compute.h
浏览文件 @
adba4384
...
...
@@ -44,7 +44,8 @@ struct GRUUnitFunctor {
static
void
compute
(
const
DeviceContext
&
context
,
GRUMetaValue
<
T
>
value
,
int
frame_size
,
int
batch_size
,
const
detail
::
ActivationType
active_node
,
const
detail
::
ActivationType
active_gate
);
const
detail
::
ActivationType
active_gate
,
bool
origin_mode
);
};
template
<
typename
DeviceContext
,
typename
T
>
...
...
@@ -52,7 +53,8 @@ struct GRUUnitGradFunctor {
static
void
compute
(
const
DeviceContext
&
context
,
GRUMetaValue
<
T
>
value
,
GRUMetaGrad
<
T
>
grad
,
int
frame_size
,
int
batch_size
,
const
detail
::
ActivationType
active_node
,
const
detail
::
ActivationType
active_gate
);
const
detail
::
ActivationType
active_gate
,
bool
origin_mode
);
};
}
// namespace math
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
adba4384
...
...
@@ -864,12 +864,14 @@ def dynamic_gru(input,
is_reverse
=
False
,
gate_activation
=
'sigmoid'
,
candidate_activation
=
'tanh'
,
h_0
=
None
):
h_0
=
None
,
origin_mode
=
False
):
"""
**Gated Recurrent Unit (GRU) Layer**
Refer to `Empirical Evaluation of Gated Recurrent Neural Networks on
Sequence Modeling <https://arxiv.org/abs/1412.3555>`_ .
if origin_mode is False, then the equation of a gru step is from paper
`Empirical Evaluation of Gated Recurrent Neural Networks on Sequence
Modeling <https://arxiv.org/pdf/1412.3555.pdf>`_ .
The formula is as follows:
...
...
@@ -883,6 +885,21 @@ def dynamic_gru(input,
h_t & = (1-u_t) \odot h_{t-1} + u_t \odot
\\
tilde{h_t}
if origin_mode is True then the equation is from paper
Learning Phrase Representations using RNN Encoder-Decoder for Statistical
Machine Translation <https://arxiv.org/pdf/1406.1078.pdf>`_
.. math::
u_t & = act_g(W_{ux}x_{t} + W_{uh}h_{t-1} + b_u)
r_t & = act_g(W_{rx}x_{t} + W_{rh}h_{t-1} + b_r)
\\
tilde{h_t} & = act_c(W_{cx}x_{t} + W_{ch}(r_t \odot h_{t-1}) + b_c)
h_t & = u_t \odot h_{t-1} + (1-u_t) \odot
\\
tilde{h_t}
The :math:`\odot` is the element-wise product of the vectors. :math:`act_g`
is the update gate and reset gate activation function and :math:`sigmoid`
is usually used for it. :math:`act_c` is the activation function for
...
...
@@ -980,7 +997,8 @@ def dynamic_gru(input,
attrs
=
{
'is_reverse'
:
is_reverse
,
'gate_activation'
:
gate_activation
,
'activation'
:
candidate_activation
'activation'
:
candidate_activation
,
'origin_mode'
:
origin_mode
})
return
hidden
...
...
@@ -991,9 +1009,14 @@ def gru_unit(input,
param_attr
=
None
,
bias_attr
=
None
,
activation
=
'tanh'
,
gate_activation
=
'sigmoid'
):
gate_activation
=
'sigmoid'
,
origin_mode
=
False
):
"""
GRU unit layer. The equation of a gru step is:
**GRU unit layer**
if origin_mode is True, then the equation of a gru step is from paper
`Learning Phrase Representations using RNN Encoder-Decoder for Statistical
Machine Translation <https://arxiv.org/pdf/1406.1078.pdf>`_
.. math::
u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)
...
...
@@ -1002,7 +1025,21 @@ def gru_unit(input,
m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m)
h_t & = dot((1-u_t), m_t) + dot(u_t, h_{t-1})
h_t & = dot(u_t, h_{t-1}) + dot((1-u_t), m_t)
if origin_mode is False, then the equation of a gru step is from paper
`Empirical Evaluation of Gated Recurrent Neural Networks on Sequence
Modeling <https://arxiv.org/pdf/1412.3555.pdf>`_
.. math::
u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)
r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r)
m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m)
h_t & = dot((1-u_t), h_{t-1}) + dot(u_t, m_t)
The inputs of gru unit includes :math:`z_t`, :math:`h_{t-1}`. In terms
of the equation above, the :math:`z_t` is split into 3 parts -
...
...
python/paddle/fluid/tests/unittests/test_gru_op.py
浏览文件 @
adba4384
...
...
@@ -31,7 +31,8 @@ def gru(
is_reverse
,
act_state
,
act_gate
,
dtype
=
'float32'
):
dtype
=
'float32'
,
origin_mode
=
False
):
def
_seq_to_batch
(
lod
,
is_reverse
):
idx_in_seq_list
=
[]
seq_lens
=
lod
[
0
]
...
...
@@ -66,7 +67,10 @@ def gru(
w_c
=
w
.
flatten
()[
D
*
D
*
2
:].
reshape
((
D
,
D
))
c
=
act_state
(
np
.
dot
(
r_h_p
,
w_c
)
+
g
[:,
D
*
2
:])
g
=
np
.
hstack
((
u_r
,
c
))
h
=
u
*
c
+
(
1
-
u
)
*
h_p
if
origin_mode
:
h
=
(
1
-
u
)
*
c
+
u
*
h_p
else
:
h
=
u
*
c
+
(
1
-
u
)
*
h_p
return
g
,
r_h_p
,
h
T
=
sum
(
lod
[
0
])
...
...
@@ -110,6 +114,7 @@ class TestGRUOp(OpTest):
self
.
act_state
=
'tanh'
self
.
act_gate
=
'sigmoid'
self
.
dtype
=
'float64'
self
.
origin_mode
=
False
self
.
set_confs
()
T
=
sum
(
self
.
lod
[
0
])
...
...
@@ -126,7 +131,8 @@ class TestGRUOp(OpTest):
batch_gate
,
batch_reset_hidden_prev
,
batch_hidden
,
hidden
=
gru
(
input
,
self
.
lod
,
h0
,
weight
,
bias
,
self
.
is_reverse
,
ACTIVATION
[
self
.
act_state
],
ACTIVATION
[
self
.
act_gate
],
self
.
dtype
)
ACTIVATION
[
self
.
act_state
],
ACTIVATION
[
self
.
act_gate
],
self
.
dtype
,
self
.
origin_mode
)
self
.
inputs
=
{
'Input'
:
(
input
,
self
.
lod
),
'Weight'
:
weight
}
if
self
.
with_bias
:
...
...
@@ -145,7 +151,8 @@ class TestGRUOp(OpTest):
self
.
attrs
=
{
'activation'
:
self
.
act_state
,
'gate_activation'
:
self
.
act_gate
,
'is_reverse'
:
self
.
is_reverse
'is_reverse'
:
self
.
is_reverse
,
'origin_mode'
:
self
.
origin_mode
}
def
test_check_output
(
self
):
...
...
@@ -155,12 +162,24 @@ class TestGRUOp(OpTest):
self
.
check_grad
([
'Input'
,
'H0'
,
'Weight'
,
'Bias'
],
[
'Hidden'
])
class
TestGRUOriginMode
(
TestGRUOp
):
def
set_confs
(
self
):
self
.
origin_mode
=
True
class
TestGRUOp2
(
TestGRUOp
):
def
set_confs
(
self
):
self
.
D
=
19
self
.
dtype
=
'float32'
class
TestGRUOp2OriginMode
(
TestGRUOp
):
def
set_confs
(
self
):
self
.
D
=
19
self
.
dtype
=
'float32'
self
.
origin_mode
=
True
class
TestGRUOpNoInitial
(
TestGRUOp
):
def
set_confs
(
self
):
self
.
with_h0
=
False
...
...
@@ -182,5 +201,11 @@ class TestGRUOpReverse(TestGRUOp):
self
.
is_reverse
=
True
class
TestGRUOpReverseOriginMode
(
TestGRUOp
):
def
set_confs
(
self
):
self
.
is_reverse
=
True
self
.
origin_mode
=
True
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_gru_unit_op.py
浏览文件 @
adba4384
...
...
@@ -53,7 +53,7 @@ class TestGRUUnitOp(OpTest):
GRUActivationType
.
relu
:
relu
,
}
def
set_inputs
(
self
):
def
set_inputs
(
self
,
origin_mode
=
False
):
batch_size
=
self
.
batch_size
frame_size
=
self
.
frame_size
self
.
op_type
=
'gru_unit'
...
...
@@ -68,10 +68,11 @@ class TestGRUUnitOp(OpTest):
}
self
.
attrs
=
{
'activation'
:
GRUActivationType
.
tanh
,
'gate_activation'
:
GRUActivationType
.
sigmoid
'gate_activation'
:
GRUActivationType
.
sigmoid
,
'origin_mode'
:
origin_mode
}
def
set_outputs
(
self
):
def
set_outputs
(
self
,
origin_mode
=
False
):
# GRU calculations
batch_size
=
self
.
batch_size
frame_size
=
self
.
frame_size
...
...
@@ -93,7 +94,10 @@ class TestGRUUnitOp(OpTest):
c
=
self
.
activate
[
self
.
attrs
[
'activation'
]](
np
.
dot
(
r_h_p
,
w_c
)
+
g
[:,
frame_size
*
2
:])
g
=
np
.
hstack
((
u_r
,
c
))
h
=
u
*
c
+
(
1
-
u
)
*
h_p
if
origin_mode
:
h
=
(
1
-
u
)
*
c
+
u
*
h_p
else
:
h
=
u
*
c
+
(
1
-
u
)
*
h_p
self
.
outputs
=
{
'Gate'
:
g
.
astype
(
'float64'
),
'ResetHiddenPrev'
:
r_h_p
.
astype
(
'float64'
),
...
...
@@ -111,8 +115,14 @@ class TestGRUUnitOp(OpTest):
self
.
check_grad
([
'Input'
,
'HiddenPrev'
,
'Weight'
],
[
'Hidden'
])
class
TestGRUUnitOpOriginMode
(
TestGRUUnitOp
):
def
setUp
(
self
):
self
.
set_inputs
(
origin_mode
=
True
)
self
.
set_outputs
(
origin_mode
=
True
)
class
TestGRUUnitOpWithBias
(
TestGRUUnitOp
):
def
set_inputs
(
self
):
def
set_inputs
(
self
,
origin_mode
=
False
):
batch_size
=
self
.
batch_size
frame_size
=
self
.
frame_size
super
(
TestGRUUnitOpWithBias
,
self
).
set_inputs
()
...
...
@@ -120,7 +130,8 @@ class TestGRUUnitOpWithBias(TestGRUUnitOp):
-
0.1
,
0.1
,
(
1
,
frame_size
*
3
)).
astype
(
'float64'
)
self
.
attrs
=
{
'activation'
:
GRUActivationType
.
identity
,
'gate_activation'
:
GRUActivationType
.
sigmoid
'gate_activation'
:
GRUActivationType
.
sigmoid
,
'origin_mode'
:
origin_mode
}
def
test_check_grad
(
self
):
...
...
@@ -132,5 +143,11 @@ class TestGRUUnitOpWithBias(TestGRUUnitOp):
no_grad_set
=
set
(
'Input'
))
class
TestGRUUnitOpWithBiasOriginMode
(
TestGRUUnitOpWithBias
):
def
setUp
(
self
):
self
.
set_inputs
(
origin_mode
=
True
)
self
.
set_outputs
(
origin_mode
=
True
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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