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5660d6a3
编写于
10月 25, 2018
作者:
M
minqiyang
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' of
https://github.com/PaddlePaddle/Paddle
into accelerate_embedding_grad
上级
5de46197
3cab25a5
变更
10
显示空白变更内容
内联
并排
Showing
10 changed file
with
420 addition
and
181 deletion
+420
-181
paddle/fluid/operators/fusion_gru_op.cc
paddle/fluid/operators/fusion_gru_op.cc
+50
-98
paddle/fluid/operators/math/CMakeLists.txt
paddle/fluid/operators/math/CMakeLists.txt
+2
-1
paddle/fluid/operators/math/jit_kernel.h
paddle/fluid/operators/math/jit_kernel.h
+9
-0
paddle/fluid/operators/math/jit_kernel_rnn.cc
paddle/fluid/operators/math/jit_kernel_rnn.cc
+180
-55
paddle/fluid/operators/math/sequence_pooling.cc
paddle/fluid/operators/math/sequence_pooling.cc
+32
-7
paddle/fluid/operators/math/sequence_pooling_test.cc
paddle/fluid/operators/math/sequence_pooling_test.cc
+126
-0
python/paddle/fluid/tests/unittests/CMakeLists.txt
python/paddle/fluid/tests/unittests/CMakeLists.txt
+3
-3
python/paddle/fluid/tests/unittests/dist_transformer.py
python/paddle/fluid/tests/unittests/dist_transformer.py
+8
-15
python/paddle/fluid/tests/unittests/test_dist_transformer.py
python/paddle/fluid/tests/unittests/test_dist_transformer.py
+4
-2
python/paddle/fluid/tests/unittests/test_fusion_gru_op.py
python/paddle/fluid/tests/unittests/test_fusion_gru_op.py
+6
-0
未找到文件。
paddle/fluid/operators/fusion_gru_op.cc
浏览文件 @
5660d6a3
...
...
@@ -16,10 +16,9 @@ limitations under the License. */
#include <cstring> // for memcpy
#include <string>
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/cpu_vec.h"
#include "paddle/fluid/operators/math/fc_compute.h"
#include "paddle/fluid/operators/math/jit_kernel.h"
#include "paddle/fluid/operators/math/sequence2batch.h"
#include "paddle/fluid/platform/cpu_info.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -174,58 +173,44 @@ class FusionGRUKernel : public framework::OpKernel<T> {
}
}
#define INIT_VEC_FUNC \
std::function<void(const int, const T *, T *)> act_gate, act_state; \
std::function<void(const int, const T*, const T*, const T*, T*)> cross; \
auto& act_gate_str = ctx.Attr<std::string>("gate_activation"); \
auto& act_state_str = ctx.Attr<std::string>("activation"); \
if (platform::jit::MayIUse(platform::jit::avx)) { \
math::VecActivations<T, platform::jit::avx> act_functor; \
act_gate = act_functor(act_gate_str); \
act_state = act_functor(act_state_str); \
cross = math::vec_cross<T, platform::jit::avx>; \
} else { \
math::VecActivations<T, platform::jit::isa_any> act_functor; \
act_gate = act_functor(act_gate_str); \
act_state = act_functor(act_state_str); \
cross = math::vec_cross<T, platform::jit::isa_any>; \
}
#define INIT_BASE_INPUT_OUTPUT \
auto* h0 = ctx.Input<Tensor>("H0"); \
auto* wx = ctx.Input<Tensor>("WeightX"); \
#define INIT_BASE_DEFINES \
auto* x = ctx.Input<LoDTensor>("X"); \
auto* wh = ctx.Input<Tensor>("WeightH"); \
auto* bias = ctx.Input<Tensor>("Bias"); \
auto* xx = ctx.Output<LoDTensor>("XX"); \
auto* hidden_out = ctx.Output<LoDTensor>("Hidden"); \
bool is_reverse = ctx.Attr<bool>("is_reverse");
#define INIT_BASE_SIZES \
auto x_lod = x->lod(); \
auto x_dims = x->dims();
/* T x M*/
\
auto wh_dims = wh->dims();
/* D x 3D*/
\
const int total_T = x_dims[0]; \
const int D3 = wh_dims[1]
#define INIT_OTHER_DEFINES \
auto* h0 = ctx.Input<Tensor>("H0"); \
auto* wx = ctx.Input<Tensor>("WeightX"); \
auto* bias = ctx.Input<Tensor>("Bias"); \
auto* hidden_out = ctx.Output<LoDTensor>("Hidden"); \
bool is_reverse = ctx.Attr<bool>("is_reverse"); \
const int M = x_dims[1]; \
const int D = wh_dims[0]; \
const int D3 = wh_dims[1]; \
const int D2 = D * 2;
const int D2 = D * 2; \
const auto& ker = math::jitkernel::KernelPool::Instance() \
.template Get<math::jitkernel::GRUKernel<T>, \
const std::string&, const std::string&>( \
ctx.Attr<std::string>("gate_activation"), \
ctx.Attr<std::string>("activation"), D); \
const T* x_data = x->data<T>(); \
const T* wx_data = wx->data<T>(); \
const T* wh_data = wh->data<T>(); \
auto place = ctx.GetPlace(); \
T* xx_data = xx->mutable_data<T>(place)
void
SeqCompute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
using
DeviceContext
=
paddle
::
platform
::
CPUDeviceContext
;
auto
*
x
=
ctx
.
Input
<
LoDTensor
>
(
"X"
);
INIT_BASE_INPUT_OUTPUT
INIT_BASE_SIZES
INIT_VEC_FUNC
auto
x_lod
=
x
->
lod
();
INIT_BASE_DEFINES
;
INIT_OTHER_DEFINES
;
const
int
N
=
x_lod
[
0
].
size
()
-
1
;
const
T
*
x_data
=
x
->
data
<
T
>
();
const
T
*
h0_data
=
h0
?
h0
->
data
<
T
>
()
:
nullptr
;
const
T
*
wx_data
=
wx
->
data
<
T
>
();
const
T
*
wh_data
=
wh
->
data
<
T
>
();
const
T
*
wh_state_data
=
wh_data
+
D
*
D2
;
T
*
xx_data
=
xx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
T
*
hidden_out_data
=
hidden_out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
T
*
hidden_out_data
=
hidden_out
->
mutable_data
<
T
>
(
place
);
auto
blas
=
math
::
GetBlas
<
DeviceContext
,
T
>
(
ctx
);
math
::
FCCompute
<
DeviceContext
,
T
>
(
blas
,
total_T
,
D3
,
M
,
x_data
,
wx_data
,
xx_data
,
...
...
@@ -252,14 +237,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
if
(
h0_data
)
{
prev_hidden_data
=
h0_data
+
bid
*
D
;
}
else
{
// W: {W_update, W_reset; W_state}
// update gate
act_gate
(
D
,
xx_data
,
xx_data
);
// state gate
act_state
(
D
,
xx_data
+
D2
,
xx_data
+
D2
);
// out = a*b
blas
.
VMUL
(
D
,
xx_data
,
xx_data
+
D2
,
hidden_out_data
);
// save prev
ker
->
ComputeH1
(
xx_data
,
hidden_out_data
);
prev_hidden_data
=
hidden_out_data
;
tstart
=
1
;
move_step
();
...
...
@@ -269,17 +247,12 @@ class FusionGRUKernel : public framework::OpKernel<T> {
blas
.
GEMM
(
CblasNoTrans
,
CblasNoTrans
,
1
,
D2
,
D
,
static_cast
<
T
>
(
1
),
prev_hidden_data
,
D
,
wh_data
,
D2
,
static_cast
<
T
>
(
1
),
xx_data
,
D3
);
act_gate
(
D2
,
xx_data
,
xx_data
);
// rt = rt*ht_1 inplace result
blas
.
VMUL
(
D
,
prev_hidden_data
,
xx_data
+
D
,
hidden_out_data
);
ker
->
ComputeHtPart1
(
xx_data
,
prev_hidden_data
,
hidden_out_data
);
// gemm rt * Ws
blas
.
GEMM
(
CblasNoTrans
,
CblasNoTrans
,
1
,
D
,
D
,
static_cast
<
T
>
(
1
),
hidden_out_data
,
D
,
wh_state_data
,
D
,
static_cast
<
T
>
(
1
),
xx_data
+
D2
,
D3
);
act_state
(
D
,
xx_data
+
D2
,
xx_data
+
D2
);
// out = zt*ht~ + (1-zt)*ht_1
cross
(
D
,
xx_data
,
xx_data
+
D2
,
prev_hidden_data
,
hidden_out_data
);
ker
->
ComputeHtPart2
(
xx_data
,
prev_hidden_data
,
hidden_out_data
);
// save prev
prev_hidden_data
=
hidden_out_data
;
move_step
();
...
...
@@ -289,28 +262,19 @@ class FusionGRUKernel : public framework::OpKernel<T> {
void
BatchCompute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
using
DeviceContext
=
paddle
::
platform
::
CPUDeviceContext
;
auto
*
x
=
ctx
.
Input
<
LoDTensor
>
(
"X"
);
INIT_BASE_INPUT_OUTPUT
INIT_BASE_SIZES
if
(
x
->
lod
()[
0
].
size
()
==
2
)
{
INIT_BASE_DEFINES
;
if
(
x_lod
[
0
].
size
()
==
2
)
{
xx
->
Resize
({
total_T
,
D3
});
SeqCompute
(
ctx
);
return
;
}
INIT_VEC_FUNC
INIT_OTHER_DEFINES
;
auto
*
reordered_h0
=
ctx
.
Output
<
Tensor
>
(
"ReorderedH0"
);
auto
*
batched_input
=
ctx
.
Output
<
LoDTensor
>
(
"BatchedInput"
);
auto
*
batched_out
=
ctx
.
Output
<
LoDTensor
>
(
"BatchedOut"
);
const
T
*
x_data
=
x
->
data
<
T
>
();
const
T
*
wx_data
=
wx
->
data
<
T
>
();
const
T
*
wh_data
=
wh
->
data
<
T
>
();
T
*
xx_data
=
xx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
T
*
batched_input_data
=
batched_input
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
T
*
batched_out_data
=
batched_out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
hidden_out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
T
*
batched_input_data
=
batched_input
->
mutable_data
<
T
>
(
place
);
T
*
batched_out_data
=
batched_out
->
mutable_data
<
T
>
(
place
);
hidden_out
->
mutable_data
<
T
>
(
place
);
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
auto
blas
=
math
::
GetBlas
<
DeviceContext
,
T
>
(
dev_ctx
);
math
::
LoDTensor2BatchFunctor
<
DeviceContext
,
T
>
to_batch
;
...
...
@@ -336,7 +300,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
T
*
prev_hidden_data
=
nullptr
;
if
(
h0
)
{
// reorder h0
T
*
reordered_h0_data
=
reordered_h0
->
mutable_data
<
T
>
(
ctx
.
GetPlace
()
);
T
*
reordered_h0_data
=
reordered_h0
->
mutable_data
<
T
>
(
place
);
const
T
*
h0_data
=
h0
->
data
<
T
>
();
prev_hidden_data
=
reordered_h0_data
;
size_t
sz
=
sizeof
(
T
)
*
D
;
...
...
@@ -350,12 +314,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
T
*
cur_out_data
=
batched_out_data
;
// W: {W_update, W_reset; W_state}
for
(
int
i
=
0
;
i
<
max_bs
;
++
i
)
{
// update gate
act_gate
(
D
,
cur_in_data
,
cur_in_data
);
// state gate
act_state
(
D
,
cur_in_data
+
D2
,
cur_in_data
+
D2
);
// out = a*b
blas
.
VMUL
(
D
,
cur_in_data
,
cur_in_data
+
D2
,
cur_out_data
);
ker
->
ComputeH1
(
cur_in_data
,
cur_out_data
);
// add offset
cur_in_data
+=
D3
;
cur_out_data
+=
D
;
...
...
@@ -380,10 +339,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
T
*
cur_out_data
=
batched_out_data
;
T
*
cur_prev_hidden_data
=
prev_hidden_data
;
for
(
int
i
=
0
;
i
<
cur_bs
;
++
i
)
{
act_gate
(
D2
,
cur_batched_data
,
cur_batched_data
);
// rt = rt*ht_1 inplace result
blas
.
VMUL
(
D
,
cur_prev_hidden_data
,
cur_batched_data
+
D
,
cur_out_data
);
ker
->
ComputeHtPart1
(
cur_batched_data
,
cur_prev_hidden_data
,
cur_out_data
);
cur_batched_data
+=
D3
;
cur_prev_hidden_data
+=
D
;
cur_out_data
+=
D
;
...
...
@@ -397,12 +354,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
cur_prev_hidden_data
=
prev_hidden_data
;
for
(
int
i
=
0
;
i
<
cur_bs
;
++
i
)
{
// ht~ = act_state(...)
act_state
(
D
,
cur_batched_data
+
D2
,
cur_batched_data
+
D2
);
// out = zt*ht~ + (1-zt)*ht_1
cross
(
D
,
cur_batched_data
,
cur_batched_data
+
D2
,
cur_prev_hidden_data
,
ker
->
ComputeHtPart2
(
cur_batched_data
,
cur_prev_hidden_data
,
cur_out_data
);
cur_batched_data
+=
D3
;
cur_prev_hidden_data
+=
D
;
cur_out_data
+=
D
;
...
...
@@ -416,9 +369,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
batched_out
->
set_lod
(
batched_lod
);
to_seq
(
dev_ctx
,
*
batched_out
,
hidden_out
);
}
#undef INIT_VEC_FUNC
#undef INIT_BASE_SIZES
#undef INIT_BASE_INPUT_OUTPUT
#undef INIT_OTHER_DEFINES
#undef INIT_BASE_DEFINES
};
}
// namespace operators
...
...
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @
5660d6a3
...
...
@@ -68,6 +68,7 @@ cc_test(selected_rows_functor_test SRCS selected_rows_functor_test.cc DEPS selec
cc_test
(
im2col_test SRCS im2col_test.cc DEPS im2col
)
cc_test
(
vol2col_test SRCS vol2col_test.cc DEPS vol2col
)
cc_test
(
sequence_padding_test SRCS sequence_padding_test.cc DEPS sequence_padding
)
cc_test
(
sequence_pooling_test SRCS sequence_pooling_test.cc DEPS sequence_pooling
)
if
(
WITH_GPU
)
nv_test
(
math_function_gpu_test SRCS math_function_test.cu DEPS math_function
)
nv_test
(
selected_rows_functor_gpu_test SRCS selected_rows_functor_test.cu DEPS selected_rows_functor math_function
)
...
...
@@ -75,6 +76,6 @@ endif()
cc_test
(
concat_test SRCS concat_test.cc DEPS concat_and_split
)
cc_test
(
cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info
)
cc_library
(
jit_kernel
SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_
lstm
.cc
SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_
rnn
.cc
DEPS cpu_info cblas
)
cc_test
(
jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel
)
paddle/fluid/operators/math/jit_kernel.h
浏览文件 @
5660d6a3
...
...
@@ -142,6 +142,15 @@ class LSTMKernel : public Kernel {
const
T
*
wp_data
=
nullptr
)
const
=
0
;
};
template
<
typename
T
>
class
GRUKernel
:
public
Kernel
{
public:
// compute h1 without h0
virtual
void
ComputeH1
(
T
*
gates
,
T
*
ht
)
const
=
0
;
virtual
void
ComputeHtPart1
(
T
*
gates
,
const
T
*
ht_1
,
T
*
ht
)
const
=
0
;
virtual
void
ComputeHtPart2
(
T
*
gates
,
const
T
*
ht_1
,
T
*
ht
)
const
=
0
;
};
}
// namespace jitkernel
}
// namespace math
}
// namespace operators
...
...
paddle/fluid/operators/math/jit_kernel_
lstm
.cc
→
paddle/fluid/operators/math/jit_kernel_
rnn
.cc
浏览文件 @
5660d6a3
...
...
@@ -136,6 +136,23 @@ static std::shared_ptr<const VActKernel<T>> GetActKernel(
return
nullptr
;
}
#ifdef __AVX__
template
<
jit
::
cpu_isa_t
isa
>
static
std
::
unique_ptr
<
AVXAct
>
GetAVXAct
(
const
std
::
string
&
type
)
{
if
(
type
==
"sigmoid"
)
{
return
std
::
unique_ptr
<
AVXAct
>
(
new
AVXActImpl
<
kSigmoid
,
isa
>
());
}
else
if
(
type
==
"relu"
)
{
return
std
::
unique_ptr
<
AVXAct
>
(
new
AVXActImpl
<
kRelu
,
isa
>
());
}
else
if
(
type
==
"tanh"
)
{
return
std
::
unique_ptr
<
AVXAct
>
(
new
AVXActImpl
<
kTanh
,
isa
>
());
}
else
if
(
type
==
"identity"
||
type
==
""
)
{
return
std
::
unique_ptr
<
AVXAct
>
(
new
AVXActImpl
<
kIdentity
,
isa
>
());
}
PADDLE_THROW
(
"Not support type: %s"
,
type
);
return
nullptr
;
}
#endif
/* LSTM JitKernel */
template
<
typename
T
,
jit
::
cpu_isa_t
isa
,
jit_block
>
class
LSTMKernelImpl
:
public
LSTMKernel
<
T
>
{
...
...
@@ -198,21 +215,9 @@ class LSTMKernelImpl : public LSTMKernel<T> {
const std::string& act_gate, const std::string& act_cand, \
const std::string& act_cell, int d) \
: LSTMKernel<float>() { \
auto GetAVXAct = [&](const std::string& type) -> std::unique_ptr<AVXAct> { \
if (type == "sigmoid") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kSigmoid, isa>()); \
} else if (type == "relu") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kRelu, isa>()); \
} else if (type == "tanh") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kTanh, isa>()); \
} else if (type == "identity" || type == "") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kIdentity, isa>()); \
} \
PADDLE_THROW("Not support type: %s", type); \
}; \
avx_act_gate_ = GetAVXAct(act_gate); \
avx_act_cand_ = GetAVXAct(act_cand); \
avx_act_cell_ = GetAVXAct(act_cell); \
avx_act_gate_ = GetAVXAct<isa>(act_gate); \
avx_act_cand_ = GetAVXAct<isa>(act_cand); \
avx_act_cell_ = GetAVXAct<isa>(act_cell); \
} \
template <> \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeCtHt( \
...
...
@@ -354,6 +359,126 @@ REGISTER_JITKERNEL_ARGS(lstm, LSTMKernel, JITKERNEL_DECLARE_LSTM,
#undef JITKERNEL_DECLARE_LSTM
#undef JITKERNEL_KEY_LSTM
#undef JITKERNEL_NEW_LSTM_IMPL
/* GRU JitKernel */
template
<
typename
T
,
jit
::
cpu_isa_t
isa
,
jit_block
>
class
GRUKernelImpl
:
public
GRUKernel
<
T
>
{
public:
explicit
GRUKernelImpl
(
const
std
::
string
&
act_gate
,
const
std
::
string
&
act_state
,
int
d
)
:
GRUKernel
<
T
>
()
{
d_
=
d
;
d2_
=
d
*
2
;
act_gate_d2_
=
GetActKernel
<
T
>
(
act_gate
,
d2_
);
act_gate_d_
=
GetActKernel
<
T
>
(
act_gate
,
d
);
act_state_d_
=
GetActKernel
<
T
>
(
act_state
,
d
);
vmul_d_
=
KernelPool
::
Instance
().
template
Get
<
VMulKernel
<
T
>
>
(
d
);
}
void
ComputeH1
(
T
*
gates
,
T
*
ht
)
const
override
{
act_gate_d_
->
Compute
(
gates
,
gates
);
act_state_d_
->
Compute
(
gates
+
d2_
,
gates
+
d2_
);
vmul_d_
->
Compute
(
gates
,
gates
+
d2_
,
ht
);
}
void
ComputeHtPart1
(
T
*
gates
,
const
T
*
ht_1
,
T
*
ht
)
const
override
{
// W: {W_update, W_reset; W_state}
act_gate_d2_
->
Compute
(
gates
,
gates
);
vmul_d_
->
Compute
(
ht_1
,
gates
+
d_
,
ht
);
}
void
ComputeHtPart2
(
T
*
gates
,
const
T
*
ht_1
,
T
*
ht
)
const
override
{
T
*
y
=
gates
+
d2_
;
act_state_d_
->
Compute
(
y
,
y
);
// out = zt*ht~ + (1-zt)*ht_1
for
(
int
i
=
0
;
i
<
d_
;
++
i
)
{
ht
[
i
]
=
gates
[
i
]
*
y
[
i
]
+
(
static_cast
<
T
>
(
1
)
-
gates
[
i
])
*
ht_1
[
i
];
}
}
private:
int
d_
,
d2_
;
std
::
shared_ptr
<
const
VActKernel
<
T
>>
act_gate_d2_
,
act_gate_d_
,
act_state_d_
;
std
::
shared_ptr
<
const
VMulKernel
<
T
>>
vmul_d_
;
#ifdef __AVX__
std
::
unique_ptr
<
const
AVXAct
>
avx_act_gate_
,
avx_act_state_
;
#endif
};
#define INTRI8_FLOAT(isa) \
template <> \
GRUKernelImpl<float, isa, kEQ8>::GRUKernelImpl( \
const std::string& act_gate, const std::string& act_state, int d) \
: GRUKernel<float>() { \
avx_act_gate_ = GetAVXAct<isa>(act_gate); \
avx_act_state_ = GetAVXAct<isa>(act_state); \
} \
template <> \
void GRUKernelImpl<float, isa, kEQ8>::ComputeH1(float* gates, float* ht) \
const { \
__m256 u, s; \
/* W: {W_update, W_reset; W_state} */
\
u = _mm256_loadu_ps(gates); \
s = _mm256_loadu_ps(gates + 16); \
s = _mm256_mul_ps(avx_act_gate_->Compute(u), avx_act_state_->Compute(s)); \
_mm256_storeu_ps(ht, s); \
} \
template <> \
void GRUKernelImpl<float, isa, kEQ8>::ComputeHtPart1( \
float* gates, const float* ht_1, float* ht) const { \
/* not exactly equal the any implementation */
\
__m256 r, ht0; \
r = _mm256_loadu_ps(gates + 8); \
ht0 = _mm256_loadu_ps(ht_1); \
r = _mm256_mul_ps(avx_act_gate_->Compute(r), ht0); \
_mm256_storeu_ps(ht, r); \
} \
template <> \
void GRUKernelImpl<float, isa, kEQ8>::ComputeHtPart2( \
float* gates, const float* ht_1, float* ht) const { \
/* not exactly equal the any implementation */
\
__m256 u, s, ht0; \
u = _mm256_loadu_ps(gates); \
s = _mm256_loadu_ps(gates + 16); \
ht0 = _mm256_loadu_ps(ht_1); \
u = avx_act_gate_->Compute(u); \
s = _mm256_mul_ps(u, avx_act_state_->Compute(s)); \
u = _mm256_sub_ps(_mm256_set1_ps(1.f), u); \
u = _mm256_mul_ps(u, ht0); \
u = _mm256_add_ps(s, u); \
_mm256_storeu_ps(ht, u); \
}
#ifdef __AVX__
INTRI8_FLOAT
(
jit
::
avx
);
#endif
#ifdef __AVX2__
INTRI8_FLOAT
(
jit
::
avx2
);
#endif
#ifdef __AVX512F__
INTRI8_FLOAT
(
jit
::
avx512f
);
#endif
#define JITKERNEL_DECLARE_GRU(ker_class, ker_dtype) \
template <> \
std::shared_ptr<const GRUKernel<ker_dtype>> KernelPool::Get< \
GRUKernel<ker_dtype>, const std::string&, const std::string&, int>( \
const std::string& act_gate, const std::string& act_state, int d)
#define JITKERNEL_KEY_GRU(ker_key, dtype_key) \
#ker_key #dtype_key + std::to_string(d) + act_gate + act_state
#define JITKERNEL_NEW_GRU_IMPL(ker, dtype, isa, k) \
p = std::dynamic_pointer_cast<ker<dtype>>( \
std::make_shared<ker##Impl<dtype, isa, k>>(act_gate, act_state, d));
REGISTER_JITKERNEL_ARGS
(
gru
,
GRUKernel
,
JITKERNEL_DECLARE_GRU
,
JITKERNEL_KEY_GRU
,
JITKERNEL_NEW_GRU_IMPL
);
#undef INTRI8_FLOAT
#undef JITKERNEL_NEW_GRU_IMPL
#undef JITKERNEL_KEY_GRU
#undef JITKERNEL_DECLARE_GRU
}
// namespace jitkernel
}
// namespace math
}
// namespace operators
...
...
paddle/fluid/operators/math/sequence_pooling.cc
浏览文件 @
5660d6a3
...
...
@@ -157,6 +157,31 @@ class FirstSeqPoolFunctor {
}
};
template
<
typename
T
>
class
SumSeqPoolGradFunctor
{
public:
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
framework
::
Tensor
&
out_grad
,
framework
::
LoDTensor
*
in_grad
)
{
auto
lod
=
in_grad
->
lod
()[
0
];
int64_t
out_w
=
out_grad
.
numel
()
/
out_grad
.
dims
()[
0
];
int64_t
in_w
=
in_grad
->
numel
()
/
in_grad
->
dims
()[
0
];
PADDLE_ENFORCE
(
in_w
==
out_w
);
const
T
*
out_g_data
=
out_grad
.
data
<
T
>
();
T
*
in_g_data
=
in_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
blas
=
math
::
GetBlas
<
platform
::
CPUDeviceContext
,
T
>
(
context
);
for
(
int
i
=
0
;
i
<
static_cast
<
int
>
(
lod
.
size
())
-
1
;
++
i
)
{
int64_t
h
=
static_cast
<
int64_t
>
(
lod
[
i
+
1
]
-
lod
[
i
]);
int64_t
in_offset
=
lod
[
i
]
*
in_w
;
const
T
*
out_pos
=
out_g_data
+
i
*
out_w
;
T
*
in_pos
=
in_g_data
+
in_offset
;
for
(
int
r
=
0
;
r
!=
h
;
++
r
)
{
blas
.
VCOPY
(
in_w
,
out_pos
,
in_pos
+
r
*
in_w
);
}
}
}
};
template
<
typename
T
>
class
SequencePoolFunctor
<
platform
::
CPUDeviceContext
,
T
>
{
public:
...
...
@@ -231,9 +256,15 @@ class SequencePoolGradFunctor<platform::CPUDeviceContext, T> {
math
::
SetConstant
<
platform
::
CPUDeviceContext
,
T
>
functor
;
functor
(
context
,
in_grad
,
0
);
}
if
(
pooltype
==
"SUM"
)
{
math
::
SumSeqPoolGradFunctor
<
T
>
sum_pool_grad
;
sum_pool_grad
(
context
,
out_grad
,
in_grad
);
return
;
}
auto
lod
=
in_grad
->
lod
()[
0
];
auto
&
place
=
*
context
.
eigen_device
();
auto
blas
=
math
::
GetBlas
<
platform
::
CPUDeviceContext
,
T
>
(
context
);
for
(
int
i
=
0
;
i
<
static_cast
<
int
>
(
lod
.
size
())
-
1
;
++
i
)
{
auto
in_g_t
=
in_grad
->
Slice
(
static_cast
<
int
>
(
lod
[
i
]),
static_cast
<
int
>
(
lod
[
i
+
1
]));
...
...
@@ -247,12 +278,6 @@ class SequencePoolGradFunctor<platform::CPUDeviceContext, T> {
if
(
pooltype
==
"AVERAGE"
)
{
in_g_e
.
device
(
place
)
=
(
out_g_e
/
static_cast
<
T
>
(
h
)).
broadcast
(
bcast
);
}
else
if
(
pooltype
==
"SUM"
)
{
const
T
*
out_g_data
=
out_g_t
.
data
<
T
>
();
T
*
in_g_data
=
in_g_t
.
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
int
r
=
0
;
r
!=
h
;
++
r
)
{
blas
.
VCOPY
(
w
,
out_g_data
,
in_g_data
+
r
*
w
);
}
}
else
if
(
pooltype
==
"SQRT"
)
{
in_g_e
.
device
(
place
)
=
(
out_g_e
/
std
::
sqrt
(
static_cast
<
T
>
(
h
))).
broadcast
(
bcast
);
...
...
paddle/fluid/operators/math/sequence_pooling_test.cc
0 → 100644
浏览文件 @
5660d6a3
/* 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. */
#include "paddle/fluid/operators/math/sequence_pooling.h"
#include <gtest/gtest.h>
#include <vector>
template
<
typename
DeviceContext
,
typename
Place
,
typename
T
>
void
TestSequencePoolingSum
(
const
paddle
::
framework
::
LoD
&
lod
)
{
paddle
::
framework
::
LoDTensor
cpu_out_grad
;
paddle
::
framework
::
LoDTensor
cpu_in_grad
;
paddle
::
framework
::
LoDTensor
out_grad
;
paddle
::
framework
::
LoDTensor
in_grad
;
const
size_t
second_dim
=
128u
;
// construct out_grad's tensor in cpu
const
size_t
out_first_dim
=
lod
[
0
].
size
()
-
1
;
auto
out_dims
=
paddle
::
framework
::
make_ddim
(
{
static_cast
<
int64_t
>
(
out_first_dim
),
static_cast
<
int64_t
>
(
second_dim
)});
cpu_out_grad
.
mutable_data
<
T
>
(
out_dims
,
paddle
::
platform
::
CPUPlace
());
for
(
int64_t
i
=
0
;
i
<
cpu_out_grad
.
numel
();
++
i
)
{
cpu_out_grad
.
data
<
T
>
()[
i
]
=
static_cast
<
T
>
(
i
);
}
// copy to dst out_grad
auto
*
place
=
new
Place
();
DeviceContext
*
context
=
new
DeviceContext
(
*
place
);
if
(
paddle
::
platform
::
is_cpu_place
(
*
place
))
{
out_grad
=
cpu_out_grad
;
}
else
{
TensorCopySync
(
cpu_out_grad
,
*
place
,
&
out_grad
);
}
// construct in_grad
in_grad
.
set_lod
(
lod
);
auto
in_dims
=
paddle
::
framework
::
make_ddim
(
{
static_cast
<
int64_t
>
(
lod
[
0
].
back
()),
static_cast
<
int64_t
>
(
second_dim
)});
in_grad
.
mutable_data
<
T
>
(
in_dims
,
context
->
GetPlace
());
// check tensor contruction result
PADDLE_ENFORCE_EQ
(
in_grad
.
dims
().
size
(),
out_grad
.
dims
().
size
());
for
(
int64_t
i
=
1
;
i
<
out_grad
.
dims
().
size
();
++
i
)
{
PADDLE_ENFORCE_EQ
(
in_grad
.
dims
()[
i
],
out_grad
.
dims
()[
i
]);
}
// call functor
paddle
::
operators
::
math
::
SequencePoolGradFunctor
<
DeviceContext
,
T
>
()(
*
context
,
"SUM"
,
out_grad
,
&
in_grad
);
if
(
paddle
::
platform
::
is_cpu_place
(
*
place
))
{
cpu_in_grad
=
in_grad
;
}
else
{
TensorCopySync
(
in_grad
,
paddle
::
platform
::
CPUPlace
(),
&
cpu_in_grad
);
cpu_in_grad
.
set_lod
(
in_grad
.
lod
());
}
EXPECT_EQ
(
in_grad
.
numel
(),
lod
[
0
].
back
()
*
second_dim
);
EXPECT_EQ
(
in_grad
.
lod
(),
lod
);
if
(
paddle
::
platform
::
is_cpu_place
(
*
place
))
{
for
(
int64_t
i
=
0
;
i
<
in_grad
.
lod
()[
0
].
size
()
-
1
;
++
i
)
{
int64_t
begin
=
in_grad
.
lod
()[
0
][
i
];
int64_t
end
=
in_grad
.
lod
()[
0
][
i
+
1
];
paddle
::
framework
::
Tensor
tmp
=
in_grad
.
Slice
(
begin
,
end
);
for
(
int64_t
j
=
0
;
j
!=
tmp
.
numel
()
/
second_dim
;
++
j
)
{
for
(
int64_t
m
=
0
;
m
!=
second_dim
;
++
m
)
{
EXPECT_EQ
(
tmp
.
data
<
T
>
()[
m
+
j
*
second_dim
],
out_grad
.
data
<
T
>
()[
m
+
i
*
second_dim
]);
}
}
}
}
else
{
for
(
int64_t
i
=
0
;
i
<
cpu_in_grad
.
lod
()[
0
].
size
()
-
1
;
++
i
)
{
int64_t
begin
=
cpu_in_grad
.
lod
()[
0
][
i
];
int64_t
end
=
cpu_in_grad
.
lod
()[
0
][
i
+
1
];
paddle
::
framework
::
Tensor
tmp
=
cpu_in_grad
.
Slice
(
begin
,
end
);
for
(
int64_t
j
=
0
;
j
!=
tmp
.
numel
()
/
second_dim
;
++
j
)
{
for
(
int64_t
m
=
0
;
m
!=
second_dim
;
++
m
)
{
EXPECT_EQ
(
tmp
.
data
<
T
>
()[
m
+
j
*
second_dim
],
cpu_out_grad
.
data
<
T
>
()[
m
+
i
*
second_dim
]);
}
}
}
}
delete
place
;
delete
context
;
}
TEST
(
SequencePoolingGrad
,
CPU_SUM
)
{
paddle
::
framework
::
LoD
lod1
;
lod1
.
push_back
(
std
::
vector
<
size_t
>
{
0
,
10
});
TestSequencePoolingSum
<
paddle
::
platform
::
CPUDeviceContext
,
paddle
::
platform
::
CPUPlace
,
float
>
(
lod1
);
paddle
::
framework
::
LoD
lod2
;
lod2
.
push_back
(
std
::
vector
<
size_t
>
{
0
,
2
,
7
,
10
});
TestSequencePoolingSum
<
paddle
::
platform
::
CPUDeviceContext
,
paddle
::
platform
::
CPUPlace
,
float
>
(
lod2
);
}
#ifdef PADDLE_WITH_CUDA
TEST
(
SequencePoolingGrad
,
CUDA_SUM
)
{
paddle
::
framework
::
LoD
lod1
;
lod1
.
push_back
(
std
::
vector
<
size_t
>
{
0
,
10
});
TestSequencePoolingSum
<
paddle
::
platform
::
CUDADeviceContext
,
paddle
::
platform
::
CUDAPlace
,
float
>
(
lod1
);
paddle
::
framework
::
LoD
lod2
;
lod2
.
push_back
(
std
::
vector
<
size_t
>
{
0
,
2
,
7
,
10
});
TestSequencePoolingSum
<
paddle
::
platform
::
CUDADeviceContext
,
paddle
::
platform
::
CUDAPlace
,
float
>
(
lod2
);
}
#endif
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @
5660d6a3
...
...
@@ -78,9 +78,9 @@ if(WITH_DISTRIBUTE)
set_tests_properties
(
test_dist_word2vec PROPERTIES TIMEOUT 200
)
py_test_modules
(
test_dist_se_resnext MODULES test_dist_se_resnext
)
set_tests_properties
(
test_dist_se_resnext PROPERTIES TIMEOUT 1000
)
# TODO: fix this test
#
py_test_modules(test_dist_transformer MODULES test_dist_transformer)
#
set_tests_properties(test_dist_transformer PROPERTIES TIMEOUT 1000)
py_test_modules
(
test_dist_transformer MODULES test_dist_transformer
)
set_tests_properties
(
test_dist_transformer PROPERTIES TIMEOUT 1000
)
endif
(
NOT APPLE
)
py_test_modules
(
test_dist_transpiler MODULES test_dist_transpiler
)
endif
()
...
...
python/paddle/fluid/tests/unittests/dist_transformer.py
浏览文件 @
5660d6a3
...
...
@@ -35,7 +35,7 @@ import paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.layers
as
layers
from
paddle.fluid
import
core
from
test_dist_base
import
TestDistRunnerBase
,
runtime_main
from
test_dist_base
import
TestDistRunnerBase
,
runtime_main
,
RUN_STEP
import
paddle.compat
as
cpt
from
paddle.compat
import
long_type
...
...
@@ -562,18 +562,12 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
for
pass_id
in
six
.
moves
.
xrange
(
TrainTaskConfig
.
pass_num
):
pass_start_time
=
time
.
time
()
for
batch_id
,
data
in
enumerate
(
train_data
()):
if
batch_id
>=
5
:
if
batch_id
>=
RUN_STEP
:
break
feed_list
=
[]
total_num_token
=
0
#if TrainTaskConfig.local:
# lr_rate = lr_scheduler.update_learning_rate()
#for place_id, data_buffer in enumerate(
# split_data(
# data, num_part=dev_count)):
if
TrainTaskConfig
.
local
:
lr_rate
=
lr_scheduler
.
update_learning_rate
()
...
...
@@ -619,7 +613,6 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
init
=
True
# Validate and save the model for inference.
if
batch_id
==
0
or
batch_id
==
4
:
if
TrainTaskConfig
.
val_file_pattern
is
not
None
:
val_avg_cost
,
val_ppl
=
test
()
print
(
"[%f]"
%
val_avg_cost
)
...
...
@@ -1701,7 +1694,7 @@ class DistTransformer2x2(TestDistRunnerBase):
def
run_trainer
(
self
,
args
):
TrainTaskConfig
.
use_gpu
=
args
.
use_cuda
sum_cost
,
avg_cost
,
predict
,
token_num
,
local_lr_scheduler
=
get_model
(
sum_cost
,
avg_cost
,
predict
,
token_num
,
local_lr_scheduler
,
test_program
=
get_model
(
args
.
is_dist
,
not
args
.
sync_mode
)
if
args
.
is_dist
:
...
...
python/paddle/fluid/tests/unittests/test_dist_transformer.py
浏览文件 @
5660d6a3
...
...
@@ -61,7 +61,8 @@ class TestDistTransformer2x2Sync(TestDistBase):
def
test_dist_train
(
self
):
download_files
()
self
.
check_with_place
(
"dist_transformer.py"
,
delta
=
1e-5
)
self
.
check_with_place
(
"dist_transformer.py"
,
delta
=
1e-5
,
check_error_log
=
False
)
class
TestDistTransformer2x2Async
(
TestDistBase
):
...
...
@@ -70,7 +71,8 @@ class TestDistTransformer2x2Async(TestDistBase):
def
test_dist_train
(
self
):
download_files
()
self
.
check_with_place
(
"dist_transformer.py"
,
delta
=
1.0
)
self
.
check_with_place
(
"dist_transformer.py"
,
delta
=
1.0
,
check_error_log
=
False
)
if
__name__
==
"__main__"
:
...
...
python/paddle/fluid/tests/unittests/test_fusion_gru_op.py
浏览文件 @
5660d6a3
...
...
@@ -125,6 +125,12 @@ class TestFusionGRUOpMD2(TestFusionGRUOp):
self
.
D
=
8
class
TestFusionGRUOpMD3
(
TestFusionGRUOp
):
def
set_confs
(
self
):
self
.
M
=
17
self
.
D
=
15
class
TestFusionGRUOpBS1
(
TestFusionGRUOp
):
def
set_confs
(
self
):
self
.
lod
=
[[
3
]]
...
...
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