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41eeb771
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41eeb771
编写于
10月 17, 2018
作者:
T
Tao Luo
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' into clean_inference_lib
上级
a35e7f4b
6447155d
变更
43
展开全部
隐藏空白更改
内联
并排
Showing
43 changed file
with
3015 addition
and
404 deletion
+3015
-404
paddle/fluid/API.spec
paddle/fluid/API.spec
+3
-1
paddle/fluid/framework/executor.cc
paddle/fluid/framework/executor.cc
+1
-1
paddle/fluid/framework/feed_fetch_method.cc
paddle/fluid/framework/feed_fetch_method.cc
+1
-2
paddle/fluid/framework/naive_executor.cc
paddle/fluid/framework/naive_executor.cc
+1
-1
paddle/fluid/framework/operator.cc
paddle/fluid/framework/operator.cc
+11
-3
paddle/fluid/framework/tensor_util.cc
paddle/fluid/framework/tensor_util.cc
+20
-0
paddle/fluid/framework/tensor_util_test.cc
paddle/fluid/framework/tensor_util_test.cc
+14
-0
paddle/fluid/framework/var_desc.h
paddle/fluid/framework/var_desc.h
+1
-0
paddle/fluid/framework/variable.h
paddle/fluid/framework/variable.h
+5
-1
paddle/fluid/framework/variable_test.cc
paddle/fluid/framework/variable_test.cc
+6
-5
paddle/fluid/inference/api/analysis_predictor.cc
paddle/fluid/inference/api/analysis_predictor.cc
+13
-0
paddle/fluid/inference/api/analysis_predictor.h
paddle/fluid/inference/api/analysis_predictor.h
+1
-0
paddle/fluid/operators/CMakeLists.txt
paddle/fluid/operators/CMakeLists.txt
+1
-1
paddle/fluid/operators/fill_constant_op.cc
paddle/fluid/operators/fill_constant_op.cc
+8
-1
paddle/fluid/operators/fusion_lstm_op.cc
paddle/fluid/operators/fusion_lstm_op.cc
+102
-261
paddle/fluid/operators/isfinite_op.cc
paddle/fluid/operators/isfinite_op.cc
+3
-1
paddle/fluid/operators/math/CMakeLists.txt
paddle/fluid/operators/math/CMakeLists.txt
+4
-2
paddle/fluid/operators/math/cpu_lstm_compute.h
paddle/fluid/operators/math/cpu_lstm_compute.h
+0
-64
paddle/fluid/operators/math/cpu_vec.h
paddle/fluid/operators/math/cpu_vec.h
+16
-19
paddle/fluid/operators/math/cpu_vec_test.cc
paddle/fluid/operators/math/cpu_vec_test.cc
+6
-10
paddle/fluid/operators/math/jit_kernel.cc
paddle/fluid/operators/math/jit_kernel.cc
+41
-0
paddle/fluid/operators/math/jit_kernel.h
paddle/fluid/operators/math/jit_kernel.h
+142
-0
paddle/fluid/operators/math/jit_kernel_blas.cc
paddle/fluid/operators/math/jit_kernel_blas.cc
+391
-0
paddle/fluid/operators/math/jit_kernel_exp.cc
paddle/fluid/operators/math/jit_kernel_exp.cc
+400
-0
paddle/fluid/operators/math/jit_kernel_lstm.cc
paddle/fluid/operators/math/jit_kernel_lstm.cc
+308
-0
paddle/fluid/operators/math/jit_kernel_macro.h
paddle/fluid/operators/math/jit_kernel_macro.h
+111
-0
paddle/fluid/operators/math/jit_kernel_test.cc
paddle/fluid/operators/math/jit_kernel_test.cc
+749
-0
paddle/fluid/operators/parallel_do_op.cc
paddle/fluid/operators/parallel_do_op.cc
+20
-1
paddle/fluid/operators/reshape_op.cc
paddle/fluid/operators/reshape_op.cc
+3
-2
paddle/fluid/operators/sequence_concat_op.cc
paddle/fluid/operators/sequence_concat_op.cc
+4
-2
paddle/fluid/operators/sequence_unpad_op.cc
paddle/fluid/operators/sequence_unpad_op.cc
+153
-0
paddle/fluid/operators/sequence_unpad_op.cu
paddle/fluid/operators/sequence_unpad_op.cu
+30
-0
paddle/fluid/operators/sequence_unpad_op.h
paddle/fluid/operators/sequence_unpad_op.h
+104
-0
paddle/fluid/platform/cpu_info.cc
paddle/fluid/platform/cpu_info.cc
+1
-1
paddle/fluid/platform/cpu_info.h
paddle/fluid/platform/cpu_info.h
+1
-1
paddle/fluid/platform/init.cc
paddle/fluid/platform/init.cc
+1
-1
paddle/fluid/platform/profiler.cc
paddle/fluid/platform/profiler.cc
+2
-2
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+136
-2
python/paddle/fluid/tests/unittests/dist_simnet_bow.py
python/paddle/fluid/tests/unittests/dist_simnet_bow.py
+17
-5
python/paddle/fluid/tests/unittests/test_dist_simnet_bow.py
python/paddle/fluid/tests/unittests/test_dist_simnet_bow.py
+74
-4
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+21
-0
python/paddle/fluid/tests/unittests/test_sequence_unpad_op.py
...on/paddle/fluid/tests/unittests/test_sequence_unpad_op.py
+75
-0
python/paddle/fluid/transpiler/distribute_transpiler.py
python/paddle/fluid/transpiler/distribute_transpiler.py
+14
-10
未找到文件。
paddle/fluid/API.spec
浏览文件 @
41eeb771
...
...
@@ -75,7 +75,8 @@ paddle.fluid.layers.conv2d_transpose ArgSpec(args=['input', 'num_filters', 'outp
paddle.fluid.layers.conv3d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))
paddle.fluid.layers.sequence_expand ArgSpec(args=['x', 'y', 'ref_level', 'name'], varargs=None, keywords=None, defaults=(-1, None))
paddle.fluid.layers.sequence_expand_as ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_pad ArgSpec(args=['x', 'pad_value', 'maxlen'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_pad ArgSpec(args=['x', 'pad_value', 'maxlen', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.sequence_unpad ArgSpec(args=['x', 'length', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.lstm_unit ArgSpec(args=['x_t', 'hidden_t_prev', 'cell_t_prev', 'forget_bias', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(0.0, None, None, None))
paddle.fluid.layers.reduce_sum ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None))
paddle.fluid.layers.reduce_mean ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None))
...
...
@@ -84,6 +85,7 @@ paddle.fluid.layers.reduce_min ArgSpec(args=['input', 'dim', 'keep_dim', 'name']
paddle.fluid.layers.reduce_prod ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None))
paddle.fluid.layers.sequence_first_step ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.sequence_last_step ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.sequence_slice ArgSpec(args=['input', 'offset', 'length', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.dropout ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name'], varargs=None, keywords=None, defaults=(False, None, None))
paddle.fluid.layers.split ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None))
paddle.fluid.layers.ctc_greedy_decoder ArgSpec(args=['input', 'blank', 'name'], varargs=None, keywords=None, defaults=(None,))
...
...
paddle/fluid/framework/executor.cc
浏览文件 @
41eeb771
...
...
@@ -101,7 +101,7 @@ void InitializeVariable(Variable* var, proto::VarType::Type var_type) {
}
else
if
(
var_type
==
proto
::
VarType
::
FETCH_LIST
)
{
var
->
GetMutable
<
FeedFetchList
>
();
}
else
if
(
var_type
==
proto
::
VarType
::
STEP_SCOPES
)
{
var
->
GetMutable
<
std
::
vector
<
framework
::
Scope
>>
();
var
->
GetMutable
<
std
::
vector
<
framework
::
Scope
*
>>
();
}
else
if
(
var_type
==
proto
::
VarType
::
LOD_RANK_TABLE
)
{
var
->
GetMutable
<
LoDRankTable
>
();
}
else
if
(
var_type
==
proto
::
VarType
::
LOD_TENSOR_ARRAY
)
{
...
...
paddle/fluid/framework/feed_fetch_method.cc
浏览文件 @
41eeb771
...
...
@@ -27,8 +27,7 @@ void SetFeedVariable(Scope* scope, const LoDTensor& input,
// be created.
VLOG
(
3
)
<<
"SetFeedVariable name="
<<
var_name
<<
" index="
<<
index
;
Variable
*
g_feed_value
=
scope
->
Var
(
var_name
);
auto
&
feed_inputs
=
*
(
g_feed_value
->
GetMutable
<
std
::
vector
<
paddle
::
framework
::
LoDTensor
>>
());
auto
&
feed_inputs
=
*
(
g_feed_value
->
GetMutable
<
FeedFetchList
>
());
if
(
index
>=
feed_inputs
.
size
())
{
feed_inputs
.
resize
(
index
+
1
);
}
...
...
paddle/fluid/framework/naive_executor.cc
浏览文件 @
41eeb771
...
...
@@ -37,7 +37,7 @@ static void InitializeVariable(Variable *var, proto::VarType::Type var_type) {
}
else
if
(
var_type
==
proto
::
VarType
::
FETCH_LIST
)
{
var
->
GetMutable
<
FeedFetchList
>
();
}
else
if
(
var_type
==
proto
::
VarType
::
STEP_SCOPES
)
{
var
->
GetMutable
<
std
::
vector
<
framework
::
Scope
>>
();
var
->
GetMutable
<
std
::
vector
<
framework
::
Scope
*
>>
();
}
else
if
(
var_type
==
proto
::
VarType
::
LOD_RANK_TABLE
)
{
var
->
GetMutable
<
LoDRankTable
>
();
}
else
if
(
var_type
==
proto
::
VarType
::
LOD_TENSOR_ARRAY
)
{
...
...
paddle/fluid/framework/operator.cc
浏览文件 @
41eeb771
...
...
@@ -149,9 +149,17 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
platform
::
SetDeviceId
(
dev_id
);
#endif
}
platform
::
DeviceContextPool
&
pool
=
platform
::
DeviceContextPool
::
Instance
();
platform
::
RecordEvent
record_event
(
Type
(),
pool
.
Get
(
place
));
RunImpl
(
scope
,
place
);
// The profile has a process-wide mutex, results in serious performance issue
// in concurrency scenerio. Here use an `if` to fix this issue.
// Please not remove the `if`, ask @Superjomn if there are any concern.
if
(
platform
::
IsProfileEnabled
())
{
platform
::
DeviceContextPool
&
pool
=
platform
::
DeviceContextPool
::
Instance
();
platform
::
RecordEvent
record_event
(
Type
(),
pool
.
Get
(
place
));
RunImpl
(
scope
,
place
);
}
else
{
RunImpl
(
scope
,
place
);
}
VLOG
(
3
)
<<
place
<<
" "
<<
DebugStringEx
(
&
scope
);
}
...
...
paddle/fluid/framework/tensor_util.cc
浏览文件 @
41eeb771
...
...
@@ -36,6 +36,11 @@ void TensorCopy(const Tensor& src, const platform::Place& dst_place,
auto
size
=
src
.
numel
()
*
SizeOfType
(
src
.
type
());
if
(
platform
::
is_cpu_place
(
src_place
)
&&
platform
::
is_cpu_place
(
dst_place
))
{
if
(
src_ptr
==
dst_ptr
)
{
VLOG
(
3
)
<<
"Skip copy the same data async from "
<<
src_place
<<
" to "
<<
dst_place
;
return
;
}
memory
::
Copy
(
boost
::
get
<
platform
::
CPUPlace
>
(
dst_place
),
dst_ptr
,
boost
::
get
<
platform
::
CPUPlace
>
(
src_place
),
src_ptr
,
size
);
}
...
...
@@ -71,6 +76,11 @@ void TensorCopy(const Tensor& src, const platform::Place& dst_place,
auto
stream
=
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
).
stream
();
if
(
platform
::
is_same_place
(
src_place
,
dst_place
))
{
if
(
src_ptr
==
dst_ptr
)
{
VLOG
(
3
)
<<
"Skip copy the same data async from "
<<
src_place
<<
" to "
<<
dst_place
;
return
;
}
memory
::
Copy
(
dst_gpu_place
,
dst_ptr
,
src_gpu_place
,
src_ptr
,
size
,
stream
);
}
else
{
...
...
@@ -114,6 +124,11 @@ void TensorCopySync(const Tensor& src, const platform::Place& dst_place,
auto
dst_ptr
=
dst
->
mutable_data
(
dst_place
,
src
.
type
());
auto
size
=
src
.
numel
()
*
SizeOfType
(
src
.
type
());
if
(
platform
::
is_cpu_place
(
src_place
)
&&
platform
::
is_cpu_place
(
dst_place
))
{
if
(
src_ptr
==
dst_ptr
)
{
VLOG
(
3
)
<<
"Skip copy the same data from "
<<
src_place
<<
" to "
<<
dst_place
;
return
;
}
memory
::
Copy
(
boost
::
get
<
platform
::
CPUPlace
>
(
dst_place
),
dst_ptr
,
boost
::
get
<
platform
::
CPUPlace
>
(
src_place
),
src_ptr
,
size
);
}
...
...
@@ -130,6 +145,11 @@ void TensorCopySync(const Tensor& src, const platform::Place& dst_place,
memory
::
Copy
(
dst_gpu_place
,
dst_ptr
,
src_cpu_place
,
src_ptr
,
size
,
nullptr
);
}
else
if
(
platform
::
is_gpu_place
(
src_place
)
&&
platform
::
is_gpu_place
(
dst_place
))
{
if
(
src_ptr
==
dst_ptr
&&
platform
::
is_same_place
(
src_place
,
dst_place
))
{
VLOG
(
3
)
<<
"Skip copy the same data from "
<<
src_place
<<
" to "
<<
dst_place
;
return
;
}
auto
src_gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
src_place
);
auto
dst_gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
dst_place
);
memory
::
Copy
(
dst_gpu_place
,
dst_ptr
,
src_gpu_place
,
src_ptr
,
size
,
nullptr
);
...
...
paddle/fluid/framework/tensor_util_test.cc
浏览文件 @
41eeb771
...
...
@@ -41,6 +41,11 @@ TEST(TensorCopy, Tensor) {
EXPECT_EQ
(
src_ptr
[
i
],
dst_ptr
[
i
]);
}
TensorCopy
(
dst_tensor
,
*
cpu_place
,
&
dst_tensor
);
for
(
size_t
i
=
0
;
i
<
9
;
++
i
)
{
EXPECT_EQ
(
src_ptr
[
i
],
dst_ptr
[
i
]);
}
EXPECT_TRUE
(
dst_tensor
.
layout
()
==
src_tensor
.
layout
());
Tensor
slice_tensor
=
src_tensor
.
Slice
(
1
,
2
);
...
...
@@ -82,6 +87,15 @@ TEST(TensorCopy, Tensor) {
EXPECT_EQ
(
src_ptr
[
i
],
dst_ptr
[
i
]);
}
// Copy the same tensor
TensorCopy
(
gpu_tensor
,
*
gpu_place
,
gpu_ctx
,
&
gpu_tensor
);
gpu_ctx
.
Wait
();
const
int
*
dst_ptr_tmp
=
dst_tensor
.
data
<
int
>
();
EXPECT_NE
(
src_ptr
,
dst_ptr_tmp
);
for
(
size_t
i
=
0
;
i
<
9
;
++
i
)
{
EXPECT_EQ
(
src_ptr
[
i
],
dst_ptr_tmp
[
i
]);
}
Tensor
slice_tensor
=
src_tensor
.
Slice
(
1
,
2
);
// CPU Slice Tensor to GPU Tensor
...
...
paddle/fluid/framework/var_desc.h
浏览文件 @
41eeb771
...
...
@@ -59,6 +59,7 @@ class VarDesc {
public:
explicit
VarDesc
(
const
std
::
string
&
name
)
{
desc_
.
set_name
(
name
);
// TODO(paddle-dev): Why default to lodtensor.
desc_
.
mutable_type
()
->
set_type
(
proto
::
VarType
::
LOD_TENSOR
);
}
...
...
paddle/fluid/framework/variable.h
浏览文件 @
41eeb771
...
...
@@ -38,8 +38,12 @@ class Variable {
template
<
typename
T
>
T
*
GetMutable
()
{
if
(
!
IsType
<
T
>
()
)
{
if
(
!
holder_
)
{
holder_
.
reset
(
new
PlaceholderImpl
<
T
>
(
new
T
()));
}
else
{
PADDLE_ENFORCE
(
IsType
<
T
>
(),
"Variable must be type %s, the holding type is %s"
,
typeid
(
T
).
name
(),
holder_
->
Type
().
name
());
}
return
static_cast
<
T
*>
(
holder_
->
Ptr
());
}
...
...
paddle/fluid/framework/variable_test.cc
浏览文件 @
41eeb771
...
...
@@ -33,9 +33,10 @@ TEST(Variable, GetMutable) {
const
Tensor
&
tt
=
v
->
Get
<
Tensor
>
();
EXPECT_EQ
(
1234
,
tt
.
content_
);
std
::
string
*
s
=
v
->
GetMutable
<
std
::
string
>
();
*
s
=
"hello"
;
const
std
::
string
&
ss
=
v
->
Get
<
std
::
string
>
();
EXPECT_EQ
(
"hello"
,
ss
);
try
{
v
->
GetMutable
<
std
::
string
>
();
}
catch
(
std
::
exception
&
e
)
{
return
;
}
EXPECT_TRUE
(
false
);
}
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @
41eeb771
...
...
@@ -340,6 +340,19 @@ bool AnalysisPredictor::LoadProgramDesc() {
}
return
true
;
}
AnalysisPredictor
::~
AnalysisPredictor
()
{
#if !defined(_WIN32)
if
(
FLAGS_profile
)
{
platform
::
DisableProfiler
(
platform
::
EventSortingKey
::
kTotal
,
"./profile.log"
);
}
#endif
if
(
sub_scope_
)
{
scope_
->
DeleteScope
(
sub_scope_
);
}
}
std
::
unique_ptr
<
PaddlePredictor
>
AnalysisPredictor
::
Clone
()
{
auto
*
x
=
new
AnalysisPredictor
(
config_
);
x
->
Init
(
scope_
,
inference_program_
);
...
...
paddle/fluid/inference/api/analysis_predictor.h
浏览文件 @
41eeb771
...
...
@@ -72,6 +72,7 @@ class AnalysisPredictor : public PaddlePredictor {
template
<
typename
T
>
void
GetFetchOne
(
const
framework
::
LoDTensor
&
fetchs
,
PaddleTensor
*
output_data
);
~
AnalysisPredictor
();
private:
contrib
::
AnalysisConfig
config_
;
...
...
paddle/fluid/operators/CMakeLists.txt
浏览文件 @
41eeb771
...
...
@@ -300,7 +300,7 @@ op_library(flatten_op DEPS reshape_op)
op_library
(
sequence_pad_op DEPS sequence_padding
)
op_library
(
unstack_op DEPS stack_op
)
op_library
(
fake_quantize_op DEPS memory
)
op_library
(
fusion_lstm_op DEPS
cpu_lstm_compute
)
op_library
(
fusion_lstm_op DEPS
jit_kernel
)
if
(
WITH_GPU
)
op_library
(
conv_op DEPS vol2col depthwise_conv im2col
)
op_library
(
layer_norm_op DEPS cub
)
...
...
paddle/fluid/operators/fill_constant_op.cc
浏览文件 @
41eeb771
...
...
@@ -70,6 +70,12 @@ class FillConstantOp : public framework::OperatorBase {
}
};
class
FillConstantOpVarTypeInference
:
public
framework
::
VarTypeInference
{
public:
void
operator
()(
const
framework
::
OpDesc
&
op_desc
,
framework
::
BlockDesc
*
block
)
const
override
{}
};
class
FillConstantOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
...
...
@@ -102,4 +108,5 @@ Fill up a variable with specified constant value.
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
fill_constant
,
ops
::
FillConstantOp
,
ops
::
FillConstantInferShape
,
ops
::
FillConstantOpMaker
,
paddle
::
framework
::
EmptyGradOpMaker
);
paddle
::
framework
::
EmptyGradOpMaker
,
ops
::
FillConstantOpVarTypeInference
);
paddle/fluid/operators/fusion_lstm_op.cc
浏览文件 @
41eeb771
...
...
@@ -15,11 +15,9 @@ limitations under the License. */
#include "paddle/fluid/operators/fusion_lstm_op.h"
#include <string>
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/cpu_lstm_compute.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
{
...
...
@@ -219,121 +217,55 @@ This operator fuse the X into LSTM, more details can refer to LSTM op.
template
<
typename
T
>
class
FuisonLSTMKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
#define INIT_VEC_FUNC \
std::function<void(const int, const T *, T *)> act_gate, act_cell, act_cand; \
auto& act_gate_str = ctx.Attr<std::string>("gate_activation"); \
auto& act_cell_str = ctx.Attr<std::string>("cell_activation"); \
auto& act_cand_str = ctx.Attr<std::string>("candidate_activation"); \
if (platform::jit::MayIUse(platform::jit::avx)) { \
math::VecActivations<T, platform::jit::avx> act_functor; \
act_gate = act_functor(act_gate_str); \
act_cell = act_functor(act_cell_str); \
act_cand = act_functor(act_cand_str); \
} else { \
math::VecActivations<T, platform::jit::isa_any> act_functor; \
act_gate = act_functor(act_gate_str); \
act_cell = act_functor(act_cell_str); \
act_cand = act_functor(act_cand_str); \
}
#define INIT_BASE_INPUT_OUTPUT \
auto* x = ctx.Input<LoDTensor>("X"); \
auto* h0 = ctx.Input<Tensor>("H0"); \
auto* c0 = ctx.Input<Tensor>("C0"); \
auto* wx = ctx.Input<Tensor>("WeightX"); \
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"); \
auto* cell_out = ctx.Output<LoDTensor>("Cell"); \
bool is_reverse = ctx.Attr<bool>("is_reverse"); \
bool use_peepholes = ctx.Attr<bool>("use_peepholes");
#define INIT_BASE_SIZES \
auto x_dims = x->dims();
/* T x M*/
\
auto wh_dims = wh->dims();
/* D x 4D*/
\
const int M = x_dims[1]; \
const int D = wh_dims[0]; \
const int D2 = D * 2; \
const int D3 = D * 3; \
const int D4 = wh_dims[1];
#define INIT_BASE_INPUT_DATAS \
const T* x_data = x->data<T>(); \
const T* wx_data = wx->data<T>(); \
const T* wh_data = wh->data<T>(); \
/* diagonal weight*/
\
const T* wc_data = bias->data<T>() + D4; \
/* for peephole only*/
\
T* checked_cell_data = nullptr; \
auto place = ctx.GetPlace(); \
if (use_peepholes) { \
/* w_ic * Ct-1, w_fc * Ct-1 ; w_oc * Ct => ih*/
\
auto* checked_cell = ctx.Output<Tensor>("CheckedCell"); \
checked_cell_data = checked_cell->mutable_data<T>(place); \
}
/// Compute LSTM
#define INIT_BASE_DEFINES \
using DeviceContext = paddle::platform::CPUDeviceContext; \
auto* x = ctx.Input<LoDTensor>("X"); \
auto* h0 = ctx.Input<Tensor>("H0"); \
auto* c0 = ctx.Input<Tensor>("C0"); \
auto* wx = ctx.Input<Tensor>("WeightX"); \
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"); \
auto* cell_out = ctx.Output<LoDTensor>("Cell"); \
bool is_reverse = ctx.Attr<bool>("is_reverse"); \
bool use_peepholes = ctx.Attr<bool>("use_peepholes"); \
auto x_dims = x->dims();
/* T x M*/
\
auto wh_dims = wh->dims();
/* D x 4D*/
\
const int M = x_dims[1]; \
const int D = wh_dims[0]; \
const int D4 = wh_dims[1]
#define INIT_OTHER_DEFINES \
const T* x_data = x->data<T>(); \
const T* wx_data = wx->data<T>(); \
const T* wh_data = wh->data<T>(); \
/* diagonal weight*/
\
const T* wp_data = bias->data<T>() + D4; \
/* for peephole only*/
\
T* checked_cell_data = nullptr; \
auto place = ctx.GetPlace(); \
if (use_peepholes) { \
/* w_ic * Ct-1, w_fc * Ct-1 ; w_oc * Ct => ih*/
\
auto* checked_cell = ctx.Output<Tensor>("CheckedCell"); \
checked_cell_data = checked_cell->mutable_data<T>(place); \
} \
const auto& ker = \
math::jitkernel::KernelPool::Instance() \
.template Get<math::jitkernel::LSTMKernel<T>, const std::string&, \
const std::string&, const std::string&>( \
ctx.Attr<std::string>("gate_activation"), \
ctx.Attr<std::string>("candidate_activation"), \
ctx.Attr<std::string>("cell_activation"), D, use_peepholes)
// Wh GEMM
#define GEMM_WH_ADDON(bs, prev, out) \
blas.GEMM(CblasNoTrans, CblasNoTrans, bs, D4, D, static_cast<T>(1), prev, D, \
wh_data, D4, static_cast<T>(1), out, D4)
#define GET_Ct(ct_1, gates, ct) \
/* C_t = C_t-1 * fgated + cand_gated * igated*/
\
act_cand(D, gates, gates); \
blas.VMUL(D, gates, gates + D, gates + D); \
blas.VMUL(D, ct_1, gates + D2, gates + D2); \
blas.VADD(D, gates + D, gates + D2, ct)
#define GET_Ht(ct, gates, ht) \
/* H_t = act_cell(C_t) * ogated */
\
act_cell(D, ct, gates + D2); \
blas.VMUL(D, gates + D2, gates + D3, ht)
#define GET_Ct_NOH0C0(gates, ct) \
/* C_t = igated * cgated*/
\
act_gate(D, gates + D, gates + D); \
act_cand(D, gates, gates); \
blas.VMUL(D, gates, gates + D, ct)
#define COMPUTE_CtHt_NOH0C0(gates, ct, ht) \
GET_Ct_NOH0C0(gates, ct); \
act_gate(D, gates + D3, gates + D3); \
GET_Ht(ct, gates, ht)
#define COMPUTE_CtHt_PEEPHOLE_NOH0C0(gates, ct, ht) \
GET_Ct_NOH0C0(gates, ct); \
/* get outgated, put W_oc * C_t on igated */
\
blas.VMUL(D, wc_data + D2, ct, gates + D); \
blas.VADD(D, gates + D, gates + D3, gates + D3); \
act_gate(D, gates + D3, gates + D3); \
GET_Ht(ct, gates, ht)
#define COMPUTE_CtHt(gates, ct_1, ct, ht) \
act_gate(D3, gates + D, gates + D); \
GET_Ct(ct_1, gates, ct); \
GET_Ht(ct, gates, ht)
#define COMPUTE_CtHt_PEEPHOLE(gates, ct_1, ct, ht) \
/* get fgated and igated*/
\
blas.VMUL(D, wc_data, ct_1, checked_cell_data); \
blas.VMUL(D, wc_data + D, ct_1, checked_cell_data + D); \
blas.VADD(D2, checked_cell_data, gates + D, gates + D); \
act_gate(D2, gates + D, gates + D); \
GET_Ct(ct_1, gates, ct); \
/* get ogated*/
\
blas.VMUL(D, wc_data + D2, ct, gates + D); \
blas.VADD(D, gates + D, gates + D3, gates + D3); \
act_gate(D, gates + D3, gates + D3); \
GET_Ht(ct, gates, ht)
void
SeqCompute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
using
DeviceContext
=
paddle
::
platform
::
CPUDeviceContext
;
INIT_BASE_INPUT_OUTPUT
INIT_BASE_SIZES
INIT_VEC_FUNC
INIT_BASE_INPUT_DATAS
INIT_BASE_DEFINES
;
INIT_OTHER_DEFINES
;
auto
x_lod
=
x
->
lod
();
const
int
total_T
=
x_dims
[
0
];
const
int
N
=
x_lod
[
0
].
size
()
-
1
;
...
...
@@ -357,89 +289,47 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
gate_offset
=
-
D
;
}
#define MOVE_ONE_STEP \
prev_h_data = h_out_data; \
prev_c_data = c_out_data; \
xx_data = xx_data + xx_offset; \
h_out_data = h_out_data + gate_offset; \
c_out_data = c_out_data + gate_offset
#define PROCESS_H0C0_DEFINES \
int bid = is_reverse ? N - 1 - i : i; \
int seq_len = x_lod[0][bid + 1] - x_lod[0][bid]; \
const T* prev_c_data = nullptr; \
const T* prev_h_data = nullptr; \
int tstart = 0
#define PROCESS_H0C0_PEEPHOLE \
PROCESS_H0C0_DEFINES; \
if (h0_data) { \
prev_h_data = h0_data + bid * D; \
prev_c_data = c0_data + bid * D; \
} else { \
COMPUTE_CtHt_PEEPHOLE_NOH0C0(xx_data, c_out_data, h_out_data); \
MOVE_ONE_STEP; \
tstart = 1; \
}
#define PROCESS_H0C0 \
PROCESS_H0C0_DEFINES; \
if (h0_data) { \
prev_h_data = h0_data + bid * D; \
prev_c_data = c0_data + bid * D; \
} else { \
COMPUTE_CtHt_NOH0C0(xx_data, c_out_data, h_out_data); \
MOVE_ONE_STEP; \
tstart = 1; \
}
if
(
use_peepholes
)
{
for
(
int
i
=
0
;
i
<
N
;
++
i
)
{
PROCESS_H0C0_PEEPHOLE
for
(
int
step
=
tstart
;
step
<
seq_len
;
++
step
)
{
GEMM_WH_ADDON
(
1
,
prev_h_data
,
xx_data
);
COMPUTE_CtHt_PEEPHOLE
(
xx_data
,
prev_c_data
,
c_out_data
,
h_out_data
);
MOVE_ONE_STEP
;
}
}
}
else
{
// TODO(TJ): unly workaround, clean me
std
::
function
<
void
(
T
*
,
const
T
*
,
T
*
,
T
*
)
>
compute_ctht
;
if
(
platform
::
jit
::
MayIUse
(
platform
::
jit
::
avx
)
&&
act_gate_str
==
"sigmoid"
&&
act_cand_str
==
"tanh"
&&
act_cell_str
==
"tanh"
&&
D
==
8
)
{
compute_ctht
=
math
::
lstm_compute_ctht
<
T
>
;
for
(
int
i
=
0
;
i
<
N
;
++
i
)
{
int
bid
=
is_reverse
?
N
-
1
-
i
:
i
;
int
seq_len
=
x_lod
[
0
][
bid
+
1
]
-
x_lod
[
0
][
bid
];
const
T
*
prev_c_data
=
nullptr
;
const
T
*
prev_h_data
=
nullptr
;
int
tstart
=
0
;
if
(
h0_data
)
{
prev_h_data
=
h0_data
+
bid
*
D
;
prev_c_data
=
c0_data
+
bid
*
D
;
}
else
{
compute_ctht
=
[
&
](
T
*
gates
,
const
T
*
ct_1
,
T
*
ct
,
T
*
ht
)
{
COMPUTE_CtHt
(
gates
,
ct_1
,
ct
,
ht
);
};
ker
->
ComputeC1H1
(
xx_data
,
c_out_data
,
h_out_data
,
wp_data
);
tstart
=
1
;
// move one step
prev_h_data
=
h_out_data
;
prev_c_data
=
c_out_data
;
xx_data
=
xx_data
+
xx_offset
;
h_out_data
=
h_out_data
+
gate_offset
;
c_out_data
=
c_out_data
+
gate_offset
;
}
for
(
int
i
=
0
;
i
<
N
;
++
i
)
{
PROCESS_H0C0
for
(
int
step
=
tstart
;
step
<
seq_len
;
++
step
)
{
GEMM_WH_ADDON
(
1
,
prev_h_data
,
xx_data
);
compute_ctht
(
xx_data
,
prev_c_data
,
c_out_data
,
h_out_data
);
MOVE_ONE_STEP
;
}
for
(
int
step
=
tstart
;
step
<
seq_len
;
++
step
)
{
GEMM_WH_ADDON
(
1
,
prev_h_data
,
xx_data
);
ker
->
ComputeCtHt
(
xx_data
,
prev_c_data
,
c_out_data
,
h_out_data
,
wp_data
,
checked_cell_data
);
// move one step
prev_h_data
=
h_out_data
;
prev_c_data
=
c_out_data
;
xx_data
=
xx_data
+
xx_offset
;
h_out_data
=
h_out_data
+
gate_offset
;
c_out_data
=
c_out_data
+
gate_offset
;
}
}
#undef PROCESS_H0C0_DEFINES
#undef PROCESS_H0C0_PEEPHOLE
#undef PROCESS_H0C0
#undef MOVE_ONE_STEP
}
void
BatchCompute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
using
DeviceContext
=
platform
::
CPUDeviceContext
;
INIT_BASE_INPUT_OUTPUT
INIT_BASE_SIZES
INIT_BASE_DEFINES
;
if
(
x
->
lod
()[
0
].
size
()
==
2
)
{
xx
->
Resize
({
x_dims
[
0
],
D4
});
SeqCompute
(
ctx
);
return
;
}
INIT_VEC_FUNC
INIT_BASE_INPUT_DATAS
INIT_OTHER_DEFINES
;
auto
*
reordered_h0
=
ctx
.
Output
<
Tensor
>
(
"ReorderedH0"
);
auto
*
reordered_c0
=
ctx
.
Output
<
Tensor
>
(
"ReorderedC0"
);
...
...
@@ -487,8 +377,8 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
prev_c_data
=
reordered_c0_data
;
size_t
sz
=
sizeof
(
T
)
*
D
;
for
(
int
i
=
0
;
i
<
max_bs
;
++
i
)
{
std
::
memcpy
(
reordered_h0_data
,
h0_data
+
seq_order
[
i
]
*
D
,
sz
);
std
::
memcpy
(
reordered_c0_data
,
c0_data
+
seq_order
[
i
]
*
D
,
sz
);
blas
.
VCOPY
(
sz
,
h0_data
+
seq_order
[
i
]
*
D
,
reordered_h0_data
);
blas
.
VCOPY
(
sz
,
c0_data
+
seq_order
[
i
]
*
D
,
reordered_c0_data
);
reordered_h0_data
+=
D
;
reordered_c0_data
+=
D
;
}
...
...
@@ -498,13 +388,7 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
T
*
cur_h_out_data
=
batched_h_out_data
;
T
*
cur_c_out_data
=
batched_c_out_data
;
for
(
int
i
=
0
;
i
<
max_bs
;
++
i
)
{
GET_Ct_NOH0C0
(
cur_in_data
,
cur_c_out_data
);
if
(
use_peepholes
)
{
blas
.
VMUL
(
D
,
wc_data
+
D2
,
cur_c_out_data
,
cur_in_data
+
D
);
blas
.
VADD
(
D
,
cur_in_data
+
D
,
cur_in_data
+
D3
,
cur_in_data
+
D3
);
}
act_gate
(
D
,
cur_in_data
+
D3
,
cur_in_data
+
D3
);
GET_Ht
(
cur_c_out_data
,
cur_in_data
,
cur_h_out_data
);
ker
->
ComputeC1H1
(
cur_in_data
,
cur_c_out_data
,
cur_h_out_data
,
wp_data
);
cur_in_data
+=
D4
;
cur_c_out_data
+=
D
;
cur_h_out_data
+=
D
;
...
...
@@ -513,71 +397,37 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
prev_h_data
=
batched_h_out_data
;
prev_c_data
=
batched_c_out_data
;
}
// compute kernel part
const
auto
&
batch_starts
=
batched_lod
[
0
];
const
int
max_seq_len
=
batch_starts
.
size
()
-
1
;
const
int
offset
=
tstart
*
max_bs
*
D
;
batched_input_data
=
batched_input_data
+
offset
*
4
;
batched_h_out_data
=
batched_h_out_data
+
offset
;
batched_c_out_data
=
batched_c_out_data
+
offset
;
#define DEFINE_CUR \
T* cur_in_data = batched_input_data; \
T* cur_prev_c_data = prev_c_data; \
T* cur_c_out_data = batched_c_out_data; \
T* cur_h_out_data = batched_h_out_data
#define MOVE_ONE_BATCH \
cur_in_data += D4; \
cur_prev_c_data += D; \
cur_c_out_data += D; \
cur_h_out_data += D
#define MOVE_ONE_STEP \
prev_c_data = batched_c_out_data; \
prev_h_data = batched_h_out_data; \
batched_c_out_data = cur_c_out_data; \
batched_h_out_data = cur_h_out_data; \
batched_input_data = cur_in_data
if
(
use_peepholes
)
{
for
(
int
step
=
tstart
;
step
<
max_seq_len
;
++
step
)
{
const
int
cur_bs
=
batch_starts
[
step
+
1
]
-
batch_starts
[
step
];
GEMM_WH_ADDON
(
cur_bs
,
prev_h_data
,
batched_input_data
);
DEFINE_CUR
;
for
(
int
i
=
0
;
i
<
cur_bs
;
++
i
)
{
COMPUTE_CtHt_PEEPHOLE
(
cur_in_data
,
cur_prev_c_data
,
cur_c_out_data
,
cur_h_out_data
);
MOVE_ONE_BATCH
;
}
MOVE_ONE_STEP
;
}
}
else
{
// TODO(TJ): unly workaround, clean me
std
::
function
<
void
(
T
*
,
const
T
*
,
T
*
,
T
*
)
>
compute_ctht
;
if
(
platform
::
jit
::
MayIUse
(
platform
::
jit
::
avx
)
&&
act_gate_str
==
"sigmoid"
&&
act_cand_str
==
"tanh"
&&
act_cell_str
==
"tanh"
&&
D
==
8
)
{
compute_ctht
=
math
::
lstm_compute_ctht
<
T
>
;
}
else
{
compute_ctht
=
[
&
](
T
*
gates
,
const
T
*
ct_1
,
T
*
ct
,
T
*
ht
)
{
COMPUTE_CtHt
(
gates
,
ct_1
,
ct
,
ht
);
};
}
for
(
int
step
=
tstart
;
step
<
max_seq_len
;
++
step
)
{
const
int
cur_bs
=
batch_starts
[
step
+
1
]
-
batch_starts
[
step
];
GEMM_WH_ADDON
(
cur_bs
,
prev_h_data
,
batched_input_data
);
DEFINE_CUR
;
for
(
int
i
=
0
;
i
<
cur_bs
;
++
i
)
{
compute_ctht
(
cur_in_data
,
cur_prev_c_data
,
cur_c_out_data
,
cur_h_out_data
);
MOVE_ONE_BATCH
;
}
MOVE_ONE_STEP
;
for
(
int
step
=
tstart
;
step
<
max_seq_len
;
++
step
)
{
const
int
cur_bs
=
batch_starts
[
step
+
1
]
-
batch_starts
[
step
];
GEMM_WH_ADDON
(
cur_bs
,
prev_h_data
,
batched_input_data
);
T
*
cur_in_data
=
batched_input_data
;
T
*
cur_prev_c_data
=
prev_c_data
;
T
*
cur_c_out_data
=
batched_c_out_data
;
T
*
cur_h_out_data
=
batched_h_out_data
;
for
(
int
i
=
0
;
i
<
cur_bs
;
++
i
)
{
ker
->
ComputeCtHt
(
cur_in_data
,
cur_prev_c_data
,
cur_c_out_data
,
cur_h_out_data
,
wp_data
,
checked_cell_data
);
// move one batch
cur_in_data
+=
D4
;
cur_prev_c_data
+=
D
;
cur_c_out_data
+=
D
;
cur_h_out_data
+=
D
;
}
// move one step
prev_c_data
=
batched_c_out_data
;
prev_h_data
=
batched_h_out_data
;
batched_c_out_data
=
cur_c_out_data
;
batched_h_out_data
=
cur_h_out_data
;
batched_input_data
=
cur_in_data
;
}
#undef MOVE_ONE_STEP
#undef MOVE_ONE_BATCH
#undef DEFINE_CUR
math
::
Batch2LoDTensorFunctor
<
DeviceContext
,
T
>
to_seq
;
batched_h_out
->
set_lod
(
batched_lod
);
...
...
@@ -594,18 +444,9 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
}
}
#undef COMPUTE_CtHt_PEEPHOLE
#undef COMPUTE_CtHt
#undef GET_Ct_NOH0C0
#undef COMPUTE_CtHt_NOH0C0
#undef COMPUTE_CtHt_PEEPHOLE_NOH0C0
#undef GET_Ht
#undef GET_Ct
#undef GEMM_WH_ADDON
#undef INIT_BASE_INPUT_DATAS
#undef INIT_BASE_SIZES
#undef INIT_BASE_INPUT_OUTPUT
#undef INIT_VEC_FUNC
#undef INIT_OTHER_DEFINES
#undef INIT_BASE_DEFINES
};
}
// namespace operators
...
...
paddle/fluid/operators/isfinite_op.cc
浏览文件 @
41eeb771
...
...
@@ -60,7 +60,7 @@ class OverflowOpMaker : public framework::OpProtoAndCheckerMaker {
"(Tensor) 1-dim tensor, contains a bool scalar. The output "
"tensor of overflow operator."
);
AddComment
(
string
::
Sprintf
(
R"DOC(
Overflow operator.
Overflow
%s
operator.
$$Out = any(X)$$
...
...
@@ -69,6 +69,8 @@ Out = Inf if any X contains Inf,
Out = Nan if any X contains Nan,
Out = 0 if no Inf/Nan detected.
If X contains both Inf/Nan, it will return the first indicator it meeted.
%s
)DOC"
,
GetName
(),
GetComments
()));
}
...
...
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @
41eeb771
...
...
@@ -45,8 +45,6 @@ math_library(im2col)
if
(
NOT WIN32
)
# windows do not support avx functions yet.
math_library
(
gru_compute DEPS activation_functions math_function
)
math_library
(
lstm_compute DEPS activation_functions
)
# TODO(TJ): ugly workaround, clean me
cc_library
(
cpu_lstm_compute SRCS cpu_lstm_compute.cc DEPS activation_functions cblas cpu_info
)
endif
(
NOT WIN32
)
cc_library
(
blas SRCS blas.cc DEPS cblas framework_proto device_context
)
...
...
@@ -76,3 +74,7 @@ if(WITH_GPU)
endif
()
cc_test
(
concat_test SRCS concat_test.cc DEPS concat
)
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
DEPS cpu_info cblas activation_functions
)
cc_test
(
jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel
)
paddle/fluid/operators/math/cpu_lstm_compute.h
已删除
100644 → 0
浏览文件 @
a35e7f4b
/* 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. */
#pragma once
#include <string>
#include "paddle/fluid/operators/math/cpu_vec.h"
#include "paddle/fluid/platform/cpu_info.h"
#ifdef __AVX__
#include <immintrin.h>
#endif
namespace
paddle
{
namespace
operators
{
namespace
math
{
// TODO(TJ): ugly workaround, clean me
template
<
typename
T
>
void
lstm_compute_ctht
(
T
*
gates
,
const
T
*
ct_1
,
T
*
ct
,
T
*
ht
)
{
// gates: W_ch, W_ih, W_fh, W_oh
vec_sigmoid
<
T
,
platform
::
jit
::
avx
>
(
24
,
gates
+
8
,
gates
+
8
);
vec_tanh
<
T
,
platform
::
jit
::
avx
>
(
8
,
gates
,
gates
);
const
T
*
i
=
gates
+
8
,
*
f
=
gates
+
16
,
*
o
=
gates
+
24
;
const
T
min
=
SIGMOID_THRESHOLD_MIN
;
const
T
max
=
SIGMOID_THRESHOLD_MAX
;
for
(
int
d
=
0
;
d
<
8
;
++
d
)
{
// C_t = C_t-1 * fgated + cand_gated * igated
ct
[
d
]
=
ct_1
[
d
]
*
f
[
d
]
+
gates
[
d
]
*
i
[
d
];
// H_t = act_cell(C_t) * ogated
T
tmp
=
ct
[
d
]
*
2
;
tmp
=
static_cast
<
T
>
(
0
)
-
((
tmp
<
min
)
?
min
:
((
tmp
>
max
)
?
max
:
tmp
));
vec_exp
<
T
>
(
1
,
&
tmp
,
&
tmp
);
tmp
=
static_cast
<
T
>
(
2
)
/
(
static_cast
<
T
>
(
1
)
+
tmp
)
-
static_cast
<
T
>
(
1
);
ht
[
d
]
=
tmp
*
o
[
d
];
}
}
#ifdef __AVX__
namespace
detail
{
namespace
forward
{
namespace
avx
{
__m256
Sigmoid
(
const
__m256
a
);
__m256
Tanh
(
const
__m256
a
);
}
// namespace avx
}
// namespace forward
}
// namespace detail
template
<
>
void
lstm_compute_ctht
<
float
>
(
float
*
gates
,
const
float
*
ct_1
,
float
*
ct
,
float
*
ht
);
#endif
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/math/cpu_vec.h
浏览文件 @
41eeb771
...
...
@@ -125,10 +125,8 @@ inline void vec_scal<float, platform::jit::avx2>(const int n, const float a,
}
template
<
>
inline
void
vec_scal
<
float
,
platform
::
jit
::
avx512_common
>
(
const
int
n
,
const
float
a
,
const
float
*
x
,
float
*
y
)
{
inline
void
vec_scal
<
float
,
platform
::
jit
::
avx512f
>
(
const
int
n
,
const
float
a
,
const
float
*
x
,
float
*
y
)
{
// TODO(TJ): enable me
vec_scal
<
float
,
platform
::
jit
::
avx2
>
(
n
,
a
,
x
,
y
);
}
...
...
@@ -181,10 +179,10 @@ inline void vec_bias_sub<float, platform::jit::avx2>(const int n, const float a,
}
template
<
>
inline
void
vec_bias_sub
<
float
,
platform
::
jit
::
avx512
_common
>
(
const
int
n
,
const
float
a
,
const
float
*
x
,
float
*
y
)
{
inline
void
vec_bias_sub
<
float
,
platform
::
jit
::
avx512
f
>
(
const
int
n
,
const
float
a
,
const
float
*
x
,
float
*
y
)
{
// TODO(TJ): enable me
vec_bias_sub
<
float
,
platform
::
jit
::
avx2
>
(
n
,
a
,
x
,
y
);
}
...
...
@@ -242,7 +240,7 @@ inline void vec_cross<float, platform::jit::avx2>(const int n, const float* x,
}
template
<
>
inline
void
vec_cross
<
float
,
platform
::
jit
::
avx512
_common
>
(
inline
void
vec_cross
<
float
,
platform
::
jit
::
avx512
f
>
(
const
int
n
,
const
float
*
x
,
const
float
*
y
,
const
float
*
z
,
float
*
out
)
{
// TODO(TJ): enable me
vec_cross
<
float
,
platform
::
jit
::
avx
>
(
n
,
x
,
y
,
z
,
out
);
...
...
@@ -296,10 +294,10 @@ inline void vec_add_bias<float, platform::jit::avx2>(const int n, const float a,
}
template
<
>
inline
void
vec_add_bias
<
float
,
platform
::
jit
::
avx512
_common
>
(
const
int
n
,
const
float
a
,
const
float
*
x
,
float
*
y
)
{
inline
void
vec_add_bias
<
float
,
platform
::
jit
::
avx512
f
>
(
const
int
n
,
const
float
a
,
const
float
*
x
,
float
*
y
)
{
// TODO(TJ): enable me
vec_add_bias
<
float
,
platform
::
jit
::
avx2
>
(
n
,
a
,
x
,
y
);
}
...
...
@@ -390,9 +388,9 @@ inline void vec_sigmoid<float, platform::jit::avx2>(const int n, const float* x,
}
template
<
>
inline
void
vec_sigmoid
<
float
,
platform
::
jit
::
avx512
_common
>
(
const
int
n
,
const
float
*
x
,
float
*
y
)
{
inline
void
vec_sigmoid
<
float
,
platform
::
jit
::
avx512
f
>
(
const
int
n
,
const
float
*
x
,
float
*
y
)
{
// TODO(TJ): enable me
vec_sigmoid
<
float
,
platform
::
jit
::
avx2
>
(
n
,
x
,
y
);
}
...
...
@@ -454,9 +452,8 @@ inline void vec_relu<float, platform::jit::avx2>(const int n, const float* x,
}
template
<
>
inline
void
vec_relu
<
float
,
platform
::
jit
::
avx512_common
>
(
const
int
n
,
const
float
*
x
,
float
*
y
)
{
inline
void
vec_relu
<
float
,
platform
::
jit
::
avx512f
>
(
const
int
n
,
const
float
*
x
,
float
*
y
)
{
// TODO(TJ): enable me
vec_relu
<
float
,
platform
::
jit
::
avx2
>
(
n
,
x
,
y
);
}
...
...
paddle/fluid/operators/math/cpu_vec_test.cc
浏览文件 @
41eeb771
...
...
@@ -110,7 +110,7 @@ TEST(CpuVecTest, sigmoid) {
TestAndBench
<
float
>
(
sz
,
vec_sigmoid
<
float
>
,
ref_sigmoid
<
float
>
);
TestAndBench
<
float
>
(
sz
,
vec_sigmoid
<
float
,
jit
::
avx
>
,
ref_sigmoid
<
float
>
);
TestAndBench
<
float
>
(
sz
,
vec_sigmoid
<
float
,
jit
::
avx2
>
,
ref_sigmoid
<
float
>
);
TestAndBench
<
float
>
(
sz
,
vec_sigmoid
<
float
,
jit
::
avx512
_common
>
,
TestAndBench
<
float
>
(
sz
,
vec_sigmoid
<
float
,
jit
::
avx512
f
>
,
ref_sigmoid
<
float
>
);
}
TestAndBench
<
double
>
(
30
,
vec_sigmoid
<
double
>
,
ref_sigmoid
<
double
>
);
...
...
@@ -123,8 +123,7 @@ TEST(CpuVecTest, tanh) {
TestAndBench
<
float
>
(
sz
,
vec_tanh
<
float
>
,
ref_tanh
<
float
>
);
TestAndBench
<
float
>
(
sz
,
vec_tanh
<
float
,
jit
::
avx
>
,
ref_tanh
<
float
>
);
TestAndBench
<
float
>
(
sz
,
vec_tanh
<
float
,
jit
::
avx2
>
,
ref_tanh
<
float
>
);
TestAndBench
<
float
>
(
sz
,
vec_tanh
<
float
,
jit
::
avx512_common
>
,
ref_tanh
<
float
>
);
TestAndBench
<
float
>
(
sz
,
vec_tanh
<
float
,
jit
::
avx512f
>
,
ref_tanh
<
float
>
);
}
TestAndBench
<
double
>
(
30
,
vec_tanh
<
double
>
,
ref_tanh
<
double
>
);
}
...
...
@@ -136,8 +135,7 @@ TEST(CpuVecTest, relu) {
TestAndBench
<
float
>
(
sz
,
vec_relu
<
float
>
,
ref_relu
<
float
>
);
TestAndBench
<
float
>
(
sz
,
vec_relu
<
float
,
jit
::
avx
>
,
ref_relu
<
float
>
);
TestAndBench
<
float
>
(
sz
,
vec_relu
<
float
,
jit
::
avx2
>
,
ref_relu
<
float
>
);
TestAndBench
<
float
>
(
sz
,
vec_relu
<
float
,
jit
::
avx512_common
>
,
ref_relu
<
float
>
);
TestAndBench
<
float
>
(
sz
,
vec_relu
<
float
,
jit
::
avx512f
>
,
ref_relu
<
float
>
);
}
TestAndBench
<
double
>
(
30
,
vec_relu
<
double
>
,
ref_relu
<
double
>
);
}
...
...
@@ -170,7 +168,7 @@ TEST(CpuVecTest, inplace_sigmoid) {
TestInplace
<
float
>
(
sz
,
vec_sigmoid
<
float
>
,
ref_sigmoid
<
float
>
);
TestInplace
<
float
>
(
sz
,
vec_sigmoid
<
float
,
jit
::
avx
>
,
ref_sigmoid
<
float
>
);
TestInplace
<
float
>
(
sz
,
vec_sigmoid
<
float
,
jit
::
avx2
>
,
ref_sigmoid
<
float
>
);
TestInplace
<
float
>
(
sz
,
vec_sigmoid
<
float
,
jit
::
avx512
_common
>
,
TestInplace
<
float
>
(
sz
,
vec_sigmoid
<
float
,
jit
::
avx512
f
>
,
ref_sigmoid
<
float
>
);
}
TestInplace
<
double
>
(
30
,
vec_sigmoid
<
double
>
,
ref_sigmoid
<
double
>
);
...
...
@@ -183,8 +181,7 @@ TEST(CpuVecTest, inplace_tanh) {
TestInplace
<
float
>
(
sz
,
vec_tanh
<
float
>
,
ref_tanh
<
float
>
);
TestInplace
<
float
>
(
sz
,
vec_tanh
<
float
,
jit
::
avx
>
,
ref_tanh
<
float
>
);
TestInplace
<
float
>
(
sz
,
vec_tanh
<
float
,
jit
::
avx2
>
,
ref_tanh
<
float
>
);
TestInplace
<
float
>
(
sz
,
vec_tanh
<
float
,
jit
::
avx512_common
>
,
ref_tanh
<
float
>
);
TestInplace
<
float
>
(
sz
,
vec_tanh
<
float
,
jit
::
avx512f
>
,
ref_tanh
<
float
>
);
}
TestInplace
<
double
>
(
30
,
vec_tanh
<
double
>
,
ref_tanh
<
double
>
);
}
...
...
@@ -196,8 +193,7 @@ TEST(CpuVecTest, inplace_relu) {
TestInplace
<
float
>
(
sz
,
vec_relu
<
float
>
,
ref_relu
<
float
>
);
TestInplace
<
float
>
(
sz
,
vec_relu
<
float
,
jit
::
avx
>
,
ref_relu
<
float
>
);
TestInplace
<
float
>
(
sz
,
vec_relu
<
float
,
jit
::
avx2
>
,
ref_relu
<
float
>
);
TestInplace
<
float
>
(
sz
,
vec_relu
<
float
,
jit
::
avx512_common
>
,
ref_relu
<
float
>
);
TestInplace
<
float
>
(
sz
,
vec_relu
<
float
,
jit
::
avx512f
>
,
ref_relu
<
float
>
);
}
TestInplace
<
double
>
(
30
,
vec_relu
<
double
>
,
ref_relu
<
double
>
);
}
paddle/fluid/operators/math/
cpu_lstm_compute
.cc
→
paddle/fluid/operators/math/
jit_kernel
.cc
浏览文件 @
41eeb771
/* 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/cpu_lstm_compute.h"
#include "paddle/fluid/operators/math/jit_kernel.h"
#include <iostream>
#include <string>
namespace
paddle
{
namespace
operators
{
namespace
math
{
#ifdef __AVX__
template
<
>
void
lstm_compute_ctht
<
float
>
(
float
*
gates
,
const
float
*
ct_1
,
float
*
ct
,
float
*
ht
)
{
namespace
act
=
detail
::
forward
::
avx
;
// gates: W_ch, W_ih, W_fh, W_oh
__m256
c
,
i
,
f
,
o
;
c
=
_mm256_loadu_ps
(
gates
);
i
=
_mm256_loadu_ps
(
gates
+
8
);
f
=
_mm256_loadu_ps
(
gates
+
16
);
o
=
_mm256_loadu_ps
(
gates
+
24
);
/* C_t = C_t-1 * fgated + cand_gated * igated*/
c
=
_mm256_mul_ps
(
act
::
Tanh
(
c
),
act
::
Sigmoid
(
i
));
i
=
_mm256_loadu_ps
(
ct_1
);
f
=
_mm256_mul_ps
(
i
,
act
::
Sigmoid
(
f
));
f
=
_mm256_add_ps
(
c
,
f
);
_mm256_storeu_ps
(
ct
,
f
);
/* H_t = act_cell(C_t) * ogated */
o
=
_mm256_mul_ps
(
act
::
Tanh
(
f
),
act
::
Sigmoid
(
o
));
_mm256_storeu_ps
(
ht
,
o
);
namespace
jitkernel
{
namespace
jit
=
platform
::
jit
;
KernelPool
&
KernelPool
::
Instance
()
{
static
thread_local
KernelPool
g_jit_kernels
;
return
g_jit_kernels
;
}
std
::
shared_ptr
<
const
Kernel
>
KernelPool
::
Get
(
const
std
::
string
&
key
)
const
{
if
(
kers_
.
find
(
key
)
==
kers_
.
end
())
{
return
nullptr
;
}
return
kers_
.
at
(
key
);
}
#endif
}
// namespace jitkernel
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/math/jit_kernel.h
0 → 100644
浏览文件 @
41eeb771
/* 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. */
#pragma once
#include <functional>
#include <memory> // for shared_ptr
#include <string>
#include <unordered_map>
#include "paddle/fluid/platform/cpu_info.h"
#include "paddle/fluid/platform/macros.h"
// Note: Only support on CPU yet.
namespace
paddle
{
namespace
operators
{
namespace
math
{
namespace
jitkernel
{
#define SIGMOID_THRESHOLD_MIN -40.0
#define SIGMOID_THRESHOLD_MAX 13.0
#define EXP_MAX_INPUT 40.0
#define AVX_FLOAT_BLOCK 8
#define AVX2_FLOAT_BLOCK 8
#define AVX512_FLOAT_BLOCK 16
typedef
enum
{
kLT8
,
kEQ8
,
kGT8LT16
,
kEQ16
,
kGT16
}
jit_block
;
class
Kernel
{
public:
Kernel
()
=
default
;
virtual
~
Kernel
()
=
default
;
int
num_
{
0
};
int
end_
{
0
};
int
rest_
{
0
};
DISABLE_COPY_AND_ASSIGN
(
Kernel
);
};
class
KernelPool
{
public:
static
KernelPool
&
Instance
();
template
<
typename
Ker
,
typename
...
ARGS
>
std
::
shared_ptr
<
const
Ker
>
Get
(
ARGS
...
args
);
std
::
shared_ptr
<
const
Kernel
>
Get
(
const
std
::
string
&
key
)
const
;
private:
KernelPool
()
=
default
;
std
::
unordered_map
<
std
::
string
,
std
::
shared_ptr
<
const
Kernel
>>
kers_
;
DISABLE_COPY_AND_ASSIGN
(
KernelPool
);
};
template
<
typename
T
>
class
VMulKernel
:
public
Kernel
{
public:
virtual
void
Compute
(
const
T
*
x
,
const
T
*
y
,
T
*
z
)
const
=
0
;
};
template
<
typename
T
>
class
VAddKernel
:
public
Kernel
{
public:
virtual
void
Compute
(
const
T
*
x
,
const
T
*
y
,
T
*
z
)
const
=
0
;
};
template
<
typename
T
>
class
VScalKernel
:
public
Kernel
{
public:
virtual
void
Compute
(
const
T
a
,
const
T
*
x
,
T
*
y
)
const
=
0
;
virtual
void
Compute
(
const
T
a
,
T
*
x
)
const
=
0
;
};
template
<
typename
T
>
class
VAddBiasKernel
:
public
Kernel
{
public:
virtual
void
Compute
(
const
T
a
,
const
T
*
x
,
T
*
y
)
const
=
0
;
};
template
<
typename
T
>
class
VActKernel
:
public
Kernel
{
public:
virtual
void
Compute
(
const
T
*
x
,
T
*
y
)
const
=
0
;
};
template
<
typename
T
>
class
VReluKernel
:
public
VActKernel
<
T
>
{
public:
virtual
void
Compute
(
const
T
*
x
,
T
*
y
)
const
=
0
;
};
template
<
typename
T
>
class
VIdentityKernel
:
public
VActKernel
<
T
>
{
public:
virtual
void
Compute
(
const
T
*
x
,
T
*
y
)
const
=
0
;
};
template
<
typename
T
>
class
VExpKernel
:
public
VActKernel
<
T
>
{
public:
virtual
void
Compute
(
const
T
*
x
,
T
*
y
)
const
=
0
;
};
template
<
typename
T
>
class
VSigmoidKernel
:
public
VActKernel
<
T
>
{
public:
virtual
void
Compute
(
const
T
*
x
,
T
*
y
)
const
=
0
;
};
template
<
typename
T
>
class
VTanhKernel
:
public
VActKernel
<
T
>
{
public:
virtual
void
Compute
(
const
T
*
x
,
T
*
y
)
const
=
0
;
};
template
<
typename
T
>
class
LSTMKernel
:
public
Kernel
{
public:
virtual
void
ComputeCtHt
(
T
*
gates
,
const
T
*
ct_1
,
T
*
ct
,
T
*
ht
,
/* below only used in peephole*/
const
T
*
wp_data
=
nullptr
,
T
*
checked
=
nullptr
)
const
=
0
;
// compute c1 and h1 without c0 or h0
virtual
void
ComputeC1H1
(
T
*
gates
,
T
*
ct
,
T
*
ht
,
/* below only used in peephole*/
const
T
*
wp_data
=
nullptr
)
const
=
0
;
};
}
// namespace jitkernel
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/math/jit_kernel_blas.cc
0 → 100644
浏览文件 @
41eeb771
/* 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/jit_kernel.h"
#include <string>
#include "paddle/fluid/operators/math/jit_kernel_macro.h"
#ifdef PADDLE_WITH_MKLML
#include "paddle/fluid/platform/dynload/mklml.h"
#endif
#ifdef __AVX__
#include <immintrin.h>
#endif
namespace
paddle
{
namespace
operators
{
namespace
math
{
namespace
jitkernel
{
namespace
jit
=
platform
::
jit
;
/* VMUL JitKernel */
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
,
jit_block
>
class
VMulKernelImpl
:
public
VMulKernel
<
T
>
{
public:
explicit
VMulKernelImpl
(
int
d
)
:
VMulKernel
<
T
>
()
{
this
->
num_
=
d
;
}
void
Compute
(
const
T
*
x
,
const
T
*
y
,
T
*
z
)
const
override
{
for
(
int
i
=
0
;
i
<
this
->
num_
;
++
i
)
{
z
[
i
]
=
x
[
i
]
*
y
[
i
];
}
}
};
#ifdef PADDLE_WITH_MKLML
#define MKL_FLOAT(isa, block) \
template <> \
void VMulKernelImpl<float, isa, block>::Compute( \
const float* x, const float* y, float* z) const { \
platform::dynload::vsMul(this->num_, x, y, z); \
}
#define MKL_DOUBLE(isa, block) \
template <> \
void VMulKernelImpl<double, isa, block>::Compute( \
const double* x, const double* y, double* z) const { \
platform::dynload::vdMul(this->num_, x, y, z); \
}
FOR_EACH_ISA
(
MKL_FLOAT
,
kGT16
);
FOR_EACH_ISA_BLOCK
(
MKL_DOUBLE
);
#endif
#define INTRI8_FLOAT(isa) \
template <> \
void VMulKernelImpl<float, isa, kEQ8>::Compute( \
const float* x, const float* y, float* z) const { \
__m256 tmpx, tmpy; \
tmpx = _mm256_loadu_ps(x); \
tmpy = _mm256_loadu_ps(y); \
tmpx = _mm256_mul_ps(tmpx, tmpy); \
_mm256_storeu_ps(z, tmpx); \
}
// avx > for > mkl
#ifdef __AVX__
INTRI8_FLOAT
(
jit
::
avx
);
#endif
#ifdef __AVX2__
INTRI8_FLOAT
(
jit
::
avx2
);
#endif
#ifdef __AVX512F__
INTRI8_FLOAT
(
jit
::
avx512f
);
#endif
// TODO(TJ): eq16 test and complete avx512
#undef INTRI8_FLOAT
#undef MKL_FLOAT
#undef MKL_DOUBLE
/* VADD JitKernel */
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
,
jit_block
>
class
VAddKernelImpl
:
public
VAddKernel
<
T
>
{
public:
explicit
VAddKernelImpl
(
int
d
)
:
VAddKernel
<
T
>
()
{
this
->
num_
=
d
;
}
void
Compute
(
const
T
*
x
,
const
T
*
y
,
T
*
z
)
const
override
{
for
(
int
i
=
0
;
i
<
this
->
num_
;
++
i
)
{
z
[
i
]
=
x
[
i
]
+
y
[
i
];
}
}
};
#ifdef PADDLE_WITH_MKLML
#define MKL_FLOAT(isa, block) \
template <> \
void VAddKernelImpl<float, isa, block>::Compute( \
const float* x, const float* y, float* z) const { \
platform::dynload::vsAdd(this->num_, x, y, z); \
}
#define MKL_DOUBLE(isa, block) \
template <> \
void VAddKernelImpl<double, isa, block>::Compute( \
const double* x, const double* y, double* z) const { \
platform::dynload::vdAdd(this->num_, x, y, z); \
}
FOR_EACH_ISA
(
MKL_FLOAT
,
kGT16
);
FOR_EACH_ISA_BLOCK
(
MKL_DOUBLE
);
#endif
#define INTRI8_FLOAT(isa) \
template <> \
void VAddKernelImpl<float, isa, kEQ8>::Compute( \
const float* x, const float* y, float* z) const { \
__m256 tmpx, tmpy; \
tmpx = _mm256_loadu_ps(x); \
tmpy = _mm256_loadu_ps(y); \
tmpx = _mm256_add_ps(tmpx, tmpy); \
_mm256_storeu_ps(z, tmpx); \
}
#ifdef __AVX__
INTRI8_FLOAT
(
jit
::
avx
);
#endif
#ifdef __AVX2__
INTRI8_FLOAT
(
jit
::
avx2
);
#endif
#ifdef __AVX512F__
INTRI8_FLOAT
(
jit
::
avx512f
);
#endif
// TODO(TJ): eq16 test and complete avx512
#undef INTRI8_FLOAT
#undef MKL_FLOAT
#undef MKL_DOUBLE
/* VSCAL JitKernel */
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
,
jit_block
>
class
VScalKernelImpl
:
public
VScalKernel
<
T
>
{
public:
explicit
VScalKernelImpl
(
int
d
)
:
VScalKernel
<
T
>
()
{
this
->
num_
=
d
;
}
void
Compute
(
const
T
a
,
const
T
*
x
,
T
*
y
)
const
override
{
for
(
int
i
=
0
;
i
<
this
->
num_
;
++
i
)
{
y
[
i
]
=
a
*
x
[
i
];
}
}
void
Compute
(
const
T
a
,
T
*
x
)
const
override
{
for
(
int
i
=
0
;
i
<
this
->
num_
;
++
i
)
{
x
[
i
]
=
a
*
x
[
i
];
}
}
};
#ifdef PADDLE_WITH_MKLML
#define MKL_FLOAT(isa, block) \
template <> \
void VScalKernelImpl<float, isa, block>::Compute(const float a, float* x) \
const { \
platform::dynload::cblas_sscal(this->num_, a, x, 1); \
}
#define MKL_DOUBLE(isa, block) \
template <> \
void VScalKernelImpl<double, isa, block>::Compute(const double a, double* x) \
const { \
platform::dynload::cblas_dscal(this->num_, a, x, 1); \
}
FOR_EACH_ISA
(
MKL_FLOAT
,
kGT16
);
FOR_EACH_ISA_BLOCK
(
MKL_DOUBLE
);
#endif
#define INTRI8_FLOAT(isa) \
template <> \
void VScalKernelImpl<float, isa, kEQ8>::Compute( \
const float a, const float* x, float* y) const { \
__m256 tmp; \
__m256 scalar = _mm256_set1_ps(a); \
tmp = _mm256_loadu_ps(x); \
tmp = _mm256_mul_ps(tmp, scalar); \
_mm256_storeu_ps(y, tmp); \
}
#define INTRI8_INPLACE_FLOAT(isa) \
template <> \
void VScalKernelImpl<float, isa, kEQ8>::Compute(const float a, float* x) \
const { \
__m256 tmp; \
__m256 scalar = _mm256_set1_ps(a); \
tmp = _mm256_loadu_ps(x); \
tmp = _mm256_mul_ps(tmp, scalar); \
_mm256_storeu_ps(x, tmp); \
}
#ifdef __AVX__
INTRI8_FLOAT
(
jit
::
avx
);
INTRI8_INPLACE_FLOAT
(
jit
::
avx
);
#endif
#ifdef __AVX2__
INTRI8_FLOAT
(
jit
::
avx2
);
INTRI8_INPLACE_FLOAT
(
jit
::
avx2
);
#endif
#ifdef __AVX512F__
INTRI8_FLOAT
(
jit
::
avx512f
);
INTRI8_INPLACE_FLOAT
(
jit
::
avx512f
);
#endif
// TODO(TJ): eq16 test and complete avx512
#undef INTRI8_FLOAT
#undef INTRI8_INPLACE_FLOAT
#undef MKL_FLOAT
#undef MKL_DOUBLE
/* VAddBias JitKernel */
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
,
jit_block
>
class
VAddBiasKernelImpl
:
public
VAddBiasKernel
<
T
>
{
public:
explicit
VAddBiasKernelImpl
(
int
d
)
:
VAddBiasKernel
<
T
>
()
{
this
->
num_
=
d
;
}
void
Compute
(
const
T
a
,
const
T
*
x
,
T
*
y
)
const
override
{
for
(
int
i
=
0
;
i
<
this
->
num_
;
++
i
)
{
y
[
i
]
=
x
[
i
]
+
a
;
}
}
};
#define INTRI8_FLOAT(isa) \
template <> \
void VAddBiasKernelImpl<float, isa, kEQ8>::Compute( \
const float a, const float* x, float* y) const { \
__m256 tmp = _mm256_loadu_ps(x); \
tmp = _mm256_add_ps(tmp, _mm256_set1_ps(a)); \
_mm256_storeu_ps(y, tmp); \
}
#define INTRI16_FLOAT(isa) \
template <> \
void VAddBiasKernelImpl<float, isa, kEQ16>::Compute( \
const float a, const float* x, float* y) const { \
__m256 tmp0 = _mm256_loadu_ps(x); \
__m256 tmp1 = _mm256_loadu_ps(x + 8); \
tmp0 = _mm256_add_ps(tmp0, _mm256_set1_ps(a)); \
tmp1 = _mm256_add_ps(tmp1, _mm256_set1_ps(a)); \
_mm256_storeu_ps(y, tmp0); \
_mm256_storeu_ps(y + 8, tmp1); \
}
#ifdef __AVX__
INTRI8_FLOAT
(
jit
::
avx
);
INTRI16_FLOAT
(
jit
::
avx
);
#endif
#ifdef __AVX2__
INTRI8_FLOAT
(
jit
::
avx2
);
INTRI16_FLOAT
(
jit
::
avx2
);
#endif
#ifdef __AVX512F__
INTRI8_FLOAT
(
jit
::
avx512f
);
INTRI16_FLOAT
(
jit
::
avx512f
);
#endif
// TODO(TJ): eq16 test and complete avx512
#undef INTRI8_FLOAT
#undef INTRI16_FLOAT
/* VRelu JitKernel */
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
,
jit_block
>
class
VReluKernelImpl
:
public
VReluKernel
<
T
>
{
public:
explicit
VReluKernelImpl
(
int
d
)
:
VReluKernel
<
T
>
()
{
this
->
num_
=
d
;
}
void
Compute
(
const
T
*
x
,
T
*
y
)
const
override
{
for
(
int
i
=
0
;
i
<
this
->
num_
;
++
i
)
{
y
[
i
]
=
x
[
i
]
>
0
?
x
[
i
]
:
0
;
}
}
};
#define INTRI8_FLOAT(isa) \
template <> \
void VReluKernelImpl<float, isa, kEQ8>::Compute(const float* x, float* y) \
const { \
__m256 tmp = _mm256_loadu_ps(x); \
tmp = _mm256_max_ps(tmp, _mm256_setzero_ps()); \
_mm256_storeu_ps(y, tmp); \
}
#define INTRI16_FLOAT(isa) \
template <> \
void VReluKernelImpl<float, isa, kEQ16>::Compute(const float* x, float* y) \
const { \
__m256 zeros = _mm256_setzero_ps(); \
__m256 tmp0 = _mm256_loadu_ps(x); \
__m256 tmp1 = _mm256_loadu_ps(x + 8); \
tmp0 = _mm256_max_ps(tmp0, zeros); \
tmp1 = _mm256_max_ps(tmp1, zeros); \
_mm256_storeu_ps(y, tmp0); \
_mm256_storeu_ps(y + 8, tmp1); \
}
#define INTRI_GT8LT16_FLOAT(isa) \
template <> \
VReluKernelImpl<float, isa, kGT8LT16>::VReluKernelImpl(int d) \
: VReluKernel<float>() { \
this->num_ = d; \
this->end_ = AVX_FLOAT_BLOCK; \
this->rest_ = d - AVX_FLOAT_BLOCK; \
} \
template <> \
void VReluKernelImpl<float, isa, kGT8LT16>::Compute(const float* x, \
float* y) const { \
__m256 zeros = _mm256_setzero_ps(); \
__m256 tmp0 = _mm256_loadu_ps(x); \
__m256 tmp1 = _mm256_loadu_ps(x + this->rest_); \
tmp0 = _mm256_max_ps(tmp0, zeros); \
tmp1 = _mm256_max_ps(tmp1, zeros); \
_mm256_storeu_ps(y, tmp0); \
_mm256_storeu_ps(y + this->rest_, tmp1); \
}
#define INTRI_GT16_FLOAT(isa) \
template <> \
VReluKernelImpl<float, isa, kGT16>::VReluKernelImpl(int d) \
: VReluKernel<float>() { \
this->num_ = d; \
this->end_ = d - d % AVX_FLOAT_BLOCK; \
this->rest_ = d - AVX_FLOAT_BLOCK; \
} \
template <> \
void VReluKernelImpl<float, isa, kGT16>::Compute(const float* x, float* y) \
const { \
__m256 zeros = _mm256_setzero_ps(); \
for (int i = 0; i < this->end_; i += AVX_FLOAT_BLOCK) { \
__m256 tmp = _mm256_loadu_ps(x + i); \
tmp = _mm256_max_ps(tmp, zeros); \
_mm256_storeu_ps(y + i, tmp); \
} \
__m256 tmp = _mm256_loadu_ps(x + this->rest_); \
tmp = _mm256_max_ps(tmp, zeros); \
_mm256_storeu_ps(y + this->rest_, tmp); \
}
#ifdef __AVX__
INTRI8_FLOAT
(
jit
::
avx
);
INTRI16_FLOAT
(
jit
::
avx
);
INTRI_GT8LT16_FLOAT
(
jit
::
avx
);
INTRI_GT16_FLOAT
(
jit
::
avx
);
#endif
#ifdef __AVX2__
INTRI8_FLOAT
(
jit
::
avx2
);
INTRI16_FLOAT
(
jit
::
avx2
);
INTRI_GT8LT16_FLOAT
(
jit
::
avx2
);
INTRI_GT16_FLOAT
(
jit
::
avx2
);
#endif
#ifdef __AVX512F__
// TODO(TJ): refine avx512
INTRI8_FLOAT
(
jit
::
avx512f
);
INTRI16_FLOAT
(
jit
::
avx512f
);
INTRI_GT8LT16_FLOAT
(
jit
::
avx512f
);
INTRI_GT16_FLOAT
(
jit
::
avx512f
);
#endif
#undef INTRI8_FLOAT
#undef INTRI16_FLOAT
#undef INTRI_GT8LT16_FLOAT
#undef INTRI_GT16_FLOAT
/* An empty JitKernel */
template
<
typename
T
,
platform
::
jit
::
cpu_isa_t
isa
,
jit_block
>
class
VIdentityKernelImpl
:
public
VIdentityKernel
<
T
>
{
public:
explicit
VIdentityKernelImpl
(
int
d
)
:
VIdentityKernel
<
T
>
()
{
this
->
num_
=
d
;
}
void
Compute
(
const
T
*
x
,
T
*
y
)
const
override
{}
};
REGISTER_JITKERNEL
(
vmul
,
VMulKernel
);
REGISTER_JITKERNEL
(
vadd
,
VAddKernel
);
REGISTER_JITKERNEL
(
vscal
,
VScalKernel
);
REGISTER_JITKERNEL
(
vaddb
,
VAddBiasKernel
);
REGISTER_JITKERNEL
(
vrelu
,
VReluKernel
);
REGISTER_JITKERNEL
(
videntity
,
VIdentityKernel
);
}
// namespace jitkernel
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/math/jit_kernel_exp.cc
0 → 100644
浏览文件 @
41eeb771
/* 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/jit_kernel.h"
#include <cmath> // for exp
#include <string>
#include "paddle/fluid/operators/math/jit_kernel_macro.h"
#ifdef PADDLE_WITH_MKLML
#include "paddle/fluid/platform/dynload/mklml.h"
#endif
#ifdef __AVX__
#include <immintrin.h>
#endif
namespace
paddle
{
namespace
operators
{
namespace
math
{
#ifdef __AVX__
namespace
detail
{
__m256
Exp
(
__m256
a
);
}
// namespace detail
#endif
namespace
jitkernel
{
namespace
jit
=
platform
::
jit
;
/* VExp JitKernel */
template
<
typename
T
,
jit
::
cpu_isa_t
isa
,
jit_block
>
class
VExpKernelImpl
:
public
VExpKernel
<
T
>
{
public:
explicit
VExpKernelImpl
(
int
d
)
:
VExpKernel
<
T
>
()
{
this
->
num_
=
d
;
}
void
Compute
(
const
T
*
x
,
T
*
y
)
const
override
{
for
(
int
i
=
0
;
i
<
this
->
num_
;
++
i
)
{
y
[
i
]
=
std
::
exp
(
x
[
i
]);
}
}
};
#ifdef PADDLE_WITH_MKLML
#define MKL_FLOAT(isa, block) \
template <> \
void VExpKernelImpl<float, isa, block>::Compute(const float* x, float* y) \
const { \
platform::dynload::vsExp(this->num_, x, y); \
}
#define MKL_DOUBLE(isa, block) \
template <> \
void VExpKernelImpl<double, isa, block>::Compute(const double* x, double* y) \
const { \
platform::dynload::vdExp(this->num_, x, y); \
}
FOR_EACH_ISA
(
MKL_FLOAT
,
kLT8
);
FOR_EACH_ISA
(
MKL_FLOAT
,
kGT8LT16
);
FOR_EACH_ISA
(
MKL_FLOAT
,
kGT16
);
FOR_EACH_ISA_BLOCK
(
MKL_DOUBLE
);
#endif
#define INTRI8_FLOAT(isa) \
template <> \
void VExpKernelImpl<float, isa, kEQ8>::Compute(const float* x, float* y) \
const { \
__m256 tmp = _mm256_loadu_ps(x); \
_mm256_storeu_ps(y, detail::Exp(tmp)); \
}
#define INTRI16_FLOAT(isa) \
template <> \
void VExpKernelImpl<float, isa, kEQ16>::Compute(const float* x, float* y) \
const { \
__m256 tmp0 = _mm256_loadu_ps(x); \
__m256 tmp1 = _mm256_loadu_ps(x + 8); \
tmp0 = detail::Exp(tmp0); \
tmp1 = detail::Exp(tmp1); \
_mm256_storeu_ps(y, tmp0); \
_mm256_storeu_ps(y + 8, tmp1); \
}
#ifdef __AVX__
INTRI8_FLOAT
(
jit
::
avx
);
INTRI16_FLOAT
(
jit
::
avx
);
#endif
#ifdef __AVX2__
INTRI8_FLOAT
(
jit
::
avx2
);
INTRI16_FLOAT
(
jit
::
avx2
);
#endif
#ifdef __AVX512F__
INTRI8_FLOAT
(
jit
::
avx512f
);
INTRI16_FLOAT
(
jit
::
avx512f
);
#endif
// TODO(TJ): eq16 test and complete avx512
#undef INTRI8_FLOAT
#undef INTRI16_FLOAT
#undef MKL_FLOAT
#undef MKL_DOUBLE
REGISTER_JITKERNEL
(
vexp
,
VExpKernel
);
/* VSigmoid JitKernel */
template
<
typename
T
,
jit
::
cpu_isa_t
isa
,
jit_block
>
class
VSigmoidKernelImpl
:
public
VSigmoidKernel
<
T
>
{
public:
explicit
VSigmoidKernelImpl
(
int
d
)
:
VSigmoidKernel
<
T
>
()
{
this
->
num_
=
d
;
vexp_
=
KernelPool
::
Instance
().
template
Get
<
VExpKernel
<
T
>
>
(
d
);
}
void
Compute
(
const
T
*
x
,
T
*
y
)
const
override
{
const
T
min
=
SIGMOID_THRESHOLD_MIN
;
const
T
max
=
SIGMOID_THRESHOLD_MAX
;
for
(
int
i
=
0
;
i
<
this
->
num_
;
++
i
)
{
y
[
i
]
=
(
x
[
i
]
<
min
)
?
min
:
((
x
[
i
]
>
max
)
?
max
:
x
[
i
]);
y
[
i
]
=
static_cast
<
T
>
(
0
)
-
y
[
i
];
}
vexp_
->
Compute
(
y
,
y
);
for
(
int
i
=
0
;
i
<
this
->
num_
;
++
i
)
{
y
[
i
]
=
static_cast
<
T
>
(
1
)
/
(
static_cast
<
T
>
(
1
)
+
y
[
i
]);
}
}
private:
std
::
shared_ptr
<
const
VExpKernel
<
T
>>
vexp_
;
};
#define INTRI_SIGMOID(tmp, min, max) \
tmp = _mm256_max_ps(tmp, min); \
tmp = _mm256_min_ps(tmp, max); \
tmp = _mm256_sub_ps(_mm256_set1_ps(0.0f), tmp); \
tmp = detail::Exp(tmp); \
tmp = _mm256_add_ps(_mm256_set1_ps(1.0f), tmp); \
tmp = _mm256_div_ps(_mm256_set1_ps(1.0f), tmp)
#define INTRI8_FLOAT(isa) \
template <> \
void VSigmoidKernelImpl<float, isa, kEQ8>::Compute(const float* x, float* y) \
const { \
__m256 max = _mm256_set1_ps(SIGMOID_THRESHOLD_MAX); \
__m256 min = _mm256_set1_ps(SIGMOID_THRESHOLD_MIN); \
__m256 tmp = _mm256_loadu_ps(x); \
INTRI_SIGMOID(tmp, min, max); \
_mm256_storeu_ps(y, tmp); \
}
#define INTRI16_FLOAT(isa) \
template <> \
void VSigmoidKernelImpl<float, isa, kEQ16>::Compute(const float* x, \
float* y) const { \
__m256 max = _mm256_set1_ps(SIGMOID_THRESHOLD_MAX); \
__m256 min = _mm256_set1_ps(SIGMOID_THRESHOLD_MIN); \
__m256 tmp0 = _mm256_loadu_ps(x); \
__m256 tmp1 = _mm256_loadu_ps(x + 8); \
INTRI_SIGMOID(tmp0, min, max); \
INTRI_SIGMOID(tmp1, min, max); \
_mm256_storeu_ps(y, tmp0); \
_mm256_storeu_ps(y + 8, tmp1); \
}
#define INTRI_GT8LT16_FLOAT(isa) \
template <> \
VSigmoidKernelImpl<float, isa, kGT8LT16>::VSigmoidKernelImpl(int d) \
: VSigmoidKernel<float>() { \
this->num_ = d; \
this->end_ = AVX_FLOAT_BLOCK; \
this->rest_ = d - this->end_; \
vexp_ = \
KernelPool::Instance().template Get<VExpKernel<float>>(this->rest_); \
} \
template <> \
void VSigmoidKernelImpl<float, isa, kGT8LT16>::Compute(const float* x, \
float* y) const { \
__m256 max = _mm256_set1_ps(SIGMOID_THRESHOLD_MAX); \
__m256 min = _mm256_set1_ps(SIGMOID_THRESHOLD_MIN); \
__m256 tmp = _mm256_loadu_ps(x); \
INTRI_SIGMOID(tmp, min, max); \
_mm256_storeu_ps(y, tmp); \
const float min_ = SIGMOID_THRESHOLD_MIN; \
const float max_ = SIGMOID_THRESHOLD_MAX; \
for (int i = this->end_; i < this->num_; ++i) { \
y[i] = (x[i] < min_) ? min_ : ((x[i] > max_) ? max_ : x[i]); \
y[i] = 0.f - y[i]; \
} \
vexp_->Compute(y + this->end_, y + this->end_); \
for (int i = this->end_; i < this->num_; ++i) { \
y[i] = 1.f / (1.f + y[i]); \
} \
}
#define INTRI_GT16_FLOAT(isa) \
template <> \
VSigmoidKernelImpl<float, isa, kGT16>::VSigmoidKernelImpl(int d) \
: VSigmoidKernel<float>() { \
this->num_ = d; \
this->rest_ = d % AVX_FLOAT_BLOCK; \
this->end_ = d - this->rest_; \
vexp_ = \
KernelPool::Instance().template Get<VExpKernel<float>>(this->rest_); \
} \
template <> \
void VSigmoidKernelImpl<float, isa, kGT16>::Compute(const float* x, \
float* y) const { \
__m256 max = _mm256_set1_ps(SIGMOID_THRESHOLD_MAX); \
__m256 min = _mm256_set1_ps(SIGMOID_THRESHOLD_MIN); \
for (int i = 0; i < this->end_; i += AVX_FLOAT_BLOCK) { \
__m256 tmp = _mm256_loadu_ps(x + i); \
INTRI_SIGMOID(tmp, min, max); \
_mm256_storeu_ps(y + i, tmp); \
} \
const float min_ = SIGMOID_THRESHOLD_MIN; \
const float max_ = SIGMOID_THRESHOLD_MAX; \
for (int i = this->end_; i < this->num_; ++i) { \
y[i] = (x[i] < min_) ? min_ : ((x[i] > max_) ? max_ : x[i]); \
y[i] = 0.f - y[i]; \
} \
vexp_->Compute(y + this->end_, y + this->end_); \
for (int i = this->end_; i < this->num_; ++i) { \
y[i] = 1.f / (1.f + y[i]); \
} \
}
#ifdef __AVX__
INTRI8_FLOAT
(
jit
::
avx
);
INTRI16_FLOAT
(
jit
::
avx
);
INTRI_GT8LT16_FLOAT
(
jit
::
avx
);
INTRI_GT16_FLOAT
(
jit
::
avx
);
#endif
#ifdef __AVX2__
INTRI8_FLOAT
(
jit
::
avx2
);
INTRI16_FLOAT
(
jit
::
avx2
);
// INTRI_GT8LT16_FLOAT(jit::avx2);
// INTRI_GT16_FLOAT(jit::avx2);
#endif
#ifdef __AVX512F__
INTRI8_FLOAT
(
jit
::
avx512f
);
INTRI16_FLOAT
(
jit
::
avx512f
);
// INTRI_GT8LT16_FLOAT(jit::avx512f);
// INTRI_GT16_FLOAT(jit::avx512f);
#endif
#undef INTRI8_FLOAT
#undef INTRI16_FLOAT
#undef INTRI_GT8LT16_FLOAT
#undef INTRI_GT16_FLOAT
#undef INTRI_VSIGMOID
REGISTER_JITKERNEL
(
vsigmoid
,
VSigmoidKernel
);
/* VTanh JitKernel */
template
<
typename
T
,
jit
::
cpu_isa_t
isa
,
jit_block
>
class
VTanhKernelImpl
:
public
VTanhKernel
<
T
>
{
public:
explicit
VTanhKernelImpl
(
int
d
)
:
VTanhKernel
<
T
>
()
{
this
->
num_
=
d
;
vscal_
=
KernelPool
::
Instance
().
template
Get
<
VScalKernel
<
T
>
>
(
d
);
vsigmoid_
=
KernelPool
::
Instance
().
template
Get
<
VSigmoidKernel
<
T
>
>
(
d
);
vaddbias_
=
KernelPool
::
Instance
().
template
Get
<
VAddBiasKernel
<
T
>
>
(
d
);
}
void
Compute
(
const
T
*
x
,
T
*
y
)
const
override
{
vscal_
->
Compute
(
static_cast
<
T
>
(
2
),
x
,
y
);
vsigmoid_
->
Compute
(
y
,
y
);
vscal_
->
Compute
(
static_cast
<
T
>
(
2
),
y
);
vaddbias_
->
Compute
(
static_cast
<
T
>
(
-
1
),
y
,
y
);
}
private:
std
::
shared_ptr
<
const
VScalKernel
<
T
>>
vscal_
;
std
::
shared_ptr
<
const
VSigmoidKernel
<
T
>>
vsigmoid_
;
std
::
shared_ptr
<
const
VAddBiasKernel
<
T
>>
vaddbias_
;
};
#define INTRI_VTANH(tmp) \
tmp = _mm256_mul_ps(_mm256_set1_ps(-2.0f), tmp); \
tmp = _mm256_min_ps(tmp, _mm256_set1_ps(EXP_MAX_INPUT)); \
tmp = detail::Exp(tmp); \
tmp = _mm256_add_ps(_mm256_set1_ps(1.0f), tmp); \
tmp = _mm256_div_ps(_mm256_set1_ps(2.0f), tmp); \
tmp = _mm256_sub_ps(tmp, _mm256_set1_ps(1.0f))
#define INTRI8_FLOAT(isa) \
template <> \
void VTanhKernelImpl<float, isa, kEQ8>::Compute(const float* x, float* y) \
const { \
__m256 tmp = _mm256_loadu_ps(x); \
INTRI_VTANH(tmp); \
_mm256_storeu_ps(y, tmp); \
}
#define INTRI16_FLOAT(isa) \
template <> \
void VTanhKernelImpl<float, isa, kEQ16>::Compute(const float* x, float* y) \
const { \
__m256 tmp0 = _mm256_loadu_ps(x); \
__m256 tmp1 = _mm256_loadu_ps(x + 8); \
INTRI_VTANH(tmp0); \
INTRI_VTANH(tmp1); \
_mm256_storeu_ps(y, tmp0); \
_mm256_storeu_ps(y + 8, tmp1); \
}
#define INTRI_GT8LT16_FLOAT(isa) \
template <> \
VTanhKernelImpl<float, isa, kGT8LT16>::VTanhKernelImpl(int d) \
: VTanhKernel<float>() { \
this->num_ = d; \
this->end_ = AVX_FLOAT_BLOCK; \
this->rest_ = d - this->end_; \
vscal_ = \
KernelPool::Instance().template Get<VScalKernel<float>>(this->rest_); \
vsigmoid_ = KernelPool::Instance().template Get<VSigmoidKernel<float>>( \
this->rest_); \
vaddbias_ = KernelPool::Instance().template Get<VAddBiasKernel<float>>( \
this->rest_); \
} \
template <> \
void VTanhKernelImpl<float, isa, kGT8LT16>::Compute(const float* x, \
float* y) const { \
__m256 tmp = _mm256_loadu_ps(x); \
INTRI_VTANH(tmp); \
_mm256_storeu_ps(y, tmp); \
x += AVX_FLOAT_BLOCK; \
y += AVX_FLOAT_BLOCK; \
vscal_->Compute(2.f, x, y); \
vsigmoid_->Compute(y, y); \
vscal_->Compute(2.f, y); \
vaddbias_->Compute(-1.f, y, y); \
}
#define INTRI_GT16_FLOAT(isa) \
template <> \
VTanhKernelImpl<float, isa, kGT16>::VTanhKernelImpl(int d) \
: VTanhKernel<float>() { \
this->num_ = d; \
this->rest_ = d % AVX_FLOAT_BLOCK; \
this->end_ = d - this->rest_; \
vscal_ = \
KernelPool::Instance().template Get<VScalKernel<float>>(this->rest_); \
vsigmoid_ = KernelPool::Instance().template Get<VSigmoidKernel<float>>( \
this->rest_); \
vaddbias_ = KernelPool::Instance().template Get<VAddBiasKernel<float>>( \
this->rest_); \
} \
template <> \
void VTanhKernelImpl<float, isa, kGT16>::Compute(const float* x, float* y) \
const { \
for (int i = 0; i < this->end_; i += AVX_FLOAT_BLOCK) { \
__m256 tmp = _mm256_loadu_ps(x + i); \
INTRI_VTANH(tmp); \
_mm256_storeu_ps(y + i, tmp); \
} \
x += this->end_; \
y += this->end_; \
vscal_->Compute(2.f, x, y); \
vsigmoid_->Compute(y, y); \
vscal_->Compute(2.f, y); \
vaddbias_->Compute(-1.f, y, y); \
}
#ifdef __AVX__
INTRI8_FLOAT
(
jit
::
avx
);
INTRI16_FLOAT
(
jit
::
avx
);
INTRI_GT8LT16_FLOAT
(
jit
::
avx
);
INTRI_GT16_FLOAT
(
jit
::
avx
);
#endif
#ifdef __AVX2__
INTRI8_FLOAT
(
jit
::
avx2
);
INTRI16_FLOAT
(
jit
::
avx2
);
// maybe use avx at gt8lt16 and gt16
#endif
#ifdef __AVX512F__
INTRI8_FLOAT
(
jit
::
avx512f
);
INTRI16_FLOAT
(
jit
::
avx512f
);
// maybe use avx at gt8lt16 and gt16
#endif
#undef INTRI8_FLOAT
#undef INTRI16_FLOAT
#undef INTRI_GT8LT16_FLOAT
#undef INTRI_GT16_FLOAT
#undef INTRI_VTANH
REGISTER_JITKERNEL
(
vtanh
,
VTanhKernel
);
#undef JITKERNEL_NEW_ACT_IMPL
}
// namespace jitkernel
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/math/jit_kernel_lstm.cc
0 → 100644
浏览文件 @
41eeb771
/* 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/jit_kernel.h"
#include <string>
#include "paddle/fluid/operators/math/jit_kernel_macro.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/macros.h"
#ifdef __AVX__
#include <immintrin.h>
#endif
namespace
paddle
{
namespace
operators
{
namespace
math
{
#ifdef __AVX__
namespace
detail
{
__m256
Exp
(
__m256
a
);
}
// namespace detail
#endif
namespace
jitkernel
{
namespace
jit
=
platform
::
jit
;
#ifdef __AVX__
typedef
enum
{
kSigmoid
,
kRelu
,
kTanh
,
kIdentity
}
act_type
;
class
AVXAct
{
public:
virtual
~
AVXAct
()
=
default
;
virtual
__m256
Compute
(
__m256
x
)
const
=
0
;
};
template
<
act_type
type
>
class
AVXActImpl
:
public
AVXAct
{
public:
__m256
Compute
(
__m256
x
)
const
override
{
PADDLE_THROW
(
"Unkown type!"
);
}
};
template
<
>
__m256
AVXActImpl
<
kSigmoid
>::
Compute
(
__m256
x
)
const
{
__m256
ones
=
_mm256_set1_ps
(
1.0
f
);
x
=
_mm256_max_ps
(
x
,
_mm256_set1_ps
(
SIGMOID_THRESHOLD_MIN
));
x
=
_mm256_min_ps
(
x
,
_mm256_set1_ps
(
SIGMOID_THRESHOLD_MAX
));
x
=
_mm256_sub_ps
(
_mm256_set1_ps
(
0.0
f
),
x
);
x
=
detail
::
Exp
(
x
);
x
=
_mm256_add_ps
(
ones
,
x
);
return
_mm256_div_ps
(
ones
,
x
);
}
template
<
>
__m256
AVXActImpl
<
kTanh
>::
Compute
(
__m256
x
)
const
{
__m256
ones
=
_mm256_set1_ps
(
1.0
f
);
x
=
_mm256_mul_ps
(
_mm256_set1_ps
(
-
2.0
f
),
x
);
x
=
_mm256_min_ps
(
x
,
_mm256_set1_ps
(
EXP_MAX_INPUT
));
x
=
detail
::
Exp
(
x
);
x
=
_mm256_add_ps
(
ones
,
x
);
x
=
_mm256_div_ps
(
_mm256_set1_ps
(
2.0
f
),
x
);
return
_mm256_sub_ps
(
x
,
ones
);
}
template
<
>
__m256
AVXActImpl
<
kRelu
>::
Compute
(
__m256
x
)
const
{
return
_mm256_max_ps
(
x
,
_mm256_setzero_ps
());
}
template
<
>
__m256
AVXActImpl
<
kIdentity
>::
Compute
(
__m256
x
)
const
{
return
x
;
}
#endif
template
<
typename
T
>
static
std
::
shared_ptr
<
const
VActKernel
<
T
>>
GetActKernel
(
const
std
::
string
&
type
,
int
n
)
{
if
(
type
==
"sigmoid"
)
{
return
std
::
dynamic_pointer_cast
<
const
VActKernel
<
T
>>
(
KernelPool
::
Instance
().
template
Get
<
VSigmoidKernel
<
T
>
>
(
n
));
}
else
if
(
type
==
"relu"
)
{
return
std
::
dynamic_pointer_cast
<
const
VActKernel
<
T
>>
(
KernelPool
::
Instance
().
template
Get
<
VReluKernel
<
T
>
>
(
n
));
}
else
if
(
type
==
"tanh"
)
{
return
std
::
dynamic_pointer_cast
<
const
VActKernel
<
T
>>
(
KernelPool
::
Instance
().
template
Get
<
VTanhKernel
<
T
>
>
(
n
));
}
else
if
(
type
==
"identity"
||
type
==
""
)
{
return
std
::
dynamic_pointer_cast
<
const
VActKernel
<
T
>>
(
KernelPool
::
Instance
().
template
Get
<
VIdentityKernel
<
T
>
>
(
n
));
}
PADDLE_THROW
(
"Not support type: %s"
,
type
);
return
nullptr
;
}
/* LSTM JitKernel */
template
<
typename
T
,
jit
::
cpu_isa_t
isa
,
jit_block
>
class
LSTMKernelImpl
:
public
LSTMKernel
<
T
>
{
public:
explicit
LSTMKernelImpl
(
const
std
::
string
&
act_gate
,
const
std
::
string
&
act_cand
,
const
std
::
string
&
act_cell
,
int
d
)
:
LSTMKernel
<
T
>
()
{
d_
=
d
;
d2_
=
d
*
2
;
d3_
=
d
*
3
;
act_gate_d3_
=
GetActKernel
<
T
>
(
act_gate
,
d3_
);
act_gate_d_
=
GetActKernel
<
T
>
(
act_gate
,
d
);
act_cand_d_
=
GetActKernel
<
T
>
(
act_cand
,
d
);
act_cell_d_
=
GetActKernel
<
T
>
(
act_cell
,
d
);
vmul_d_
=
KernelPool
::
Instance
().
template
Get
<
VMulKernel
<
T
>
>
(
d
);
vadd_d_
=
KernelPool
::
Instance
().
template
Get
<
VAddKernel
<
T
>
>
(
d
);
#ifdef __AVX__
auto
GetAVXAct
=
[
&
](
const
std
::
string
&
type
)
->
std
::
unique_ptr
<
AVXAct
>
{
if
(
type
==
"sigmoid"
)
{
return
std
::
unique_ptr
<
AVXAct
>
(
new
AVXActImpl
<
kSigmoid
>
());
}
else
if
(
type
==
"relu"
)
{
return
std
::
unique_ptr
<
AVXAct
>
(
new
AVXActImpl
<
kRelu
>
());
}
else
if
(
type
==
"tanh"
)
{
return
std
::
unique_ptr
<
AVXAct
>
(
new
AVXActImpl
<
kTanh
>
());
}
else
if
(
type
==
"identity"
||
type
==
""
)
{
return
std
::
unique_ptr
<
AVXAct
>
(
new
AVXActImpl
<
kIdentity
>
());
}
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
);
#endif
}
void
ComputeCtHt
(
T
*
gates
,
const
T
*
ct_1
,
T
*
ct
,
T
*
ht
,
const
T
*
wp_data
,
T
*
checked
)
const
override
{
// gates: W_ch, W_ih, W_fh, W_oh
act_gate_d3_
->
Compute
(
gates
+
d_
,
gates
+
d_
);
/* C_t = C_t-1 * fgated + cand_gated * igated */
act_cand_d_
->
Compute
(
gates
,
gates
);
vmul_d_
->
Compute
(
gates
,
gates
+
d_
,
gates
+
d_
);
vmul_d_
->
Compute
(
ct_1
,
gates
+
d2_
,
gates
+
d2_
);
vadd_d_
->
Compute
(
gates
+
d_
,
gates
+
d2_
,
ct
);
/* H_t = act_cell(C_t) * ogated */
act_cell_d_
->
Compute
(
ct
,
gates
+
d2_
);
vmul_d_
->
Compute
(
gates
+
d2_
,
gates
+
d3_
,
ht
);
}
void
ComputeC1H1
(
T
*
gates
,
T
*
ct
,
T
*
ht
,
const
T
*
wp_data
)
const
override
{
/* C_t = igated * cgated*/
act_gate_d_
->
Compute
(
gates
+
d_
,
gates
+
d_
);
act_cand_d_
->
Compute
(
gates
,
gates
);
vmul_d_
->
Compute
(
gates
,
gates
+
d_
,
ct
);
/* H_t = act_cell(C_t) * ogated */
act_gate_d_
->
Compute
(
gates
+
d3_
,
gates
+
d3_
);
act_cell_d_
->
Compute
(
ct
,
gates
+
d2_
);
vmul_d_
->
Compute
(
gates
+
d2_
,
gates
+
d3_
,
ht
);
}
private:
int
d_
,
d2_
,
d3_
;
std
::
shared_ptr
<
const
VActKernel
<
T
>>
act_gate_d3_
,
act_gate_d_
,
act_cand_d_
,
act_cell_d_
;
std
::
shared_ptr
<
const
VMulKernel
<
T
>>
vmul_d_
;
std
::
shared_ptr
<
const
VAddKernel
<
T
>>
vadd_d_
;
#ifdef __AVX__
std
::
unique_ptr
<
const
AVXAct
>
avx_act_gate_
,
avx_act_cand_
,
avx_act_cell_
;
#endif
};
#define INTRI8_FLOAT(isa) \
template <> \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeCtHt( \
float* gates, const float* ct_1, float* ct, float* ht, \
const float* wp_data, float* checked) const { \
/* gates: W_ch, W_ih, W_fh, W_oh */
\
__m256 c, i, f, o; \
c = _mm256_loadu_ps(gates); \
i = _mm256_loadu_ps(gates + 8); \
f = _mm256_loadu_ps(gates + 16); \
o = _mm256_loadu_ps(gates + 24); \
/* C_t = C_t-1 * fgated + cand_gated * igated*/
\
c = _mm256_mul_ps(avx_act_cand_->Compute(c), avx_act_gate_->Compute(i)); \
i = _mm256_loadu_ps(ct_1); \
f = _mm256_mul_ps(i, avx_act_gate_->Compute(f)); \
f = _mm256_add_ps(c, f); \
_mm256_storeu_ps(ct, f); \
/* H_t = act_cell(C_t) * ogated */
\
o = _mm256_mul_ps(avx_act_cell_->Compute(f), avx_act_gate_->Compute(o)); \
_mm256_storeu_ps(ht, o); \
}
// TODO(TJ): optimize keq16
#ifdef __AVX__
INTRI8_FLOAT
(
jit
::
avx
);
#endif
#ifdef __AVX2__
INTRI8_FLOAT
(
jit
::
avx2
);
#endif
#ifdef __AVX512F__
INTRI8_FLOAT
(
jit
::
avx512f
);
#endif
/* Peephole JitKernel */
template
<
typename
T
,
jit
::
cpu_isa_t
isa
,
jit_block
>
class
PeepholeKernelImpl
:
public
LSTMKernel
<
T
>
{
public:
explicit
PeepholeKernelImpl
(
const
std
::
string
&
act_gate
,
const
std
::
string
&
act_cand
,
const
std
::
string
&
act_cell
,
int
d
)
:
LSTMKernel
<
T
>
()
{
d_
=
d
;
d2_
=
d
*
2
;
d3_
=
d
*
3
;
act_gate_d_
=
GetActKernel
<
T
>
(
act_gate
,
d
);
act_cand_d_
=
GetActKernel
<
T
>
(
act_cand
,
d
);
act_cell_d_
=
GetActKernel
<
T
>
(
act_cell
,
d
);
vmul_d_
=
KernelPool
::
Instance
().
template
Get
<
VMulKernel
<
T
>
>
(
d
);
vadd_d_
=
KernelPool
::
Instance
().
template
Get
<
VAddKernel
<
T
>
>
(
d
);
vadd_d2_
=
KernelPool
::
Instance
().
template
Get
<
VAddKernel
<
T
>
>
(
d2_
);
act_gate_d2_
=
GetActKernel
<
T
>
(
act_gate
,
d2_
);
}
void
ComputeCtHt
(
T
*
gates
,
const
T
*
ct_1
,
T
*
ct
,
T
*
ht
,
const
T
*
wp_data
,
T
*
checked
)
const
override
{
/* get fgated and igated*/
vmul_d_
->
Compute
(
wp_data
,
ct_1
,
checked
);
vmul_d_
->
Compute
(
wp_data
+
d_
,
ct_1
,
checked
+
d_
);
vadd_d2_
->
Compute
(
checked
,
gates
+
d_
,
gates
+
d_
);
act_gate_d2_
->
Compute
(
gates
+
d_
,
gates
+
d_
);
/* C_t = C_t-1 * fgated + cand_gated * igated*/
act_cand_d_
->
Compute
(
gates
,
gates
);
vmul_d_
->
Compute
(
gates
,
gates
+
d_
,
gates
+
d_
);
vmul_d_
->
Compute
(
ct_1
,
gates
+
d2_
,
gates
+
d2_
);
vadd_d_
->
Compute
(
gates
+
d_
,
gates
+
d2_
,
ct
);
/* get ogated*/
vmul_d_
->
Compute
(
wp_data
+
d2_
,
ct
,
gates
+
d_
);
vadd_d_
->
Compute
(
gates
+
d_
,
gates
+
d3_
,
gates
+
d3_
);
act_gate_d_
->
Compute
(
gates
+
d3_
,
gates
+
d3_
);
/* H_t = act_cell(C_t) * ogated */
act_cell_d_
->
Compute
(
ct
,
gates
+
d2_
);
vmul_d_
->
Compute
(
gates
+
d2_
,
gates
+
d3_
,
ht
);
}
void
ComputeC1H1
(
T
*
gates
,
T
*
ct
,
T
*
ht
,
const
T
*
wp_data
)
const
override
{
/* C_t = igated * cgated*/
act_gate_d_
->
Compute
(
gates
+
d_
,
gates
+
d_
);
act_cand_d_
->
Compute
(
gates
,
gates
);
vmul_d_
->
Compute
(
gates
,
gates
+
d_
,
ct
);
/* get outgated, put W_oc * C_t on igated */
vmul_d_
->
Compute
(
wp_data
+
d2_
,
ct
,
gates
+
d_
);
vadd_d_
->
Compute
(
gates
+
d_
,
gates
+
d3_
,
gates
+
d3_
);
/* H_t = act_cell(C_t) * ogated */
act_gate_d_
->
Compute
(
gates
+
d3_
,
gates
+
d3_
);
act_cell_d_
->
Compute
(
ct
,
gates
+
d2_
);
vmul_d_
->
Compute
(
gates
+
d2_
,
gates
+
d3_
,
ht
);
}
private:
int
d_
,
d2_
,
d3_
;
std
::
shared_ptr
<
const
VActKernel
<
T
>>
act_gate_d2_
,
act_gate_d_
,
act_cand_d_
,
act_cell_d_
;
std
::
shared_ptr
<
const
VMulKernel
<
T
>>
vmul_d_
;
std
::
shared_ptr
<
const
VAddKernel
<
T
>>
vadd_d_
,
vadd_d2_
;
};
#define JITKERNEL_DECLARE_LSTM(ker_class, ker_dtype) \
template <> \
std::shared_ptr<const LSTMKernel<ker_dtype>> \
KernelPool::Get<LSTMKernel<ker_dtype>, const std::string&, \
const std::string&, const std::string&, int, bool>( \
const std::string& act_gate, const std::string& act_cand, \
const std::string& act_cell, int d, bool use_peephole)
#define JITKERNEL_KEY_LSTM(ker_key, dtype_key) \
#ker_key #dtype_key + std::to_string(d) + act_gate + act_cand + act_cell + \
(use_peephole ? "p" : "n")
#define JITKERNEL_NEW_LSTM_IMPL(ker, dtype, isa, k) \
if (use_peephole) { \
p = std::dynamic_pointer_cast<ker<dtype>>( \
std::make_shared<PeepholeKernelImpl<dtype, isa, k>>( \
act_gate, act_cand, act_cell, d)); \
} else { \
p = std::dynamic_pointer_cast<ker<dtype>>( \
std::make_shared<ker##Impl<dtype, isa, k>>(act_gate, act_cand, \
act_cell, d)); \
}
REGISTER_JITKERNEL_ARGS
(
lstm
,
LSTMKernel
,
JITKERNEL_DECLARE_LSTM
,
JITKERNEL_KEY_LSTM
,
JITKERNEL_NEW_LSTM_IMPL
);
#undef INTRI8_FLOAT
#undef JITKERNEL_DECLARE_LSTM
#undef JITKERNEL_KEY_LSTM
#undef JITKERNEL_NEW_LSTM_IMPL
}
// namespace jitkernel
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/math/jit_kernel_macro.h
0 → 100644
浏览文件 @
41eeb771
/* 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. */
#pragma once
#include <string>
#include "paddle/fluid/platform/cpu_info.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
namespace
jitkernel
{
namespace
jit
=
platform
::
jit
;
#define SEARCH_BLOCK(macro_, ker, dtype, isa) \
if (d < AVX_FLOAT_BLOCK) { \
macro_(ker, dtype, isa, kLT8); \
} else if (d == AVX_FLOAT_BLOCK) { \
macro_(ker, dtype, isa, kEQ8); \
} else if (d > AVX_FLOAT_BLOCK && d < AVX512_FLOAT_BLOCK) { \
macro_(ker, dtype, isa, kGT8LT16); \
} else if (d == AVX512_FLOAT_BLOCK) { \
macro_(ker, dtype, isa, kEQ16); \
} else { \
macro_(ker, dtype, isa, kGT16); \
}
#define SEARCH_ISA_BLOCK(macro_, ker, dtype) \
if (jit::MayIUse(jit::avx512f)) { \
SEARCH_BLOCK(macro_, ker, dtype, jit::avx512f); \
} else if (jit::MayIUse(jit::avx2)) { \
SEARCH_BLOCK(macro_, ker, dtype, jit::avx2); \
} else if (jit::MayIUse(jit::avx)) { \
SEARCH_BLOCK(macro_, ker, dtype, jit::avx); \
} else { \
SEARCH_BLOCK(macro_, ker, dtype, jit::isa_any); \
}
#define JITKERNEL_DECLARE(ker_class, ker_dtype) \
template <> \
std::shared_ptr<const ker_class<ker_dtype>> \
KernelPool::Get<ker_class<ker_dtype>, int>(int d)
#define JITKERNEL_KEY(ker_key, dtype_key) \
#ker_key #dtype_key + std::to_string(d)
#define JITKERNEL_NEW_IMPL(ker, dtype, isa, k) \
p = std::dynamic_pointer_cast<ker<dtype>>( \
std::make_shared<ker##Impl<dtype, isa, k>>(d))
#define JITKERNEL_WITH_DTYPE(ker_key, ker_class, ker_dtype, dtype_key, \
marco_declare, macro_key, macro_impl) \
marco_declare(ker_class, ker_dtype) { \
std::string key = macro_key(ker_key, dtype_key); \
if (kers_.find(key) == kers_.end()) { \
std::shared_ptr<ker_class<ker_dtype>> p; \
SEARCH_ISA_BLOCK(macro_impl, ker_class, ker_dtype); \
kers_.insert({key, std::dynamic_pointer_cast<Kernel>(p)}); \
return p; \
} \
return std::dynamic_pointer_cast<const ker_class<ker_dtype>>( \
kers_.at(key)); \
}
#define REGISTER_JITKERNEL(ker_key, ker_class) \
JITKERNEL_WITH_DTYPE(ker_key, ker_class, float, f, JITKERNEL_DECLARE, \
JITKERNEL_KEY, JITKERNEL_NEW_IMPL); \
JITKERNEL_WITH_DTYPE(ker_key, ker_class, double, d, JITKERNEL_DECLARE, \
JITKERNEL_KEY, JITKERNEL_NEW_IMPL)
#define REGISTER_JITKERNEL_ARGS(ker_key, ker_class, marco_declare, macro_key, \
macro_impl) \
JITKERNEL_WITH_DTYPE(ker_key, ker_class, float, f, marco_declare, macro_key, \
macro_impl); \
JITKERNEL_WITH_DTYPE(ker_key, ker_class, double, d, marco_declare, \
macro_key, macro_impl)
#define FOR_EACH_ISA(macro_, block) \
macro_(jit::avx512f, block); \
macro_(jit::avx2, block); \
macro_(jit::avx, block); \
macro_(jit::isa_any, block)
#define FOR_EACH_BLOCK(macro_, isa) \
macro_(isa, kLT8); \
macro_(isa, kEQ8); \
macro_(isa, kGT8LT16); \
macro_(isa, kEQ16); \
macro_(isa, kGT16)
#define FOR_EACH_ISA_BLOCK(macro_) \
FOR_EACH_BLOCK(macro_, jit::avx512f); \
FOR_EACH_BLOCK(macro_, jit::avx2); \
FOR_EACH_BLOCK(macro_, jit::avx); \
FOR_EACH_BLOCK(macro_, jit::isa_any)
}
// namespace jitkernel
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/math/jit_kernel_test.cc
0 → 100644
浏览文件 @
41eeb771
此差异已折叠。
点击以展开。
paddle/fluid/operators/parallel_do_op.cc
浏览文件 @
41eeb771
...
...
@@ -397,6 +397,24 @@ class ParallelDoGradOpShapeInference : public framework::InferShapeBase {
}
};
class
ParallelDoGradOpVarTypeInference
:
public
framework
::
VarTypeInference
{
public:
void
operator
()(
const
framework
::
OpDesc
&
op_desc
,
framework
::
BlockDesc
*
block
)
const
override
{
framework
::
BlockDesc
*
sub_block
=
boost
::
get
<
framework
::
BlockDesc
*>
(
op_desc
.
GetAttr
(
kParallelBlock
));
for
(
auto
&
out_vars
:
op_desc
.
Outputs
())
{
for
(
auto
&
out_var
:
out_vars
.
second
)
{
auto
&
var
=
block
->
FindRecursiveOrCreateVar
(
out_var
);
auto
sub_var
=
sub_block
->
FindRecursiveOrCreateVar
(
out_var
);
if
(
sub_var
.
GetType
()
!=
var
.
GetType
())
{
var
.
SetType
(
sub_var
.
GetType
());
}
}
}
}
};
}
// namespace operators
}
// namespace paddle
...
...
@@ -404,4 +422,5 @@ REGISTER_OPERATOR(parallel_do, paddle::operators::ParallelDoOp,
paddle
::
operators
::
ParallelDoOpProtoMaker
,
paddle
::
operators
::
ParallelDoGradOpDescMaker
);
REGISTER_OPERATOR
(
parallel_do_grad
,
paddle
::
operators
::
ParallelDoGradOp
,
paddle
::
operators
::
ParallelDoGradOpShapeInference
);
paddle
::
operators
::
ParallelDoGradOpShapeInference
,
paddle
::
operators
::
ParallelDoGradOpVarTypeInference
);
paddle/fluid/operators/reshape_op.cc
浏览文件 @
41eeb771
...
...
@@ -164,7 +164,7 @@ dimension value will be copied from Input(X) at runtime. Note that the index of
[2, 3, 4], Attr(shape) = [2, 3, 2, 0] is an invalid input.
3. Input(Shape) has a higher priority than Attr(shape) if it is provided, while
Attr(shape) still should be set correctly to gurantee shape inference in
Attr(shape) still should be set correctly to gurantee shape inference in
compile-time.
)DOC"
);
...
...
@@ -259,7 +259,6 @@ class Reshape2Op : public ReshapeOp {
:
ReshapeOp
(
type
,
inputs
,
outputs
,
attrs
)
{}
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
ReshapeOp
::
InferShape
(
ctx
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"XShape"
),
"Output(XShape) of ReshapeOp should not be null."
);
const
auto
&
x_dims
=
ctx
->
GetInputDim
(
"X"
);
...
...
@@ -270,6 +269,8 @@ class Reshape2Op : public ReshapeOp {
}
ctx
->
SetOutputDim
(
"XShape"
,
framework
::
make_ddim
(
xshape_dims
));
ctx
->
ShareLoD
(
"X"
,
/*->*/
"XShape"
);
ReshapeOp
::
InferShape
(
ctx
);
}
};
...
...
paddle/fluid/operators/sequence_concat_op.cc
浏览文件 @
41eeb771
...
...
@@ -90,11 +90,13 @@ REGISTER_OPERATOR(sequence_concat, paddle::framework::OperatorWithKernel,
paddle
::
framework
::
DefaultGradOpDescMaker
<
false
>
);
template
<
typename
T
>
using
Kernel
=
op
::
SeqConcatKernel
<
paddle
::
platform
::
CPUDeviceContext
,
T
>
;
REGISTER_OP_CPU_KERNEL
(
sequence_concat
,
Kernel
<
float
>
,
Kernel
<
double
>
);
REGISTER_OP_CPU_KERNEL
(
sequence_concat
,
Kernel
<
float
>
,
Kernel
<
double
>
,
Kernel
<
int64_t
>
);
REGISTER_OPERATOR
(
sequence_concat_grad
,
paddle
::
framework
::
OperatorWithKernel
,
op
::
SeqConcatGradShapeInferer
);
template
<
typename
T
>
using
GradKernel
=
op
::
SeqConcatGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
T
>
;
REGISTER_OP_CPU_KERNEL
(
sequence_concat_grad
,
GradKernel
<
float
>
,
GradKernel
<
double
>
);
GradKernel
<
double
>
,
GradKernel
<
int64_t
>
);
paddle/fluid/operators/sequence_unpad_op.cc
0 → 100644
浏览文件 @
41eeb771
/* 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/sequence_unpad_op.h"
namespace
paddle
{
namespace
operators
{
class
SequenceUnpadOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of SequenceUnpadOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Length"
),
"Input(Length) of SequenceUnpadOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of SequenceUnpadOp should not be null."
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
PADDLE_ENFORCE_GE
(
x_dims
.
size
(),
2
,
"The rank of Input(X) can't be less than 2."
);
auto
len_dims
=
ctx
->
GetInputDim
(
"Length"
);
PADDLE_ENFORCE
(
len_dims
.
size
()
==
2
&&
len_dims
[
1
]
==
1
,
"The shape of Input(Length) should be [batch_size, 1]."
);
PADDLE_ENFORCE
(
len_dims
[
0
]
==
x_dims
[
0
],
"Input(X) and Input(Length) should have the same first dimension."
);
int64_t
out_dim_0
=
-
1
;
if
(
ctx
->
IsRuntime
())
{
out_dim_0
=
x_dims
[
0
]
*
x_dims
[
1
];
}
std
::
vector
<
int64_t
>
out_dims_vec
{
out_dim_0
};
if
(
x_dims
.
size
()
==
2
)
{
out_dims_vec
.
push_back
(
1
);
}
else
{
for
(
size_t
i
=
2
;
i
<
x_dims
.
size
();
++
i
)
{
out_dims_vec
.
push_back
(
x_dims
[
i
]);
}
}
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
out_dims_vec
));
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
data_type
=
framework
::
GetDataTypeOfVar
(
ctx
.
InputVar
(
"X"
));
return
framework
::
OpKernelType
(
data_type
,
ctx
.
device_context
());
}
};
class
SequenceUnpadOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"(LoDTensor, default LoDTensor<float>) Input tensor which "
"contains the padded sequences with equal length."
);
AddInput
(
"Length"
,
"(LoDTensor) The input tensor which specifies the actual ength of "
"sequences after unpadding."
);
AddOutput
(
"Out"
,
"(LoDTensor) The output tensor which contains unpadded sequences."
);
AddComment
(
R"DOC(
Sequence Unpad Operator
This operator removes the padding data in the input sequences and convert
them into sequences with actual length as output, identitied by lod
information.
Example:
Given input tensor Input(X):
X.data = [[ 1.0, 2.0, 3.0, 4.0, 5.0],
[ 6.0, 7.0, 8.0, 9.0, 10.0],
[11.0, 12.0, 13.0, 14.0, 15.0]],
`
in which there are 3 sequences padded to length 5, and the acutal length
specified by Input(Length):
Length.data = [[2], [3], [4]],
after unpadding, Output(Out) will be:
Out.data = [[1.0, 2.0, 6.0, 7.0, 8.0, 11.0, 12.0, 13.0, 14.0]]
Out.lod = [[0, 2, 5, 9]]
)DOC"
);
}
};
class
SequenceUnpadGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of SequenceUnpadGradOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) of SequenceUnpadGradOp should not be null."
);
if
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)))
{
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
ctx
->
ShareLoD
(
"X"
,
/*->*/
framework
::
GradVarName
(
"X"
));
}
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
data_type
=
framework
::
GetDataTypeOfVar
(
ctx
.
InputVar
(
"X"
));
return
framework
::
OpKernelType
(
data_type
,
ctx
.
device_context
());
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
sequence_unpad
,
ops
::
SequenceUnpadOp
,
ops
::
SequenceUnpadOpMaker
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OPERATOR
(
sequence_unpad_grad
,
ops
::
SequenceUnpadGradOp
);
REGISTER_OP_CPU_KERNEL
(
sequence_unpad
,
ops
::
SequenceUnpadOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
SequenceUnpadOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
SequenceUnpadOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
,
ops
::
SequenceUnpadOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
REGISTER_OP_CPU_KERNEL
(
sequence_unpad_grad
,
ops
::
SequenceUnpadGradOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
SequenceUnpadGradOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
SequenceUnpadGradOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
,
ops
::
SequenceUnpadGradOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
paddle/fluid/operators/sequence_unpad_op.cu
0 → 100644
浏览文件 @
41eeb771
/* 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/sequence_unpad_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
sequence_unpad
,
ops
::
SequenceUnpadOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
SequenceUnpadOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
SequenceUnpadOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
,
ops
::
SequenceUnpadOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
);
REGISTER_OP_CUDA_KERNEL
(
sequence_unpad_grad
,
ops
::
SequenceUnpadGradOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
SequenceUnpadGradOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
SequenceUnpadGradOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
,
ops
::
SequenceUnpadGradOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
);
paddle/fluid/operators/sequence_unpad_op.h
0 → 100644
浏览文件 @
41eeb771
/* 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. */
#pragma once
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/sequence_padding.h"
namespace
paddle
{
namespace
operators
{
using
LoDTensor
=
framework
::
LoDTensor
;
using
LoD
=
framework
::
LoD
;
template
<
typename
DeviceContext
,
typename
T
>
class
SequenceUnpadOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
x_t
=
ctx
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
len_t
=
ctx
.
Input
<
LoDTensor
>
(
"Length"
);
auto
*
out_t
=
ctx
.
Output
<
LoDTensor
>
(
"Out"
);
out_t
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
int64_t
*
seq_len_ptr
=
nullptr
;
if
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()))
{
LoDTensor
seq_len_cpu
;
seq_len_cpu
.
Resize
(
len_t
->
dims
());
seq_len_ptr
=
seq_len_cpu
.
mutable_data
<
int64_t
>
(
platform
::
CPUPlace
());
framework
::
TensorCopy
(
*
len_t
,
platform
::
CPUPlace
(),
ctx
.
template
device_context
<
DeviceContext
>(),
&
seq_len_cpu
);
}
else
{
seq_len_ptr
=
len_t
->
data
<
int64_t
>
();
}
size_t
batch_size
=
x_t
->
dims
()[
0
];
std
::
vector
<
size_t
>
out_lod0
(
batch_size
+
1
,
0
);
for
(
size_t
i
=
0
;
i
<
batch_size
;
++
i
)
{
out_lod0
[
i
+
1
]
=
out_lod0
[
i
]
+
seq_len_ptr
[
i
];
}
framework
::
LoD
out_lod
;
out_lod
.
push_back
(
out_lod0
);
out_t
->
set_lod
(
out_lod
);
std
::
vector
<
int64_t
>
out_dims_vec
{
static_cast
<
int64_t
>
(
out_lod0
.
back
())};
if
(
x_t
->
dims
().
size
()
==
2
)
{
out_dims_vec
.
push_back
(
1
);
}
else
{
for
(
size_t
i
=
2
;
i
<
x_t
->
dims
().
size
();
++
i
)
{
out_dims_vec
.
push_back
(
x_t
->
dims
()[
i
]);
}
}
out_t
->
Resize
(
framework
::
make_ddim
(
out_dims_vec
));
int64_t
padded_length
=
x_t
->
dims
()[
1
];
math
::
UnpaddingLoDTensorFunctor
<
DeviceContext
,
T
>
()(
ctx
.
template
device_context
<
DeviceContext
>(),
*
x_t
,
out_t
,
padded_length
,
0
,
false
,
math
::
kBatchLengthWidth
);
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
SequenceUnpadGradOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
d_x
=
ctx
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
if
(
d_x
)
{
const
auto
*
d_out
=
ctx
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
const
auto
*
x_t
=
ctx
.
Input
<
LoDTensor
>
(
"X"
);
d_x
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
int
padded_length
=
x_t
->
dims
()[
1
];
LoDTensor
zero_pads
;
zero_pads
.
Resize
({
1
,
1
});
zero_pads
.
mutable_data
<
T
>
(
ctx
.
GetPlace
());
math
::
SetConstant
<
DeviceContext
,
T
>
set_zero
;
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
set_zero
(
dev_ctx
,
&
zero_pads
,
static_cast
<
T
>
(
0
));
math
::
PaddingLoDTensorFunctor
<
DeviceContext
,
T
>
()(
ctx
.
template
device_context
<
DeviceContext
>(),
*
d_out
,
d_x
,
zero_pads
,
padded_length
,
0
,
false
,
math
::
kBatchLengthWidth
);
}
}
};
}
// namespace operators
}
// namespace paddle
paddle/fluid/platform/cpu_info.cc
浏览文件 @
41eeb771
...
...
@@ -128,7 +128,7 @@ bool MayIUse(const cpu_isa_t cpu_isa) {
return
cpu
.
has
(
Cpu
::
tAVX
);
case
avx2
:
return
cpu
.
has
(
Cpu
::
tAVX2
);
case
avx512
_common
:
case
avx512
f
:
return
cpu
.
has
(
Cpu
::
tAVX512F
);
case
avx512_core
:
return
true
&&
cpu
.
has
(
Cpu
::
tAVX512F
)
&&
cpu
.
has
(
Cpu
::
tAVX512BW
)
&&
...
...
paddle/fluid/platform/cpu_info.h
浏览文件 @
41eeb771
...
...
@@ -43,7 +43,7 @@ typedef enum {
sse42
,
avx
,
avx2
,
avx512
_common
,
avx512
f
,
avx512_core
,
avx512_core_vnni
,
avx512_mic
,
...
...
paddle/fluid/platform/init.cc
浏览文件 @
41eeb771
...
...
@@ -116,7 +116,7 @@ void InitDevices(bool init_p2p, const std::vector<int> devices) {
platform
::
SetNumThreads
(
FLAGS_paddle_num_threads
);
#endif
if
(
platform
::
jit
::
MayIUse
(
platform
::
jit
::
avx512
_common
))
{
if
(
platform
::
jit
::
MayIUse
(
platform
::
jit
::
avx512
f
))
{
#ifndef __AVX512F__
LOG
(
WARNING
)
<<
"AVX512F is available, Please re-compile on local machine"
;
#endif
...
...
paddle/fluid/platform/profiler.cc
浏览文件 @
41eeb771
...
...
@@ -370,8 +370,8 @@ void ParseEvents(const std::vector<std::vector<Event>>& events,
std
::
vector
<
std
::
vector
<
Event
>>
merged_events_list
;
if
(
merge_thread
)
{
std
::
vector
<
Event
>
merged_events
;
for
(
in
t
i
=
0
;
i
<
events
.
size
();
++
i
)
{
for
(
in
t
j
=
0
;
j
<
events
[
i
].
size
();
++
j
)
{
for
(
size_
t
i
=
0
;
i
<
events
.
size
();
++
i
)
{
for
(
size_
t
j
=
0
;
j
<
events
[
i
].
size
();
++
j
)
{
merged_events
.
push_back
(
events
[
i
][
j
]);
}
}
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
41eeb771
...
...
@@ -56,6 +56,7 @@ __all__ = [
'sequence_expand'
,
'sequence_expand_as'
,
'sequence_pad'
,
'sequence_unpad'
,
'lstm_unit'
,
'reduce_sum'
,
'reduce_mean'
,
...
...
@@ -64,6 +65,7 @@ __all__ = [
'reduce_prod'
,
'sequence_first_step'
,
'sequence_last_step'
,
'sequence_slice'
,
'dropout'
,
'split'
,
'ctc_greedy_decoder'
,
...
...
@@ -1902,6 +1904,76 @@ def sequence_last_step(input):
return
sequence_pool
(
input
=
input
,
pool_type
=
"last"
)
def
sequence_slice
(
input
,
offset
,
length
,
name
=
None
):
"""
**Sequence Slice Layer**
The layer crops a subsequence from given sequence with given start
offset and subsequence length.
It only supports sequence data (LoDTensor with lod_level equal to 1).
.. code-block:: text
- Case:
Given the input Variable **input**:
input.data = [[a1, a2], [b1, b2], [c1, c2], [d1, d2], [e1, e2]],
input.lod = [[3, 2]],
input.dims = (5, 2),
with offset.data = [[0], [1]] and length.data = [[2], [1]],
the output Variable will be
out.data = [[a1, a2], [b1, b2], [e1, e2]],
out.lod = [[2, 1]],
out.dims = (3, 2).
NOTE: The first dimension size of **input**, **offset** and **length**
should be equal. The **offset** should start from 0.
Args:
input(Variable): The input Variable which consists of the complete
sequences.
offset(Variable): The offset to slice each sequence.
length(Variable): The length of each subsequence.
name(str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
Returns:
Variable: The output subsequences.
Examples:
.. code-block:: python
import numpy as np
seqs = fluid.layers.data(name='x', shape=[10, 5],
dtype='float32', lod_level=1)
offset = fluid.layers.assign(input=np.array([[0, 1]]).astype("int32"))
length = fluid.layers.assign(input=np.array([[2, 1]]).astype("int32"))
subseqs = fluid.layers.sequence_slice(input=seqs, offset=offset,
length=length)
"""
helper
=
LayerHelper
(
"sequence_slice"
,
**
locals
())
dtype
=
helper
.
input_dtype
()
out
=
helper
.
create_tmp_variable
(
dtype
)
offset
.
stop_gradient
=
True
length
.
stop_gradient
=
True
helper
.
append_op
(
type
=
"sequence_slice"
,
inputs
=
{
"X"
:
input
,
"Offset"
:
offset
,
"Length"
:
length
},
outputs
=
{
"Out"
:
out
})
return
out
@
templatedoc
()
def
pool2d
(
input
,
pool_size
=-
1
,
...
...
@@ -2793,7 +2865,7 @@ def sequence_expand_as(x, y, name=None):
@
templatedoc
()
def
sequence_pad
(
x
,
pad_value
,
maxlen
=
None
):
def
sequence_pad
(
x
,
pad_value
,
maxlen
=
None
,
name
=
None
):
"""
${comment}
...
...
@@ -2807,7 +2879,9 @@ def sequence_pad(x, pad_value, maxlen=None):
None or any positive int. When it is None, all sequences will be
padded up to the length of the longest one among them; when it a
certain positive value, it must be greater than the length of the
longest original sequence."
longest original sequence.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The padded sequence batch and the original lengths before
...
...
@@ -2844,6 +2918,66 @@ def sequence_pad(x, pad_value, maxlen=None):
return
out
,
length
def
sequence_unpad
(
x
,
length
,
name
=
None
):
"""
**Sequence Unpad Layer**
This layer removes the padding data in the input sequences and convert
them into sequences with actual length as output, identitied by lod
information.
.. code-block:: text
Example:
Given input Variable **x**:
x.data = [[ 1.0, 2.0, 3.0, 4.0, 5.0],
[ 6.0, 7.0, 8.0, 9.0, 10.0],
[11.0, 12.0, 13.0, 14.0, 15.0]],
in which there are 3 sequences padded to length 5, and the acutal length
specified by input Variable **length**:
length.data = [[2], [3], [4]],
after unpadding, the output Variable will be:
out.data = [[1.0, 2.0, 6.0, 7.0, 8.0, 11.0, 12.0, 13.0, 14.0]]
out.lod = [[2, 3, 4]]
Args:
x(Variable): Input Variable which contains the padded sequences with
equal length.
length(Variable): The Variable that specifies the actual ength of
sequences after unpadding.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The Variable contains the unpadded sequences.
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[10, 5], dtype='float32')
len = fluid.layers.data(name='length', shape=[1], dtype='int64')
out = fluid.layers.sequence_unpad(x=x, length=len)
"""
helper
=
LayerHelper
(
'sequence_unpad'
,
input
=
x
,
**
locals
())
dtype
=
helper
.
input_dtype
()
out
=
helper
.
create_tmp_variable
(
dtype
)
length
.
stop_gradient
=
True
helper
.
append_op
(
type
=
'sequence_unpad'
,
inputs
=
{
'X'
:
x
,
'Length'
:
length
},
outputs
=
{
'Out'
:
out
})
return
out
def
beam_search
(
pre_ids
,
pre_scores
,
ids
,
...
...
python/paddle/fluid/tests/unittests/dist_simnet_bow.py
浏览文件 @
41eeb771
...
...
@@ -81,7 +81,10 @@ def get_optimizer():
return
optimizer
def
train_network
(
batch_size
,
is_distributed
=
False
,
is_sparse
=
False
):
def
train_network
(
batch_size
,
is_distributed
=
False
,
is_sparse
=
False
,
is_self_contained_lr
=
False
):
# query
q
=
fluid
.
layers
.
data
(
name
=
"query_ids"
,
shape
=
[
1
],
dtype
=
"int64"
,
lod_level
=
1
)
...
...
@@ -93,7 +96,9 @@ def train_network(batch_size, is_distributed=False, is_sparse=False):
param_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Constant
(
value
=
0.01
),
name
=
"__emb__"
,
learning_rate
=
emb_lr
),
learning_rate
=
emb_lr
)
if
is_self_contained_lr
else
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Constant
(
value
=
0.01
),
name
=
"__emb__"
),
is_sparse
=
is_sparse
)
## vsum
q_sum
=
fluid
.
layers
.
sequence_pool
(
input
=
q_emb
,
pool_type
=
'sum'
)
...
...
@@ -119,7 +124,9 @@ def train_network(batch_size, is_distributed=False, is_sparse=False):
param_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Constant
(
value
=
0.01
),
name
=
"__emb__"
,
learning_rate
=
emb_lr
),
learning_rate
=
emb_lr
)
if
is_self_contained_lr
else
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Constant
(
value
=
0.01
),
name
=
"__emb__"
),
is_sparse
=
is_sparse
)
## vsum
pt_sum
=
fluid
.
layers
.
sequence_pool
(
input
=
pt_emb
,
pool_type
=
'sum'
)
...
...
@@ -144,7 +151,9 @@ def train_network(batch_size, is_distributed=False, is_sparse=False):
param_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Constant
(
value
=
0.01
),
name
=
"__emb__"
,
learning_rate
=
emb_lr
),
learning_rate
=
emb_lr
)
if
is_self_contained_lr
else
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Constant
(
value
=
0.01
),
name
=
"__emb__"
),
is_sparse
=
is_sparse
)
## vsum
nt_sum
=
fluid
.
layers
.
sequence_pool
(
input
=
nt_emb
,
pool_type
=
'sum'
)
...
...
@@ -220,7 +229,10 @@ class TestDistSimnetBow2x2(TestDistRunnerBase):
def
get_model
(
self
,
batch_size
=
2
):
# Train program
avg_cost
,
acc
,
predict
=
\
train_network
(
batch_size
,
bool
(
int
(
os
.
environ
[
"IS_DISTRIBUTED"
])),
bool
(
int
(
os
.
environ
[
"IS_SPARSE"
])))
train_network
(
batch_size
,
bool
(
int
(
os
.
environ
[
"IS_DISTRIBUTED"
])),
bool
(
int
(
os
.
environ
[
"IS_SPARSE"
])),
bool
(
int
(
os
.
environ
[
"IS_SELF_CONTAINED_LR"
])))
inference_program
=
fluid
.
default_main_program
().
clone
()
...
...
python/paddle/fluid/tests/unittests/test_dist_simnet_bow.py
浏览文件 @
41eeb771
...
...
@@ -25,7 +25,11 @@ class TestDistSimnetBowDense2x2(TestDistBase):
self
.
_enforce_place
=
"CPU"
def
test_simnet_bow
(
self
):
need_envs
=
{
"IS_DISTRIBUTED"
:
'0'
,
"IS_SPARSE"
:
'0'
}
need_envs
=
{
"IS_DISTRIBUTED"
:
'0'
,
"IS_SPARSE"
:
'0'
,
'IS_SELF_CONTAINED_LR'
:
'1'
}
self
.
check_with_place
(
"dist_simnet_bow.py"
,
delta
=
1e-5
,
...
...
@@ -39,7 +43,11 @@ class TestDistSimnetBow2x2DenseAsync(TestDistBase):
self
.
_enforce_place
=
"CPU"
def
test_simnet_bow
(
self
):
need_envs
=
{
"IS_DISTRIBUTED"
:
'0'
,
"IS_SPARSE"
:
'0'
}
need_envs
=
{
"IS_DISTRIBUTED"
:
'0'
,
"IS_SPARSE"
:
'0'
,
'IS_SELF_CONTAINED_LR'
:
'1'
}
self
.
check_with_place
(
"dist_simnet_bow.py"
,
delta
=
100
,
...
...
@@ -53,7 +61,11 @@ class TestDistSimnetBowSparse2x2(TestDistBase):
self
.
_enforce_place
=
"CPU"
def
test_simnet_bow
(
self
):
need_envs
=
{
"IS_DISTRIBUTED"
:
'0'
,
"IS_SPARSE"
:
'1'
}
need_envs
=
{
"IS_DISTRIBUTED"
:
'0'
,
"IS_SPARSE"
:
'1'
,
'IS_SELF_CONTAINED_LR'
:
'1'
}
self
.
check_with_place
(
"dist_simnet_bow.py"
,
delta
=
1e-5
,
...
...
@@ -67,7 +79,11 @@ class TestDistSimnetBow2x2SparseAsync(TestDistBase):
self
.
_enforce_place
=
"CPU"
def
test_simnet_bow
(
self
):
need_envs
=
{
"IS_DISTRIBUTED"
:
'0'
,
"IS_SPARSE"
:
'1'
}
need_envs
=
{
"IS_DISTRIBUTED"
:
'0'
,
"IS_SPARSE"
:
'1'
,
'IS_SELF_CONTAINED_LR'
:
'1'
}
self
.
check_with_place
(
"dist_simnet_bow.py"
,
delta
=
100
,
...
...
@@ -75,5 +91,59 @@ class TestDistSimnetBow2x2SparseAsync(TestDistBase):
need_envs
=
need_envs
)
class
TestDistSimnetBow2x2LookupTableSync
(
TestDistBase
):
def
_setup_config
(
self
):
self
.
_sync_mode
=
True
self
.
_enforce_place
=
"CPU"
def
test_simnet_bow
(
self
):
need_envs
=
{
"IS_DISTRIBUTED"
:
'1'
,
"IS_SPARSE"
:
'1'
,
'IS_SELF_CONTAINED_LR'
:
'1'
}
self
.
check_with_place
(
"dist_simnet_bow.py"
,
delta
=
1e-5
,
check_error_log
=
False
,
need_envs
=
need_envs
)
class
TestDistSimnetBow2x2LookupTableAsync
(
TestDistBase
):
def
_setup_config
(
self
):
self
.
_sync_mode
=
False
self
.
_enforce_place
=
"CPU"
def
test_simnet_bow
(
self
):
need_envs
=
{
"IS_DISTRIBUTED"
:
'1'
,
"IS_SPARSE"
:
'1'
,
'IS_SELF_CONTAINED_LR'
:
'1'
}
self
.
check_with_place
(
"dist_simnet_bow.py"
,
delta
=
100
,
check_error_log
=
False
,
need_envs
=
need_envs
)
class
TestDistSimnetBow2x2LookupTableNotContainLRSync
(
TestDistBase
):
def
_setup_config
(
self
):
self
.
_sync_mode
=
True
self
.
_enforce_place
=
"CPU"
def
test_simnet_bow
(
self
):
need_envs
=
{
"IS_DISTRIBUTED"
:
'1'
,
"IS_SPARSE"
:
'1'
,
'IS_SELF_CONTAINED_LR'
:
'0'
}
self
.
check_with_place
(
"dist_simnet_bow.py"
,
delta
=
1e-5
,
check_error_log
=
False
,
need_envs
=
need_envs
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
41eeb771
...
...
@@ -194,6 +194,14 @@ class TestBook(unittest.TestCase):
self
.
assertIsNotNone
(
layers
.
sequence_expand
(
x
=
x
,
y
=
y
,
ref_level
=
1
))
print
(
str
(
program
))
def
test_sequence_unpad
(
self
):
program
=
Program
()
with
program_guard
(
program
):
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
10
,
5
],
dtype
=
'float32'
)
length
=
layers
.
data
(
name
=
'length'
,
shape
=
[
1
],
dtype
=
'int64'
)
self
.
assertIsNotNone
(
layers
.
sequence_unpad
(
x
=
x
,
length
=
length
))
print
(
str
(
program
))
def
test_lstm_unit
(
self
):
program
=
Program
()
with
program_guard
(
program
):
...
...
@@ -406,6 +414,19 @@ class TestBook(unittest.TestCase):
self
.
assertIsNotNone
(
out
)
print
(
str
(
program
))
def
test_sequence_slice
(
self
):
program
=
Program
()
with
program_guard
(
program
):
import
numpy
as
np
seqs
=
layers
.
data
(
name
=
'x'
,
shape
=
[
10
,
5
],
dtype
=
'float32'
,
lod_level
=
1
)
offset
=
layers
.
assign
(
input
=
np
.
array
([[
0
,
1
]]).
astype
(
'int32'
))
length
=
layers
.
assign
(
input
=
np
.
array
([[
2
,
1
]]).
astype
(
'int32'
))
out
=
layers
.
sequence_slice
(
input
=
seqs
,
offset
=
offset
,
length
=
length
)
self
.
assertIsNotNone
(
out
)
print
(
str
(
program
))
def
test_lod_reset
(
self
):
program
=
Program
()
with
program_guard
(
program
):
...
...
python/paddle/fluid/tests/unittests/test_sequence_unpad_op.py
0 → 100644
浏览文件 @
41eeb771
# 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.
import
unittest
import
six
import
numpy
as
np
from
op_test
import
OpTest
class
TestSequenceUnpadOp
(
OpTest
):
def
init
(
self
):
self
.
length
=
[
2
,
3
,
4
]
self
.
x_shape
=
(
3
,
5
)
self
.
dtype
=
"float32"
def
compute
(
self
):
assert
len
(
self
.
length
)
==
self
.
x_shape
[
0
]
x
=
np
.
random
.
random
(
self
.
x_shape
).
astype
(
self
.
dtype
)
out_lod
=
[
self
.
length
]
out
=
x
[
0
,
0
:
self
.
length
[
0
]]
for
i
in
six
.
moves
.
xrange
(
1
,
x
.
shape
[
0
]):
out
=
np
.
append
(
out
,
x
[
i
,
0
:
self
.
length
[
i
]],
axis
=
0
)
out_shape
=
(
sum
(
self
.
length
),
)
if
len
(
self
.
x_shape
)
==
2
:
out_shape
=
out_shape
+
(
1
,
)
else
:
out_shape
=
out_shape
+
self
.
x_shape
[
2
:]
self
.
inputs
=
{
'X'
:
x
,
'Length'
:
np
.
array
(
self
.
length
).
astype
(
'int64'
).
reshape
(
-
1
,
1
)
}
self
.
outputs
=
{
'Out'
:
(
out
.
reshape
(
out_shape
),
out_lod
)}
def
setUp
(
self
):
self
.
op_type
=
'sequence_unpad'
self
.
init
()
self
.
compute
()
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
"X"
],
"Out"
)
class
TestSequenceUnpadOp2
(
TestSequenceUnpadOp
):
def
init
(
self
):
self
.
length
=
[
2
,
3
,
4
]
self
.
x_shape
=
(
3
,
5
,
4
,
3
)
self
.
dtype
=
"float32"
class
TestSequenceUnpadOp3
(
TestSequenceUnpadOp
):
def
init
(
self
):
self
.
length
=
[
5
,
2
,
3
,
4
]
self
.
x_shape
=
(
4
,
5
,
3
,
3
,
6
)
self
.
dtype
=
"float64"
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @
41eeb771
...
...
@@ -1119,6 +1119,7 @@ to transpile() call.")
def
_split_table_grad_and_add_send_vars
(
self
,
program
,
pserver_endpoints
):
# 2. add split_ids_op and send_op to send gradient to pservers
# there should only be one table_name
all_ops
=
program
.
global_block
().
ops
table_grad_name
=
grad_var_name
(
self
.
table_name
)
...
...
@@ -1143,7 +1144,7 @@ to transpile() call.")
if
self
.
sync_mode
else
[]
},
attrs
=
{
"sync_mode"
:
self
.
sync_mode
,
"sync_mode"
:
not
self
.
sync_mode
,
"epmap"
:
pserver_endpoints
,
RPC_OP_ROLE_ATTR_NAME
:
RPC_OP_ROLE_ATTR_VALUE
,
OP_ROLE_VAR_ATTR_NAME
:
[
...
...
@@ -1189,7 +1190,15 @@ to transpile() call.")
def
_create_table_optimize_block
(
self
,
pserver_index
,
pserver_program
,
pre_block_idx
,
grad_to_block_id
):
# STEP: create table optimize block
table_opt_block
=
pserver_program
.
_create_block
(
pre_block_idx
)
# create table param and grad var in pserver program
# create table optimize block in pserver program
table_opt_op
=
[
op
for
op
in
self
.
optimize_ops
if
'Param'
in
op
.
input_names
and
op
.
input
(
"Param"
)[
0
]
==
self
.
table_name
][
0
]
origin_param_var
=
self
.
origin_program
.
global_block
().
vars
[
self
.
table_name
]
...
...
@@ -1205,19 +1214,16 @@ to transpile() call.")
dtype
=
origin_param_var
.
dtype
,
type
=
core
.
VarDesc
.
VarType
.
SELECTED_ROWS
,
persistable
=
True
)
# parameter must be selected rows
param_var
.
desc
.
set_type
(
core
.
VarDesc
.
VarType
.
SELECTED_ROWS
)
grad_var
=
pserver_program
.
global_block
().
_clone_variable
(
self
.
origin_program
.
global_block
().
vars
[
grad_var_name
(
self
.
table_name
)])
# create table optimize block in pserver program
table_opt_op
=
[
op
for
op
in
self
.
optimize_ops
if
'Param'
in
op
.
input_names
and
op
.
input
(
"Param"
)[
0
]
==
self
.
table_name
][
0
]
table_opt_block
=
pserver_program
.
_create_block
(
pre_block_idx
)
lr_var
=
pserver_program
.
global_block
().
_clone_variable
(
self
.
origin_program
.
global_block
().
vars
[
table_opt_op
.
input
(
"LearningRate"
)[
0
]])
if
self
.
sync_mode
:
# create grad vars in pserver program
...
...
@@ -1249,8 +1255,6 @@ to transpile() call.")
grad_var
=
pserver_program
.
global_block
().
_rename_var
(
origin_grad_name
,
splited_grad_name
)
lr_var
=
pserver_program
.
global_block
().
vars
[
table_opt_op
.
input
(
"LearningRate"
)[
0
]]
inputs
=
{
"Param"
:
[
param_var
],
"Grad"
:
[
grad_var
],
...
...
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