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体验新版 GitCode,发现更多精彩内容 >>
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6057f362
编写于
3月 06, 2019
作者:
T
tensor-tang
提交者:
GitHub
3月 06, 2019
浏览文件
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差异文件
Merge pull request #15996 from tensor-tang/op/embgrad
refine embeddingseqpool grad
上级
c67afb0f
12eb9aec
变更
15
显示空白变更内容
内联
并排
Showing
15 changed file
with
313 addition
and
37 deletion
+313
-37
paddle/fluid/operators/fused/fused_embedding_seq_pool_op.h
paddle/fluid/operators/fused/fused_embedding_seq_pool_op.h
+10
-13
paddle/fluid/operators/jit/benchmark.cc
paddle/fluid/operators/jit/benchmark.cc
+23
-0
paddle/fluid/operators/jit/gen/CMakeLists.txt
paddle/fluid/operators/jit/gen/CMakeLists.txt
+1
-0
paddle/fluid/operators/jit/gen/vbroadcast.cc
paddle/fluid/operators/jit/gen/vbroadcast.cc
+91
-0
paddle/fluid/operators/jit/gen/vbroadcast.h
paddle/fluid/operators/jit/gen/vbroadcast.h
+53
-0
paddle/fluid/operators/jit/helper.cc
paddle/fluid/operators/jit/helper.cc
+2
-0
paddle/fluid/operators/jit/kernel_base.h
paddle/fluid/operators/jit/kernel_base.h
+9
-0
paddle/fluid/operators/jit/kernel_key.cc
paddle/fluid/operators/jit/kernel_key.cc
+5
-0
paddle/fluid/operators/jit/more/mkl/CMakeLists.txt
paddle/fluid/operators/jit/more/mkl/CMakeLists.txt
+2
-0
paddle/fluid/operators/jit/more/mkl/mkl.cc
paddle/fluid/operators/jit/more/mkl/mkl.cc
+18
-0
paddle/fluid/operators/jit/more/mkl/mkl.h
paddle/fluid/operators/jit/more/mkl/mkl.h
+10
-0
paddle/fluid/operators/jit/refer/CMakeLists.txt
paddle/fluid/operators/jit/refer/CMakeLists.txt
+2
-0
paddle/fluid/operators/jit/refer/refer.cc
paddle/fluid/operators/jit/refer/refer.cc
+3
-0
paddle/fluid/operators/jit/refer/refer.h
paddle/fluid/operators/jit/refer/refer.h
+17
-0
paddle/fluid/operators/jit/test.cc
paddle/fluid/operators/jit/test.cc
+67
-24
未找到文件。
paddle/fluid/operators/fused/fused_embedding_seq_pool_op.h
浏览文件 @
6057f362
...
...
@@ -22,7 +22,6 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/operators/jit/kernels.h"
#include "paddle/fluid/operators/math/blas.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -47,7 +46,7 @@ struct EmbeddingVSumFunctor {
auto
*
output
=
output_t
->
mutable_data
<
T
>
(
context
.
GetPlace
());
PADDLE_ENFORCE_LE
(
table_width
*
idx_width
,
out_width
);
PADDLE_ENFORCE_GT
(
ids_lod
.
size
(),
1UL
);
PADDLE_ENFORCE_GT
(
ids_lod
.
size
(),
1UL
,
"The LoD[0] could NOT be empty"
);
jit
::
emb_seq_pool_attr_t
attr
(
table_height
,
table_width
,
0
,
idx_width
,
out_width
,
jit
::
SeqPoolType
::
kSum
);
...
...
@@ -83,11 +82,11 @@ class FusedEmbeddingSeqPoolKernel : public framework::OpKernel<T> {
FusedEmbeddingSeqPoolLastDim
(
table_var
->
dims
(),
ids_t
->
dims
());
const
auto
&
ids_lod
=
ids_t
->
lod
();
// in run time, the LoD of ids must be 1
PADDLE_ENFORCE
(
ids_lod
.
size
(),
1
u
,
"The LoD level of Input(Ids) must be 1"
);
PADDLE_ENFORCE_GE
(
ids_lod
[
0
].
size
(),
1u
,
"The LoD could NOT be empty
"
);
PADDLE_ENFORCE
(
ids_lod
.
size
(),
1
UL
,
"The LoD level of Input(Ids) must be 1
"
);
int64_t
batch_size
=
ids_lod
[
0
].
size
()
-
1
;
// in run time, the shape from Ids -> output
// should be [seq_length, 1] -> [batch_size,
embedding_size
]
// should be [seq_length, 1] -> [batch_size,
last_dim
]
output_t
->
Resize
({
batch_size
,
last_dim
});
if
(
combiner_type
==
"sum"
)
{
...
...
@@ -125,7 +124,7 @@ class FusedEmbeddingSeqPoolGradKernel : public framework::OpKernel<T> {
auto
*
ids_data
=
ids
->
data
<
int64_t
>
();
int64_t
ids_num
=
ids
->
numel
();
auto
lod
=
ids
->
lod
()[
0
];
int64_t
row
_width
=
d_output
->
dims
()[
1
];
int64_t
out
_width
=
d_output
->
dims
()[
1
];
framework
::
Vector
<
int64_t
>
*
new_rows
=
d_table
->
mutable_rows
();
new_rows
->
resize
(
ids_num
);
...
...
@@ -136,15 +135,13 @@ class FusedEmbeddingSeqPoolGradKernel : public framework::OpKernel<T> {
T
*
d_table_data
=
d_table_value
->
mutable_data
<
T
>
(
context
.
GetPlace
());
const
T
*
d_output_data
=
d_output
->
data
<
T
>
();
auto
blas
=
math
::
GetBlas
<
platform
::
CPUDeviceContext
,
T
>
(
context
);
auto
vbroadcast
=
jit
::
Get
<
jit
::
kVBroadcast
,
jit
::
VBroadcastTuples
<
T
>
,
platform
::
CPUPlace
>
(
out_width
);
for
(
int
i
=
0
;
i
<
static_cast
<
int
>
(
lod
.
size
())
-
1
;
++
i
)
{
int64_t
h
=
static_cast
<
int64_t
>
(
lod
[
i
+
1
]
-
lod
[
i
]);
int64_t
in_offset
=
lod
[
i
]
*
row_width
;
const
T
*
out_pos
=
d_output_data
+
i
*
row_width
;
T
*
in_pos
=
d_table_data
+
in_offset
;
for
(
int
r
=
0
;
r
!=
h
;
++
r
)
{
blas
.
VCOPY
(
row_width
,
out_pos
,
in_pos
+
r
*
row_width
);
}
const
T
*
src
=
d_output_data
+
i
*
out_width
;
T
*
dst
=
d_table_data
+
lod
[
i
]
*
out_width
;
vbroadcast
(
src
,
dst
,
h
,
out_width
);
}
}
else
{
LOG
(
ERROR
)
<<
"Dense is not supported in fused_embedding_seq_pool_op now"
;
...
...
paddle/fluid/operators/jit/benchmark.cc
浏览文件 @
6057f362
...
...
@@ -474,6 +474,23 @@ void BenchCRFDecodingKernel() {
}
}
template
<
jit
::
KernelType
KT
,
typename
T
,
typename
PlaceType
>
void
BenchVBroadcastKernel
()
{
for
(
int64_t
w
:
{
1
,
16
,
64
,
100
,
256
})
{
Tensor
x
;
x
.
Resize
({
w
});
RandomVec
<
T
>
(
w
,
x
.
mutable_data
<
T
>
(
PlaceType
()));
const
T
*
x_data
=
x
.
data
<
T
>
();
for
(
int
h
:
TestSizes
())
{
Tensor
y
;
y
.
Resize
({
h
*
w
});
T
*
y_data
=
y
.
mutable_data
<
T
>
(
PlaceType
());
BenchAllImpls
<
KT
,
jit
::
VBroadcastTuples
<
T
>
,
PlaceType
>
(
w
,
x_data
,
y_data
,
static_cast
<
int64_t
>
(
h
),
w
);
}
}
}
using
T
=
float
;
using
CPUPlace
=
paddle
::
platform
::
CPUPlace
;
...
...
@@ -498,6 +515,7 @@ BENCH_FP32_CPU(kVSquare) { BenchXYNKernel<jit::kVSquare, T, CPUPlace>(); }
BENCH_FP32_CPU
(
kVExp
)
{
BenchXYNKernel
<
jit
::
kVExp
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kVSigmoid
)
{
BenchXYNKernel
<
jit
::
kVSigmoid
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kVTanh
)
{
BenchXYNKernel
<
jit
::
kVTanh
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kVCopy
)
{
BenchXYNKernel
<
jit
::
kVCopy
,
T
,
CPUPlace
>
();
}
// lstm and peephole
BENCH_FP32_CPU
(
kLSTMCtHt
)
{
BenchLSTMKernel
<
jit
::
kLSTMCtHt
,
T
,
CPUPlace
>
();
}
...
...
@@ -535,6 +553,11 @@ BENCH_FP32_CPU(kCRFDecoding) {
BenchCRFDecodingKernel
<
jit
::
kCRFDecoding
,
T
,
CPUPlace
>
();
}
// vbroadcast function
BENCH_FP32_CPU
(
kVBroadcast
)
{
BenchVBroadcastKernel
<
jit
::
kVBroadcast
,
T
,
CPUPlace
>
();
}
// Benchmark all jit kernels including jitcode, mkl and refer.
// To use this tool, run command: ./benchmark [options...]
// Options:
...
...
paddle/fluid/operators/jit/gen/CMakeLists.txt
浏览文件 @
6057f362
...
...
@@ -33,3 +33,4 @@ USE_JITKERNEL_GEN(kHMax)
USE_JITKERNEL_GEN
(
kHSum
)
USE_JITKERNEL_GEN
(
kEmbSeqPool
)
USE_JITKERNEL_GEN
(
kSgd
)
USE_JITKERNEL_GEN
(
kVBroadcast
)
paddle/fluid/operators/jit/gen/vbroadcast.cc
0 → 100644
浏览文件 @
6057f362
/* Copyright (c) 2019 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/jit/gen/vbroadcast.h"
#include <memory>
#include <vector>
#include "paddle/fluid/operators/jit/registry.h"
#include "paddle/fluid/platform/cpu_info.h"
namespace
paddle
{
namespace
operators
{
namespace
jit
{
namespace
gen
{
void
VBroadcastJitCode
::
genCode
()
{
preCode
();
constexpr
int
block
=
YMM_FLOAT_BLOCK
;
constexpr
int
max_num_regs
=
16
;
const
int
num_block
=
w_
/
block
;
const
int
num_groups
=
num_block
/
max_num_regs
;
const
size_t
block_size
=
sizeof
(
float
)
*
block
;
std
::
vector
<
int
>
groups
(
num_groups
,
max_num_regs
);
int
rest_num_regs
=
num_block
%
max_num_regs
;
if
(
rest_num_regs
>
0
)
{
groups
.
push_back
(
rest_num_regs
);
}
// protect param_h
mov
(
reg_height
,
param_h
);
Label
l_next_h
;
xor_
(
reg_h_i
,
reg_h_i
);
mov
(
reg_ptr_dst_i
,
param_dst
);
L
(
l_next_h
);
{
mov
(
reg_ptr_src_i
,
param_src
);
for
(
int
num_regs
:
groups
)
{
size_t
w_offset
=
0
;
for
(
int
reg_i
=
0
;
reg_i
<
num_regs
;
++
reg_i
)
{
vmovups
(
ymm_t
(
reg_i
),
ptr
[
reg_ptr_src_i
+
w_offset
]);
w_offset
+=
block_size
;
}
add
(
reg_ptr_src_i
,
num_regs
*
block_size
);
w_offset
=
0
;
for
(
int
reg_i
=
0
;
reg_i
<
num_regs
;
++
reg_i
)
{
vmovups
(
ptr
[
reg_ptr_dst_i
+
w_offset
],
ymm_t
(
reg_i
));
w_offset
+=
block_size
;
}
add
(
reg_ptr_dst_i
,
num_regs
*
block_size
);
}
// end of groups
inc
(
reg_h_i
);
cmp
(
reg_h_i
,
reg_height
);
jl
(
l_next_h
,
T_NEAR
);
}
// end of l_next_h
postCode
();
}
class
VBroadcastCreator
:
public
JitCodeCreator
<
int64_t
>
{
public:
bool
UseMe
(
const
int64_t
&
w
)
const
override
{
return
platform
::
MayIUse
(
platform
::
avx
)
&&
w
%
YMM_FLOAT_BLOCK
==
0
;
}
size_t
CodeSize
(
const
int64_t
&
w
)
const
override
{
return
96
+
(
w
/
YMM_FLOAT_BLOCK
)
*
16
*
8
;
}
std
::
unique_ptr
<
GenBase
>
CreateJitCode
(
const
int64_t
&
w
)
const
override
{
PADDLE_ENFORCE_GT
(
w
,
0
);
return
make_unique
<
VBroadcastJitCode
>
(
w
,
CodeSize
(
w
));
}
};
}
// namespace gen
}
// namespace jit
}
// namespace operators
}
// namespace paddle
namespace
gen
=
paddle
::
operators
::
jit
::
gen
;
REGISTER_JITKERNEL_GEN
(
kVBroadcast
,
gen
::
VBroadcastCreator
);
paddle/fluid/operators/jit/gen/vbroadcast.h
0 → 100644
浏览文件 @
6057f362
/* 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 "glog/logging.h"
#include "paddle/fluid/operators/jit/gen/jitcode.h"
namespace
paddle
{
namespace
operators
{
namespace
jit
{
namespace
gen
{
class
VBroadcastJitCode
:
public
JitCode
{
public:
explicit
VBroadcastJitCode
(
const
int64_t
&
w
,
size_t
code_size
=
256
*
1024
,
void
*
code_ptr
=
nullptr
)
:
JitCode
(
code_size
,
code_ptr
),
w_
(
w
)
{
this
->
genCode
();
}
DECLARE_JIT_CODE
(
VBroadcastJitCode
);
void
genCode
()
override
;
private:
int
w_
;
reg64_t
param_src
{
abi_param1
};
reg64_t
param_dst
{
abi_param2
};
reg64_t
param_h
{
abi_param3
};
reg64_t
param_w
{
abi_param4
};
reg64_t
reg_height
{
r9
};
reg64_t
reg_h_i
{
r10
};
reg64_t
reg_ptr_src_i
{
r11
};
reg64_t
reg_ptr_dst_i
{
r12
};
};
}
// namespace gen
}
// namespace jit
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/jit/helper.cc
浏览文件 @
6057f362
...
...
@@ -36,6 +36,8 @@ const char* to_string(KernelType kt) {
ONE_CASE
(
kVScal
);
ONE_CASE
(
kVAddBias
);
ONE_CASE
(
kVRelu
);
ONE_CASE
(
kVBroadcast
);
ONE_CASE
(
kVCopy
);
ONE_CASE
(
kVIdentity
);
ONE_CASE
(
kVExp
);
ONE_CASE
(
kVSquare
);
...
...
paddle/fluid/operators/jit/kernel_base.h
浏览文件 @
6057f362
...
...
@@ -41,6 +41,8 @@ typedef enum {
kVAdd
,
kVAddBias
,
kVAddRelu
,
kVBroadcast
,
kVCopy
,
kVExp
,
kVIdentity
,
kVMul
,
...
...
@@ -133,6 +135,13 @@ struct GRUTuples {
typedef
void
(
*
func_type
)(
gru_t
*
,
const
gru_attr_t
*
);
};
template
<
typename
T
>
struct
VBroadcastTuples
{
typedef
T
data_type
;
typedef
int64_t
attr_type
;
typedef
void
(
*
func_type
)(
const
T
*
,
T
*
,
int64_t
,
int64_t
);
};
typedef
struct
seq_pool_attr_s
{
int
h
,
w
;
// h should always be the first one
SeqPoolType
type
;
...
...
paddle/fluid/operators/jit/kernel_key.cc
浏览文件 @
6057f362
...
...
@@ -24,6 +24,11 @@ size_t JitCodeKey<int>(const int& d) {
return
d
;
}
template
<
>
size_t
JitCodeKey
<
int64_t
>
(
const
int64_t
&
d
)
{
return
d
;
}
// TODO(TJ): refine and benchmark JitCodeKey generatation
constexpr
int
act_type_shift
=
3
;
// suppot 2^3 act types
static
inline
int
act_type_convert
(
KernelType
type
)
{
...
...
paddle/fluid/operators/jit/more/mkl/CMakeLists.txt
浏览文件 @
6057f362
...
...
@@ -9,9 +9,11 @@ USE_JITKERNEL_MORE(kVAdd, mkl)
USE_JITKERNEL_MORE
(
kVScal, mkl
)
USE_JITKERNEL_MORE
(
kVExp, mkl
)
USE_JITKERNEL_MORE
(
kVSquare, mkl
)
USE_JITKERNEL_MORE
(
kVCopy, mkl
)
USE_JITKERNEL_MORE
(
kVSigmoid, mkl
)
USE_JITKERNEL_MORE
(
kVTanh, mkl
)
USE_JITKERNEL_MORE
(
kSeqPool, mkl
)
USE_JITKERNEL_MORE
(
kSoftmax, mkl
)
USE_JITKERNEL_MORE
(
kEmbSeqPool, mkl
)
USE_JITKERNEL_MORE
(
kSgd, mkl
)
USE_JITKERNEL_MORE
(
kVBroadcast, mkl
)
paddle/fluid/operators/jit/more/mkl/mkl.cc
浏览文件 @
6057f362
...
...
@@ -154,6 +154,21 @@ bool VSquareKernel<float>::UseMe(const int& d) const {
return
d
>
7
;
}
template
<
>
bool
VCopyKernel
<
float
>::
UseMe
(
const
int
&
d
)
const
{
return
d
>
15
;
}
template
<
>
bool
VBroadcastKernel
<
float
>::
UseMe
(
const
int64_t
&
d
)
const
{
return
d
>
127
;
}
template
<
>
bool
VBroadcastKernel
<
double
>::
UseMe
(
const
int64_t
&
attr
)
const
{
return
true
;
}
template
<
>
bool
VSigmoidKernel
<
float
>::
UseMe
(
const
int
&
d
)
const
{
return
d
>
7
;
...
...
@@ -223,6 +238,7 @@ AWALYS_USE_ME_WITH_DOUBLE(VExp);
AWALYS_USE_ME_WITH_DOUBLE
(
VSigmoid
);
AWALYS_USE_ME_WITH_DOUBLE
(
VTanh
);
AWALYS_USE_ME_WITH_DOUBLE
(
VSquare
);
AWALYS_USE_ME_WITH_DOUBLE
(
VCopy
);
AWALYS_USE_ME_WITH_DOUBLE
(
Softmax
);
#undef AWALYS_USE_ME_WITH_DOUBLE
...
...
@@ -244,6 +260,8 @@ REGISTER_MKL_KERNEL(kVAdd, VAdd);
REGISTER_MKL_KERNEL
(
kVScal
,
VScal
);
REGISTER_MKL_KERNEL
(
kVExp
,
VExp
);
REGISTER_MKL_KERNEL
(
kVSquare
,
VSquare
);
REGISTER_MKL_KERNEL
(
kVCopy
,
VCopy
);
REGISTER_MKL_KERNEL
(
kVBroadcast
,
VBroadcast
);
REGISTER_MKL_KERNEL
(
kVSigmoid
,
VSigmoid
);
REGISTER_MKL_KERNEL
(
kVTanh
,
VTanh
);
REGISTER_MKL_KERNEL
(
kSeqPool
,
SeqPool
);
...
...
paddle/fluid/operators/jit/more/mkl/mkl.h
浏览文件 @
6057f362
...
...
@@ -50,6 +50,13 @@ void VCopy(const T* x, T* y, int n);
template
<
typename
T
>
void
VAXPY
(
T
a
,
const
T
*
x
,
T
*
y
,
int
n
);
template
<
typename
T
>
void
VBroadcast
(
const
T
*
x
,
T
*
y
,
int64_t
y_h
,
int64_t
x_len
)
{
for
(
int64_t
h
=
0
;
h
<
y_h
;
++
h
)
{
VCopy
(
x
,
y
+
h
*
x_len
,
x_len
);
}
}
template
<
typename
T
>
void
VSigmoid
(
const
T
*
x
,
T
*
y
,
int
n
)
{
const
T
min
=
SIGMOID_THRESHOLD_MIN
;
...
...
@@ -192,6 +199,7 @@ DECLARE_MKL_KERNEL(VExp, XYNTuples);
DECLARE_MKL_KERNEL
(
VSigmoid
,
XYNTuples
);
DECLARE_MKL_KERNEL
(
VTanh
,
XYNTuples
);
DECLARE_MKL_KERNEL
(
VSquare
,
XYNTuples
);
DECLARE_MKL_KERNEL
(
VCopy
,
XYNTuples
);
DECLARE_MKL_KERNEL
(
SeqPool
,
SeqPoolTuples
);
...
...
@@ -201,6 +209,8 @@ DECLARE_MKL_KERNEL(Softmax, SoftmaxTuples);
DECLARE_MKL_KERNEL
(
Sgd
,
SgdTuples
);
DECLARE_MKL_KERNEL
(
VBroadcast
,
VBroadcastTuples
);
#undef DECLARE_MKL_KERNEL
}
// namespace mkl
...
...
paddle/fluid/operators/jit/refer/CMakeLists.txt
浏览文件 @
6057f362
...
...
@@ -13,6 +13,7 @@ USE_JITKERNEL_REFER(kVAddRelu)
USE_JITKERNEL_REFER
(
kVSub
)
USE_JITKERNEL_REFER
(
kVScal
)
USE_JITKERNEL_REFER
(
kVAddBias
)
USE_JITKERNEL_REFER
(
kVCopy
)
USE_JITKERNEL_REFER
(
kVRelu
)
USE_JITKERNEL_REFER
(
kVIdentity
)
USE_JITKERNEL_REFER
(
kVExp
)
...
...
@@ -34,3 +35,4 @@ USE_JITKERNEL_REFER(kHMax)
USE_JITKERNEL_REFER
(
kSoftmax
)
USE_JITKERNEL_REFER
(
kEmbSeqPool
)
USE_JITKERNEL_REFER
(
kSgd
)
USE_JITKERNEL_REFER
(
kVBroadcast
)
paddle/fluid/operators/jit/refer/refer.cc
浏览文件 @
6057f362
...
...
@@ -30,6 +30,7 @@ REGISTER_REFER_KERNEL(kVScal, VScal);
REGISTER_REFER_KERNEL
(
kVAddBias
,
VAddBias
);
REGISTER_REFER_KERNEL
(
kVRelu
,
VRelu
);
REGISTER_REFER_KERNEL
(
kVCopy
,
VCopy
);
REGISTER_REFER_KERNEL
(
kVIdentity
,
VIdentity
);
REGISTER_REFER_KERNEL
(
kVSquare
,
VSquare
);
REGISTER_REFER_KERNEL
(
kVExp
,
VExp
);
...
...
@@ -61,4 +62,6 @@ REGISTER_REFER_KERNEL(kEmbSeqPool, EmbSeqPool);
REGISTER_REFER_KERNEL
(
kSgd
,
Sgd
);
REGISTER_REFER_KERNEL
(
kVBroadcast
,
VBroadcast
);
#undef REGISTER_REFER_KERNEL
paddle/fluid/operators/jit/refer/refer.h
浏览文件 @
6057f362
...
...
@@ -70,6 +70,20 @@ void VAddBias(const T* a, const T* x, T* y, int n) {
}
}
template
<
typename
T
>
void
VCopy
(
const
T
*
x
,
T
*
y
,
int
n
)
{
std
::
memcpy
(
y
,
x
,
n
*
sizeof
(
T
));
}
// x shape: (x_len)
// y shape: (h, x_len)
template
<
typename
T
>
void
VBroadcast
(
const
T
*
x
,
T
*
y
,
int64_t
y_h
,
int64_t
x_len
)
{
for
(
int64_t
h
=
0
;
h
<
y_h
;
++
h
)
{
VCopy
(
x
,
y
+
h
*
x_len
,
x_len
);
}
}
template
<
typename
T
>
void
VRelu
(
const
T
*
x
,
T
*
y
,
int
n
)
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
...
...
@@ -500,6 +514,7 @@ DECLARE_REFER_KERNEL(VExp, XYNTuples);
DECLARE_REFER_KERNEL
(
VSigmoid
,
XYNTuples
);
DECLARE_REFER_KERNEL
(
VTanh
,
XYNTuples
);
DECLARE_REFER_KERNEL
(
VSquare
,
XYNTuples
);
DECLARE_REFER_KERNEL
(
VCopy
,
XYNTuples
);
// lstm_t*, const lstm_attr_t*
DECLARE_REFER_KERNEL
(
LSTMCtHt
,
LSTMTuples
);
...
...
@@ -528,6 +543,8 @@ DECLARE_REFER_KERNEL(EmbSeqPool, EmbSeqPoolTuples);
DECLARE_REFER_KERNEL
(
Sgd
,
SgdTuples
);
DECLARE_REFER_KERNEL
(
VBroadcast
,
VBroadcastTuples
);
#undef DECLARE_REFER_KERNEL
}
// namespace refer
...
...
paddle/fluid/operators/jit/test.cc
浏览文件 @
6057f362
...
...
@@ -26,8 +26,8 @@ limitations under the License. */
DEFINE_double
(
acc
,
1e-5
,
"Test accuracy threshold."
);
template
<
typename
T
>
void
RandomVec
(
const
int
n
,
T
*
a
,
const
T
lower
=
static_cast
<
T
>
(
-
2
0
.
f
),
const
T
upper
=
static_cast
<
T
>
(
2
0
.
f
))
{
void
RandomVec
(
const
int
n
,
T
*
a
,
const
T
lower
=
static_cast
<
T
>
(
-
2.
f
),
const
T
upper
=
static_cast
<
T
>
(
2.
f
))
{
static
unsigned
int
seed
=
100
;
std
::
mt19937
rng
(
seed
++
);
std
::
uniform_real_distribution
<
double
>
uniform_dist
(
0
,
1
);
...
...
@@ -157,6 +157,26 @@ struct TestFuncWithRefer<jit::XRNTuples<T>, std::vector<T>, T> {
}
};
template
<
typename
T
>
struct
TestFuncWithRefer
<
jit
::
VBroadcastTuples
<
T
>
,
std
::
vector
<
T
>
,
std
::
vector
<
T
>
,
int64_t
,
typename
jit
::
VBroadcastTuples
<
T
>::
attr_type
>
{
void
operator
()(
const
typename
jit
::
VBroadcastTuples
<
T
>::
func_type
tgt
,
const
std
::
vector
<
T
>&
x
,
const
std
::
vector
<
T
>&
yref
,
int64_t
h
,
const
typename
jit
::
VBroadcastTuples
<
T
>::
attr_type
&
attr
)
{
EXPECT_TRUE
(
tgt
!=
nullptr
);
EXPECT_EQ
(
x
.
size
(),
static_cast
<
size_t
>
(
attr
));
EXPECT_EQ
(
yref
.
size
(),
x
.
size
()
*
h
);
std
::
vector
<
T
>
y
(
yref
.
size
());
const
T
*
x_data
=
x
.
data
();
const
T
*
yref_data
=
yref
.
data
();
T
*
y_data
=
y
.
data
();
tgt
(
x_data
,
y_data
,
h
,
attr
);
ExpectEQ
<
T
>
(
y_data
,
yref_data
,
yref
.
size
());
}
};
template
<
typename
T
>
struct
TestFuncWithRefer
<
jit
::
XYNTuples
<
T
>
,
std
::
vector
<
T
>
,
std
::
vector
<
T
>>
{
void
operator
()(
const
typename
jit
::
XYNTuples
<
T
>::
func_type
tgt
,
...
...
@@ -514,7 +534,7 @@ void TestKernelXRNTuples() {
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
XRNTuples
<
T
>>
();
EXPECT_TRUE
(
ref
!=
nullptr
);
std
::
vector
<
T
>
x
(
d
);
RandomVec
<
T
>
(
d
,
x
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
d
,
x
.
data
());
T
ref_res
;
ref
(
x
.
data
(),
&
ref_res
,
d
);
TestAllImpls
<
KT
,
jit
::
XRNTuples
<
T
>
,
PlaceType
,
std
::
vector
<
T
>
,
T
>
(
d
,
x
,
...
...
@@ -532,7 +552,7 @@ void TestKernelXYNTuples() {
std
::
vector
<
T
>
x
(
d
),
yref
(
d
);
std
::
vector
<
T
>
xinp
(
d
);
// inplace test
RandomVec
<
T
>
(
d
,
x
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
d
,
x
.
data
());
std
::
copy
(
x
.
begin
(),
x
.
end
(),
xinp
.
begin
());
const
T
*
x_data
=
x
.
data
();
...
...
@@ -566,7 +586,7 @@ void TestKernelLSTMTuples() {
EXPECT_TRUE
(
ref
!=
nullptr
);
std
::
vector
<
T
>
xsrc
(
4
*
d
),
wp
(
3
*
d
),
ct_1
(
d
);
std
::
vector
<
T
>
ct_ref
(
d
),
ht_ref
(
d
),
checked
(
2
*
d
);
RandomVec
<
T
>
(
4
*
d
,
xsrc
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
4
*
d
,
xsrc
.
data
());
RandomVec
<
T
>
(
3
*
d
,
wp
.
data
(),
-
1.
f
,
1.
f
);
RandomVec
<
T
>
(
d
,
ct_1
.
data
(),
-
1.
f
,
1.
f
);
// x could be changed after compute, so copy to save src
...
...
@@ -614,8 +634,8 @@ void TestKernelGRUTuples() {
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
GRUTuples
<
T
>>
();
EXPECT_TRUE
(
ref
!=
nullptr
);
std
::
vector
<
T
>
xsrc
(
3
*
d
),
ht_1
(
d
),
ht_ref
(
d
);
RandomVec
<
T
>
(
3
*
d
,
xsrc
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
d
,
ht_1
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
3
*
d
,
xsrc
.
data
());
RandomVec
<
T
>
(
d
,
ht_1
.
data
());
// x could be changed after compute, so copy to save src
std
::
vector
<
T
>
x
(
xsrc
.
size
());
std
::
copy
(
xsrc
.
begin
(),
xsrc
.
end
(),
x
.
begin
());
...
...
@@ -651,7 +671,7 @@ void TestKernelSeqPoolTuples() {
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
SeqPoolTuples
<
T
>>
();
EXPECT_TRUE
(
ref
!=
nullptr
);
std
::
vector
<
T
>
x
(
h
*
w
),
yref
(
w
);
RandomVec
<
T
>
(
h
*
w
,
x
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
h
*
w
,
x
.
data
());
const
T
*
x_data
=
x
.
data
();
T
*
yref_data
=
yref
.
data
();
ref
(
x_data
,
yref_data
,
&
attr
);
...
...
@@ -676,8 +696,8 @@ void TestKernelMatMulTuples() {
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
MatMulTuples
<
T
>>
();
EXPECT_TRUE
(
ref
!=
nullptr
);
std
::
vector
<
T
>
a
(
m
*
k
),
b
(
k
*
n
),
c
(
m
*
n
);
RandomVec
<
T
>
(
m
*
k
,
a
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
k
*
n
,
b
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
m
*
k
,
a
.
data
());
RandomVec
<
T
>
(
k
*
n
,
b
.
data
());
const
T
*
a_data
=
a
.
data
();
const
T
*
b_data
=
b
.
data
();
T
*
c_data
=
c
.
data
();
...
...
@@ -699,7 +719,7 @@ void TestKernelSoftmaxTuples() {
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
SoftmaxTuples
<
T
>>
();
EXPECT_TRUE
(
ref
!=
nullptr
);
std
::
vector
<
T
>
x
(
bs
*
n
),
y
(
bs
*
n
);
RandomVec
<
T
>
(
bs
*
n
,
x
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
bs
*
n
,
x
.
data
());
const
T
*
x_data
=
x
.
data
();
T
*
y_data
=
y
.
data
();
...
...
@@ -726,7 +746,7 @@ void TestKernelEmbSeqPoolTuples() {
test_sizes
.
erase
(
std
::
remove
(
test_sizes
.
begin
(),
test_sizes
.
end
(),
1000
));
for
(
int
tbl_w
:
test_sizes
)
{
std
::
vector
<
T
>
table
(
tbl_h
*
tbl_w
);
RandomVec
<
T
>
(
tbl_h
*
tbl_w
,
table
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
tbl_h
*
tbl_w
,
table
.
data
());
const
T
*
table_data
=
table
.
data
();
for
(
auto
type
:
pool_types
)
{
for
(
int
idx_w
:
{
1
,
2
,
10
,
16
})
{
...
...
@@ -772,14 +792,14 @@ void TestKernelSgdTuples() {
for
(
int
grad_w
:
TestSizes
())
{
std
::
vector
<
T
>
param
(
param_h
*
grad_w
);
std
::
vector
<
T
>
param_out
(
param_h
*
grad_w
);
RandomVec
<
T
>
(
param_h
*
grad_w
,
param
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
param_h
*
grad_w
,
param
.
data
());
const
T
*
param_data
=
param
.
data
();
T
*
out_data
=
param_out
.
data
();
for
(
int
rows_size
=
1
;
rows_size
<=
param_h
;
++
rows_size
)
{
std
::
vector
<
T
>
grad
(
rows_size
*
grad_w
);
std
::
vector
<
int64_t
>
rows
=
UnDuplicatedRandomVec
(
rows_size
,
0
,
rows_size
-
1
);
RandomVec
<
T
>
(
rows_size
*
grad_w
,
grad
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
rows_size
*
grad_w
,
grad
.
data
());
const
int64_t
*
rows_data
=
rows
.
data
();
const
T
*
grad_data
=
grad
.
data
();
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
SgdTuples
<
T
>>
();
...
...
@@ -815,8 +835,8 @@ void TestKernelNCHW16CMulNCTuples() {
int
sz
=
n
*
c
*
h
*
w
;
std
::
vector
<
T
>
x
(
sz
),
y
(
n
*
c
),
zref
(
sz
);
std
::
vector
<
T
>
ztgt
(
sz
),
zjit
(
sz
);
RandomVec
<
T
>
(
sz
,
x
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
n
*
c
,
y
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
sz
,
x
.
data
());
RandomVec
<
T
>
(
n
*
c
,
y
.
data
());
const
T
*
x_data
=
x
.
data
();
const
T
*
y_data
=
y
.
data
();
...
...
@@ -873,11 +893,11 @@ void TestKernelLayerNormTuples() {
int
sz
=
left
*
right
;
std
::
vector
<
T
>
x
(
sz
),
mean
(
left
),
var
(
left
),
scale
(
right
),
bias
(
right
),
outref
(
sz
);
RandomVec
<
T
>
(
sz
,
x
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
left
,
mean
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
left
,
var
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
right
,
scale
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
right
,
bias
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
sz
,
x
.
data
());
RandomVec
<
T
>
(
left
,
mean
.
data
());
RandomVec
<
T
>
(
left
,
var
.
data
());
RandomVec
<
T
>
(
right
,
scale
.
data
());
RandomVec
<
T
>
(
right
,
bias
.
data
());
const
T
*
scale_data
=
scale
.
data
();
const
T
*
bias_data
=
bias
.
data
();
...
...
@@ -903,7 +923,7 @@ void TestKernelCRFDecodingTuples() {
VLOG
(
10
)
<<
"===== Test JITKernel "
<<
jit
::
to_string
(
KT
);
constexpr
int
state_trans_base_idx
=
2
;
auto
test_sizes
=
TestSizes
();
test_sizes
.
erase
(
std
::
remove
(
test_sizes
.
begin
(),
test_sizes
.
end
(),
1
000
));
test_sizes
.
erase
(
std
::
remove
(
test_sizes
.
begin
(),
test_sizes
.
end
(),
2
000
));
for
(
int
seq_len
:
{
1
,
11
,
17
,
50
})
{
for
(
int
tag_num
:
test_sizes
)
{
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
CRFDecodingTuples
<
T
>>
();
...
...
@@ -912,8 +932,8 @@ void TestKernelCRFDecodingTuples() {
int
w_sz
=
(
tag_num
+
state_trans_base_idx
)
*
tag_num
;
std
::
vector
<
T
>
x
(
x_sz
),
w
(
w_sz
),
alpharef
(
x_sz
);
std
::
vector
<
int
>
trackref
(
x_sz
);
RandomVec
<
T
>
(
x_sz
,
x
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
w_sz
,
w
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
x_sz
,
x
.
data
());
RandomVec
<
T
>
(
w_sz
,
w
.
data
());
ref
(
seq_len
,
(
const
T
*
)
x
.
data
(),
(
const
T
*
)
w
.
data
(),
alpharef
.
data
(),
trackref
.
data
(),
tag_num
);
...
...
@@ -926,6 +946,27 @@ void TestKernelCRFDecodingTuples() {
}
}
template
<
jit
::
KernelType
KT
,
typename
T
,
typename
PlaceType
>
void
TestKernelVBroadcastTuples
()
{
VLOG
(
10
)
<<
"===== Test JITKernel "
<<
jit
::
to_string
(
KT
);
for
(
int
w
:
TestSizes
())
{
std
::
vector
<
T
>
x
(
w
);
RandomVec
<
T
>
(
w
,
x
.
data
());
const
T
*
x_data
=
x
.
data
();
for
(
int64_t
h
:
{
1
,
2
,
6
})
{
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
VBroadcastTuples
<
T
>>
();
EXPECT_TRUE
(
ref
!=
nullptr
);
std
::
vector
<
T
>
y
(
w
*
h
);
T
*
y_data
=
y
.
data
();
ref
(
x_data
,
y_data
,
h
,
w
);
TestAllImpls
<
KT
,
jit
::
VBroadcastTuples
<
T
>
,
PlaceType
,
std
::
vector
<
T
>
,
std
::
vector
<
T
>
,
int64_t
>
(
static_cast
<
int64_t
>
(
w
),
x
,
y
,
h
,
static_cast
<
int64_t
>
(
w
));
}
}
}
#define TEST_CPU_KERNEL(test_tuple, kernel_type) \
TEST(JITKernel, kernel_type) { \
TestKernel##test_tuple<jit::kernel_type, float, CPUPlace>(); \
...
...
@@ -949,6 +990,7 @@ TEST_CPU_KERNEL(XYNTuples, kVSquare);
TEST_CPU_KERNEL
(
XYNTuples
,
kVExp
);
TEST_CPU_KERNEL
(
XYNTuples
,
kVSigmoid
);
TEST_CPU_KERNEL
(
XYNTuples
,
kVTanh
);
TEST_CPU_KERNEL
(
XYNTuples
,
kVCopy
);
TEST_CPU_KERNEL
(
LSTMTuples
,
kLSTMCtHt
);
TEST_CPU_KERNEL
(
LSTMTuples
,
kLSTMC1H1
);
...
...
@@ -966,6 +1008,7 @@ TEST_CPU_KERNEL(EmbSeqPoolTuples, kEmbSeqPool);
TEST_CPU_KERNEL
(
SgdTuples
,
kSgd
);
TEST_CPU_KERNEL
(
LayerNormTuples
,
kLayerNorm
);
TEST_CPU_KERNEL
(
CRFDecodingTuples
,
kCRFDecoding
);
TEST_CPU_KERNEL
(
VBroadcastTuples
,
kVBroadcast
);
TEST
(
JITKernel_key
,
lstm
)
{
jit
::
lstm_attr_t
attr1
(
8
,
jit
::
kVIdentity
,
jit
::
kVSigmoid
,
jit
::
kVTanh
);
...
...
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