Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
PaddlePaddle
PaddleDetection
提交
6057f362
P
PaddleDetection
项目概览
PaddlePaddle
/
PaddleDetection
大约 1 年 前同步成功
通知
695
Star
11112
Fork
2696
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
184
列表
看板
标记
里程碑
合并请求
40
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
PaddleDetection
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
184
Issue
184
列表
看板
标记
里程碑
合并请求
40
合并请求
40
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
未验证
提交
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
操作
浏览文件
下载
差异文件
Merge pull request #15996 from tensor-tang/op/embgrad
refine embeddingseqpool grad
上级
c67afb0f
12eb9aec
变更
15
隐藏空白更改
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. */
...
@@ -22,7 +22,6 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/operators/jit/kernels.h"
#include "paddle/fluid/operators/jit/kernels.h"
#include "paddle/fluid/operators/math/blas.h"
namespace
paddle
{
namespace
paddle
{
namespace
operators
{
namespace
operators
{
...
@@ -47,7 +46,7 @@ struct EmbeddingVSumFunctor {
...
@@ -47,7 +46,7 @@ struct EmbeddingVSumFunctor {
auto
*
output
=
output_t
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
*
output
=
output_t
->
mutable_data
<
T
>
(
context
.
GetPlace
());
PADDLE_ENFORCE_LE
(
table_width
*
idx_width
,
out_width
);
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
,
jit
::
emb_seq_pool_attr_t
attr
(
table_height
,
table_width
,
0
,
idx_width
,
out_width
,
jit
::
SeqPoolType
::
kSum
);
out_width
,
jit
::
SeqPoolType
::
kSum
);
...
@@ -83,11 +82,11 @@ class FusedEmbeddingSeqPoolKernel : public framework::OpKernel<T> {
...
@@ -83,11 +82,11 @@ class FusedEmbeddingSeqPoolKernel : public framework::OpKernel<T> {
FusedEmbeddingSeqPoolLastDim
(
table_var
->
dims
(),
ids_t
->
dims
());
FusedEmbeddingSeqPoolLastDim
(
table_var
->
dims
(),
ids_t
->
dims
());
const
auto
&
ids_lod
=
ids_t
->
lod
();
const
auto
&
ids_lod
=
ids_t
->
lod
();
// in run time, the LoD of ids must be 1
// 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
(
ids_lod
.
size
(),
1
UL
,
PADDLE_ENFORCE_GE
(
ids_lod
[
0
].
size
(),
1u
,
"The LoD could NOT be empty
"
);
"The LoD level of Input(Ids) must be 1
"
);
int64_t
batch_size
=
ids_lod
[
0
].
size
()
-
1
;
int64_t
batch_size
=
ids_lod
[
0
].
size
()
-
1
;
// in run time, the shape from Ids -> output
// 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
});
output_t
->
Resize
({
batch_size
,
last_dim
});
if
(
combiner_type
==
"sum"
)
{
if
(
combiner_type
==
"sum"
)
{
...
@@ -125,7 +124,7 @@ class FusedEmbeddingSeqPoolGradKernel : public framework::OpKernel<T> {
...
@@ -125,7 +124,7 @@ class FusedEmbeddingSeqPoolGradKernel : public framework::OpKernel<T> {
auto
*
ids_data
=
ids
->
data
<
int64_t
>
();
auto
*
ids_data
=
ids
->
data
<
int64_t
>
();
int64_t
ids_num
=
ids
->
numel
();
int64_t
ids_num
=
ids
->
numel
();
auto
lod
=
ids
->
lod
()[
0
];
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
();
framework
::
Vector
<
int64_t
>
*
new_rows
=
d_table
->
mutable_rows
();
new_rows
->
resize
(
ids_num
);
new_rows
->
resize
(
ids_num
);
...
@@ -136,15 +135,13 @@ class FusedEmbeddingSeqPoolGradKernel : public framework::OpKernel<T> {
...
@@ -136,15 +135,13 @@ class FusedEmbeddingSeqPoolGradKernel : public framework::OpKernel<T> {
T
*
d_table_data
=
d_table_value
->
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
d_table_data
=
d_table_value
->
mutable_data
<
T
>
(
context
.
GetPlace
());
const
T
*
d_output_data
=
d_output
->
data
<
T
>
();
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
)
{
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
h
=
static_cast
<
int64_t
>
(
lod
[
i
+
1
]
-
lod
[
i
]);
int64_t
in_offset
=
lod
[
i
]
*
row_width
;
const
T
*
src
=
d_output_data
+
i
*
out_width
;
const
T
*
out_pos
=
d_output_data
+
i
*
row_width
;
T
*
dst
=
d_table_data
+
lod
[
i
]
*
out_width
;
T
*
in_pos
=
d_table_data
+
in_offset
;
vbroadcast
(
src
,
dst
,
h
,
out_width
);
for
(
int
r
=
0
;
r
!=
h
;
++
r
)
{
blas
.
VCOPY
(
row_width
,
out_pos
,
in_pos
+
r
*
row_width
);
}
}
}
}
else
{
}
else
{
LOG
(
ERROR
)
<<
"Dense is not supported in fused_embedding_seq_pool_op now"
;
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() {
...
@@ -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
T
=
float
;
using
CPUPlace
=
paddle
::
platform
::
CPUPlace
;
using
CPUPlace
=
paddle
::
platform
::
CPUPlace
;
...
@@ -498,6 +515,7 @@ BENCH_FP32_CPU(kVSquare) { BenchXYNKernel<jit::kVSquare, T, 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
(
kVExp
)
{
BenchXYNKernel
<
jit
::
kVExp
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kVSigmoid
)
{
BenchXYNKernel
<
jit
::
kVSigmoid
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kVSigmoid
)
{
BenchXYNKernel
<
jit
::
kVSigmoid
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kVTanh
)
{
BenchXYNKernel
<
jit
::
kVTanh
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kVTanh
)
{
BenchXYNKernel
<
jit
::
kVTanh
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kVCopy
)
{
BenchXYNKernel
<
jit
::
kVCopy
,
T
,
CPUPlace
>
();
}
// lstm and peephole
// lstm and peephole
BENCH_FP32_CPU
(
kLSTMCtHt
)
{
BenchLSTMKernel
<
jit
::
kLSTMCtHt
,
T
,
CPUPlace
>
();
}
BENCH_FP32_CPU
(
kLSTMCtHt
)
{
BenchLSTMKernel
<
jit
::
kLSTMCtHt
,
T
,
CPUPlace
>
();
}
...
@@ -535,6 +553,11 @@ BENCH_FP32_CPU(kCRFDecoding) {
...
@@ -535,6 +553,11 @@ BENCH_FP32_CPU(kCRFDecoding) {
BenchCRFDecodingKernel
<
jit
::
kCRFDecoding
,
T
,
CPUPlace
>
();
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.
// Benchmark all jit kernels including jitcode, mkl and refer.
// To use this tool, run command: ./benchmark [options...]
// To use this tool, run command: ./benchmark [options...]
// Options:
// Options:
...
...
paddle/fluid/operators/jit/gen/CMakeLists.txt
浏览文件 @
6057f362
...
@@ -33,3 +33,4 @@ USE_JITKERNEL_GEN(kHMax)
...
@@ -33,3 +33,4 @@ USE_JITKERNEL_GEN(kHMax)
USE_JITKERNEL_GEN
(
kHSum
)
USE_JITKERNEL_GEN
(
kHSum
)
USE_JITKERNEL_GEN
(
kEmbSeqPool
)
USE_JITKERNEL_GEN
(
kEmbSeqPool
)
USE_JITKERNEL_GEN
(
kSgd
)
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) {
...
@@ -36,6 +36,8 @@ const char* to_string(KernelType kt) {
ONE_CASE
(
kVScal
);
ONE_CASE
(
kVScal
);
ONE_CASE
(
kVAddBias
);
ONE_CASE
(
kVAddBias
);
ONE_CASE
(
kVRelu
);
ONE_CASE
(
kVRelu
);
ONE_CASE
(
kVBroadcast
);
ONE_CASE
(
kVCopy
);
ONE_CASE
(
kVIdentity
);
ONE_CASE
(
kVIdentity
);
ONE_CASE
(
kVExp
);
ONE_CASE
(
kVExp
);
ONE_CASE
(
kVSquare
);
ONE_CASE
(
kVSquare
);
...
...
paddle/fluid/operators/jit/kernel_base.h
浏览文件 @
6057f362
...
@@ -41,6 +41,8 @@ typedef enum {
...
@@ -41,6 +41,8 @@ typedef enum {
kVAdd
,
kVAdd
,
kVAddBias
,
kVAddBias
,
kVAddRelu
,
kVAddRelu
,
kVBroadcast
,
kVCopy
,
kVExp
,
kVExp
,
kVIdentity
,
kVIdentity
,
kVMul
,
kVMul
,
...
@@ -133,6 +135,13 @@ struct GRUTuples {
...
@@ -133,6 +135,13 @@ struct GRUTuples {
typedef
void
(
*
func_type
)(
gru_t
*
,
const
gru_attr_t
*
);
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
{
typedef
struct
seq_pool_attr_s
{
int
h
,
w
;
// h should always be the first one
int
h
,
w
;
// h should always be the first one
SeqPoolType
type
;
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) {
...
@@ -24,6 +24,11 @@ size_t JitCodeKey<int>(const int& d) {
return
d
;
return
d
;
}
}
template
<
>
size_t
JitCodeKey
<
int64_t
>
(
const
int64_t
&
d
)
{
return
d
;
}
// TODO(TJ): refine and benchmark JitCodeKey generatation
// TODO(TJ): refine and benchmark JitCodeKey generatation
constexpr
int
act_type_shift
=
3
;
// suppot 2^3 act types
constexpr
int
act_type_shift
=
3
;
// suppot 2^3 act types
static
inline
int
act_type_convert
(
KernelType
type
)
{
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)
...
@@ -9,9 +9,11 @@ USE_JITKERNEL_MORE(kVAdd, mkl)
USE_JITKERNEL_MORE
(
kVScal, mkl
)
USE_JITKERNEL_MORE
(
kVScal, mkl
)
USE_JITKERNEL_MORE
(
kVExp, mkl
)
USE_JITKERNEL_MORE
(
kVExp, mkl
)
USE_JITKERNEL_MORE
(
kVSquare, mkl
)
USE_JITKERNEL_MORE
(
kVSquare, mkl
)
USE_JITKERNEL_MORE
(
kVCopy, mkl
)
USE_JITKERNEL_MORE
(
kVSigmoid, mkl
)
USE_JITKERNEL_MORE
(
kVSigmoid, mkl
)
USE_JITKERNEL_MORE
(
kVTanh, mkl
)
USE_JITKERNEL_MORE
(
kVTanh, mkl
)
USE_JITKERNEL_MORE
(
kSeqPool, mkl
)
USE_JITKERNEL_MORE
(
kSeqPool, mkl
)
USE_JITKERNEL_MORE
(
kSoftmax, mkl
)
USE_JITKERNEL_MORE
(
kSoftmax, mkl
)
USE_JITKERNEL_MORE
(
kEmbSeqPool, mkl
)
USE_JITKERNEL_MORE
(
kEmbSeqPool, mkl
)
USE_JITKERNEL_MORE
(
kSgd, 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 {
...
@@ -154,6 +154,21 @@ bool VSquareKernel<float>::UseMe(const int& d) const {
return
d
>
7
;
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
<
>
template
<
>
bool
VSigmoidKernel
<
float
>::
UseMe
(
const
int
&
d
)
const
{
bool
VSigmoidKernel
<
float
>::
UseMe
(
const
int
&
d
)
const
{
return
d
>
7
;
return
d
>
7
;
...
@@ -223,6 +238,7 @@ AWALYS_USE_ME_WITH_DOUBLE(VExp);
...
@@ -223,6 +238,7 @@ AWALYS_USE_ME_WITH_DOUBLE(VExp);
AWALYS_USE_ME_WITH_DOUBLE
(
VSigmoid
);
AWALYS_USE_ME_WITH_DOUBLE
(
VSigmoid
);
AWALYS_USE_ME_WITH_DOUBLE
(
VTanh
);
AWALYS_USE_ME_WITH_DOUBLE
(
VTanh
);
AWALYS_USE_ME_WITH_DOUBLE
(
VSquare
);
AWALYS_USE_ME_WITH_DOUBLE
(
VSquare
);
AWALYS_USE_ME_WITH_DOUBLE
(
VCopy
);
AWALYS_USE_ME_WITH_DOUBLE
(
Softmax
);
AWALYS_USE_ME_WITH_DOUBLE
(
Softmax
);
#undef AWALYS_USE_ME_WITH_DOUBLE
#undef AWALYS_USE_ME_WITH_DOUBLE
...
@@ -244,6 +260,8 @@ REGISTER_MKL_KERNEL(kVAdd, VAdd);
...
@@ -244,6 +260,8 @@ REGISTER_MKL_KERNEL(kVAdd, VAdd);
REGISTER_MKL_KERNEL
(
kVScal
,
VScal
);
REGISTER_MKL_KERNEL
(
kVScal
,
VScal
);
REGISTER_MKL_KERNEL
(
kVExp
,
VExp
);
REGISTER_MKL_KERNEL
(
kVExp
,
VExp
);
REGISTER_MKL_KERNEL
(
kVSquare
,
VSquare
);
REGISTER_MKL_KERNEL
(
kVSquare
,
VSquare
);
REGISTER_MKL_KERNEL
(
kVCopy
,
VCopy
);
REGISTER_MKL_KERNEL
(
kVBroadcast
,
VBroadcast
);
REGISTER_MKL_KERNEL
(
kVSigmoid
,
VSigmoid
);
REGISTER_MKL_KERNEL
(
kVSigmoid
,
VSigmoid
);
REGISTER_MKL_KERNEL
(
kVTanh
,
VTanh
);
REGISTER_MKL_KERNEL
(
kVTanh
,
VTanh
);
REGISTER_MKL_KERNEL
(
kSeqPool
,
SeqPool
);
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);
...
@@ -50,6 +50,13 @@ void VCopy(const T* x, T* y, int n);
template
<
typename
T
>
template
<
typename
T
>
void
VAXPY
(
T
a
,
const
T
*
x
,
T
*
y
,
int
n
);
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
>
template
<
typename
T
>
void
VSigmoid
(
const
T
*
x
,
T
*
y
,
int
n
)
{
void
VSigmoid
(
const
T
*
x
,
T
*
y
,
int
n
)
{
const
T
min
=
SIGMOID_THRESHOLD_MIN
;
const
T
min
=
SIGMOID_THRESHOLD_MIN
;
...
@@ -192,6 +199,7 @@ DECLARE_MKL_KERNEL(VExp, XYNTuples);
...
@@ -192,6 +199,7 @@ DECLARE_MKL_KERNEL(VExp, XYNTuples);
DECLARE_MKL_KERNEL
(
VSigmoid
,
XYNTuples
);
DECLARE_MKL_KERNEL
(
VSigmoid
,
XYNTuples
);
DECLARE_MKL_KERNEL
(
VTanh
,
XYNTuples
);
DECLARE_MKL_KERNEL
(
VTanh
,
XYNTuples
);
DECLARE_MKL_KERNEL
(
VSquare
,
XYNTuples
);
DECLARE_MKL_KERNEL
(
VSquare
,
XYNTuples
);
DECLARE_MKL_KERNEL
(
VCopy
,
XYNTuples
);
DECLARE_MKL_KERNEL
(
SeqPool
,
SeqPoolTuples
);
DECLARE_MKL_KERNEL
(
SeqPool
,
SeqPoolTuples
);
...
@@ -201,6 +209,8 @@ DECLARE_MKL_KERNEL(Softmax, SoftmaxTuples);
...
@@ -201,6 +209,8 @@ DECLARE_MKL_KERNEL(Softmax, SoftmaxTuples);
DECLARE_MKL_KERNEL
(
Sgd
,
SgdTuples
);
DECLARE_MKL_KERNEL
(
Sgd
,
SgdTuples
);
DECLARE_MKL_KERNEL
(
VBroadcast
,
VBroadcastTuples
);
#undef DECLARE_MKL_KERNEL
#undef DECLARE_MKL_KERNEL
}
// namespace mkl
}
// namespace mkl
...
...
paddle/fluid/operators/jit/refer/CMakeLists.txt
浏览文件 @
6057f362
...
@@ -13,6 +13,7 @@ USE_JITKERNEL_REFER(kVAddRelu)
...
@@ -13,6 +13,7 @@ USE_JITKERNEL_REFER(kVAddRelu)
USE_JITKERNEL_REFER
(
kVSub
)
USE_JITKERNEL_REFER
(
kVSub
)
USE_JITKERNEL_REFER
(
kVScal
)
USE_JITKERNEL_REFER
(
kVScal
)
USE_JITKERNEL_REFER
(
kVAddBias
)
USE_JITKERNEL_REFER
(
kVAddBias
)
USE_JITKERNEL_REFER
(
kVCopy
)
USE_JITKERNEL_REFER
(
kVRelu
)
USE_JITKERNEL_REFER
(
kVRelu
)
USE_JITKERNEL_REFER
(
kVIdentity
)
USE_JITKERNEL_REFER
(
kVIdentity
)
USE_JITKERNEL_REFER
(
kVExp
)
USE_JITKERNEL_REFER
(
kVExp
)
...
@@ -34,3 +35,4 @@ USE_JITKERNEL_REFER(kHMax)
...
@@ -34,3 +35,4 @@ USE_JITKERNEL_REFER(kHMax)
USE_JITKERNEL_REFER
(
kSoftmax
)
USE_JITKERNEL_REFER
(
kSoftmax
)
USE_JITKERNEL_REFER
(
kEmbSeqPool
)
USE_JITKERNEL_REFER
(
kEmbSeqPool
)
USE_JITKERNEL_REFER
(
kSgd
)
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);
...
@@ -30,6 +30,7 @@ REGISTER_REFER_KERNEL(kVScal, VScal);
REGISTER_REFER_KERNEL
(
kVAddBias
,
VAddBias
);
REGISTER_REFER_KERNEL
(
kVAddBias
,
VAddBias
);
REGISTER_REFER_KERNEL
(
kVRelu
,
VRelu
);
REGISTER_REFER_KERNEL
(
kVRelu
,
VRelu
);
REGISTER_REFER_KERNEL
(
kVCopy
,
VCopy
);
REGISTER_REFER_KERNEL
(
kVIdentity
,
VIdentity
);
REGISTER_REFER_KERNEL
(
kVIdentity
,
VIdentity
);
REGISTER_REFER_KERNEL
(
kVSquare
,
VSquare
);
REGISTER_REFER_KERNEL
(
kVSquare
,
VSquare
);
REGISTER_REFER_KERNEL
(
kVExp
,
VExp
);
REGISTER_REFER_KERNEL
(
kVExp
,
VExp
);
...
@@ -61,4 +62,6 @@ REGISTER_REFER_KERNEL(kEmbSeqPool, EmbSeqPool);
...
@@ -61,4 +62,6 @@ REGISTER_REFER_KERNEL(kEmbSeqPool, EmbSeqPool);
REGISTER_REFER_KERNEL
(
kSgd
,
Sgd
);
REGISTER_REFER_KERNEL
(
kSgd
,
Sgd
);
REGISTER_REFER_KERNEL
(
kVBroadcast
,
VBroadcast
);
#undef REGISTER_REFER_KERNEL
#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) {
...
@@ -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
>
template
<
typename
T
>
void
VRelu
(
const
T
*
x
,
T
*
y
,
int
n
)
{
void
VRelu
(
const
T
*
x
,
T
*
y
,
int
n
)
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
...
@@ -500,6 +514,7 @@ DECLARE_REFER_KERNEL(VExp, XYNTuples);
...
@@ -500,6 +514,7 @@ DECLARE_REFER_KERNEL(VExp, XYNTuples);
DECLARE_REFER_KERNEL
(
VSigmoid
,
XYNTuples
);
DECLARE_REFER_KERNEL
(
VSigmoid
,
XYNTuples
);
DECLARE_REFER_KERNEL
(
VTanh
,
XYNTuples
);
DECLARE_REFER_KERNEL
(
VTanh
,
XYNTuples
);
DECLARE_REFER_KERNEL
(
VSquare
,
XYNTuples
);
DECLARE_REFER_KERNEL
(
VSquare
,
XYNTuples
);
DECLARE_REFER_KERNEL
(
VCopy
,
XYNTuples
);
// lstm_t*, const lstm_attr_t*
// lstm_t*, const lstm_attr_t*
DECLARE_REFER_KERNEL
(
LSTMCtHt
,
LSTMTuples
);
DECLARE_REFER_KERNEL
(
LSTMCtHt
,
LSTMTuples
);
...
@@ -528,6 +543,8 @@ DECLARE_REFER_KERNEL(EmbSeqPool, EmbSeqPoolTuples);
...
@@ -528,6 +543,8 @@ DECLARE_REFER_KERNEL(EmbSeqPool, EmbSeqPoolTuples);
DECLARE_REFER_KERNEL
(
Sgd
,
SgdTuples
);
DECLARE_REFER_KERNEL
(
Sgd
,
SgdTuples
);
DECLARE_REFER_KERNEL
(
VBroadcast
,
VBroadcastTuples
);
#undef DECLARE_REFER_KERNEL
#undef DECLARE_REFER_KERNEL
}
// namespace refer
}
// namespace refer
...
...
paddle/fluid/operators/jit/test.cc
浏览文件 @
6057f362
...
@@ -26,8 +26,8 @@ limitations under the License. */
...
@@ -26,8 +26,8 @@ limitations under the License. */
DEFINE_double
(
acc
,
1e-5
,
"Test accuracy threshold."
);
DEFINE_double
(
acc
,
1e-5
,
"Test accuracy threshold."
);
template
<
typename
T
>
template
<
typename
T
>
void
RandomVec
(
const
int
n
,
T
*
a
,
const
T
lower
=
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
0
.
f
))
{
const
T
upper
=
static_cast
<
T
>
(
2.
f
))
{
static
unsigned
int
seed
=
100
;
static
unsigned
int
seed
=
100
;
std
::
mt19937
rng
(
seed
++
);
std
::
mt19937
rng
(
seed
++
);
std
::
uniform_real_distribution
<
double
>
uniform_dist
(
0
,
1
);
std
::
uniform_real_distribution
<
double
>
uniform_dist
(
0
,
1
);
...
@@ -157,6 +157,26 @@ struct TestFuncWithRefer<jit::XRNTuples<T>, std::vector<T>, T> {
...
@@ -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
>
template
<
typename
T
>
struct
TestFuncWithRefer
<
jit
::
XYNTuples
<
T
>
,
std
::
vector
<
T
>
,
std
::
vector
<
T
>>
{
struct
TestFuncWithRefer
<
jit
::
XYNTuples
<
T
>
,
std
::
vector
<
T
>
,
std
::
vector
<
T
>>
{
void
operator
()(
const
typename
jit
::
XYNTuples
<
T
>::
func_type
tgt
,
void
operator
()(
const
typename
jit
::
XYNTuples
<
T
>::
func_type
tgt
,
...
@@ -514,7 +534,7 @@ void TestKernelXRNTuples() {
...
@@ -514,7 +534,7 @@ void TestKernelXRNTuples() {
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
XRNTuples
<
T
>>
();
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
XRNTuples
<
T
>>
();
EXPECT_TRUE
(
ref
!=
nullptr
);
EXPECT_TRUE
(
ref
!=
nullptr
);
std
::
vector
<
T
>
x
(
d
);
std
::
vector
<
T
>
x
(
d
);
RandomVec
<
T
>
(
d
,
x
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
d
,
x
.
data
());
T
ref_res
;
T
ref_res
;
ref
(
x
.
data
(),
&
ref_res
,
d
);
ref
(
x
.
data
(),
&
ref_res
,
d
);
TestAllImpls
<
KT
,
jit
::
XRNTuples
<
T
>
,
PlaceType
,
std
::
vector
<
T
>
,
T
>
(
d
,
x
,
TestAllImpls
<
KT
,
jit
::
XRNTuples
<
T
>
,
PlaceType
,
std
::
vector
<
T
>
,
T
>
(
d
,
x
,
...
@@ -532,7 +552,7 @@ void TestKernelXYNTuples() {
...
@@ -532,7 +552,7 @@ void TestKernelXYNTuples() {
std
::
vector
<
T
>
x
(
d
),
yref
(
d
);
std
::
vector
<
T
>
x
(
d
),
yref
(
d
);
std
::
vector
<
T
>
xinp
(
d
);
// inplace test
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
());
std
::
copy
(
x
.
begin
(),
x
.
end
(),
xinp
.
begin
());
const
T
*
x_data
=
x
.
data
();
const
T
*
x_data
=
x
.
data
();
...
@@ -566,7 +586,7 @@ void TestKernelLSTMTuples() {
...
@@ -566,7 +586,7 @@ void TestKernelLSTMTuples() {
EXPECT_TRUE
(
ref
!=
nullptr
);
EXPECT_TRUE
(
ref
!=
nullptr
);
std
::
vector
<
T
>
xsrc
(
4
*
d
),
wp
(
3
*
d
),
ct_1
(
d
);
std
::
vector
<
T
>
xsrc
(
4
*
d
),
wp
(
3
*
d
),
ct_1
(
d
);
std
::
vector
<
T
>
ct_ref
(
d
),
ht_ref
(
d
),
checked
(
2
*
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
>
(
3
*
d
,
wp
.
data
(),
-
1.
f
,
1.
f
);
RandomVec
<
T
>
(
d
,
ct_1
.
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
// x could be changed after compute, so copy to save src
...
@@ -614,8 +634,8 @@ void TestKernelGRUTuples() {
...
@@ -614,8 +634,8 @@ void TestKernelGRUTuples() {
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
GRUTuples
<
T
>>
();
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
GRUTuples
<
T
>>
();
EXPECT_TRUE
(
ref
!=
nullptr
);
EXPECT_TRUE
(
ref
!=
nullptr
);
std
::
vector
<
T
>
xsrc
(
3
*
d
),
ht_1
(
d
),
ht_ref
(
d
);
std
::
vector
<
T
>
xsrc
(
3
*
d
),
ht_1
(
d
),
ht_ref
(
d
);
RandomVec
<
T
>
(
3
*
d
,
xsrc
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
3
*
d
,
xsrc
.
data
());
RandomVec
<
T
>
(
d
,
ht_1
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
d
,
ht_1
.
data
());
// x could be changed after compute, so copy to save src
// x could be changed after compute, so copy to save src
std
::
vector
<
T
>
x
(
xsrc
.
size
());
std
::
vector
<
T
>
x
(
xsrc
.
size
());
std
::
copy
(
xsrc
.
begin
(),
xsrc
.
end
(),
x
.
begin
());
std
::
copy
(
xsrc
.
begin
(),
xsrc
.
end
(),
x
.
begin
());
...
@@ -651,7 +671,7 @@ void TestKernelSeqPoolTuples() {
...
@@ -651,7 +671,7 @@ void TestKernelSeqPoolTuples() {
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
SeqPoolTuples
<
T
>>
();
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
SeqPoolTuples
<
T
>>
();
EXPECT_TRUE
(
ref
!=
nullptr
);
EXPECT_TRUE
(
ref
!=
nullptr
);
std
::
vector
<
T
>
x
(
h
*
w
),
yref
(
w
);
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
();
const
T
*
x_data
=
x
.
data
();
T
*
yref_data
=
yref
.
data
();
T
*
yref_data
=
yref
.
data
();
ref
(
x_data
,
yref_data
,
&
attr
);
ref
(
x_data
,
yref_data
,
&
attr
);
...
@@ -676,8 +696,8 @@ void TestKernelMatMulTuples() {
...
@@ -676,8 +696,8 @@ void TestKernelMatMulTuples() {
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
MatMulTuples
<
T
>>
();
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
MatMulTuples
<
T
>>
();
EXPECT_TRUE
(
ref
!=
nullptr
);
EXPECT_TRUE
(
ref
!=
nullptr
);
std
::
vector
<
T
>
a
(
m
*
k
),
b
(
k
*
n
),
c
(
m
*
n
);
std
::
vector
<
T
>
a
(
m
*
k
),
b
(
k
*
n
),
c
(
m
*
n
);
RandomVec
<
T
>
(
m
*
k
,
a
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
m
*
k
,
a
.
data
());
RandomVec
<
T
>
(
k
*
n
,
b
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
k
*
n
,
b
.
data
());
const
T
*
a_data
=
a
.
data
();
const
T
*
a_data
=
a
.
data
();
const
T
*
b_data
=
b
.
data
();
const
T
*
b_data
=
b
.
data
();
T
*
c_data
=
c
.
data
();
T
*
c_data
=
c
.
data
();
...
@@ -699,7 +719,7 @@ void TestKernelSoftmaxTuples() {
...
@@ -699,7 +719,7 @@ void TestKernelSoftmaxTuples() {
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
SoftmaxTuples
<
T
>>
();
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
SoftmaxTuples
<
T
>>
();
EXPECT_TRUE
(
ref
!=
nullptr
);
EXPECT_TRUE
(
ref
!=
nullptr
);
std
::
vector
<
T
>
x
(
bs
*
n
),
y
(
bs
*
n
);
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
();
const
T
*
x_data
=
x
.
data
();
T
*
y_data
=
y
.
data
();
T
*
y_data
=
y
.
data
();
...
@@ -726,7 +746,7 @@ void TestKernelEmbSeqPoolTuples() {
...
@@ -726,7 +746,7 @@ void TestKernelEmbSeqPoolTuples() {
test_sizes
.
erase
(
std
::
remove
(
test_sizes
.
begin
(),
test_sizes
.
end
(),
1000
));
test_sizes
.
erase
(
std
::
remove
(
test_sizes
.
begin
(),
test_sizes
.
end
(),
1000
));
for
(
int
tbl_w
:
test_sizes
)
{
for
(
int
tbl_w
:
test_sizes
)
{
std
::
vector
<
T
>
table
(
tbl_h
*
tbl_w
);
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
();
const
T
*
table_data
=
table
.
data
();
for
(
auto
type
:
pool_types
)
{
for
(
auto
type
:
pool_types
)
{
for
(
int
idx_w
:
{
1
,
2
,
10
,
16
})
{
for
(
int
idx_w
:
{
1
,
2
,
10
,
16
})
{
...
@@ -772,14 +792,14 @@ void TestKernelSgdTuples() {
...
@@ -772,14 +792,14 @@ void TestKernelSgdTuples() {
for
(
int
grad_w
:
TestSizes
())
{
for
(
int
grad_w
:
TestSizes
())
{
std
::
vector
<
T
>
param
(
param_h
*
grad_w
);
std
::
vector
<
T
>
param
(
param_h
*
grad_w
);
std
::
vector
<
T
>
param_out
(
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
();
const
T
*
param_data
=
param
.
data
();
T
*
out_data
=
param_out
.
data
();
T
*
out_data
=
param_out
.
data
();
for
(
int
rows_size
=
1
;
rows_size
<=
param_h
;
++
rows_size
)
{
for
(
int
rows_size
=
1
;
rows_size
<=
param_h
;
++
rows_size
)
{
std
::
vector
<
T
>
grad
(
rows_size
*
grad_w
);
std
::
vector
<
T
>
grad
(
rows_size
*
grad_w
);
std
::
vector
<
int64_t
>
rows
=
std
::
vector
<
int64_t
>
rows
=
UnDuplicatedRandomVec
(
rows_size
,
0
,
rows_size
-
1
);
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
int64_t
*
rows_data
=
rows
.
data
();
const
T
*
grad_data
=
grad
.
data
();
const
T
*
grad_data
=
grad
.
data
();
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
SgdTuples
<
T
>>
();
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
SgdTuples
<
T
>>
();
...
@@ -815,8 +835,8 @@ void TestKernelNCHW16CMulNCTuples() {
...
@@ -815,8 +835,8 @@ void TestKernelNCHW16CMulNCTuples() {
int
sz
=
n
*
c
*
h
*
w
;
int
sz
=
n
*
c
*
h
*
w
;
std
::
vector
<
T
>
x
(
sz
),
y
(
n
*
c
),
zref
(
sz
);
std
::
vector
<
T
>
x
(
sz
),
y
(
n
*
c
),
zref
(
sz
);
std
::
vector
<
T
>
ztgt
(
sz
),
zjit
(
sz
);
std
::
vector
<
T
>
ztgt
(
sz
),
zjit
(
sz
);
RandomVec
<
T
>
(
sz
,
x
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
sz
,
x
.
data
());
RandomVec
<
T
>
(
n
*
c
,
y
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
n
*
c
,
y
.
data
());
const
T
*
x_data
=
x
.
data
();
const
T
*
x_data
=
x
.
data
();
const
T
*
y_data
=
y
.
data
();
const
T
*
y_data
=
y
.
data
();
...
@@ -873,11 +893,11 @@ void TestKernelLayerNormTuples() {
...
@@ -873,11 +893,11 @@ void TestKernelLayerNormTuples() {
int
sz
=
left
*
right
;
int
sz
=
left
*
right
;
std
::
vector
<
T
>
x
(
sz
),
mean
(
left
),
var
(
left
),
scale
(
right
),
bias
(
right
),
std
::
vector
<
T
>
x
(
sz
),
mean
(
left
),
var
(
left
),
scale
(
right
),
bias
(
right
),
outref
(
sz
);
outref
(
sz
);
RandomVec
<
T
>
(
sz
,
x
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
sz
,
x
.
data
());
RandomVec
<
T
>
(
left
,
mean
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
left
,
mean
.
data
());
RandomVec
<
T
>
(
left
,
var
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
left
,
var
.
data
());
RandomVec
<
T
>
(
right
,
scale
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
right
,
scale
.
data
());
RandomVec
<
T
>
(
right
,
bias
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
right
,
bias
.
data
());
const
T
*
scale_data
=
scale
.
data
();
const
T
*
scale_data
=
scale
.
data
();
const
T
*
bias_data
=
bias
.
data
();
const
T
*
bias_data
=
bias
.
data
();
...
@@ -903,7 +923,7 @@ void TestKernelCRFDecodingTuples() {
...
@@ -903,7 +923,7 @@ void TestKernelCRFDecodingTuples() {
VLOG
(
10
)
<<
"===== Test JITKernel "
<<
jit
::
to_string
(
KT
);
VLOG
(
10
)
<<
"===== Test JITKernel "
<<
jit
::
to_string
(
KT
);
constexpr
int
state_trans_base_idx
=
2
;
constexpr
int
state_trans_base_idx
=
2
;
auto
test_sizes
=
TestSizes
();
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
seq_len
:
{
1
,
11
,
17
,
50
})
{
for
(
int
tag_num
:
test_sizes
)
{
for
(
int
tag_num
:
test_sizes
)
{
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
CRFDecodingTuples
<
T
>>
();
auto
ref
=
jit
::
GetRefer
<
KT
,
jit
::
CRFDecodingTuples
<
T
>>
();
...
@@ -912,8 +932,8 @@ void TestKernelCRFDecodingTuples() {
...
@@ -912,8 +932,8 @@ void TestKernelCRFDecodingTuples() {
int
w_sz
=
(
tag_num
+
state_trans_base_idx
)
*
tag_num
;
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
<
T
>
x
(
x_sz
),
w
(
w_sz
),
alpharef
(
x_sz
);
std
::
vector
<
int
>
trackref
(
x_sz
);
std
::
vector
<
int
>
trackref
(
x_sz
);
RandomVec
<
T
>
(
x_sz
,
x
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
x_sz
,
x
.
data
());
RandomVec
<
T
>
(
w_sz
,
w
.
data
()
,
-
2.
f
,
2.
f
);
RandomVec
<
T
>
(
w_sz
,
w
.
data
());
ref
(
seq_len
,
(
const
T
*
)
x
.
data
(),
(
const
T
*
)
w
.
data
(),
alpharef
.
data
(),
ref
(
seq_len
,
(
const
T
*
)
x
.
data
(),
(
const
T
*
)
w
.
data
(),
alpharef
.
data
(),
trackref
.
data
(),
tag_num
);
trackref
.
data
(),
tag_num
);
...
@@ -926,6 +946,27 @@ void TestKernelCRFDecodingTuples() {
...
@@ -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) \
#define TEST_CPU_KERNEL(test_tuple, kernel_type) \
TEST(JITKernel, kernel_type) { \
TEST(JITKernel, kernel_type) { \
TestKernel##test_tuple<jit::kernel_type, float, CPUPlace>(); \
TestKernel##test_tuple<jit::kernel_type, float, CPUPlace>(); \
...
@@ -949,6 +990,7 @@ TEST_CPU_KERNEL(XYNTuples, kVSquare);
...
@@ -949,6 +990,7 @@ TEST_CPU_KERNEL(XYNTuples, kVSquare);
TEST_CPU_KERNEL
(
XYNTuples
,
kVExp
);
TEST_CPU_KERNEL
(
XYNTuples
,
kVExp
);
TEST_CPU_KERNEL
(
XYNTuples
,
kVSigmoid
);
TEST_CPU_KERNEL
(
XYNTuples
,
kVSigmoid
);
TEST_CPU_KERNEL
(
XYNTuples
,
kVTanh
);
TEST_CPU_KERNEL
(
XYNTuples
,
kVTanh
);
TEST_CPU_KERNEL
(
XYNTuples
,
kVCopy
);
TEST_CPU_KERNEL
(
LSTMTuples
,
kLSTMCtHt
);
TEST_CPU_KERNEL
(
LSTMTuples
,
kLSTMCtHt
);
TEST_CPU_KERNEL
(
LSTMTuples
,
kLSTMC1H1
);
TEST_CPU_KERNEL
(
LSTMTuples
,
kLSTMC1H1
);
...
@@ -966,6 +1008,7 @@ TEST_CPU_KERNEL(EmbSeqPoolTuples, kEmbSeqPool);
...
@@ -966,6 +1008,7 @@ TEST_CPU_KERNEL(EmbSeqPoolTuples, kEmbSeqPool);
TEST_CPU_KERNEL
(
SgdTuples
,
kSgd
);
TEST_CPU_KERNEL
(
SgdTuples
,
kSgd
);
TEST_CPU_KERNEL
(
LayerNormTuples
,
kLayerNorm
);
TEST_CPU_KERNEL
(
LayerNormTuples
,
kLayerNorm
);
TEST_CPU_KERNEL
(
CRFDecodingTuples
,
kCRFDecoding
);
TEST_CPU_KERNEL
(
CRFDecodingTuples
,
kCRFDecoding
);
TEST_CPU_KERNEL
(
VBroadcastTuples
,
kVBroadcast
);
TEST
(
JITKernel_key
,
lstm
)
{
TEST
(
JITKernel_key
,
lstm
)
{
jit
::
lstm_attr_t
attr1
(
8
,
jit
::
kVIdentity
,
jit
::
kVSigmoid
,
jit
::
kVTanh
);
jit
::
lstm_attr_t
attr1
(
8
,
jit
::
kVIdentity
,
jit
::
kVSigmoid
,
jit
::
kVTanh
);
...
...
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录