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
Paddle
提交
95aab366
P
Paddle
项目概览
PaddlePaddle
/
Paddle
大约 2 年 前同步成功
通知
2325
Star
20933
Fork
5424
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1423
列表
看板
标记
里程碑
合并请求
543
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
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
1,423
Issue
1,423
列表
看板
标记
里程碑
合并请求
543
合并请求
543
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
95aab366
编写于
7月 14, 2023
作者:
Z
zhupengyang
提交者:
GitHub
7月 14, 2023
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix embedding_with_eltwise_add_xpu (#55354)
上级
fce4c2de
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
123 addition
and
126 deletion
+123
-126
paddle/phi/kernels/fusion/xpu/embedding_with_eltwise_add_xpu_kernel.cc
...rnels/fusion/xpu/embedding_with_eltwise_add_xpu_kernel.cc
+123
-126
未找到文件。
paddle/phi/kernels/fusion/xpu/embedding_with_eltwise_add_xpu_kernel.cc
浏览文件 @
95aab366
...
...
@@ -19,20 +19,67 @@
namespace
phi
{
namespace
fusion
{
template
<
typename
T
,
typename
Context
>
void
EmbeddingWithEltwiseAddXpuKernel
(
const
Context
&
ctx
,
const
std
::
vector
<
const
DenseTensor
*>&
ids
,
const
std
::
vector
<
const
DenseTensor
*>&
tables
,
const
paddle
::
optional
<
DenseTensor
>&
mask
,
int64_t
padding_idx
,
DenseTensor
*
out
,
DenseTensor
*
seq_lod
,
DenseTensor
*
max_seq_len
)
{
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
int
emb_dim
=
tables
[
0
]
->
dims
()[
1
];
std
::
vector
<
int
>
table_lens
;
std
::
vector
<
const
float
*>
arg_tables
;
namespace
{
template
<
typename
T
>
void
FillSeqLod
(
int
batch_size
,
int
max_seq_len
,
const
T
*
mask
,
int
*
seq_lod
)
{
for
(
int
batch_idx
=
0
;
batch_idx
<
batch_size
;
batch_idx
++
)
{
int
cur_batch_seq_len
=
0
;
for
(
int
seq_idx
=
0
;
seq_idx
<
max_seq_len
;
seq_idx
++
)
{
int
mask_idx
=
batch_idx
*
max_seq_len
+
seq_idx
;
if
(
mask
[
mask_idx
]
>
0
)
{
cur_batch_seq_len
++
;
}
else
{
break
;
}
}
PADDLE_ENFORCE_GT
(
cur_batch_seq_len
,
0
,
errors
::
PreconditionNotMet
(
"cur_batch_seq_len should be greater than 0, but got %d."
,
cur_batch_seq_len
));
seq_lod
[
batch_idx
+
1
]
=
seq_lod
[
batch_idx
]
+
cur_batch_seq_len
;
}
}
template
<
>
void
FillSeqLod
<
float
>
(
int
batch_size
,
int
max_seq_len
,
const
float
*
mask
,
int
*
seq_lod
)
{
for
(
int
batch_idx
=
0
;
batch_idx
<
batch_size
;
batch_idx
++
)
{
int
cur_batch_seq_len
=
0
;
for
(
int
seq_idx
=
0
;
seq_idx
<
max_seq_len
;
seq_idx
++
)
{
int
mask_idx
=
batch_idx
*
max_seq_len
+
seq_idx
;
if
(
fabs
(
mask
[
mask_idx
])
>
1e-5
)
{
cur_batch_seq_len
++
;
}
else
{
break
;
}
}
PADDLE_ENFORCE_GT
(
cur_batch_seq_len
,
0
,
errors
::
PreconditionNotMet
(
"cur_batch_seq_len should be greater than 0, but got %d."
,
cur_batch_seq_len
));
seq_lod
[
batch_idx
+
1
]
=
seq_lod
[
batch_idx
]
+
cur_batch_seq_len
;
}
}
template
<
typename
TT
,
typename
TID
,
typename
Context
>
void
MultiEmbeddingKernel
(
const
Context
&
ctx
,
const
std
::
vector
<
const
DenseTensor
*>&
ids
,
const
std
::
vector
<
const
DenseTensor
*>&
tables
,
const
paddle
::
optional
<
DenseTensor
>&
mask
,
int64_t
padding_idx
,
DenseTensor
*
out
,
DenseTensor
*
seq_lod
,
DenseTensor
*
max_seq_len
)
{
using
XPUType
=
typename
XPUTypeTrait
<
TT
>::
Type
;
int64_t
emb_dim
=
tables
[
0
]
->
dims
()[
1
];
std
::
vector
<
TID
>
table_lens
;
std
::
vector
<
const
XPUType
*>
arg_tables
;
for
(
auto
*
table
:
tables
)
{
auto
&
table_dims
=
table
->
dims
();
PADDLE_ENFORCE_EQ
(
...
...
@@ -49,20 +96,7 @@ void EmbeddingWithEltwiseAddXpuKernel(
table_dims
[
1
],
emb_dim
));
table_lens
.
push_back
(
table_dims
[
0
]);
if
(
std
::
is_same
<
T
,
phi
::
dtype
::
float16
>::
value
)
{
DenseTensor
table_data_fp32_t
;
ctx
.
template
Alloc
<
float
>(
&
table_data_fp32_t
,
table
->
numel
()
*
sizeof
(
float
));
int
r
=
xpu
::
cast
<
XPUType
,
float
>
(
ctx
.
x_context
(),
reinterpret_cast
<
const
XPUType
*>
(
table
->
data
<
T
>
()),
table_data_fp32_t
.
data
<
float
>
(),
table
->
numel
());
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"cast"
);
arg_tables
.
push_back
(
table_data_fp32_t
.
data
<
float
>
());
}
else
{
arg_tables
.
push_back
(
table
->
data
<
float
>
());
}
arg_tables
.
push_back
(
reinterpret_cast
<
const
XPUType
*>
(
table
->
data
<
TT
>
()));
}
int
emb_layer_num
=
ids
.
size
();
...
...
@@ -78,118 +112,81 @@ void EmbeddingWithEltwiseAddXpuKernel(
auto
&
id_dims
=
ids
[
0
]
->
dims
();
int
batch_size
=
id_dims
[
0
];
int
max_seq_len_value
=
id_dims
[
1
];
int
ids_len
=
id_dims
[
0
]
*
id_dims
[
1
];
std
::
vector
<
std
::
vector
<
int
>>
int_ids
(
emb_layer_num
,
std
::
vector
<
int
>
(
ids_len
,
0
));
std
::
vector
<
xpu
::
VectorParam
<
int
>>
arg_ids
;
auto
*
mask_tensor
=
mask
.
get_ptr
();
if
(
mask_tensor
!=
nullptr
)
{
auto
mask_dtype
=
mask_tensor
->
dtype
();
PADDLE_ENFORCE
(
mask_dtype
==
phi
::
DataType
::
INT64
||
mask_dtype
==
phi
::
DataType
::
FLOAT32
,
errors
::
InvalidArgument
(
"The data type of mask should be int64 or float32, but got %s."
,
DataTypeToString
(
mask_dtype
)));
max_seq_len
->
Resize
({
1
});
ctx
.
template
HostAlloc
<
int
>(
max_seq_len
)[
0
]
=
max_seq_len_value
;
seq_lod
->
Resize
({
batch_size
+
1
});
int
*
seq_lod_data
=
ctx
.
template
HostAlloc
<
int
>(
seq_lod
);
seq_lod_data
[
0
]
=
0
;
for
(
int
batch_idx
=
0
;
batch_idx
<
batch_size
;
batch_idx
++
)
{
int
cur_batch_seq_len
=
0
;
for
(
int
seq_idx
=
0
;
seq_idx
<
max_seq_len_value
;
seq_idx
++
)
{
int
mask_idx
=
batch_idx
*
max_seq_len_value
+
seq_idx
;
if
((
mask_dtype
==
phi
::
DataType
::
INT64
&&
mask
->
data
<
int64_t
>
()[
mask_idx
]
>
0
)
||
(
mask_dtype
==
phi
::
DataType
::
FLOAT32
&&
fabs
(
mask
->
data
<
float
>
()[
mask_idx
])
>
1e-5
))
{
cur_batch_seq_len
++
;
}
else
{
break
;
}
}
PADDLE_ENFORCE_GT
(
cur_batch_seq_len
,
0
,
errors
::
PreconditionNotMet
(
"cur_batch_seq_len should be greater than 0, but got %d."
,
cur_batch_seq_len
));
seq_lod_data
[
batch_idx
+
1
]
=
seq_lod_data
[
batch_idx
]
+
cur_batch_seq_len
;
switch
(
mask_tensor
->
dtype
())
{
case
DataType
::
FLOAT32
:
FillSeqLod
(
batch_size
,
max_seq_len_value
,
mask_tensor
->
data
<
float
>
(),
seq_lod_data
);
break
;
case
DataType
::
INT64
:
FillSeqLod
(
batch_size
,
max_seq_len_value
,
mask_tensor
->
data
<
int64_t
>
(),
seq_lod_data
);
break
;
default:
PADDLE_THROW
(
phi
::
errors
::
Unimplemented
(
"Only support mask data type is int64 "
"or float, not support %s now."
,
DataTypeToString
(
mask_tensor
->
dtype
())));
break
;
}
out
->
Resize
({
batch_size
,
seq_lod_data
[
batch_size
],
emb_dim
});
}
for
(
int
i
=
0
;
i
<
emb_layer_num
;
i
++
)
{
if
(
ids
[
i
]
->
dtype
()
==
DataType
::
INT64
)
{
auto
*
ids_data
=
ids
[
i
]
->
data
<
int64_t
>
();
for
(
int
batch_idx
=
0
;
batch_idx
<
batch_size
;
batch_idx
++
)
{
for
(
int
j
=
0
;
j
<
seq_lod_data
[
batch_idx
+
1
]
-
seq_lod_data
[
batch_idx
];
j
++
)
{
int_ids
[
i
][
seq_lod_data
[
batch_idx
]
+
j
]
=
ids_data
[
batch_idx
*
max_seq_len_value
+
j
];
}
}
}
else
{
auto
*
ids_data
=
ids
[
i
]
->
data
<
int
>
();
for
(
int
batch_idx
=
0
;
batch_idx
<
batch_size
;
batch_idx
++
)
{
for
(
int
j
=
0
;
j
<
seq_lod_data
[
batch_idx
+
1
]
-
seq_lod_data
[
batch_idx
];
j
++
)
{
int_ids
[
i
][
seq_lod_data
[
batch_idx
]
+
j
]
=
ids_data
[
batch_idx
*
max_seq_len_value
+
j
];
}
}
}
arg_ids
.
push_back
(
xpu
::
VectorParam
<
int
>
{
int_ids
[
i
].
data
(),
ids_len
,
nullptr
});
}
}
else
{
for
(
int
i
=
0
;
i
<
emb_layer_num
;
i
++
)
{
for
(
int
j
=
0
;
j
<
ids_len
;
j
++
)
{
if
(
ids
[
i
]
->
dtype
()
==
phi
::
DataType
::
INT64
)
{
int_ids
[
i
][
j
]
=
static_cast
<
int
>
(
ids
[
i
]
->
data
<
int64_t
>
()[
j
]);
}
else
if
(
ids
[
i
]
->
dtype
()
==
phi
::
DataType
::
INT32
)
{
int_ids
[
i
][
j
]
=
ids
[
i
]
->
data
<
int
>
()[
j
];
}
}
arg_ids
.
push_back
(
xpu
::
VectorParam
<
int
>
{
int_ids
[
i
].
data
(),
ids_len
,
nullptr
});
}
int
ids_len
=
id_dims
[
0
]
*
id_dims
[
1
];
std
::
vector
<
xpu
::
VectorParam
<
TID
>>
arg_ids
;
for
(
int
i
=
0
;
i
<
emb_layer_num
;
i
++
)
{
arg_ids
.
push_back
(
xpu
::
VectorParam
<
TID
>
{
ids
[
i
]
->
data
<
TID
>
(),
ids_len
,
nullptr
});
}
ctx
.
template
Alloc
<
T
>(
out
);
if
(
std
::
is_same
<
T
,
phi
::
dtype
::
float16
>::
value
)
{
DenseTensor
out_fp32_t
;
ctx
.
template
Alloc
<
float
>(
&
out_fp32_t
,
out
->
numel
()
*
sizeof
(
float
));
int
r
=
xpu
::
multi_embedding_fusion
<
float
,
float
,
int
>
(
ctx
.
x_context
(),
arg_tables
,
/* tables */
out_fp32_t
.
data
<
float
>
(),
arg_ids
,
table_lens
,
emb_dim
,
std
::
vector
<
float
>
(
table_lens
.
size
(),
1.0
f
),
std
::
vector
<
int
>
(
table_lens
.
size
(),
padding_idx
));
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"embedding_with_eltwise_add_xpu"
);
int
r
=
xpu
::
multi_embedding_fusion
<
XPUType
,
XPUType
,
TID
>
(
ctx
.
x_context
(),
arg_tables
,
reinterpret_cast
<
XPUType
*>
(
ctx
.
template
Alloc
<
TT
>(
out
)),
arg_ids
,
table_lens
,
emb_dim
,
std
::
vector
<
float
>
(
table_lens
.
size
(),
1.0
f
),
std
::
vector
<
TID
>
(
table_lens
.
size
(),
padding_idx
));
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"embedding_with_eltwise_add_xpu"
);
}
}
// namespace
r
=
xpu
::
cast
(
ctx
.
x_context
(),
out_fp32_t
.
data
<
float
>
(),
reinterpret_cast
<
XPUTypeFP16
*>
(
out
->
data
<
T
>
()),
out
->
numel
());
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"cast"
);
}
else
{
int
r
=
xpu
::
multi_embedding_fusion
<
float
,
float
,
int
>
(
ctx
.
x_context
(),
arg_tables
,
/* tables */
out
->
data
<
float
>
(),
arg_ids
,
table_lens
,
emb_dim
,
std
::
vector
<
float
>
(
table_lens
.
size
(),
1.0
f
),
std
::
vector
<
int
>
(
table_lens
.
size
(),
padding_idx
));
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"embedding_with_eltwise_add_xpu"
);
template
<
typename
T
,
typename
Context
>
void
EmbeddingWithEltwiseAddXpuKernel
(
const
Context
&
ctx
,
const
std
::
vector
<
const
DenseTensor
*>&
ids
,
const
std
::
vector
<
const
DenseTensor
*>&
tables
,
const
paddle
::
optional
<
DenseTensor
>&
mask
,
int64_t
padding_idx
,
DenseTensor
*
out
,
DenseTensor
*
seq_lod
,
DenseTensor
*
max_seq_len
)
{
switch
(
ids
[
0
]
->
dtype
())
{
case
DataType
::
INT32
:
MultiEmbeddingKernel
<
T
,
int
,
Context
>
(
ctx
,
ids
,
tables
,
mask
,
padding_idx
,
out
,
seq_lod
,
max_seq_len
);
break
;
case
DataType
::
INT64
:
MultiEmbeddingKernel
<
T
,
int64_t
,
Context
>
(
ctx
,
ids
,
tables
,
mask
,
padding_idx
,
out
,
seq_lod
,
max_seq_len
);
break
;
default:
PADDLE_THROW
(
phi
::
errors
::
Unimplemented
(
"Only support ids data type is int64 or int32, not support %s now."
,
DataTypeToString
(
ids
[
0
]
->
dtype
())));
break
;
}
}
...
...
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