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体验新版 GitCode,发现更多精彩内容 >>
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ecd6db43
编写于
5月 10, 2022
作者:
F
fwenguang
提交者:
GitHub
5月 10, 2022
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电子邮件补丁
差异文件
[MLU] add layernorm mlu kernel (#42356)
上级
4e5fb733
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
620 addition
and
5 deletion
+620
-5
paddle/fluid/operators/layer_norm_op_mlu.cc
paddle/fluid/operators/layer_norm_op_mlu.cc
+234
-0
paddle/fluid/operators/mlu/mlu_baseop.cc
paddle/fluid/operators/mlu/mlu_baseop.cc
+39
-0
paddle/fluid/operators/mlu/mlu_baseop.h
paddle/fluid/operators/mlu/mlu_baseop.h
+38
-5
python/paddle/fluid/tests/unittests/mlu/test_layer_norm_op_mlu.py
...addle/fluid/tests/unittests/mlu/test_layer_norm_op_mlu.py
+309
-0
未找到文件。
paddle/fluid/operators/layer_norm_op_mlu.cc
0 → 100644
浏览文件 @
ecd6db43
/* Copyright (c) 2022 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/framework/op_registry.h"
#include "paddle/fluid/operators/mlu/mlu_baseop.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
DDim
=
framework
::
DDim
;
template
<
typename
T
>
class
LayerNormMLUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
auto
begin_norm_axis
=
ctx
.
Attr
<
int
>
(
"begin_norm_axis"
);
const
auto
epsilon
=
ctx
.
Attr
<
float
>
(
"epsilon"
);
const
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
const
auto
*
scale
=
ctx
.
Input
<
Tensor
>
(
"Scale"
);
const
auto
*
bias
=
ctx
.
Input
<
Tensor
>
(
"Bias"
);
auto
*
y
=
ctx
.
Output
<
Tensor
>
(
"Y"
);
auto
*
mean
=
ctx
.
Output
<
Tensor
>
(
"Mean"
);
auto
*
variance
=
ctx
.
Output
<
Tensor
>
(
"Variance"
);
auto
place
=
ctx
.
GetPlace
();
y
->
mutable_data
<
T
>
(
place
);
mean
->
mutable_data
<
T
>
(
place
);
variance
->
mutable_data
<
T
>
(
place
);
const
auto
&
x_dims
=
x
->
dims
();
std
::
vector
<
int
>
scale_bias_axes
;
std
::
vector
<
int
>
mean_var_axes
;
for
(
auto
i
=
0
;
i
<
x_dims
.
size
();
++
i
)
{
if
(
i
>=
begin_norm_axis
)
{
scale_bias_axes
.
push_back
(
x_dims
[
i
]);
}
else
{
mean_var_axes
.
push_back
(
x_dims
[
i
]);
}
}
MLUCnnlTensorDesc
x_desc
(
*
x
);
MLUCnnlTensorDesc
y_desc
(
*
y
);
MLUCnnlTensorDesc
mean_var_desc
(
mean_var_axes
.
size
(),
mean_var_axes
.
data
(),
ToCnnlDataType
<
T
>
());
// cnnl only support both of scale and bias is NULL or not.
if
(
!
scale
&&
!
bias
)
{
MLUCnnl
::
LayerNormForward
(
ctx
,
begin_norm_axis
,
x_desc
.
get
(),
GetBasePtr
(
x
),
nullptr
/*scale_bias_desc*/
,
nullptr
/*scale*/
,
nullptr
/*bias*/
,
epsilon
,
y_desc
.
get
(),
GetBasePtr
(
y
),
mean_var_desc
.
get
(),
GetBasePtr
(
mean
),
GetBasePtr
(
variance
));
}
else
{
Tensor
tmp_scale
(
x
->
dtype
());
if
(
!
scale
)
{
tmp_scale
.
mutable_data
<
T
>
(
phi
::
make_ddim
(
scale_bias_axes
),
place
);
FillMLUTensorWithHostValue
(
ctx
,
static_cast
<
T
>
(
1
),
&
tmp_scale
);
}
else
{
tmp_scale
=
*
scale
;
}
Tensor
tmp_bias
(
x
->
dtype
());
if
(
!
bias
)
{
tmp_bias
.
mutable_data
<
T
>
(
phi
::
make_ddim
(
scale_bias_axes
),
place
);
FillMLUTensorWithHostValue
(
ctx
,
static_cast
<
T
>
(
0
),
&
tmp_bias
);
}
else
{
tmp_bias
=
*
bias
;
}
// scale and bias should have same type with x/y
MLUCnnlTensorDesc
float32_desc
(
scale_bias_axes
.
size
(),
scale_bias_axes
.
data
(),
CNNL_DTYPE_FLOAT
);
MLUCnnlTensorDesc
float16_desc
(
scale_bias_axes
.
size
(),
scale_bias_axes
.
data
(),
CNNL_DTYPE_HALF
);
cnnlCastDataType_t
cast_type
=
GetCastDataType
(
VT
::
FP32
,
VT
::
FP16
);
Tensor
final_scale
(
x
->
dtype
());
if
(
final_scale
.
dtype
()
==
DataType
::
FLOAT16
&&
tmp_scale
.
dtype
()
==
DataType
::
FLOAT32
)
{
final_scale
.
mutable_data
<
T
>
(
phi
::
make_ddim
(
scale_bias_axes
),
place
);
// cast scale to fp16
MLUCnnl
::
Cast
(
ctx
,
cast_type
,
float32_desc
.
get
(),
GetBasePtr
(
&
tmp_scale
),
float16_desc
.
get
(),
GetBasePtr
(
&
final_scale
));
}
else
{
final_scale
=
tmp_scale
;
}
Tensor
final_bias
(
x
->
dtype
());
if
(
final_bias
.
dtype
()
==
DataType
::
FLOAT16
&&
tmp_bias
.
dtype
()
==
DataType
::
FLOAT32
)
{
final_bias
.
mutable_data
<
T
>
(
phi
::
make_ddim
(
scale_bias_axes
),
place
);
// cast bias to fp16
MLUCnnl
::
Cast
(
ctx
,
cast_type
,
float32_desc
.
get
(),
GetBasePtr
(
&
tmp_bias
),
float16_desc
.
get
(),
GetBasePtr
(
&
final_bias
));
}
else
{
final_bias
=
tmp_bias
;
}
MLUCnnlTensorDesc
scale_bias_desc
(
scale_bias_axes
.
size
(),
scale_bias_axes
.
data
(),
ToCnnlDataType
<
T
>
());
MLUCnnl
::
LayerNormForward
(
ctx
,
begin_norm_axis
,
x_desc
.
get
(),
GetBasePtr
(
x
),
scale_bias_desc
.
get
(),
GetBasePtr
(
&
final_scale
),
GetBasePtr
(
&
final_bias
),
epsilon
,
y_desc
.
get
(),
GetBasePtr
(
y
),
mean_var_desc
.
get
(),
GetBasePtr
(
mean
),
GetBasePtr
(
variance
));
}
}
};
template
<
typename
T
>
class
LayerNormGradMLUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
auto
begin_norm_axis
=
ctx
.
Attr
<
int
>
(
"begin_norm_axis"
);
const
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
const
auto
*
mean
=
ctx
.
Input
<
Tensor
>
(
"Mean"
);
const
auto
*
variance
=
ctx
.
Input
<
Tensor
>
(
"Variance"
);
const
auto
*
scale
=
ctx
.
Input
<
Tensor
>
(
"Scale"
);
const
auto
*
dy
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
*
dx
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
dscale
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Scale"
));
auto
*
dbias
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Bias"
));
auto
place
=
ctx
.
GetPlace
();
dx
->
mutable_data
<
T
>
(
place
);
const
auto
&
x_dims
=
x
->
dims
();
std
::
vector
<
int
>
scale_bias_axes
;
std
::
vector
<
int
>
mean_var_axes
;
for
(
auto
i
=
0
;
i
<
x_dims
.
size
();
++
i
)
{
if
(
i
>=
begin_norm_axis
)
{
scale_bias_axes
.
push_back
(
x_dims
[
i
]);
}
else
{
mean_var_axes
.
push_back
(
x_dims
[
i
]);
}
}
MLUCnnlTensorDesc
x_desc
(
*
x
);
MLUCnnlTensorDesc
dy_desc
(
*
dy
);
MLUCnnlTensorDesc
mean_var_desc
(
mean_var_axes
.
size
(),
mean_var_axes
.
data
(),
ToCnnlDataType
<
T
>
());
MLUCnnlTensorDesc
dx_desc
(
*
dx
);
Tensor
tmp_scale
(
x
->
dtype
());
if
(
!
scale
)
{
tmp_scale
.
mutable_data
<
T
>
(
phi
::
make_ddim
(
scale_bias_axes
),
place
);
FillMLUTensorWithHostValue
(
ctx
,
static_cast
<
T
>
(
1
),
&
tmp_scale
);
}
else
{
tmp_scale
=
*
scale
;
}
MLUCnnlTensorDesc
float32_desc
(
scale_bias_axes
.
size
(),
scale_bias_axes
.
data
(),
CNNL_DTYPE_FLOAT
);
MLUCnnlTensorDesc
float16_desc
(
scale_bias_axes
.
size
(),
scale_bias_axes
.
data
(),
CNNL_DTYPE_HALF
);
cnnlCastDataType_t
cast_fp32_to_fp16
=
GetCastDataType
(
VT
::
FP32
,
VT
::
FP16
);
cnnlCastDataType_t
cast_fp16_to_fp32
=
GetCastDataType
(
VT
::
FP16
,
VT
::
FP32
);
Tensor
final_scale
(
x
->
dtype
());
if
(
final_scale
.
dtype
()
==
DataType
::
FLOAT16
&&
tmp_scale
.
dtype
()
==
DataType
::
FLOAT32
)
{
final_scale
.
mutable_data
<
T
>
(
phi
::
make_ddim
(
scale_bias_axes
),
place
);
// cast scale to fp16
MLUCnnl
::
Cast
(
ctx
,
cast_fp32_to_fp16
,
float32_desc
.
get
(),
GetBasePtr
(
&
tmp_scale
),
float16_desc
.
get
(),
GetBasePtr
(
&
final_scale
));
}
else
{
final_scale
=
tmp_scale
;
}
Tensor
tmp_dscale
(
x
->
dtype
());
if
(
dscale
&&
(
tmp_dscale
.
dtype
()
==
dscale
->
dtype
()))
{
dscale
->
mutable_data
<
T
>
(
place
);
tmp_dscale
=
*
dscale
;
}
else
{
tmp_dscale
.
mutable_data
<
T
>
(
phi
::
make_ddim
(
scale_bias_axes
),
place
);
}
Tensor
tmp_dbias
(
x
->
dtype
());
if
(
dbias
&&
(
tmp_dbias
.
dtype
()
==
dbias
->
dtype
()))
{
dbias
->
mutable_data
<
T
>
(
place
);
tmp_dbias
=
*
dbias
;
}
else
{
tmp_dbias
.
mutable_data
<
T
>
(
phi
::
make_ddim
(
scale_bias_axes
),
place
);
}
MLUCnnlTensorDesc
scale_desc
(
scale_bias_axes
.
size
(),
scale_bias_axes
.
data
(),
ToCnnlDataType
<
T
>
());
MLUCnnl
::
LayerNormBackward
(
ctx
,
begin_norm_axis
,
x_desc
.
get
(),
GetBasePtr
(
x
),
dy_desc
.
get
(),
GetBasePtr
(
dy
),
scale_desc
.
get
(),
GetBasePtr
(
&
final_scale
),
mean_var_desc
.
get
(),
GetBasePtr
(
mean
),
GetBasePtr
(
variance
),
dx_desc
.
get
(),
GetBasePtr
(
dx
),
GetBasePtr
(
&
tmp_dscale
),
GetBasePtr
(
&
tmp_dbias
));
if
(
dscale
&&
(
tmp_dscale
.
dtype
()
==
DataType
::
FLOAT16
&&
dscale
->
dtype
()
==
DataType
::
FLOAT32
))
{
dscale
->
mutable_data
<
T
>
(
place
);
MLUCnnl
::
Cast
(
ctx
,
cast_fp16_to_fp32
,
float16_desc
.
get
(),
GetBasePtr
(
&
tmp_dscale
),
float32_desc
.
get
(),
GetBasePtr
(
dscale
));
}
if
(
dbias
&&
(
tmp_dbias
.
dtype
()
==
DataType
::
FLOAT16
&&
dbias
->
dtype
()
==
DataType
::
FLOAT32
))
{
dbias
->
mutable_data
<
T
>
(
place
);
MLUCnnl
::
Cast
(
ctx
,
cast_fp16_to_fp32
,
float16_desc
.
get
(),
GetBasePtr
(
&
tmp_dbias
),
float32_desc
.
get
(),
GetBasePtr
(
dbias
));
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
plat
=
paddle
::
platform
;
REGISTER_OP_MLU_KERNEL
(
layer_norm
,
ops
::
LayerNormMLUKernel
<
float
>
,
ops
::
LayerNormMLUKernel
<
plat
::
float16
>
);
REGISTER_OP_MLU_KERNEL
(
layer_norm_grad
,
ops
::
LayerNormGradMLUKernel
<
float
>
,
ops
::
LayerNormGradMLUKernel
<
plat
::
float16
>
);
paddle/fluid/operators/mlu/mlu_baseop.cc
浏览文件 @
ecd6db43
...
...
@@ -2077,6 +2077,45 @@ MLUCnnlTrigonDesc::~MLUCnnlTrigonDesc() {
}
}
/* static */
void
MLUCnnl
::
LayerNormForward
(
const
ExecutionContext
&
ctx
,
int
axis
,
const
cnnlTensorDescriptor_t
x_desc
,
const
void
*
x
,
const
cnnlTensorDescriptor_t
weight_bias_desc
,
const
void
*
weight
,
const
void
*
bias
,
float
eps
,
const
cnnlTensorDescriptor_t
y_desc
,
void
*
y
,
const
cnnlTensorDescriptor_t
mean_rstd_desc
,
void
*
saved_mean
,
void
*
saved_rstd
)
{
cnnlHandle_t
handle
=
GetHandleFromCTX
(
ctx
);
size_t
workspace_size
;
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlGetLayerNormOpWorkspaceSize
(
handle
,
axis
,
x_desc
,
&
workspace_size
));
auto
&
dev_ctx
=
GetDevCtxFromCTX
(
ctx
);
Tensor
workspace
=
ctx
.
AllocateTmpTensor
<
int8_t
,
MLUDeviceContext
>
(
{
static_cast
<
int64_t
>
(
workspace_size
)},
dev_ctx
);
void
*
workspace_ptr
=
workspace
.
mutable_data
(
ctx
.
GetPlace
());
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlLayerNormForward
(
handle
,
x_desc
,
x
,
axis
,
weight_bias_desc
,
weight
,
bias
,
eps
,
workspace_ptr
,
workspace_size
,
y_desc
,
y
,
mean_rstd_desc
,
saved_mean
,
saved_rstd
));
}
/* static */
void
MLUCnnl
::
LayerNormBackward
(
const
ExecutionContext
&
ctx
,
int
axis
,
const
cnnlTensorDescriptor_t
x_desc
,
const
void
*
x
,
const
cnnlTensorDescriptor_t
diff_z_desc
,
const
void
*
diff_z
,
const
cnnlTensorDescriptor_t
weight_bias_desc
,
const
void
*
weight
,
const
cnnlTensorDescriptor_t
mean_rstd_desc
,
const
void
*
saved_mean
,
const
void
*
saved_rstd
,
const
cnnlTensorDescriptor_t
diff_x_desc
,
void
*
diff_x
,
void
*
diff_weight
,
void
*
diff_bias
)
{
cnnlHandle_t
handle
=
GetHandleFromCTX
(
ctx
);
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlLayerNormBackward
(
handle
,
x_desc
,
x
,
axis
,
diff_z_desc
,
diff_z
,
weight_bias_desc
,
weight
,
mean_rstd_desc
,
saved_mean
,
saved_rstd
,
diff_x_desc
,
diff_x
,
diff_weight
,
diff_bias
));
}
/* static */
void
MLUCnnl
::
QuantizeParam
(
const
ExecutionContext
&
ctx
,
const
cnnlQuantizeMode_t
mode
,
const
int
bitwidth
,
const
cnnlTensorDescriptor_t
input_desc
,
...
...
paddle/fluid/operators/mlu/mlu_baseop.h
浏览文件 @
ecd6db43
...
...
@@ -146,10 +146,8 @@ const std::map<std::pair<VT::Type, VT::Type>, cnnlCastDataType_t>
{{
VT
::
FP16
,
/*cast to*/
VT
::
BOOL
},
CNNL_CAST_HALF_TO_BOOL
},
{{
VT
::
INT32
,
/*cast to*/
VT
::
FP32
},
CNNL_CAST_INT32_TO_FLOAT
},
{{
VT
::
INT32
,
/*cast to*/
VT
::
FP16
},
CNNL_CAST_INT32_TO_HALF
},
{{
VT
::
INT32
,
/*cast to*/
VT
::
INT64
},
CNNL_CAST_INT32_TO_INT64
},
{{
VT
::
INT32
,
/*cast to*/
VT
::
INT16
},
CNNL_CAST_INT32_TO_INT16
},
{{
VT
::
INT32
,
/*cast to*/
VT
::
INT8
},
CNNL_CAST_INT32_TO_INT8
},
{{
VT
::
INT32
,
/*cast to*/
VT
::
BOOL
},
CNNL_CAST_INT32_TO_BOOL
},
{{
VT
::
INT32
,
/*cast to*/
VT
::
INT16
},
CNNL_CAST_INT32_TO_INT16
},
{{
VT
::
INT16
,
/*cast to*/
VT
::
FP32
},
CNNL_CAST_INT16_TO_FLOAT
},
{{
VT
::
INT16
,
/*cast to*/
VT
::
FP16
},
CNNL_CAST_INT16_TO_HALF
},
{{
VT
::
INT16
,
/*cast to*/
VT
::
INT32
},
CNNL_CAST_INT16_TO_INT32
},
...
...
@@ -158,12 +156,21 @@ const std::map<std::pair<VT::Type, VT::Type>, cnnlCastDataType_t>
{{
VT
::
INT8
,
/*cast to*/
VT
::
INT32
},
CNNL_CAST_INT8_TO_INT32
},
{{
VT
::
UINT8
,
/*cast to*/
VT
::
FP32
},
CNNL_CAST_UINT8_TO_FLOAT
},
{{
VT
::
UINT8
,
/*cast to*/
VT
::
FP16
},
CNNL_CAST_UINT8_TO_HALF
},
{{
VT
::
UINT8
,
/*cast to*/
VT
::
INT64
},
CNNL_CAST_UINT8_TO_INT64
},
{{
VT
::
UINT8
,
/*cast to*/
VT
::
INT32
},
CNNL_CAST_UINT8_TO_INT32
},
{{
VT
::
BOOL
,
/*cast to*/
VT
::
FP32
},
CNNL_CAST_BOOL_TO_FLOAT
},
{{
VT
::
BOOL
,
/*cast to*/
VT
::
FP16
},
CNNL_CAST_BOOL_TO_HALF
},
{{
VT
::
BOOL
,
/*cast to*/
VT
::
INT32
},
CNNL_CAST_BOOL_TO_INT32
},
{{
VT
::
UINT8
,
/*cast to*/
VT
::
INT32
},
CNNL_CAST_UINT8_TO_INT32
},
{{
VT
::
INT32
,
/*cast to*/
VT
::
INT64
},
CNNL_CAST_INT32_TO_INT64
},
{{
VT
::
INT64
,
/*cast to*/
VT
::
INT32
},
CNNL_CAST_INT64_TO_INT32
},
{{
VT
::
INT32
,
/*cast to*/
VT
::
BOOL
},
CNNL_CAST_INT32_TO_BOOL
},
{{
VT
::
UINT8
,
/*cast to*/
VT
::
INT64
},
CNNL_CAST_UINT8_TO_INT64
},
{{
VT
::
INT8
,
/*cast to*/
VT
::
INT16
},
CNNL_CAST_INT8_TO_INT16
},
{{
VT
::
FP32
,
/*cast to*/
VT
::
FP64
},
CNNL_CAST_FLOAT_TO_DOUBLE
},
{{
VT
::
FP64
,
/*cast to*/
VT
::
FP32
},
CNNL_CAST_DOUBLE_TO_FLOAT
},
{{
VT
::
INT64
,
/*cast to*/
VT
::
FP32
},
CNNL_CAST_INT64_TO_FLOAT
},
{{
VT
::
INT64
,
/*cast to*/
VT
::
FP16
},
CNNL_CAST_INT64_TO_HALF
},
{{
VT
::
FP32
,
/*cast to*/
VT
::
INT64
},
CNNL_CAST_FLOAT_TO_INT64
},
{{
VT
::
FP16
,
/*cast to*/
VT
::
INT64
},
CNNL_CAST_HALF_TO_INT64
},
};
cnnlCastDataType_t
GetCastDataType
(
const
VT
::
Type
&
src_type
,
...
...
@@ -1103,6 +1110,24 @@ class MLUCnnl {
const
cnnlTensorDescriptor_t
x_backprop_desc
,
void
*
x_backprop
,
void
*
scale_backprop
,
void
*
offset_backprop
);
static
void
LayerNormForward
(
const
ExecutionContext
&
ctx
,
int
axis
,
const
cnnlTensorDescriptor_t
x_desc
,
const
void
*
x
,
const
cnnlTensorDescriptor_t
weight_bias_desc
,
const
void
*
weight
,
const
void
*
bias
,
float
eps
,
const
cnnlTensorDescriptor_t
y_desc
,
void
*
y
,
const
cnnlTensorDescriptor_t
mean_rstd_desc
,
void
*
saved_mean
,
void
*
saved_rstd
);
static
void
LayerNormBackward
(
const
ExecutionContext
&
ctx
,
int
axis
,
const
cnnlTensorDescriptor_t
x_desc
,
const
void
*
x
,
const
cnnlTensorDescriptor_t
diff_z_desc
,
const
void
*
diff_z
,
const
cnnlTensorDescriptor_t
weight_bias_desc
,
const
void
*
weight
,
const
cnnlTensorDescriptor_t
mean_rstd_desc
,
const
void
*
saved_mean
,
const
void
*
saved_rstd
,
const
cnnlTensorDescriptor_t
diff_x_desc
,
void
*
diff_x
,
void
*
diff_weight
,
void
*
diff_bias
);
static
void
Transpose
(
const
ExecutionContext
&
ctx
,
const
std
::
vector
<
int
>
perm
,
const
int
input_dim
,
const
cnnlTensorDescriptor_t
input_desc
,
...
...
@@ -1230,5 +1255,13 @@ inline void TransposeFromMLUTensor(const ExecutionContext& ctx,
GetBasePtr
(
transformed_output
));
}
template
<
typename
T
>
inline
void
FillMLUTensorWithHostValue
(
const
ExecutionContext
&
ctx
,
T
value
,
Tensor
*
out
)
{
MLUCnnlTensorDesc
out_desc
(
*
out
);
MLUCnnl
::
Fill
(
ctx
,
CNNL_POINTER_MODE_HOST
,
&
value
,
out_desc
.
get
(),
GetBasePtr
(
out
));
}
}
// namespace operators
}
// namespace paddle
python/paddle/fluid/tests/unittests/mlu/test_layer_norm_op_mlu.py
0 → 100644
浏览文件 @
ecd6db43
# Copyright (c) 2022 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.
from
__future__
import
print_function
import
unittest
import
numpy
as
np
import
paddle
from
operator
import
mul
import
paddle.fluid.core
as
core
import
paddle.fluid
as
fluid
import
paddle.nn.functional
as
F
from
functools
import
reduce
import
sys
sys
.
path
.
append
(
'..'
)
from
op_test
import
_set_use_system_allocator
from
paddle.fluid
import
Program
,
program_guard
from
paddle.fluid.contrib.mixed_precision.fp16_utils
import
_keep_layer_norm_scale_bias_to_fp32
from
test_layer_norm_op
import
_reference_layer_norm_naive
,
_reference_layer_norm_grad
paddle
.
enable_static
()
np
.
random
.
random
(
123
)
_set_use_system_allocator
(
True
)
class
TestLayerNormOp
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
use_cudnn
=
True
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
self
.
__class__
.
use_mlu
=
True
def
__assert_close
(
self
,
tensor
,
np_array
,
msg
,
atol
=
1e-4
):
self
.
assertTrue
(
np
.
allclose
(
np
.
array
(
tensor
),
np_array
,
atol
=
atol
),
msg
)
def
check_forward_backward
(
self
,
shape
,
begin_norm_axis
,
has_scale
=
True
,
has_bias
=
True
,
y_grad_scale
=
1.0
,
use_mkldnn
=
False
):
def
test_with_place
(
place
,
shape
,
begin_norm_axis
,
use_mkldnn
=
use_mkldnn
):
# attr
epsilon
=
0.00001
x_shape
=
shape
D
=
reduce
(
mul
,
x_shape
[
begin_norm_axis
:
len
(
x_shape
)],
1
)
scale_shape
=
[
D
]
np
.
random
.
seed
(
123
)
x
=
np
.
random
.
random_sample
(
x_shape
).
astype
(
np
.
float32
)
scale
=
np
.
random
.
random_sample
(
scale_shape
).
astype
(
np
.
float32
)
if
has_scale
else
None
bias
=
np
.
random
.
random_sample
(
scale_shape
).
astype
(
np
.
float32
)
if
has_bias
else
None
y_grad
=
(
np
.
random
.
random_sample
(
x_shape
)
*
y_grad_scale
).
astype
(
np
.
float32
)
# reference forward & backward
y
,
mean
,
variance
=
_reference_layer_norm_naive
(
x
,
scale
,
bias
,
epsilon
,
begin_norm_axis
)
x_grad
,
scale_grad
,
bias_grad
=
_reference_layer_norm_grad
(
x
,
y_grad
,
scale
,
bias
,
mean
,
variance
,
begin_norm_axis
)
var_dict
=
locals
()
var_dict
[
'y@GRAD'
]
=
y_grad
var_names
=
[
'x'
,
'mean'
,
'variance'
,
'y'
,
'y@GRAD'
]
if
has_scale
:
var_names
+=
[
'scale'
]
if
has_bias
:
var_names
+=
[
'bias'
]
ground_truth
=
{
name
:
var_dict
[
name
]
for
name
in
var_names
}
program
=
fluid
.
Program
()
with
fluid
.
program_guard
(
program
):
block
=
program
.
global_block
()
for
name
in
ground_truth
:
block
.
create_var
(
name
=
name
,
dtype
=
'float32'
,
shape
=
ground_truth
[
name
].
shape
)
inputs
=
{
"X"
:
block
.
var
(
'x'
)}
fetch_list
=
[
'y'
,
'mean'
,
'variance'
,
'x@GRAD'
,
]
if
has_scale
:
inputs
[
"Scale"
]
=
block
.
var
(
'scale'
)
fetch_list
+=
[
'scale@GRAD'
]
if
has_bias
:
inputs
[
"Bias"
]
=
block
.
var
(
'bias'
)
fetch_list
+=
[
'bias@GRAD'
]
layer_norm_op
=
block
.
append_op
(
type
=
"layer_norm"
,
inputs
=
inputs
,
outputs
=
{
"Y"
:
block
.
var
(
'y'
),
"Mean"
:
block
.
var
(
'mean'
),
# share the same memory
"Variance"
:
block
.
var
(
'variance'
),
# share the same memory
},
attrs
=
{
"epsilon"
:
epsilon
,
"begin_norm_axis"
:
begin_norm_axis
,
"use_mkldnn"
:
use_mkldnn
})
# generate backward op_desc
grad_op_desc_list
,
op_grad_to_var
=
core
.
get_grad_op_desc
(
layer_norm_op
.
desc
,
set
(),
[])
grad_op_desc
=
grad_op_desc_list
[
0
]
new_op_desc
=
block
.
desc
.
append_op
()
new_op_desc
.
copy_from
(
grad_op_desc
)
for
var_name
in
grad_op_desc
.
output_arg_names
():
block
.
desc
.
var
(
var_name
.
encode
(
"ascii"
))
grad_op_desc
.
infer_var_type
(
block
.
desc
)
grad_op_desc
.
infer_shape
(
block
.
desc
)
for
arg
in
grad_op_desc
.
output_arg_names
():
grad_var
=
block
.
desc
.
find_var
(
arg
.
encode
(
"ascii"
))
grad_var
.
set_dtype
(
core
.
VarDesc
.
VarType
.
FP32
)
program
.
_sync_with_cpp
()
exe
=
fluid
.
Executor
(
place
)
out
=
exe
.
run
(
program
,
feed
=
{
name
:
var_dict
[
name
]
for
name
in
[
'x'
,
'scale'
,
'bias'
,
'y@GRAD'
]
},
fetch_list
=
fetch_list
)
self
.
__assert_close
(
y
,
out
[
0
],
"y"
)
self
.
__assert_close
(
mean
,
out
[
1
],
"mean"
)
self
.
__assert_close
(
1
/
np
.
sqrt
(
variance
),
out
[
2
],
"variance"
,
1e-3
)
self
.
__assert_close
(
x_grad
,
out
[
3
],
"x_grad"
)
if
has_scale
:
self
.
__assert_close
(
scale_grad
,
out
[
fetch_list
.
index
(
'scale@GRAD'
)],
"scale_grad"
,
1e-3
)
if
has_bias
:
self
.
__assert_close
(
bias_grad
,
out
[
fetch_list
.
index
(
'bias@GRAD'
)],
"bias_grad"
)
test_with_place
(
self
.
place
,
shape
,
begin_norm_axis
)
def
test_check_forward_backward_with_scale_and_bias
(
self
):
self
.
check_forward_backward
(
shape
=
[
1
,
3
,
4
,
5
],
begin_norm_axis
=
1
)
self
.
check_forward_backward
(
shape
=
[
2
,
3
,
4
,
5
],
begin_norm_axis
=
1
)
self
.
check_forward_backward
(
shape
=
[
2
,
3
,
4
,
5
],
begin_norm_axis
=
1
,
has_scale
=
False
,
has_bias
=
True
)
self
.
check_forward_backward
(
shape
=
[
2
,
3
,
4
,
5
],
begin_norm_axis
=
1
,
has_scale
=
True
,
has_bias
=
False
)
self
.
check_forward_backward
(
shape
=
[
2
,
3
,
4
,
5
],
begin_norm_axis
=
1
,
has_scale
=
False
,
has_bias
=
False
)
self
.
check_forward_backward
(
shape
=
[
2
,
3
,
4
,
5
],
begin_norm_axis
=
3
)
self
.
check_forward_backward
(
shape
=
[
92
,
513
,
129
],
begin_norm_axis
=
2
,
y_grad_scale
=
0.1
)
self
.
check_forward_backward
(
shape
=
[
3
,
34
,
1134
],
begin_norm_axis
=
2
)
self
.
check_forward_backward
(
shape
=
[
92
,
513
,
1134
],
begin_norm_axis
=
2
,
y_grad_scale
=
0.1
)
self
.
check_forward_backward
(
shape
=
[
92
,
513
,
1134
],
begin_norm_axis
=
2
,
has_scale
=
False
,
has_bias
=
True
,
y_grad_scale
=
0.1
)
self
.
check_forward_backward
(
shape
=
[
92
,
513
,
1134
],
begin_norm_axis
=
2
,
has_scale
=
True
,
has_bias
=
False
,
y_grad_scale
=
0.1
)
self
.
check_forward_backward
(
shape
=
[
92
,
513
,
1134
],
begin_norm_axis
=
2
,
has_scale
=
False
,
has_bias
=
False
,
y_grad_scale
=
0.1
)
self
.
check_forward_backward
(
shape
=
[
512
,
1024
],
begin_norm_axis
=
1
,
has_scale
=
True
,
has_bias
=
True
)
class
TestLayerNormAPI
(
unittest
.
TestCase
):
def
test_case
(
self
):
x
=
fluid
.
layers
.
data
(
name
=
'x'
,
shape
=
[
64
,
32
,
256
],
dtype
=
'float32'
,
append_batch_size
=
False
)
x
=
fluid
.
layers
.
layer_norm
(
x
,
scale
=
True
,
shift
=
True
,
begin_norm_axis
=
1
,
epsilon
=
1e-05
,
param_attr
=
None
,
bias_attr
=
None
)
x
=
fluid
.
layers
.
layer_norm
(
x
,
scale
=
False
,
shift
=
False
,
begin_norm_axis
=
1
,
epsilon
=
1e-05
,
param_attr
=
None
,
bias_attr
=
None
)
x
=
fluid
.
layers
.
layer_norm
(
x
,
scale
=
False
,
shift
=
False
,
begin_norm_axis
=
1
,
epsilon
=
1e-05
,
param_attr
=
"scale"
,
bias_attr
=
"shift"
)
class
TestDygraphLayerNormAPIError
(
unittest
.
TestCase
):
def
test_errors
(
self
):
with
program_guard
(
Program
(),
Program
()):
paddle
.
enable_static
()
layer_norm
=
fluid
.
LayerNorm
([
32
,
32
])
# the input of LayerNorm must be Variable.
x1
=
np
.
random
.
random
((
3
,
32
,
32
)).
astype
(
'float32'
)
self
.
assertRaises
(
TypeError
,
layer_norm
,
x1
)
# the input dtype of LayerNorm must be float32 or float16
x2
=
fluid
.
layers
.
data
(
name
=
'x2'
,
shape
=
[
3
,
32
,
32
],
dtype
=
"int32"
)
self
.
assertRaises
(
TypeError
,
layer_norm
,
x2
)
class
TestFP16ScaleBiasLayerNorm
(
unittest
.
TestCase
):
def
check_main
(
self
,
x_np
,
weight_np
,
bias_np
,
dtype
):
paddle
.
disable_static
()
weight_np
=
weight_np
.
astype
(
dtype
)
bias_np
=
bias_np
.
astype
(
dtype
)
x
=
paddle
.
to_tensor
(
x_np
)
weight
=
paddle
.
to_tensor
(
weight_np
)
bias
=
paddle
.
to_tensor
(
bias_np
)
x
.
stop_gradient
=
False
weight
.
stop_gradient
=
False
bias
.
stop_gradient
=
False
y
=
F
.
layer_norm
(
x
,
x
.
shape
[
1
:],
weight
,
bias
)
x_g
,
w_g
,
b_g
=
paddle
.
grad
(
y
,
[
x
,
weight
,
bias
])
y_np
=
y
.
numpy
().
astype
(
'float32'
)
x_g_np
=
x_g
.
numpy
().
astype
(
'float32'
)
w_g_np
=
w_g
.
numpy
().
astype
(
'float16'
)
b_g_np
=
b_g
.
numpy
().
astype
(
'float32'
)
paddle
.
enable_static
()
return
y_np
,
x_g_np
,
w_g_np
,
b_g_np
def
test_main
(
self
):
x_np
=
np
.
random
.
random
([
10
,
20
]).
astype
(
'float16'
)
weight_np
=
np
.
random
.
random
([
20
]).
astype
(
'float16'
)
bias_np
=
np
.
random
.
random
([
20
]).
astype
(
'float16'
)
y_np_1
,
x_g_np_1
,
w_g_np_1
,
b_g_np_1
=
self
.
check_main
(
x_np
,
weight_np
,
bias_np
,
'float16'
)
y_np_2
,
x_g_np_2
,
w_g_np_2
,
b_g_np_2
=
self
.
check_main
(
x_np
,
weight_np
,
bias_np
,
'float32'
)
def
assert_equal
(
x
,
y
):
self
.
assertTrue
(
np
.
array_equal
(
x
,
y
))
assert_equal
(
y_np_1
,
y_np_2
)
assert_equal
(
x_g_np_1
,
x_g_np_2
)
assert_equal
(
w_g_np_1
,
w_g_np_2
)
assert_equal
(
b_g_np_1
,
b_g_np_2
)
class
TestGetSetKeepLayerNormScaleBiasFP32Flag
(
unittest
.
TestCase
):
def
test_main
(
self
):
self
.
assertTrue
(
_keep_layer_norm_scale_bias_to_fp32
())
_keep_layer_norm_scale_bias_to_fp32
(
False
)
self
.
assertFalse
(
_keep_layer_norm_scale_bias_to_fp32
())
_keep_layer_norm_scale_bias_to_fp32
(
True
)
self
.
assertTrue
(
_keep_layer_norm_scale_bias_to_fp32
())
if
__name__
==
'__main__'
:
unittest
.
main
()
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