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体验新版 GitCode,发现更多精彩内容 >>
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4d5faa3f
编写于
3月 22, 2022
作者:
M
Megvii Engine Team
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix(imperative): using DnnOprCaller to avoid early destruction of dnn_opr
GitOrigin-RevId: 4a14f53738917212810d62ce2e1908e2bd7dc572
上级
da620ca1
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
54 addition
and
63 deletion
+54
-63
imperative/python/test/integration/test_dtr.py
imperative/python/test/integration/test_dtr.py
+1
-1
imperative/src/impl/dnn_op_helper.h
imperative/src/impl/dnn_op_helper.h
+5
-5
imperative/src/impl/ops/batch_norm.cpp
imperative/src/impl/ops/batch_norm.cpp
+2
-4
imperative/src/impl/ops/convolution.cpp
imperative/src/impl/ops/convolution.cpp
+21
-26
imperative/src/impl/ops/dot.cpp
imperative/src/impl/ops/dot.cpp
+10
-11
imperative/src/impl/ops/elemwise.cpp
imperative/src/impl/ops/elemwise.cpp
+8
-9
imperative/src/impl/ops/misc.cpp
imperative/src/impl/ops/misc.cpp
+7
-7
未找到文件。
imperative/python/test/integration/test_dtr.py
浏览文件 @
4d5faa3f
...
...
@@ -91,7 +91,7 @@ class ResNet(M.Module):
def
run_dtr_resnet1202
():
batch_size
=
7
batch_size
=
6
resnet1202
=
ResNet
(
BasicBlock
,
[
200
,
200
,
200
])
opt
=
optim
.
SGD
(
resnet1202
.
parameters
(),
lr
=
0.05
,
momentum
=
0.9
,
weight_decay
=
1e-4
)
gm
=
GradManager
().
attach
(
resnet1202
.
parameters
())
...
...
imperative/src/impl/dnn_op_helper.h
浏览文件 @
4d5faa3f
...
...
@@ -12,6 +12,7 @@
#include "megbrain/comp_node.h"
#include "megbrain/comp_node_env.h"
#include "megbrain/imperative/physical_tensor.h"
#include "megbrain/rdnn/management.h"
using
namespace
megdnn
;
...
...
@@ -28,13 +29,12 @@ struct DnnOprCaller {
CompNode
cn
;
DeviceTensorND
dev_tensor
;
Workspace
workspace
;
std
::
unique_ptr
<
Opr
>
op
;
mgb
::
opr
::
intl
::
UniqPtrWithCN
<
Opr
>
op
;
DnnOprCaller
(
CompNode
cn
)
:
cn
(
cn
),
op
(
create_operator
(
cn
))
{}
DnnOprCaller
(
CompNode
cn
)
:
cn
(
cn
),
op
(
std
::
move
(
create_operator
(
cn
)
))
{}
static
std
::
unique_ptr
<
Opr
>
create_operator
(
CompNode
cn
)
{
auto
&&
handle
=
MegDNNHandle
::
get
(
CompNodeEnv
::
from_comp_node
(
cn
)).
handle
();
return
handle
->
create_operator
<
Opr
>
();
static
mgb
::
opr
::
intl
::
UniqPtrWithCN
<
Opr
>
create_operator
(
CompNode
cn
)
{
return
mgb
::
opr
::
intl
::
create_megdnn_opr
<
Opr
>
(
cn
);
}
megdnn
::
Workspace
create_workspace
(
TensorLayout
layout
)
{
...
...
imperative/src/impl/ops/batch_norm.cpp
浏览文件 @
4d5faa3f
...
...
@@ -171,7 +171,7 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
bool
empty_input
=
src_layout
.
is_empty
();
size_t
nr_inp
=
inputs
.
size
();
DeviceTensorND
ws
,
reserve
;
DeviceTensorND
reserve
;
size_t
sz
=
0
,
rsz
=
0
;
TensorLayout
w_layout
({
sz
},
dtype
::
Byte
());
...
...
@@ -186,9 +186,7 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
w_layout
=
TensorLayout
({
sz
},
dtype
::
Byte
());
r_layout
=
TensorLayout
({
rsz
},
dtype
::
Byte
());
}
auto
wk
=
Blob
::
make
(
comp_node
,
sz
);
auto
ptr
=
wk
->
storage
().
get
();
megdnn
::
Workspace
dnn_wk
(
ptr
,
sz
);
auto
dnn_wk
=
dnn_opr
.
create_workspace
(
w_layout
);
reserve
=
BlobManager
::
inst
()
->
alloc_workspace_with_defrag
(
comp_node
,
r_layout
);
// alloc memory
...
...
imperative/src/impl/ops/convolution.cpp
浏览文件 @
4d5faa3f
...
...
@@ -123,8 +123,6 @@ TensorLayout do_shape_infer(
std
::
tuple
<
SmallVector
<
LogicalTensorDesc
>
,
bool
>
infer_output_attrs_fallible
(
const
OpDef
&
def
,
const
SmallVector
<
LogicalTensorDesc
>&
inputs
)
{
auto
&&
conv
=
static_cast
<
const
Convolution
&>
(
def
);
using
Param
=
::
megdnn
::
param
::
Convolution
;
SmallVector
<
LogicalTensorDesc
>
dests
(
1
);
...
...
@@ -167,34 +165,33 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
inp_shapes
[
i
]
=
inputs
[
i
]
->
layout
();
}
oup_shapes
[
0
]
=
out_layout
;
auto
&&
dnn_opr
=
opr
::
intl
::
create_megdnn_opr
<
megdnn
::
ConvBiasForward
>
(
cn
);
dnn_opr
->
param
().
pad_h
=
conv
.
pad_h
;
dnn_opr
->
param
().
pad_w
=
conv
.
pad_w
;
dnn_opr
->
param
().
stride_h
=
conv
.
stride_h
;
dnn_opr
->
param
().
stride_w
=
conv
.
stride_w
;
dnn_opr
->
param
().
dilate_h
=
conv
.
dilate_h
;
dnn_opr
->
param
().
dilate_w
=
conv
.
dilate_w
;
dnn_opr
->
param
().
sparse
=
conv
.
sparse
;
dnn_opr
->
param
().
compute_mode
=
conv
.
compute_mode
;
dnn_opr
->
param
().
format
=
conv
.
format
;
DnnOprCaller
<
megdnn
::
ConvBiasForward
>
dnn_opr
(
cn
);
dnn_opr
.
op
->
param
().
pad_h
=
conv
.
pad_h
;
dnn_opr
.
op
->
param
().
pad_w
=
conv
.
pad_w
;
dnn_opr
.
op
->
param
().
stride_h
=
conv
.
stride_h
;
dnn_opr
.
op
->
param
().
stride_w
=
conv
.
stride_w
;
dnn_opr
.
op
->
param
().
dilate_h
=
conv
.
dilate_h
;
dnn_opr
.
op
->
param
().
dilate_w
=
conv
.
dilate_w
;
dnn_opr
.
op
->
param
().
sparse
=
conv
.
sparse
;
dnn_opr
.
op
->
param
().
compute_mode
=
conv
.
compute_mode
;
dnn_opr
.
op
->
param
().
format
=
conv
.
format
;
// shape infer
TensorLayout
shp
({
0
},
inputs
[
0
]
->
dtype
());
shp
.
ndim
=
0
;
size_t
sz
=
setup_algo
<
megdnn
::
ConvBiasForward
>
(
{
inp_shapes
[
0
],
inp_shapes
[
1
],
shp
,
shp
,
oup_shapes
[
0
]},
dnn_opr
.
get
(),
0
,
false
,
false
,
cn
,
conv
.
policy
(),
false
);
{
inp_shapes
[
0
],
inp_shapes
[
1
],
shp
,
shp
,
oup_shapes
[
0
]},
dnn_opr
.
op
.
get
()
,
0
,
false
,
false
,
cn
,
conv
.
policy
(),
false
);
// alloc memory
DeviceTensorND
bias
=
BlobManager
::
inst
()
->
alloc_workspace_with_defrag
(
cn
,
shp
);
auto
wk
=
Blob
::
make
(
cn
,
sz
);
auto
ptr
=
wk
->
storage
().
get
();
megdnn
::
Workspace
dnn_wk
(
ptr
,
sz
);
TensorLayout
w_layout
({
sz
},
dtype
::
Byte
());
auto
dnn_wk
=
dnn_opr
.
create_workspace
(
w_layout
);
// exeucte
dnn_opr
->
exec
(
dnn_opr
.
op
->
exec
(
inp_tensornds
[
0
],
inp_tensornds
[
1
],
bias
.
as_megdnn
(),
bias
.
as_megdnn
(),
out
.
as_megdnn
(),
nullptr
,
dnn_wk
);
return
{
Tensor
::
make
(
out
)};
...
...
@@ -359,7 +356,6 @@ TensorLayout do_shape_infer(
std
::
tuple
<
SmallVector
<
LogicalTensorDesc
>
,
bool
>
infer_output_attrs_fallible
(
const
OpDef
&
def
,
const
SmallVector
<
LogicalTensorDesc
>&
inputs
)
{
auto
&&
conv
=
static_cast
<
const
Convolution3D
&>
(
def
);
using
Param
=
::
megdnn
::
param
::
Convolution3D
;
SmallVector
<
LogicalTensorDesc
>
dests
(
1
);
...
...
@@ -398,24 +394,23 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
inp_shapes
[
i
]
=
inputs
[
i
]
->
layout
();
}
oup_shapes
[
0
]
=
out_layout
;
auto
&&
dnn_opr
=
opr
::
intl
::
create_megdnn_opr
<
megdnn
::
Convolution3D
>
(
cn
);
dnn_opr
->
param
()
=
conv
.
param
();
DnnOprCaller
<
megdnn
::
Convolution3D
>
dnn_opr
(
cn
);
dnn_opr
.
op
->
param
()
=
conv
.
param
();
// shape infer
size_t
sz
=
setup_algo
<
megdnn
::
Convolution3D
>
(
{
inp_shapes
[
0
],
inp_shapes
[
1
],
oup_shapes
[
0
]},
dnn_opr
.
get
(),
0
,
false
,
{
inp_shapes
[
0
],
inp_shapes
[
1
],
oup_shapes
[
0
]},
dnn_opr
.
op
.
get
(),
0
,
false
,
false
,
cn
,
conv
.
policy
(),
false
);
// alloc memory
DeviceTensorND
out
=
BlobManager
::
inst
()
->
alloc_workspace_with_defrag
(
cn
,
out_layout
);
auto
wk
=
Blob
::
make
(
cn
,
sz
);
auto
ptr
=
wk
->
storage
().
get
();
megdnn
::
Workspace
dnn_wk
(
ptr
,
sz
);
TensorLayout
w_layout
({
sz
},
dtype
::
Byte
());
auto
dnn_wk
=
dnn_opr
.
create_workspace
(
w_layout
);
// exeucte
dnn_opr
->
exec
(
inp_tensornds
[
0
],
inp_tensornds
[
1
],
out
.
as_megdnn
(),
dnn_wk
);
dnn_opr
.
op
->
exec
(
inp_tensornds
[
0
],
inp_tensornds
[
1
],
out
.
as_megdnn
(),
dnn_wk
);
return
{
Tensor
::
make
(
out
)};
}
...
...
imperative/src/impl/ops/dot.cpp
浏览文件 @
4d5faa3f
...
...
@@ -29,7 +29,7 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
using
TensorND
=
megdnn
::
TensorND
;
SmallVector
<
TensorND
>
inp_tensornds
;
inp_tensornds
.
reserve
(
inputs
.
size
());
auto
&&
dnn_opr
=
opr
::
intl
::
create_megdnn_opr
<
megdnn
::
Dot
>
(
comp_node
);
DnnOprCaller
<
megdnn
::
Dot
>
dnn_opr
(
comp_node
);
for
(
unsigned
i
=
0
;
i
<
inputs
.
size
();
++
i
)
{
auto
dnn_ten
=
inputs
[
i
]
->
dnn_tensor
();
inp_tensornds
.
push_back
(
dnn_ten
);
...
...
@@ -37,28 +37,27 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
TensorLayout
oup_layout
{
inputs
[
0
]
->
dtype
()};
auto
inp1_tensor
=
inputs
[
0
]
->
dnn_tensor
();
auto
inp2_tensor
=
inputs
[
1
]
->
dnn_tensor
();
dnn_opr
->
deduce_layout
(
inp1_tensor
.
layout
,
inp2_tensor
.
layout
,
oup_layout
);
dnn_opr
.
op
->
deduce_layout
(
inp1_tensor
.
layout
,
inp2_tensor
.
layout
,
oup_layout
);
if
(
inputs
[
0
]
->
layout
().
is_empty
()
||
inputs
[
1
]
->
layout
().
is_empty
())
{
auto
fill_opr
=
opr
::
intl
::
create_megdnn_opr
<
megdnn
::
Fill
>
(
comp_node
);
DnnOprCaller
<
megdnn
::
Fill
>
fill_opr
(
comp_node
);
DeviceTensorND
out
=
BlobManager
::
inst
()
->
alloc_workspace_with_defrag
(
comp_node
,
oup_layout
);
fill_opr
->
param
()
=
0
;
fill_opr
->
exec
(
out
.
as_megdnn
(),
{});
fill_opr
.
op
->
param
()
=
0
;
fill_opr
.
op
->
exec
(
out
.
as_megdnn
(),
{});
return
{
Tensor
::
make
(
out
)};
}
auto
wk_size
=
dnn_opr
->
get_workspace_in_bytes
(
auto
sz
=
dnn_opr
.
op
->
get_workspace_in_bytes
(
inp_tensornds
[
0
].
layout
,
inp_tensornds
[
1
].
layout
,
output_descs
[
0
].
layout
);
DeviceTensorND
out_devtensor
=
BlobManager
::
inst
()
->
alloc_workspace_with_defrag
(
comp_node
,
oup_layout
);
TensorLayout
wk_layout
{
TensorShape
{
wk_size
},
inputs
[
0
]
->
dtype
()};
DeviceTensorND
workspace
=
BlobManager
::
inst
()
->
alloc_workspace_with_defrag
(
comp_node
,
wk_layout
);
megdnn
::
Workspace
dnn_wk
(
workspace
.
raw_ptr
(),
wk_size
);
dnn_opr
->
exec
(
TensorLayout
w_layout
({
sz
},
dtype
::
Byte
());
auto
dnn_wk
=
dnn_opr
.
create_workspace
(
w_layout
);
dnn_opr
.
op
->
exec
(
inp_tensornds
[
0
],
inp_tensornds
[
1
],
out_devtensor
.
as_megdnn
(),
dnn_wk
);
return
{
Tensor
::
make
(
out_devtensor
)};
...
...
imperative/src/impl/ops/elemwise.cpp
浏览文件 @
4d5faa3f
...
...
@@ -106,9 +106,8 @@ void apply_on_device_tensornd(
mgb_assert
(
inputs
.
size
()
==
trait
.
arity
,
"%s expects %u inputs; got %zu actually"
,
trait
.
name
,
trait
.
arity
,
inputs
.
size
());
auto
&&
dnn_opr
=
opr
::
intl
::
create_megdnn_opr
<
megdnn
::
Elemwise
>
(
inputs
[
0
].
comp_node
());
opr
::
Elemwise
::
perform
(
op_def
.
mode
,
(
*
outputs
)[
0
],
inputs
,
dnn_opr
);
DnnOprCaller
<
megdnn
::
Elemwise
>
dnn_opr
(
inputs
[
0
].
comp_node
());
opr
::
Elemwise
::
perform
(
op_def
.
mode
,
(
*
outputs
)[
0
],
inputs
,
dnn_opr
.
op
);
}
SmallVector
<
TensorPtr
>
apply_on_physical_tensor
(
...
...
@@ -139,16 +138,16 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
if
(
is_empty
)
{
return
{
Tensor
::
make
(
out
)};
}
auto
&&
dnn_opr
=
opr
::
intl
::
create_megdnn_opr
<
megdnn
::
Elemwise
>
(
comp_node
);
DnnOprCaller
<
megdnn
::
Elemwise
>
dnn_opr
(
comp_node
);
dnn_opr
->
param
()
=
op_def
.
param
();
if
(
dnn_opr
->
param
().
mode
==
Mode
::
FUSE_MUL_ADD3
||
dnn_opr
->
param
().
mode
==
Mode
::
FUSE_MUL_ADD4
||
dnn_opr
.
op
->
param
()
=
op_def
.
param
();
if
(
dnn_opr
.
op
->
param
().
mode
==
Mode
::
FUSE_MUL_ADD3
||
dnn_opr
.
op
->
param
().
mode
==
Mode
::
FUSE_MUL_ADD4
||
(
inp_tensornds
.
size
()
&&
inp_tensornds
[
0
].
layout
.
dtype
.
category
()
==
DTypeCategory
::
QUANTIZED
))
{
opr
::
Elemwise
::
perform_dnn
(
comp_node
,
out
,
inp_tensornds
,
dnn_opr
);
opr
::
Elemwise
::
perform_dnn
(
comp_node
,
out
,
inp_tensornds
,
dnn_opr
.
op
);
}
else
{
dnn_opr
->
exec
(
inp_tensornds
,
out
.
as_megdnn
());
dnn_opr
.
op
->
exec
(
inp_tensornds
,
out
.
as_megdnn
());
}
return
{
Tensor
::
make
(
out
)};
...
...
imperative/src/impl/ops/misc.cpp
浏览文件 @
4d5faa3f
...
...
@@ -8,6 +8,7 @@
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*/
#include "../dnn_op_helper.h"
#include "../op_trait.h"
#include "megbrain/imperative/ops/autogen.h"
...
...
@@ -34,8 +35,7 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
auto
dest
=
outputs
[
size
];
auto
cn
=
dest
->
comp_node
();
auto
&&
dnn_opr
=
opr
::
intl
::
create_megdnn_opr
<
megdnn
::
CheckNonFinite
>
(
cn
);
size_t
wk_size
=
0
;
DnnOprCaller
<
megdnn
::
CheckNonFinite
>
dnn_opr
(
cn
);
SmallVector
<
megdnn
::
TensorND
>
srcs
(
size
);
// copy an outputs to the dnn for inplace
for
(
size_t
i
=
0
;
i
<
size
;
++
i
)
{
...
...
@@ -44,11 +44,11 @@ SmallVector<TensorPtr> apply_on_physical_tensor(
srcs
[
i
]
=
outputs
[
i
]
->
dev_tensor
().
as_megdnn
();
}
megdnn
::
CheckNonFinite
::
Param
param
({
op
.
scale
});
dnn_opr
->
param
()
=
param
;
wk_size
=
dnn_opr
->
get_workspace_in_bytes
(
srcs
,
dest
->
layout
());
auto
wk
=
Blob
::
make
(
cn
,
wk_size
);
megdnn
::
Workspace
dnn_wk
(
wk
->
storage
().
get
(),
wk_size
);
dnn_opr
->
exec
(
srcs
,
dest
->
dev_tensor
().
as_megdnn
(),
dnn_wk
);
dnn_opr
.
op
->
param
()
=
param
;
size_t
sz
=
dnn_opr
.
op
->
get_workspace_in_bytes
(
srcs
,
dest
->
layout
());
TensorLayout
w_layout
({
sz
},
dtype
::
Byte
()
);
auto
dnn_wk
=
dnn_opr
.
create_workspace
(
w_layout
);
dnn_opr
.
op
->
exec
(
srcs
,
dest
->
dev_tensor
().
as_megdnn
(),
dnn_wk
);
return
outputs
;
}
...
...
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