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体验新版 GitCode,发现更多精彩内容 >>
未验证
提交
bf748f24
编写于
10月 13, 2021
作者:
J
Jacek Czaja
提交者:
GitHub
10月 13, 2021
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Implemented LRU based cache clearing (#36290)
- Lint - Merge with develop - lint
上级
59e425cd
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
136 addition
and
146 deletion
+136
-146
paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc
paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc
+25
-24
paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc
paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc
+17
-16
paddle/fluid/operators/mkldnn/quantize_mkldnn_op.cc
paddle/fluid/operators/mkldnn/quantize_mkldnn_op.cc
+35
-70
paddle/fluid/platform/device_context.cc
paddle/fluid/platform/device_context.cc
+44
-19
paddle/fluid/platform/device_context.h
paddle/fluid/platform/device_context.h
+10
-5
paddle/fluid/platform/mkldnn_reuse.h
paddle/fluid/platform/mkldnn_reuse.h
+5
-12
未找到文件。
paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc
浏览文件 @
bf748f24
...
@@ -78,7 +78,8 @@ class ConvMKLDNNHandlerT
...
@@ -78,7 +78,8 @@ class ConvMKLDNNHandlerT
mkldnn
::
convolution_backward_weights
>
(
mkldnn
::
convolution_backward_weights
>
(
dev_ctx
,
mkldnn_engine
,
cpu_place
,
dev_ctx
,
mkldnn_engine
,
cpu_place
,
platform
::
CreateKey
(
dev_ctx
,
framework
::
vectorize
(
input
->
dims
()),
platform
::
CreateKey
(
dev_ctx
,
framework
::
vectorize
(
input
->
dims
()),
unique_name
))
{
unique_name
)),
is_test_
(
ctx
.
Attr
<
bool
>
(
"is_test"
))
{
if
(
!
this
->
isCached
())
{
if
(
!
this
->
isCached
())
{
PADDLE_ENFORCE_EQ
(
PADDLE_ENFORCE_EQ
(
input
->
layout
(),
framework
::
DataLayout
::
kMKLDNN
,
input
->
layout
(),
framework
::
DataLayout
::
kMKLDNN
,
...
@@ -159,7 +160,6 @@ class ConvMKLDNNHandlerT
...
@@ -159,7 +160,6 @@ class ConvMKLDNNHandlerT
framework
::
slice_ddim
(
filter_dims
,
2
,
filter_dims
.
size
());
framework
::
slice_ddim
(
filter_dims
,
2
,
filter_dims
.
size
());
const
auto
ksize
=
framework
::
vectorize
(
filter_data_dims
);
const
auto
ksize
=
framework
::
vectorize
(
filter_data_dims
);
const
bool
is_test
=
ctx
.
Attr
<
bool
>
(
"is_test"
);
auto
strides_temp
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
auto
strides_temp
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int64_t
>
strides
(
begin
(
strides_temp
),
end
(
strides_temp
));
std
::
vector
<
int64_t
>
strides
(
begin
(
strides_temp
),
end
(
strides_temp
));
...
@@ -214,9 +214,8 @@ class ConvMKLDNNHandlerT
...
@@ -214,9 +214,8 @@ class ConvMKLDNNHandlerT
const
auto
dst_md
=
platform
::
MKLDNNMemDesc
(
const
auto
dst_md
=
platform
::
MKLDNNMemDesc
(
dst_tz
,
platform
::
MKLDNNGetDataType
<
T_out
>
(),
chosen_memory_format
);
dst_tz
,
platform
::
MKLDNNGetDataType
<
T_out
>
(),
chosen_memory_format
);
const
auto
fwd_prop_kind
=
is_test
?
mkldnn
::
prop_kind
::
forward_inference
const
auto
fwd_prop_kind
=
is_test_
?
mkldnn
::
prop_kind
::
forward_inference
:
mkldnn
::
prop_kind
::
forward_training
;
:
mkldnn
::
prop_kind
::
forward_training
;
float
sum_scale
=
1.0
f
;
float
sum_scale
=
1.0
f
;
std
::
vector
<
float
>
output_shift_scale
;
std
::
vector
<
float
>
output_shift_scale
;
if
(
platform
::
is_int8
<
T
>
())
if
(
platform
::
is_int8
<
T
>
())
...
@@ -261,7 +260,8 @@ class ConvMKLDNNHandlerT
...
@@ -261,7 +260,8 @@ class ConvMKLDNNHandlerT
mkldnn
::
convolution_backward_weights
>
(
mkldnn
::
convolution_backward_weights
>
(
dev_ctx
,
dev_ctx
.
GetEngine
(),
cpu_place
,
dev_ctx
,
dev_ctx
.
GetEngine
(),
cpu_place
,
platform
::
CreateKey
(
dev_ctx
,
framework
::
vectorize
(
in
->
dims
()),
platform
::
CreateKey
(
dev_ctx
,
framework
::
vectorize
(
in
->
dims
()),
unique_name
))
{
unique_name
)),
is_test_
(
false
)
{
if
(
!
this
->
isBwdCached
())
{
if
(
!
this
->
isBwdCached
())
{
PADDLE_ENFORCE_EQ
(
PADDLE_ENFORCE_EQ
(
in
->
layout
(),
framework
::
DataLayout
::
kMKLDNN
,
in
->
layout
(),
framework
::
DataLayout
::
kMKLDNN
,
...
@@ -291,7 +291,7 @@ class ConvMKLDNNHandlerT
...
@@ -291,7 +291,7 @@ class ConvMKLDNNHandlerT
"Wrong format set for output_grad tensor"
));
"Wrong format set for output_grad tensor"
));
PADDLE_ENFORCE_EQ
(
PADDLE_ENFORCE_EQ
(
ctx
.
Attr
<
bool
>
(
"is_test"
)
,
false
,
is_test_
,
false
,
platform
::
errors
::
InvalidArgument
(
platform
::
errors
::
InvalidArgument
(
"is_test attribute should be set to False in training phase."
));
"is_test attribute should be set to False in training phase."
));
...
@@ -557,13 +557,14 @@ class ConvMKLDNNHandlerT
...
@@ -557,13 +557,14 @@ class ConvMKLDNNHandlerT
framework
::
vectorize
(
in_mem
->
dims
()),
framework
::
vectorize
(
in_mem
->
dims
()),
platform
::
MKLDNNGetDataType
<
T
>
(),
in_mem
->
format
());
platform
::
MKLDNNGetDataType
<
T
>
(),
in_mem
->
format
());
return
this
->
AcquireMemoryWithReorder
(
return
this
->
AcquireMemoryWithReorder
(
user_mem_md
,
mem_md
,
platform
::
to_void_cast
<
T
>
(
in_mem_data
),
key_mem
);
user_mem_md
,
mem_md
,
platform
::
to_void_cast
<
T
>
(
in_mem_data
),
key_mem
,
is_test_
);
}
else
{
}
else
{
const
std
::
string
target_key_suffix
{
key_mem_target
};
const
std
::
string
target_key_suffix
{
key_mem_target
};
const
auto
target_mem_p
=
this
->
AcquireMemory
(
target_key_suffix
);
const
auto
target_mem_p
=
this
->
AcquireMemory
(
target_key_suffix
);
user_mem_p
->
set_data_handle
(
platform
::
to_void_cast
<
T
>
(
in_mem_data
));
user_mem_p
->
set_data_handle
(
platform
::
to_void_cast
<
T
>
(
in_mem_data
));
if
(
user_mem_p
!=
target_mem_p
)
{
if
(
user_mem_p
!=
target_mem_p
)
{
this
->
AcquireReorder
(
user_mem_p
,
target_mem_p
,
key_mem
);
this
->
AcquireReorder
(
user_mem_p
,
target_mem_p
);
}
}
return
target_mem_p
;
return
target_mem_p
;
}
}
...
@@ -571,12 +572,11 @@ class ConvMKLDNNHandlerT
...
@@ -571,12 +572,11 @@ class ConvMKLDNNHandlerT
std
::
shared_ptr
<
mkldnn
::
memory
>
AcquireWeightsMemoryWithReorder
(
std
::
shared_ptr
<
mkldnn
::
memory
>
AcquireWeightsMemoryWithReorder
(
const
framework
::
Tensor
*
filter
,
const
int
groups
,
const
bool
is_conv3d
,
const
framework
::
Tensor
*
filter
,
const
int
groups
,
const
bool
is_conv3d
,
const
bool
is_test
,
const
std
::
vector
<
float
>&
scale_data
=
{
1.0
f
},
const
std
::
vector
<
float
>&
scale_data
=
{
1.0
f
},
int
mask
=
0
)
{
int
mask
=
0
)
{
// This is workaround to make execution faster, delete
// This is workaround to make execution faster, delete
// if statement after including md inside Tensor
// if statement after including md inside Tensor
auto
weights_mem_p
=
this
->
AcquireMemory
(
"@weights_mem_p_target"
);
auto
weights_mem_p
=
this
->
AcquireMemory
(
"@weights_mem_p_target"
);
if
(
is_test
&&
weights_mem_p
)
{
if
(
is_test
_
&&
weights_mem_p
)
{
return
weights_mem_p
;
return
weights_mem_p
;
}
else
{
}
else
{
const
K
*
filter_data
=
filter
->
data
<
K
>
();
const
K
*
filter_data
=
filter
->
data
<
K
>
();
...
@@ -589,16 +589,16 @@ class ConvMKLDNNHandlerT
...
@@ -589,16 +589,16 @@ class ConvMKLDNNHandlerT
return
this
->
AcquireMemoryWithReorder
(
return
this
->
AcquireMemoryWithReorder
(
user_src_md
,
this
->
fwd_pd_
->
weights_desc
(),
user_src_md
,
this
->
fwd_pd_
->
weights_desc
(),
platform
::
to_void_cast
<
K
>
(
filter_data
),
"@weights_mem_p"
,
is_test
,
{}
,
platform
::
to_void_cast
<
K
>
(
filter_data
),
"@weights_mem_p"
,
is_test
_
,
scale_data
,
mask
);
{},
scale_data
,
mask
);
}
}
}
}
std
::
shared_ptr
<
mkldnn
::
memory
>
AcquireBiasMemoryWithReorder
(
std
::
shared_ptr
<
mkldnn
::
memory
>
AcquireBiasMemoryWithReorder
(
const
framework
::
Tensor
*
bias
,
const
bool
is_test
,
const
framework
::
Tensor
*
bias
,
const
std
::
vector
<
float
>&
scale_data
=
{
1.0
f
},
int
mask
=
0
)
{
const
std
::
vector
<
float
>&
scale_data
=
{
1.0
f
},
int
mask
=
0
)
{
auto
bias_mem_p
=
this
->
AcquireMemory
(
"@bias_mem_p_target"
);
auto
bias_mem_p
=
this
->
AcquireMemory
(
"@bias_mem_p_target"
);
if
(
is_test
&&
bias_mem_p
)
{
if
(
is_test
_
&&
bias_mem_p
)
{
return
bias_mem_p
;
return
bias_mem_p
;
}
else
{
}
else
{
const
K
*
bias_data
=
bias
->
data
<
K
>
();
const
K
*
bias_data
=
bias
->
data
<
K
>
();
...
@@ -608,7 +608,7 @@ class ConvMKLDNNHandlerT
...
@@ -608,7 +608,7 @@ class ConvMKLDNNHandlerT
return
this
->
AcquireMemoryWithReorder
(
return
this
->
AcquireMemoryWithReorder
(
user_bias_md
,
this
->
fwd_pd_
->
bias_desc
(),
user_bias_md
,
this
->
fwd_pd_
->
bias_desc
(),
platform
::
to_void_cast
<
K
>
(
bias_data
),
"@bias_mem_p"
,
is_test
,
{},
platform
::
to_void_cast
<
K
>
(
bias_data
),
"@bias_mem_p"
,
is_test
_
,
{},
scale_data
,
mask
);
scale_data
,
mask
);
}
}
}
}
...
@@ -641,7 +641,7 @@ class ConvMKLDNNHandlerT
...
@@ -641,7 +641,7 @@ class ConvMKLDNNHandlerT
platform
::
GetMKLDNNFormat
(
this
->
fwd_pd_
->
dst_desc
()))
{
platform
::
GetMKLDNNFormat
(
this
->
fwd_pd_
->
dst_desc
()))
{
auto
residual_memory_p
=
this
->
AcquireResidualMemory
(
residual_param
);
auto
residual_memory_p
=
this
->
AcquireResidualMemory
(
residual_param
);
dst_memory_p
=
this
->
template
AcquireDstMemory
<
T_out
>(
output
);
dst_memory_p
=
this
->
template
AcquireDstMemory
<
T_out
>(
output
);
this
->
AcquireReorder
(
residual_memory_p
,
dst_memory_p
,
"@residual_dst"
);
this
->
AcquireReorder
(
residual_memory_p
,
dst_memory_p
);
}
else
{
}
else
{
// Changing ShareDataWith to TensorCopy results in performance drop
// Changing ShareDataWith to TensorCopy results in performance drop
// on ResNet architectures
// on ResNet architectures
...
@@ -651,6 +651,9 @@ class ConvMKLDNNHandlerT
...
@@ -651,6 +651,9 @@ class ConvMKLDNNHandlerT
}
}
return
dst_memory_p
;
return
dst_memory_p
;
}
}
private:
const
bool
is_test_
;
};
};
}
// anonymous namespace
}
// anonymous namespace
...
@@ -695,7 +698,6 @@ class ConvMKLDNNOpKernel : public framework::OpKernel<T> {
...
@@ -695,7 +698,6 @@ class ConvMKLDNNOpKernel : public framework::OpKernel<T> {
ctx
.
template
device_context
<
platform
::
MKLDNNDeviceContext
>();
ctx
.
template
device_context
<
platform
::
MKLDNNDeviceContext
>();
const
auto
&
mkldnn_engine
=
dev_ctx
.
GetEngine
();
const
auto
&
mkldnn_engine
=
dev_ctx
.
GetEngine
();
const
bool
is_test
=
ctx
.
Attr
<
bool
>
(
"is_test"
);
const
bool
is_conv3d
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
).
size
()
==
3U
;
const
bool
is_conv3d
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
).
size
()
==
3U
;
const
bool
fuse_residual_conn
=
ctx
.
Attr
<
bool
>
(
"fuse_residual_connection"
);
const
bool
fuse_residual_conn
=
ctx
.
Attr
<
bool
>
(
"fuse_residual_connection"
);
...
@@ -712,7 +714,7 @@ class ConvMKLDNNOpKernel : public framework::OpKernel<T> {
...
@@ -712,7 +714,7 @@ class ConvMKLDNNOpKernel : public framework::OpKernel<T> {
auto
src_memory_p
=
handler
.
AcquireSrcMemoryWithReorder
(
input
);
auto
src_memory_p
=
handler
.
AcquireSrcMemoryWithReorder
(
input
);
auto
weights_memory_p
=
handler
.
AcquireWeightsMemoryWithReorder
(
auto
weights_memory_p
=
handler
.
AcquireWeightsMemoryWithReorder
(
filter
,
ctx
.
Attr
<
int
>
(
"groups"
),
is_conv3d
,
is_test
);
filter
,
ctx
.
Attr
<
int
>
(
"groups"
),
is_conv3d
);
std
::
shared_ptr
<
dnnl
::
memory
>
dst_memory_p
;
std
::
shared_ptr
<
dnnl
::
memory
>
dst_memory_p
;
if
(
fuse_residual_conn
)
{
if
(
fuse_residual_conn
)
{
...
@@ -731,7 +733,7 @@ class ConvMKLDNNOpKernel : public framework::OpKernel<T> {
...
@@ -731,7 +733,7 @@ class ConvMKLDNNOpKernel : public framework::OpKernel<T> {
{
MKLDNN_ARG_DST
,
*
dst_memory_p
}};
{
MKLDNN_ARG_DST
,
*
dst_memory_p
}};
if
(
bias
)
{
if
(
bias
)
{
auto
bias_memory_p
=
handler
.
AcquireBiasMemoryWithReorder
(
bias
,
is_test
);
auto
bias_memory_p
=
handler
.
AcquireBiasMemoryWithReorder
(
bias
);
args
.
insert
({
MKLDNN_ARG_BIAS
,
*
bias_memory_p
});
args
.
insert
({
MKLDNN_ARG_BIAS
,
*
bias_memory_p
});
}
}
...
@@ -783,11 +785,10 @@ class ConvMKLDNNOpKernel : public framework::OpKernel<T> {
...
@@ -783,11 +785,10 @@ class ConvMKLDNNOpKernel : public framework::OpKernel<T> {
ctx
.
Attr
<
std
::
vector
<
float
>>
(
"Scale_weights"
);
ctx
.
Attr
<
std
::
vector
<
float
>>
(
"Scale_weights"
);
const
bool
is_multi_channel
=
scale_weights_data
.
size
()
>
1
;
const
bool
is_multi_channel
=
scale_weights_data
.
size
()
>
1
;
const
int
&
groups
=
ctx
.
Attr
<
int
>
(
"groups"
);
const
int
&
groups
=
ctx
.
Attr
<
int
>
(
"groups"
);
const
bool
&
is_test
=
ctx
.
Attr
<
bool
>
(
"is_test"
);
int
mask_reorder
=
int
mask_reorder
=
is_multi_channel
?
((
groups
!=
1
)
?
(
1
<<
1
)
+
(
1
<<
0
)
:
1
<<
0
)
:
0
;
is_multi_channel
?
((
groups
!=
1
)
?
(
1
<<
1
)
+
(
1
<<
0
)
:
1
<<
0
)
:
0
;
auto
weights_memory_p
=
handler
.
AcquireWeightsMemoryWithReorder
(
auto
weights_memory_p
=
handler
.
AcquireWeightsMemoryWithReorder
(
filter
,
groups
,
false
,
is_test
,
scale_weights_data
,
mask_reorder
);
filter
,
groups
,
false
,
scale_weights_data
,
mask_reorder
);
std
::
shared_ptr
<
dnnl
::
memory
>
dst_memory_p
;
std
::
shared_ptr
<
dnnl
::
memory
>
dst_memory_p
;
if
(
fuse_residual_conn
)
{
if
(
fuse_residual_conn
)
{
...
@@ -822,7 +823,7 @@ class ConvMKLDNNOpKernel : public framework::OpKernel<T> {
...
@@ -822,7 +823,7 @@ class ConvMKLDNNOpKernel : public framework::OpKernel<T> {
handler
.
get_int8_bias_scales
(
ctx
);
handler
.
get_int8_bias_scales
(
ctx
);
auto
bias_memory_p
=
handler
.
AcquireBiasMemoryWithReorder
(
auto
bias_memory_p
=
handler
.
AcquireBiasMemoryWithReorder
(
bias
,
is_test
,
scale_bias_data
,
mask_reorder
);
bias
,
scale_bias_data
,
mask_reorder
);
args
.
insert
({
MKLDNN_ARG_BIAS
,
*
bias_memory_p
});
args
.
insert
({
MKLDNN_ARG_BIAS
,
*
bias_memory_p
});
}
}
...
...
paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc
浏览文件 @
bf748f24
...
@@ -51,10 +51,10 @@ class ConvTransposeMKLDNNHandlerT
...
@@ -51,10 +51,10 @@ class ConvTransposeMKLDNNHandlerT
:
platform
::
MKLDNNHandlerT
<
T
,
mkldnn
::
deconvolution_forward
>
(
:
platform
::
MKLDNNHandlerT
<
T
,
mkldnn
::
deconvolution_forward
>
(
dev_ctx
,
mkldnn_engine
,
cpu_place
,
dev_ctx
,
mkldnn_engine
,
cpu_place
,
platform
::
CreateKey
(
dev_ctx
,
framework
::
vectorize
(
input
->
dims
()),
platform
::
CreateKey
(
dev_ctx
,
framework
::
vectorize
(
input
->
dims
()),
unique_name
))
{
unique_name
)),
is_test_
(
ctx
.
Attr
<
bool
>
(
"is_test"
))
{
if
(
!
this
->
isCached
())
{
if
(
!
this
->
isCached
())
{
const
bool
is_test
=
ctx
.
Attr
<
bool
>
(
"is_test"
);
PADDLE_ENFORCE_EQ
(
is_test_
,
true
,
PADDLE_ENFORCE_EQ
(
is_test
,
true
,
platform
::
errors
::
InvalidArgument
(
platform
::
errors
::
InvalidArgument
(
"ConvTransposeMKLDNN works only for inference. "
"ConvTransposeMKLDNN works only for inference. "
"The attribute
\'
is_test
\'
value should be set to "
"The attribute
\'
is_test
\'
value should be set to "
...
@@ -169,8 +169,8 @@ class ConvTransposeMKLDNNHandlerT
...
@@ -169,8 +169,8 @@ class ConvTransposeMKLDNNHandlerT
const
mkldnn
::
primitive_attr
conv_trans_attr
=
const
mkldnn
::
primitive_attr
conv_trans_attr
=
CreatePostOps
(
fuse_activation
,
fuse_alpha
,
fuse_beta
);
CreatePostOps
(
fuse_activation
,
fuse_alpha
,
fuse_beta
);
auto
fwd_prop_kind
=
is_test
?
mkldnn
::
prop_kind
::
forward_inference
auto
fwd_prop_kind
=
is_test
_
?
mkldnn
::
prop_kind
::
forward_inference
:
mkldnn
::
prop_kind
::
forward_training
;
:
mkldnn
::
prop_kind
::
forward_training
;
if
(
bias
)
{
if
(
bias
)
{
std
::
vector
<
int64_t
>
bias_tz
=
framework
::
vectorize
(
bias
->
dims
());
std
::
vector
<
int64_t
>
bias_tz
=
framework
::
vectorize
(
bias
->
dims
());
const
auto
bias_md
=
const
auto
bias_md
=
...
@@ -231,18 +231,18 @@ class ConvTransposeMKLDNNHandlerT
...
@@ -231,18 +231,18 @@ class ConvTransposeMKLDNNHandlerT
const
auto
target_src_mem_p
=
this
->
AcquireMemory
(
target_key_suffix
);
const
auto
target_src_mem_p
=
this
->
AcquireMemory
(
target_key_suffix
);
user_src_mem_p
->
set_data_handle
(
platform
::
to_void_cast
<
T
>
(
input_data
));
user_src_mem_p
->
set_data_handle
(
platform
::
to_void_cast
<
T
>
(
input_data
));
if
(
user_src_mem_p
!=
target_src_mem_p
)
{
if
(
user_src_mem_p
!=
target_src_mem_p
)
{
this
->
AcquireReorder
(
user_src_mem_p
,
target_src_mem_p
,
"@src_mem_p"
);
this
->
AcquireReorder
(
user_src_mem_p
,
target_src_mem_p
);
}
}
return
target_src_mem_p
;
return
target_src_mem_p
;
}
}
}
}
std
::
shared_ptr
<
mkldnn
::
memory
>
AcquireWeightsMemoryWithReorder
(
std
::
shared_ptr
<
mkldnn
::
memory
>
AcquireWeightsMemoryWithReorder
(
const
framework
::
Tensor
*
filter
,
const
int
&
groups
,
const
bool
&
is_test
)
{
const
framework
::
Tensor
*
filter
,
const
int
&
groups
)
{
// This is workaround to make execution faster, delete
// This is workaround to make execution faster, delete
// if statement after including md inside Tensor
// if statement after including md inside Tensor
auto
weights_mem_p
=
this
->
AcquireMemory
(
"@weights_mem_p_target"
);
auto
weights_mem_p
=
this
->
AcquireMemory
(
"@weights_mem_p_target"
);
if
(
is_test
&&
weights_mem_p
)
{
if
(
is_test
_
&&
weights_mem_p
)
{
return
weights_mem_p
;
return
weights_mem_p
;
}
else
{
}
else
{
const
K
*
filter_data
=
filter
->
data
<
K
>
();
const
K
*
filter_data
=
filter
->
data
<
K
>
();
...
@@ -277,15 +277,15 @@ class ConvTransposeMKLDNNHandlerT
...
@@ -277,15 +277,15 @@ class ConvTransposeMKLDNNHandlerT
return
this
->
template
AcquireMemoryWithReorder
<
K
>(
return
this
->
template
AcquireMemoryWithReorder
<
K
>(
user_src_md
,
this
->
fwd_pd_
->
weights_desc
(),
user_src_md
,
this
->
fwd_pd_
->
weights_desc
(),
platform
::
to_void_cast
<
K
>
(
filter_data
),
"@weights_mem_p"
,
is_test
,
platform
::
to_void_cast
<
K
>
(
filter_data
),
"@weights_mem_p"
,
is_test
_
,
iohw2oihw_reorder
);
iohw2oihw_reorder
);
}
}
}
}
std
::
shared_ptr
<
mkldnn
::
memory
>
AcquireBiasMemoryWithReorder
(
std
::
shared_ptr
<
mkldnn
::
memory
>
AcquireBiasMemoryWithReorder
(
const
framework
::
Tensor
*
bias
,
const
bool
&
is_test
)
{
const
framework
::
Tensor
*
bias
)
{
auto
bias_mem_p
=
this
->
AcquireMemory
(
"@bias_mem_p_target"
);
auto
bias_mem_p
=
this
->
AcquireMemory
(
"@bias_mem_p_target"
);
if
(
is_test
&&
bias_mem_p
)
{
if
(
is_test
_
&&
bias_mem_p
)
{
return
bias_mem_p
;
return
bias_mem_p
;
}
else
{
}
else
{
const
K
*
bias_data
=
bias
->
data
<
K
>
();
const
K
*
bias_data
=
bias
->
data
<
K
>
();
...
@@ -294,9 +294,12 @@ class ConvTransposeMKLDNNHandlerT
...
@@ -294,9 +294,12 @@ class ConvTransposeMKLDNNHandlerT
MKLDNNMemoryFormat
::
x
);
MKLDNNMemoryFormat
::
x
);
return
this
->
AcquireMemoryWithReorder
(
return
this
->
AcquireMemoryWithReorder
(
user_bias_md
,
this
->
fwd_pd_
->
bias_desc
(),
user_bias_md
,
this
->
fwd_pd_
->
bias_desc
(),
platform
::
to_void_cast
<
K
>
(
bias_data
),
"@bias_mem_p"
,
is_test
);
platform
::
to_void_cast
<
K
>
(
bias_data
),
"@bias_mem_p"
,
is_test
_
);
}
}
}
}
private:
const
bool
is_test_
;
};
};
template
<
typename
T
,
typename
K
>
template
<
typename
T
,
typename
K
>
...
@@ -325,8 +328,6 @@ class ConvTransposeMKLDNNOpKernel : public framework::OpKernel<T> {
...
@@ -325,8 +328,6 @@ class ConvTransposeMKLDNNOpKernel : public framework::OpKernel<T> {
ctx
.
template
device_context
<
platform
::
MKLDNNDeviceContext
>();
ctx
.
template
device_context
<
platform
::
MKLDNNDeviceContext
>();
const
auto
&
mkldnn_engine
=
dev_ctx
.
GetEngine
();
const
auto
&
mkldnn_engine
=
dev_ctx
.
GetEngine
();
const
bool
is_test
=
ctx
.
Attr
<
bool
>
(
"is_test"
);
const
auto
*
input
=
ctx
.
Input
<
Tensor
>
(
"Input"
);
const
auto
*
input
=
ctx
.
Input
<
Tensor
>
(
"Input"
);
const
auto
*
filter
=
ctx
.
Input
<
Tensor
>
(
"Filter"
);
const
auto
*
filter
=
ctx
.
Input
<
Tensor
>
(
"Filter"
);
const
auto
*
bias
=
const
auto
*
bias
=
...
@@ -340,7 +341,7 @@ class ConvTransposeMKLDNNOpKernel : public framework::OpKernel<T> {
...
@@ -340,7 +341,7 @@ class ConvTransposeMKLDNNOpKernel : public framework::OpKernel<T> {
output
,
unique_name
);
output
,
unique_name
);
auto
src_memory_p
=
handler
.
AcquireSrcMemoryWithReorder
(
input
);
auto
src_memory_p
=
handler
.
AcquireSrcMemoryWithReorder
(
input
);
auto
weights_memory_p
=
handler
.
AcquireWeightsMemoryWithReorder
(
auto
weights_memory_p
=
handler
.
AcquireWeightsMemoryWithReorder
(
filter
,
ctx
.
Attr
<
int
>
(
"groups"
)
,
is_test
);
filter
,
ctx
.
Attr
<
int
>
(
"groups"
));
std
::
shared_ptr
<
dnnl
::
memory
>
dst_memory_p
=
std
::
shared_ptr
<
dnnl
::
memory
>
dst_memory_p
=
handler
.
template
AcquireDstMemory
<
T_out
>(
output
);
handler
.
template
AcquireDstMemory
<
T_out
>(
output
);
...
@@ -352,7 +353,7 @@ class ConvTransposeMKLDNNOpKernel : public framework::OpKernel<T> {
...
@@ -352,7 +353,7 @@ class ConvTransposeMKLDNNOpKernel : public framework::OpKernel<T> {
{
MKLDNN_ARG_DST
,
*
dst_memory_p
}};
{
MKLDNN_ARG_DST
,
*
dst_memory_p
}};
if
(
bias
)
{
if
(
bias
)
{
auto
bias_memory_p
=
handler
.
AcquireBiasMemoryWithReorder
(
bias
,
is_test
);
auto
bias_memory_p
=
handler
.
AcquireBiasMemoryWithReorder
(
bias
);
args
.
insert
({
MKLDNN_ARG_BIAS
,
*
bias_memory_p
});
args
.
insert
({
MKLDNN_ARG_BIAS
,
*
bias_memory_p
});
}
}
auto
&
astream
=
platform
::
MKLDNNDeviceContext
::
tls
().
get_stream
();
auto
&
astream
=
platform
::
MKLDNNDeviceContext
::
tls
().
get_stream
();
...
...
paddle/fluid/operators/mkldnn/quantize_mkldnn_op.cc
浏览文件 @
bf748f24
...
@@ -64,81 +64,46 @@ class QuantOpKernel : public framework::OpKernel<T> {
...
@@ -64,81 +64,46 @@ class QuantOpKernel : public framework::OpKernel<T> {
bool
is_negative_input
=
ctx
.
Attr
<
bool
>
(
"is_negative_input"
);
bool
is_negative_input
=
ctx
.
Attr
<
bool
>
(
"is_negative_input"
);
bool
bfloat16
=
ctx
.
Attr
<
bool
>
(
"bfloat16"
);
bool
bfloat16
=
ctx
.
Attr
<
bool
>
(
"bfloat16"
);
std
::
string
key
=
// TODO(jczaja): Refactor with Acquire API
platform
::
CreateKey
(
dev_ctx
,
src_tz
,
scale_data
,
scale_shift
,
is_negative_input
,
ctx
.
OutputName
(
"Output"
));
key
=
platform
::
ExtendKeyWithThreadInfoIfNeeded
(
dev_ctx
,
key
);
const
std
::
string
key_prim
=
key
+
"@r"
;
const
std
::
string
key_src_mem
=
key
+
"@s"
;
const
std
::
string
key_dst_mem
=
key
+
"@d"
;
std
::
shared_ptr
<
mkldnn
::
memory
>
src_memory
;
std
::
shared_ptr
<
mkldnn
::
memory
>
src_memory
;
std
::
shared_ptr
<
mkldnn
::
memory
>
dst_memory
;
std
::
shared_ptr
<
mkldnn
::
memory
>
dst_memory
;
std
::
shared_ptr
<
reorder
>
reorder_p
;
std
::
shared_ptr
<
reorder
>
reorder_p
;
reorder_p
=
std
::
static_pointer_cast
<
reorder
>
(
dev_ctx
.
GetBlob
(
key_prim
));
std
::
string
out_layout
=
ctx
.
Attr
<
std
::
string
>
(
"output_format"
);
if
(
reorder_p
==
nullptr
)
{
MKLDNNMemoryFormat
out_format
=
std
::
string
out_layout
=
ctx
.
Attr
<
std
::
string
>
(
"output_format"
);
platform
::
data_format_to_memory_format
(
out_layout
);
MKLDNNMemoryFormat
out_format
=
mkldnn
::
primitive_attr
attri
;
platform
::
data_format_to_memory_format
(
out_layout
);
int
mask
=
0
;
mkldnn
::
primitive_attr
attri
;
attri
.
set_output_scales
(
mask
,
{
scale_data
});
int
mask
=
0
;
attri
.
set_output_scales
(
mask
,
{
scale_data
});
if
(
with_shift
)
{
mkldnn
::
post_ops
post_operations
;
if
(
with_shift
)
{
post_operations
.
append_sum
();
mkldnn
::
post_ops
post_operations
;
attri
.
set_post_ops
(
post_operations
);
post_operations
.
append_sum
();
uint8_t
*
output_data
=
output
->
mutable_data
<
uint8_t
>
(
ctx
.
GetPlace
());
attri
.
set_post_ops
(
post_operations
);
// memset casts scale_shift to unsigned char (uint8_t) internally
uint8_t
*
output_data
=
output
->
mutable_data
<
uint8_t
>
(
ctx
.
GetPlace
());
std
::
memset
(
output_data
,
scale_shift
,
output
->
numel
());
// memset casts scale_shift to unsigned char (uint8_t) internally
}
std
::
memset
(
output_data
,
scale_shift
,
output
->
numel
());
}
auto
src_md
=
platform
::
MKLDNNMemDesc
({
src_tz
},
memory
::
data_type
::
f32
,
input
->
format
());
auto
src_md
=
platform
::
MKLDNNMemDesc
({
src_tz
},
memory
::
data_type
::
f32
,
src_memory
=
std
::
make_shared
<
mkldnn
::
memory
>
(
src_md
,
engine
,
input
->
format
());
to_void_cast
<
T
>
(
input_data
));
src_memory
=
std
::
make_shared
<
mkldnn
::
memory
>
(
src_md
,
engine
,
to_void_cast
<
T
>
(
input_data
));
std
::
shared_ptr
<
mkldnn
::
memory
::
desc
>
dst_md
;
if
(
bfloat16
)
{
std
::
shared_ptr
<
mkldnn
::
memory
::
desc
>
dst_md
;
platform
::
SetDstMemoryQuantized
<
paddle
::
platform
::
bfloat16
>
(
if
(
bfloat16
)
{
ctx
,
output
,
dst_tz
,
engine
,
dst_md
,
dst_memory
,
out_format
);
platform
::
SetDstMemoryQuantized
<
paddle
::
platform
::
bfloat16
>
(
}
else
if
(
is_negative_input
&&
!
with_shift
)
{
ctx
,
output
,
dst_tz
,
engine
,
dst_md
,
dst_memory
,
out_format
);
platform
::
SetDstMemoryQuantized
<
int8_t
>
(
ctx
,
output
,
dst_tz
,
engine
,
}
else
if
(
is_negative_input
&&
!
with_shift
)
{
dst_md
,
dst_memory
,
out_format
);
platform
::
SetDstMemoryQuantized
<
int8_t
>
(
ctx
,
output
,
dst_tz
,
engine
,
dst_md
,
dst_memory
,
out_format
);
}
else
{
platform
::
SetDstMemoryQuantized
<
uint8_t
>
(
ctx
,
output
,
dst_tz
,
engine
,
dst_md
,
dst_memory
,
out_format
);
}
auto
reorder_pd
=
std
::
shared_ptr
<
reorder
::
primitive_desc
>
(
new
reorder
::
primitive_desc
(
*
src_memory
,
*
dst_memory
,
attri
));
reorder_p
=
std
::
shared_ptr
<
reorder
>
(
new
reorder
(
*
reorder_pd
));
dev_ctx
.
SetBlob
(
key_prim
,
reorder_p
);
dev_ctx
.
SetBlob
(
key_src_mem
,
src_memory
);
dev_ctx
.
SetBlob
(
key_dst_mem
,
dst_memory
);
}
else
{
}
else
{
src_memory
=
std
::
static_pointer_cast
<
mkldnn
::
memory
>
(
platform
::
SetDstMemoryQuantized
<
uint8_t
>
(
ctx
,
output
,
dst_tz
,
engine
,
dev_ctx
.
GetBlob
(
key_src_mem
));
dst_md
,
dst_memory
,
out_format
);
src_memory
->
set_data_handle
(
to_void_cast
<
T
>
(
input_data
));
dst_memory
=
std
::
static_pointer_cast
<
mkldnn
::
memory
>
(
dev_ctx
.
GetBlob
(
key_dst_mem
));
auto
place
=
ctx
.
GetPlace
();
if
(
bfloat16
)
{
dst_memory
->
set_data_handle
(
output
->
mutable_data
<
paddle
::
platform
::
bfloat16
>
(
place
));
}
else
if
(
with_shift
||
!
is_negative_input
)
{
uint8_t
*
output_data
=
output
->
mutable_data
<
uint8_t
>
(
ctx
.
GetPlace
());
if
(
with_shift
)
std
::
memset
(
output_data
,
scale_shift
,
output
->
numel
());
dst_memory
->
set_data_handle
(
output_data
);
}
else
{
dst_memory
->
set_data_handle
(
output
->
mutable_data
<
int8_t
>
(
ctx
.
GetPlace
()));
}
}
}
auto
reorder_pd
=
std
::
shared_ptr
<
reorder
::
primitive_desc
>
(
new
reorder
::
primitive_desc
(
*
src_memory
,
*
dst_memory
,
attri
));
reorder_p
=
std
::
shared_ptr
<
reorder
>
(
new
reorder
(
*
reorder_pd
));
auto
&
astream
=
platform
::
MKLDNNDeviceContext
::
tls
().
get_stream
();
auto
&
astream
=
platform
::
MKLDNNDeviceContext
::
tls
().
get_stream
();
{
{
...
...
paddle/fluid/platform/device_context.cc
浏览文件 @
bf748f24
...
@@ -11,6 +11,12 @@ See the License for the specific language governing permissions and
...
@@ -11,6 +11,12 @@ See the License for the specific language governing permissions and
limitations under the License. */
limitations under the License. */
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/device_context.h"
#include <set>
#include <set>
#include <utility>
#ifdef _WIN32
#include <intrin.h>
#else
#include <x86intrin.h>
#endif
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
#include "paddle/fluid/memory/allocation/cuda_device_context_allocator.h"
#include "paddle/fluid/memory/allocation/cuda_device_context_allocator.h"
...
@@ -666,7 +672,7 @@ void MKLDNNDeviceContext::ResetBlobMap(void* ptr) {
...
@@ -666,7 +672,7 @@ void MKLDNNDeviceContext::ResetBlobMap(void* ptr) {
// of this executor
// of this executor
for
(
auto
&
s
:
*
p_exec_items_
)
{
for
(
auto
&
s
:
*
p_exec_items_
)
{
for
(
auto
&
v
:
(
*
s
.
second
)[
ptr
])
{
for
(
auto
&
v
:
(
*
s
.
second
)[
ptr
])
{
(
v
.
first
)
->
erase
(
v
.
second
);
(
v
.
first
)
->
second
.
erase
(
v
.
second
);
}
}
s
.
second
->
erase
(
ptr
);
s
.
second
->
erase
(
ptr
);
}
}
...
@@ -677,12 +683,27 @@ void MKLDNNDeviceContext::ResetBlobMap(void* ptr) {
...
@@ -677,12 +683,27 @@ void MKLDNNDeviceContext::ResetBlobMap(void* ptr) {
}
}
}
}
void
MKLDNNDeviceContext
::
RemoveShapeEntriesWithExecutor
(
void
)
const
{
std
::
string
MKLDNNDeviceContext
::
PickLeastUsedShape
(
p_exec_items_
->
erase
(
p_exec_items_
->
begin
());
BlobPtr_t
<
ShapeBlob
>
sb
)
const
{
auto
ancient_one
=
sb
->
begin
();
for
(
auto
v
=
std
::
next
(
sb
->
begin
());
v
!=
sb
->
end
();
++
v
)
{
if
(
v
->
second
->
first
<
ancient_one
->
second
->
first
)
{
ancient_one
=
v
;
}
}
VLOG
(
2
)
<<
"num_shapes: "
<<
sb
->
size
()
<<
", remove all blobs of shape: "
<<
ancient_one
->
first
;
return
ancient_one
->
first
;
}
void
MKLDNNDeviceContext
::
RemoveShapeEntriesWithExecutor
(
std
::
string
shape_to_be_removed
)
const
{
p_exec_items_
->
erase
(
shape_to_be_removed
);
}
}
void
MKLDNNDeviceContext
::
LinkEntryWithExecutor
(
BlobPtr_t
<
KeyBlob
>
pblob
,
void
MKLDNNDeviceContext
::
LinkEntryWithExecutor
(
KeyBlob
::
iterator
it
)
const
{
BlobPtr_t
<
std
::
pair
<
unsigned
long
long
,
KeyBlob
>>
pblob
,
KeyBlob
::
iterator
it
)
const
{
// Take current input shape from TLS
// Take current input shape from TLS
// Take current executor addess from TLS
// Take current executor addess from TLS
// and for this executor's items add the one defined with arguments
// and for this executor's items add the one defined with arguments
...
@@ -719,7 +740,7 @@ void MKLDNNDeviceContext::SetBlob(const std::string& name,
...
@@ -719,7 +740,7 @@ void MKLDNNDeviceContext::SetBlob(const std::string& name,
BlobPtr_t
<
void
>
data
)
const
{
BlobPtr_t
<
void
>
data
)
const
{
BlobMap
*
pMap
=
p_blobmap_
.
get
();
BlobMap
*
pMap
=
p_blobmap_
.
get
();
BlobPtr_t
<
ShapeBlob
>
sBlob
=
nullptr
;
BlobPtr_t
<
ShapeBlob
>
sBlob
=
nullptr
;
BlobPtr_t
<
KeyBlob
>
pBlob
=
nullptr
;
BlobPtr_t
<
std
::
pair
<
unsigned
long
long
,
KeyBlob
>
>
pBlob
=
nullptr
;
int
sid
=
tls
().
get_cur_mkldnn_session_id
();
int
sid
=
tls
().
get_cur_mkldnn_session_id
();
...
@@ -748,22 +769,24 @@ void MKLDNNDeviceContext::SetBlob(const std::string& name,
...
@@ -748,22 +769,24 @@ void MKLDNNDeviceContext::SetBlob(const std::string& name,
sBlob
->
size
()
&&
sBlob
->
size
()
&&
(
sBlob
->
size
()
>=
(
sBlob
->
size
()
>=
static_cast
<
size_t
>
(
tls
().
cur_input_shape_cache_capacity
)))
{
static_cast
<
size_t
>
(
tls
().
cur_input_shape_cache_capacity
)))
{
VLOG
(
2
)
<<
"sid="
<<
sid
auto
shape_to_be_erased
=
PickLeastUsedShape
(
sBlob
);
<<
", remove all blobs of shape: "
<<
sBlob
->
begin
()
->
first
;
sBlob
->
erase
(
shape_to_be_erased
);
sBlob
->
erase
(
sBlob
->
begin
()
->
first
);
RemoveShapeEntriesWithExecutor
(
shape_to_be_erased
);
RemoveShapeEntriesWithExecutor
();
}
}
pBlob
=
std
::
make_shared
<
KeyBlob
>
();
pBlob
=
std
::
make_shared
<
std
::
pair
<
unsigned
long
long
,
KeyBlob
>>
();
pBlob
->
first
=
__rdtsc
();
(
*
sBlob
)[
tls
().
cur_input_shape_str
]
=
pBlob
;
(
*
sBlob
)[
tls
().
cur_input_shape_str
]
=
pBlob
;
}
else
{
}
else
{
pBlob
=
key_it
->
second
;
pBlob
=
key_it
->
second
;
// Update time stamp
pBlob
->
first
=
__rdtsc
();
}
}
// Find Blob via name
// Find Blob via name
auto
blob_it
=
pBlob
->
find
(
name
);
auto
blob_it
=
pBlob
->
second
.
find
(
name
);
if
(
blob_it
==
pBlob
->
end
())
{
if
(
blob_it
==
pBlob
->
second
.
end
())
{
auto
el
=
auto
el
=
pBlob
->
second
.
insert
(
pBlob
->
insert
(
std
::
make_pair
(
name
,
data
));
// (*pBlob)[name] = data;
std
::
make_pair
(
name
,
data
));
// (*pBlob)[name] = data;
// Register new element in per executor map
// Register new element in per executor map
// to have easily erased when executor terminated
// to have easily erased when executor terminated
LinkEntryWithExecutor
(
pBlob
,
el
.
first
);
LinkEntryWithExecutor
(
pBlob
,
el
.
first
);
...
@@ -779,7 +802,7 @@ unsigned int MKLDNNDeviceContext::GetCachedObjectsNumber(void) const {
...
@@ -779,7 +802,7 @@ unsigned int MKLDNNDeviceContext::GetCachedObjectsNumber(void) const {
unsigned
int
num_entries
=
0
;
unsigned
int
num_entries
=
0
;
for
(
auto
const
&
l3
:
*
p_blobmap_
)
{
for
(
auto
const
&
l3
:
*
p_blobmap_
)
{
for
(
auto
const
&
l2
:
*
(
l3
.
second
))
{
for
(
auto
const
&
l2
:
*
(
l3
.
second
))
{
num_entries
+=
(
l2
.
second
)
->
size
();
num_entries
+=
(
l2
.
second
->
second
).
size
();
}
}
}
}
return
num_entries
;
return
num_entries
;
...
@@ -789,7 +812,7 @@ MKLDNNDeviceContext::BlobPtr_t<void> MKLDNNDeviceContext::GetBlob(
...
@@ -789,7 +812,7 @@ MKLDNNDeviceContext::BlobPtr_t<void> MKLDNNDeviceContext::GetBlob(
const
std
::
string
&
name
)
const
{
const
std
::
string
&
name
)
const
{
BlobMap
*
pMap
=
p_blobmap_
.
get
();
BlobMap
*
pMap
=
p_blobmap_
.
get
();
BlobPtr_t
<
ShapeBlob
>
sBlob
=
nullptr
;
BlobPtr_t
<
ShapeBlob
>
sBlob
=
nullptr
;
BlobPtr_t
<
KeyBlob
>
pBlob
=
nullptr
;
BlobPtr_t
<
std
::
pair
<
unsigned
long
long
,
KeyBlob
>
>
pBlob
=
nullptr
;
int
sid
=
tls
().
get_cur_mkldnn_session_id
();
int
sid
=
tls
().
get_cur_mkldnn_session_id
();
...
@@ -813,12 +836,14 @@ MKLDNNDeviceContext::BlobPtr_t<void> MKLDNNDeviceContext::GetBlob(
...
@@ -813,12 +836,14 @@ MKLDNNDeviceContext::BlobPtr_t<void> MKLDNNDeviceContext::GetBlob(
pBlob
=
sBlob_it
->
second
;
pBlob
=
sBlob_it
->
second
;
// Find Blob via name
// Find Blob via name
auto
key_it
=
pBlob
->
find
(
name
);
auto
key_it
=
pBlob
->
second
.
find
(
name
);
if
(
key_it
==
pBlob
->
end
())
{
if
(
key_it
==
pBlob
->
second
.
end
())
{
VLOG
(
2
)
<<
"GetBlob sid="
<<
sid
<<
", miss blob="
<<
name
<<
"
\n
"
;
VLOG
(
2
)
<<
"GetBlob sid="
<<
sid
<<
", miss blob="
<<
name
<<
"
\n
"
;
return
nullptr
;
return
nullptr
;
}
}
// Update timestamp
sBlob_it
->
second
->
first
=
__rdtsc
();
// TODO(windows)
VLOG
(
2
)
<<
"GetBlob sid="
<<
sid
<<
", get blob="
<<
name
<<
"
\n
"
;
VLOG
(
2
)
<<
"GetBlob sid="
<<
sid
<<
", get blob="
<<
name
<<
"
\n
"
;
// lock will be automatically released when out of scope
// lock will be automatically released when out of scope
...
...
paddle/fluid/platform/device_context.h
浏览文件 @
bf748f24
...
@@ -757,18 +757,20 @@ class MKLDNNDeviceContext : public CPUDeviceContext {
...
@@ -757,18 +757,20 @@ class MKLDNNDeviceContext : public CPUDeviceContext {
// Following three maps are used to cache MKLDNN primitives.
// Following three maps are used to cache MKLDNN primitives.
// There relations are:
// There relations are:
// - BlobMap = Map<cur_thread_id, ShapeBlob>
// - BlobMap = Map<cur_thread_id, ShapeBlob>
// - ShapeBlob = Map<cur_input_shape_str,
KeyBlob
>
// - ShapeBlob = Map<cur_input_shape_str,
<unsigned long long, KeyBlob>
>
// - KeyBlob = Map<blob_name, blob>
// - KeyBlob = Map<blob_name, blob>
using
KeyBlob
=
umap_key_string_t
<
void
>
;
using
KeyBlob
=
umap_key_string_t
<
void
>
;
using
ShapeBlob
=
umap_key_string_t
<
KeyBlob
>
;
using
ShapeBlob
=
umap_key_string_t
<
std
::
pair
<
unsigned
long
long
,
KeyBlob
>
>
;
using
BlobMap
=
umap_value_smart_t
<
int
,
ShapeBlob
>
;
using
BlobMap
=
umap_value_smart_t
<
int
,
ShapeBlob
>
;
// Auxillary two-level structure (shape, executor) to easier control
// Auxillary two-level structure (shape, executor) to easier control
// clearing cache objects related to specific executor
// clearing cache objects related to specific executor
using
ExecKey
=
void
*
;
using
ExecKey
=
void
*
;
using
ExecMapCacheIterPair
=
std
::
pair
<
BlobPtr_t
<
KeyBlob
>
,
KeyBlob
::
iterator
>
;
using
ExecMapCacheIterPair
=
std
::
pair
<
BlobPtr_t
<
std
::
pair
<
unsigned
long
long
,
KeyBlob
>>
,
KeyBlob
::
iterator
>
;
using
ExecMap
=
using
ExecMap
=
std
::
unordered_map
<
ExecKey
,
std
::
vector
<
ExecMapCacheIterPair
>>
;
std
::
unordered_map
<
ExecKey
,
std
::
vector
<
ExecMapCacheIterPair
>>
;
using
ExecShape
=
std
::
unordered_map
<
std
::
string
,
std
::
shared_ptr
<
ExecMap
>>
;
using
ExecShape
=
std
::
unordered_map
<
std
::
string
,
std
::
shared_ptr
<
ExecMap
>>
;
...
@@ -779,8 +781,11 @@ class MKLDNNDeviceContext : public CPUDeviceContext {
...
@@ -779,8 +781,11 @@ class MKLDNNDeviceContext : public CPUDeviceContext {
const
mkldnn
::
engine
&
GetEngine
()
const
{
return
tls
().
get_engine
();
}
const
mkldnn
::
engine
&
GetEngine
()
const
{
return
tls
().
get_engine
();
}
// Register object to currently used executor's map
// Register object to currently used executor's map
void
LinkEntryWithExecutor
(
BlobPtr_t
<
KeyBlob
>
,
KeyBlob
::
iterator
)
const
;
void
LinkEntryWithExecutor
(
void
RemoveShapeEntriesWithExecutor
(
void
)
const
;
BlobPtr_t
<
std
::
pair
<
unsigned
long
long
,
KeyBlob
>>
pblob
,
KeyBlob
::
iterator
it
)
const
;
void
RemoveShapeEntriesWithExecutor
(
std
::
string
)
const
;
std
::
string
PickLeastUsedShape
(
BlobPtr_t
<
ShapeBlob
>
sb
)
const
;
// Remove all entries from the blob map
// Remove all entries from the blob map
void
ResetBlobMap
(
void
*
ptr
);
void
ResetBlobMap
(
void
*
ptr
);
...
...
paddle/fluid/platform/mkldnn_reuse.h
浏览文件 @
bf748f24
...
@@ -500,18 +500,9 @@ class MKLDNNHandlerT {
...
@@ -500,18 +500,9 @@ class MKLDNNHandlerT {
}
}
void
AcquireReorder
(
const
std
::
shared_ptr
<
mkldnn
::
memory
>&
user_memory_p
,
void
AcquireReorder
(
const
std
::
shared_ptr
<
mkldnn
::
memory
>&
user_memory_p
,
const
std
::
shared_ptr
<
mkldnn
::
memory
>&
target_memory_p
,
const
std
::
shared_ptr
<
mkldnn
::
memory
>&
target_memory_p
)
{
const
std
::
string
&
suffix
)
{
auto
reorder_p
=
const
auto
key_reorder_p
=
key_
+
suffix
+
"reorder_p"
;
std
::
make_shared
<
mkldnn
::
reorder
>
(
*
user_memory_p
,
*
target_memory_p
);
auto
reorder_p
=
std
::
static_pointer_cast
<
mkldnn
::
reorder
>
(
dev_ctx_
.
GetBlob
(
key_reorder_p
));
if
(
reorder_p
==
nullptr
)
{
reorder_p
=
std
::
make_shared
<
mkldnn
::
reorder
>
(
*
user_memory_p
,
*
target_memory_p
);
dev_ctx_
.
SetBlob
(
key_reorder_p
,
reorder_p
);
}
auto
&
astream
=
platform
::
MKLDNNDeviceContext
::
tls
().
get_stream
();
auto
&
astream
=
platform
::
MKLDNNDeviceContext
::
tls
().
get_stream
();
...
@@ -578,6 +569,8 @@ class MKLDNNHandlerT {
...
@@ -578,6 +569,8 @@ class MKLDNNHandlerT {
std
::
static_pointer_cast
<
dnnl
::
memory
>
(
dev_ctx_
.
GetBlob
(
user_key
));
std
::
static_pointer_cast
<
dnnl
::
memory
>
(
dev_ctx_
.
GetBlob
(
user_key
));
user_memory_p
->
set_data_handle
(
ptr
);
user_memory_p
->
set_data_handle
(
ptr
);
// TODO(jczaja): Here we detect if reorder is cached it means it is needed
// need to change this to get rid of keys
auto
reorder_p
=
std
::
static_pointer_cast
<
mkldnn
::
reorder
>
(
auto
reorder_p
=
std
::
static_pointer_cast
<
mkldnn
::
reorder
>
(
dev_ctx_
.
GetBlob
(
key_reorder_p
));
dev_ctx_
.
GetBlob
(
key_reorder_p
));
if
(
reorder_p
!=
nullptr
)
{
if
(
reorder_p
!=
nullptr
)
{
...
...
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