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体验新版 GitCode,发现更多精彩内容 >>
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9942565f
编写于
2月 12, 2018
作者:
武
武毅
提交者:
GitHub
2月 12, 2018
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差异文件
Merge pull request #8386 from typhoonzero/fix_dist_transpiler_develop
Fix dist transpiler develop
上级
da02a581
dca9941e
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
34 addition
and
56 deletion
+34
-56
paddle/fluid/operators/concat_op.h
paddle/fluid/operators/concat_op.h
+2
-2
paddle/fluid/operators/split_op.h
paddle/fluid/operators/split_op.h
+1
-1
paddle/fluid/operators/strided_memcpy.h
paddle/fluid/operators/strided_memcpy.h
+4
-4
python/paddle/v2/fluid/distribute_transpiler.py
python/paddle/v2/fluid/distribute_transpiler.py
+27
-46
python/paddle/v2/fluid/framework.py
python/paddle/v2/fluid/framework.py
+0
-3
未找到文件。
paddle/fluid/operators/concat_op.h
浏览文件 @
9942565f
...
...
@@ -38,7 +38,7 @@ class ConcatKernel : public framework::OpKernel<T> {
auto
in_stride
=
framework
::
stride_numel
(
in
->
dims
());
StridedNumelCopyWithAxis
<
T
>
(
ctx
.
device_context
(),
axis
,
out
->
data
<
T
>
()
+
output_offset
,
out_stride
,
in
->
data
<
T
>
(),
in_stride
);
in
->
data
<
T
>
(),
in_stride
,
in_stride
[
axis
]
);
output_offset
+=
in_stride
[
axis
];
}
}
...
...
@@ -59,7 +59,7 @@ class ConcatGradKernel : public framework::OpKernel<T> {
auto
out_stride
=
framework
::
stride_numel
(
out
->
dims
());
StridedNumelCopyWithAxis
<
T
>
(
ctx
.
device_context
(),
axis
,
out
->
data
<
T
>
(),
out_stride
,
in
->
data
<
T
>
()
+
input_offset
,
in_stride
);
in_stride
,
out_stride
[
axis
]
);
input_offset
+=
out_stride
[
axis
];
}
}
...
...
paddle/fluid/operators/split_op.h
浏览文件 @
9942565f
...
...
@@ -38,7 +38,7 @@ class SplitOpKernel : public framework::OpKernel<T> {
auto
out_stride
=
framework
::
stride_numel
(
out
->
dims
());
StridedNumelCopyWithAxis
<
T
>
(
ctx
.
device_context
(),
axis
,
out
->
data
<
T
>
(),
out_stride
,
in
->
data
<
T
>
()
+
input_offset
,
in_stride
);
in_stride
,
out_stride
[
axis
]
);
input_offset
+=
out_stride
[
axis
];
}
}
...
...
paddle/fluid/operators/strided_memcpy.h
浏览文件 @
9942565f
...
...
@@ -54,7 +54,8 @@ inline void StridedNumelCopyWithAxis(const platform::DeviceContext& ctx,
int64_t
axis
,
T
*
dst
,
const
framework
::
DDim
&
dst_stride_numel
,
const
T
*
src
,
const
framework
::
DDim
&
src_stride_numel
)
{
const
framework
::
DDim
&
src_stride_numel
,
int64_t
size
)
{
int64_t
before
=
dst_stride_numel
[
0
]
/
dst_stride_numel
[
axis
];
int64_t
src_after
=
src_stride_numel
[
axis
];
int64_t
dst_after
=
dst_stride_numel
[
axis
];
...
...
@@ -82,15 +83,14 @@ inline void StridedNumelCopyWithAxis(const platform::DeviceContext& ctx,
if
(
platform
::
is_cpu_place
(
place
))
{
auto
&
cpu_place
=
boost
::
get
<
platform
::
CPUPlace
>
(
place
);
memory
::
Copy
(
cpu_place
,
dst
+
i
*
dst_after
,
cpu_place
,
src
+
i
*
src_after
,
sizeof
(
T
)
*
s
rc_after
);
src
+
i
*
src_after
,
sizeof
(
T
)
*
s
ize
);
}
else
{
#ifdef PADDLE_WITH_CUDA
auto
&
gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
place
);
auto
&
cuda_ctx
=
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
);
memory
::
Copy
(
gpu_place
,
dst
+
i
*
dst_after
,
gpu_place
,
src
+
i
*
src_after
,
sizeof
(
T
)
*
src_after
,
cuda_ctx
.
stream
());
src
+
i
*
src_after
,
sizeof
(
T
)
*
size
,
cuda_ctx
.
stream
());
#else
PADDLE_THROW
(
"Paddle is not compiled with GPU"
);
#endif
...
...
python/paddle/v2/fluid/distribute_transpiler.py
浏览文件 @
9942565f
...
...
@@ -121,6 +121,7 @@ def split_dense_variable(var_list,
block_size
+=
dim1
-
remains
# update split_count after aligning
split_count
=
int
(
math
.
ceil
(
var_numel
/
float
(
block_size
)))
print
(
"###split var "
,
var
.
name
,
var
.
shape
,
block_size
,
split_count
)
for
block_id
in
xrange
(
split_count
):
curr_block_size
=
min
(
block_size
,
var_numel
-
(
(
block_id
)
*
block_size
))
...
...
@@ -191,7 +192,6 @@ class DistributeTranspiler:
for
b
in
param_blocks
:
varname
,
block_id
,
_
=
b
.
split
(
":"
)
send_outputs
.
append
(
param_var_mapping
[
varname
][
int
(
block_id
)])
# let send_op know which endpoint to send which var to, eplist has the same
# order as send_inputs.
eplist
=
split_method
(
send_inputs
,
pserver_endpoints
)
...
...
@@ -230,21 +230,6 @@ class DistributeTranspiler:
outputs
=
{
"Out"
:
[
orig_param
]},
attrs
=
{
"axis"
:
0
})
self
.
lr_param_mapping
=
self
.
_create_lr_param_mapping
()
def
_create_lr_param_mapping
(
self
):
lr_mapping
=
dict
()
for
_
,
opt_op
in
enumerate
(
self
.
optimize_ops
):
if
not
opt_op
.
inputs
or
not
opt_op
.
inputs
.
has_key
(
"LearningRate"
)
\
or
not
opt_op
.
inputs
.
has_key
(
"Param"
):
continue
lr
=
opt_op
.
inputs
[
"LearningRate"
].
name
param
=
opt_op
.
inputs
[
"Param"
].
name
if
not
lr_mapping
.
has_key
(
lr
):
lr_mapping
.
update
({
lr
:
list
()})
lr_mapping
[
lr
].
append
(
param
)
return
lr_mapping
def
_create_vars_from_blocklist
(
self
,
program
,
block_list
):
# Create respective variables using the block_list
block_map
=
dict
()
...
...
@@ -271,6 +256,7 @@ class DistributeTranspiler:
splited_shape
=
[
rows
]
if
len
(
orig_shape
)
>=
2
:
splited_shape
.
extend
(
orig_shape
[
1
:])
print
(
"###splited: "
,
size
,
rows
,
splited_shape
)
var
=
program
.
global_block
().
create_var
(
name
=
"%s.block%d"
%
(
varname
,
i
),
psersistable
=
False
,
...
...
@@ -278,6 +264,7 @@ class DistributeTranspiler:
type
=
orig_var
.
type
,
shape
=
splited_shape
)
# flattend splited var
var_mapping
[
varname
].
append
(
var
)
print
(
"###created split var "
,
var
)
return
var_mapping
def
_clone_var
(
self
,
block
,
var
):
...
...
@@ -369,18 +356,9 @@ class DistributeTranspiler:
pass
return
orig_shape
def
_fetch_var_names
(
self
,
param_dict
):
res
=
[]
if
not
param_dict
:
return
res
for
_
,
values
in
param_dict
.
iteritems
():
if
not
isinstance
(
values
,
list
):
values
=
[
values
]
res
+=
[
v
.
name
for
v
in
values
]
return
res
def
_append_pserver_ops
(
self
,
optimize_block
,
opt_op
,
endpoint
):
program
=
optimize_block
.
program
pserver_block
=
program
.
global_block
()
new_inputs
=
dict
()
# update param/grad shape first, then other inputs like
# moment can use the updated shape
...
...
@@ -395,11 +373,11 @@ class DistributeTranspiler:
# do not append this op if current endpoint
# is not dealing with this grad block
return
merged_var
=
p
rogram
.
global_block
()
.
vars
[
grad_block
.
name
]
merged_var
=
p
server_block
.
vars
[
grad_block
.
name
]
# append merging ops if trainers > 1
if
self
.
trainers
>
1
:
vars2merge
=
self
.
_create_var_for_trainers
(
p
rogram
.
global_block
()
,
grad_block
,
self
.
trainers
)
p
server_block
,
grad_block
,
self
.
trainers
)
optimize_block
.
append_op
(
type
=
"sum"
,
inputs
=
{
"X"
:
vars2merge
},
...
...
@@ -419,29 +397,27 @@ class DistributeTranspiler:
break
if
not
param_block
:
return
tmpvar
=
p
rogram
.
global_block
()
.
create_var
(
tmpvar
=
p
server_block
.
create_var
(
name
=
param_block
.
name
,
persistable
=
True
,
dtype
=
param_block
.
dtype
,
shape
=
param_block
.
shape
)
new_inputs
[
key
]
=
tmpvar
elif
key
==
"LearningRate"
:
# leraning rate variable has already be created by non-optimize op,
# don't create it once again.
new_inputs
[
key
]
=
program
.
global_block
().
vars
[
opt_op
.
input
(
key
)[
0
]]
new_inputs
[
key
]
=
pserver_block
.
vars
[
opt_op
.
input
(
key
)[
0
]]
for
key
in
opt_op
.
input_names
:
new_shape
=
None
if
key
in
[
"Param"
,
"Grad"
,
"LearningRate"
]:
continue
var
=
program
.
global_block
().
vars
[
opt_op
.
input
(
key
)[
0
]]
var
=
self
.
program
.
global_block
().
vars
[
opt_op
.
input
(
key
)[
0
]]
# update accumulator variable shape
param_shape
=
new_inputs
[
"Param"
].
shape
new_shape
=
self
.
_get_optimizer_input_shape
(
opt_op
.
type
,
key
,
var
.
shape
,
param_shape
)
tmpvar
=
p
rogram
.
global_block
()
.
create_var
(
tmpvar
=
p
server_block
.
create_var
(
name
=
var
.
name
,
persistable
=
var
.
persistable
,
dtype
=
var
.
dtype
,
...
...
@@ -449,11 +425,14 @@ class DistributeTranspiler:
new_inputs
[
key
]
=
tmpvar
# change output's ParamOut variable
opt_op
.
outputs
[
"ParamOut"
]
=
new_inputs
[
"Param"
]
outputs
=
self
.
_get_output_map_from_op
(
self
.
program
.
global_block
().
vars
,
opt_op
)
outputs
[
"ParamOut"
]
=
new_inputs
[
"Param"
]
optimize_block
.
append_op
(
type
=
opt_op
.
type
,
inputs
=
new_inputs
,
outputs
=
o
pt_op
.
o
utputs
,
outputs
=
outputs
,
attrs
=
opt_op
.
attrs
)
def
_append_pserver_non_opt_ops
(
self
,
optimize_block
,
opt_op
):
...
...
@@ -497,11 +476,12 @@ class DistributeTranspiler:
# If one op's input is another op's output or
# one op's output is another op's input, we say
# the two operator is connected.
op1_input_names
=
self
.
_fetch_var_names
(
op1
.
inputs
)
op1_output_names
=
self
.
_fetch_var_names
(
op1
.
outputs
)
op1_input_names
=
op1
.
desc
.
input_arg_names
()
op1_output_names
=
op1
.
desc
.
output_arg_names
()
op2_input_names
=
op2
.
desc
.
input_arg_names
()
op2_output_names
=
op2
.
desc
.
output_arg_names
()
op2_input_names
=
self
.
_fetch_var_names
(
op2
.
inputs
)
op2_output_names
=
self
.
_fetch_var_names
(
op2
.
outputs
)
if
set
(
op1_output_names
)
&
set
(
op2_input_names
)
or
\
set
(
op1_input_names
)
&
set
(
op2_output_names
):
return
True
...
...
@@ -521,8 +501,8 @@ class DistributeTranspiler:
def
_is_opt_op
(
self
,
op
):
# NOTE: It's a HACK implement.
# optimize op: SGDOptimize, MomentumOptimizer, AdamOptimizer and etc...
if
op
.
inputs
and
op
.
inputs
.
has_key
(
"Param"
)
\
and
op
.
inputs
.
has_key
(
"LearningRate"
)
:
if
"Param"
in
op
.
input_names
and
\
"LearningRate"
in
op
.
input_names
:
return
True
return
False
...
...
@@ -530,12 +510,12 @@ class DistributeTranspiler:
param_names
=
[
p
.
name
for
p
in
self
.
param_grad_ep_mapping
[
endpoint
][
"params"
]
]
if
op
.
input
s
[
"Param"
].
name
in
param_names
:
if
op
.
input
(
"Param"
)
in
param_names
:
return
True
else
:
for
n
in
param_names
:
param
=
op
.
input
s
[
"Param"
].
name
if
same_or_split_var
(
n
,
param
)
and
n
!=
op
.
inputs
[
"Param"
].
name
:
param
=
op
.
input
(
"Param"
)[
0
]
if
same_or_split_var
(
n
,
param
)
and
n
!=
param
:
return
True
return
False
return
False
...
...
@@ -551,6 +531,8 @@ class DistributeTranspiler:
"""
# step5
pserver_program
=
Program
()
print
(
"param mapping on pserver: #### "
,
self
.
param_grad_ep_mapping
[
endpoint
][
"params"
])
for
v
in
self
.
param_grad_ep_mapping
[
endpoint
][
"params"
]:
self
.
_clone_var
(
pserver_program
.
global_block
(),
v
)
for
v
in
self
.
param_grad_ep_mapping
[
endpoint
][
"grads"
]:
...
...
@@ -564,7 +546,6 @@ class DistributeTranspiler:
persistable
=
True
,
dtype
=
v
.
dtype
,
shape
=
v
.
shape
)
# step6
optimize_block
=
pserver_program
.
create_block
(
0
)
# step 6.1
...
...
python/paddle/v2/fluid/framework.py
浏览文件 @
9942565f
...
...
@@ -400,9 +400,6 @@ class Operator(object):
"""
self
.
block
=
block
self
.
desc
=
desc
# for clone a new operator
self
.
inputs
=
inputs
self
.
outputs
=
outputs
self
.
attrs
=
attrs
if
len
(
self
.
desc
.
type
())
!=
0
:
return
...
...
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