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9fec1618
编写于
1月 18, 2021
作者:
H
hutuxian
提交者:
GitHub
1月 18, 2021
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差异文件
Ascend Framework Part3: Ascend Parser (#30391)
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e207fe63
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2
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2 changed file
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+768
-0
python/paddle/distributed/fleet/meta_optimizers/ascend/ascend_optimizer.py
...tributed/fleet/meta_optimizers/ascend/ascend_optimizer.py
+179
-0
python/paddle/distributed/fleet/meta_optimizers/ascend/ascend_parser.py
...distributed/fleet/meta_optimizers/ascend/ascend_parser.py
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python/paddle/distributed/fleet/meta_optimizers/ascend/ascend_optimizer.py
0 → 100644
浏览文件 @
9fec1618
# Copyright (c) 2021 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.
import
paddle.fluid.framework
as
framework
from
paddle.fluid.optimizer
import
Optimizer
import
paddle.fluid.core
as
core
import
numpy
as
np
import
ascend_parser
class
AscendIRParser
(
object
):
def
__init__
(
self
):
self
.
graph_idx
=
0
def
_construct_input_map
(
self
,
input_varlist
):
ret_map
=
{}
ge_in_operator
=
[]
for
id
,
var
in
enumerate
(
input_varlist
):
if
var
.
is_data
:
# input data
ge_input
=
core
.
GEOperatorFactory
.
create_operator
(
var
.
name
,
"Data"
).
set_attr_int32
(
"index"
,
id
)
ret_map
[
var
.
name
]
=
ge_input
ge_in_operator
.
append
(
ge_input
)
else
:
# param, learning ...
ge_input
=
core
.
GEOperatorFactory
.
create_operator
(
var
.
name
,
"Variable"
)
ge_input
.
update_output_desc
(
"y"
,
core
.
GETensorDesc
(
core
.
GEShape
(
var
.
shape
),
core
.
GEFormat
.
FORMAT_ND
,
core
.
GEDataType
.
DT_FLOAT
))
ret_map
[
var
.
name
]
=
ge_input
return
ge_in_operator
,
ret_map
def
parse_op
(
self
,
op
):
if
op
.
type
in
ascend_parser
.
registerd_op
:
print
(
"Op[%s] has been registered, begin to parse it"
%
(
op
.
type
))
op_parser
=
self
.
parser_factory
.
create_parse
(
ascend_parser
.
registerd_op
[
op
.
type
])
op_parser
.
apply
(
op
)
else
:
print
(
"Op[%s] has not been registered, so we have to skip it"
%
(
op
.
type
))
def
_parse_program
(
self
,
graph_name
,
program
,
input_varlist
=
[],
fetch_list
=
[]):
begin_graph_idx
=
self
.
graph_idx
ge_in_operator
=
[]
ge_out_operator
=
[]
self
.
var2geop
=
{}
block
=
program
.
global_block
()
if
len
(
block
.
ops
)
==
0
:
print
(
"There is no ops in program %s"
%
(
graph_name
))
return
[]
graph
=
core
.
GEGraph
(
graph_name
)
ge_in_operator
,
self
.
var2geop
=
self
.
_construct_input_map
(
input_varlist
)
self
.
parser_factory
=
ascend_parser
.
AscendParserFactory
(
graph
,
self
.
var2geop
)
for
i
,
curop
in
list
(
enumerate
(
block
.
ops
)):
self
.
parse_op
(
curop
)
# Set fetch_var for GE
for
e
in
fetch_list
:
name
=
e
if
not
isinstance
(
e
,
str
):
name
=
e
.
name
ge_out_operator
.
append
(
self
.
var2geop
[
name
])
# (Debug) If you want to print back prop vars, append/assign the varname in ge_out_operator here, such as:
# if graph_name == "main":
# ge_out_operator.append(self.var2geop["reduce_sum_0.tmp_0@GRAD"])
# Add ops that may be input of a graph, such as const.
for
varname
,
geop
in
self
.
var2geop
.
items
():
if
varname
.
startswith
(
"geinput"
):
ge_in_operator
.
append
(
geop
)
graph
.
set_inputs
(
ge_in_operator
).
set_outputs
(
ge_out_operator
)
# Remove ops of origin program
op_num
=
len
(
block
.
ops
)
for
i
in
range
(
op_num
-
1
,
-
1
,
-
1
):
block
.
_remove_op
(
i
)
input_varlist
=
[
var
for
var
in
input_varlist
if
var
.
is_data
]
block
.
append_op
(
type
=
"ascend_trigger"
,
inputs
=
{
"FeedList"
:
input_varlist
},
outputs
=
{
"FetchList"
:
fetch_list
},
attrs
=
{
'graph_idx'
:
self
.
graph_idx
})
self
.
graph_idx
+=
1
return
graph
def
parse_program
(
self
,
startup_program
,
main_program
,
input_varlist
,
fetch_list
):
startup_graph
=
self
.
_parse_program
(
"startup"
,
startup_program
)
main_graph
=
self
.
_parse_program
(
"main"
,
main_program
,
input_varlist
,
fetch_list
)
return
startup_graph
,
main_graph
# AscendOptimizer is a wrapper for basic optimizer now
# We will make it part of fleet meta_optimizer in the future
class
AscendOptimizer
(
Optimizer
):
def
__init__
(
self
,
optimizer
,
fetch_list
=
[]):
self
.
inner_opt
=
optimizer
self
.
fetch_list
=
fetch_list
def
__del__
(
self
):
core
.
ge_finalize
()
def
_can_apply
(
self
):
if
not
self
.
user_defined_strategy
.
ascend
:
return
False
# TODO(hutuxian): other check here
return
True
def
_disable_strategy
(
self
,
dist_strategy
):
dist_strategy
.
ascend
=
False
dist_strategy
.
ascend_configs
=
{}
def
_get_input_varlist
(
program
):
ret_list
=
[]
for
var
in
program
.
list_vars
():
if
var
.
is_data
or
var
.
persistable
:
ret_list
.
append
(
var
)
return
ret_list
def
minimize
(
self
,
loss
,
startup_program
=
None
,
parameter_list
=
None
,
no_grad_set
=
None
):
minimized
=
self
.
inner_opt
.
minimize
(
loss
,
startup_program
=
startup_program
)
self
.
ascend_instance
=
core
.
AscendInstance
()
# Config about Graph Engine can be found in https://support.huaweicloud.com/
config
=
{
"ge.exec.deviceId"
:
"0"
,
"ge.graphRunMode"
:
"1"
,
"ge.exec.precision_mode"
:
"must_keep_origin_dtype"
}
core
.
ge_initialize
(
config
)
# Init Session
self
.
ascend_instance
.
init_global_resources
()
main_block
=
loss
.
block
self
.
parser
=
AscendIRParser
()
input_varlist
=
_get_input_varlist
(
main_block
.
program
)
startup_graph
,
main_graph
=
self
.
parser
.
parse_program
(
startup_program
,
main_block
.
program
,
input_varlist
,
self
.
fetch_list
)
self
.
ascend_instance
.
add_ascend_subgraph
(
0
,
startup_graph
)
self
.
ascend_instance
.
add_ascend_subgraph
(
1
,
main_graph
)
return
minimized
python/paddle/distributed/fleet/meta_optimizers/ascend/ascend_parser.py
0 → 100644
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9fec1618
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