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cf5de26f
编写于
12月 07, 2021
作者:
W
Wilber
提交者:
GitHub
12月 07, 2021
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
ut support block (#37909)
上级
b48545ee
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
120 addition
and
5 deletion
+120
-5
python/paddle/fluid/tests/unittests/ir/inference/program_config.py
...ddle/fluid/tests/unittests/ir/inference/program_config.py
+120
-5
未找到文件。
python/paddle/fluid/tests/unittests/ir/inference/program_config.py
浏览文件 @
cf5de26f
...
...
@@ -14,6 +14,7 @@
from
typing
import
Optional
,
List
,
Callable
,
Dict
,
Any
,
Set
import
numpy
as
np
import
enum
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
...
...
@@ -57,6 +58,12 @@ class TensorConfig:
return
str
({
'shape'
:
self
.
shape
,
'lod'
:
self
.
lod
,
'dtype'
:
self
.
dtype
})
class
VarType
(
enum
.
Enum
):
LOD_TENSOR
=
1
LOD_TENSOR_ARRAY
=
2
STEP_SCOPES
=
3
class
OpConfig
:
''' A config builder for generating a Op. '''
...
...
@@ -65,10 +72,14 @@ class OpConfig:
inputs
:
Dict
[
str
,
List
[
str
]],
outputs
:
Dict
[
str
,
List
[
str
]],
attrs
:
Dict
[
str
,
Any
]
=
None
,
outputs_var_type
:
Dict
[
str
,
VarType
]
=
None
,
outputs_dtype
:
Dict
[
str
,
np
.
dtype
]
=
None
,
**
kwargs
):
self
.
type
=
type
self
.
inputs
=
inputs
self
.
outputs
=
outputs
self
.
outputs_dtype
=
outputs_dtype
self
.
outputs_var_type
=
outputs_var_type
self
.
attrs
=
attrs
if
self
.
attrs
is
None
:
self
.
attrs
=
dict
()
...
...
@@ -80,6 +91,88 @@ class OpConfig:
return
log_str
_OP_WITHOUT_KERNEL_SET
=
{
'feed'
,
'fetch'
,
'recurrent'
,
'go'
,
'rnn_memory_helper_grad'
,
'conditional_block'
,
'while'
,
'send'
,
'recv'
,
'listen_and_serv'
,
'fl_listen_and_serv'
,
'ncclInit'
,
'select'
,
'checkpoint_notify'
,
'gen_bkcl_id'
,
'c_gen_bkcl_id'
,
'gen_nccl_id'
,
'c_gen_nccl_id'
,
'c_comm_init'
,
'c_sync_calc_stream'
,
'c_sync_comm_stream'
,
'queue_generator'
,
'dequeue'
,
'enqueue'
,
'heter_listen_and_serv'
,
'c_wait_comm'
,
'c_wait_compute'
,
'c_gen_hccl_id'
,
'c_comm_init_hccl'
,
'copy_cross_scope'
}
class
BlockConfig
:
''' A config builder for generating a Block. '''
def
__init__
(
self
,
ops
:
List
[
OpConfig
],
vars
:
List
[
str
],
vars_dtype
:
Dict
[
str
,
np
.
dtype
]
=
None
,
vars_var_type
:
Dict
[
str
,
VarType
]
=
None
,
vars_lod_level
:
Dict
[
str
,
int
]
=
None
):
self
.
ops
=
ops
self
.
vars
=
vars
self
.
vars_dtype
=
vars_dtype
self
.
vars_var_type
=
vars_var_type
self
.
vars_lod_level
=
vars_lod_level
def
fill_block_desc
(
self
,
block_desc
):
for
name
in
self
.
vars
:
var_desc
=
block_desc
.
var
(
cpt
.
to_bytes
(
name
))
var_desc
.
set_type
(
core
.
VarDesc
.
VarType
.
LOD_TENSOR
)
if
self
.
vars_lod_level
is
not
None
and
name
in
self
.
vars_lod_level
.
keys
(
):
var_desc
.
set_lod_level
(
self
.
vars_lod_level
[
name
])
if
self
.
vars_var_type
is
not
None
and
name
in
self
.
vars_var_type
.
keys
(
):
if
self
.
vars_var_type
[
name
]
==
VarType
.
LOD_TENSOR_ARRAY
:
var_desc
.
set_type
(
core
.
VarDesc
.
VarType
.
LOD_TENSOR_ARRAY
)
elif
self
.
vars_var_type
[
name
]
==
VarType
.
STEP_SCOPES
:
var_desc
.
set_type
(
core
.
VarDesc
.
VarType
.
STEP_SCOPES
)
continue
var_desc
.
set_dtype
(
convert_np_dtype_to_dtype_
(
np
.
float32
))
if
self
.
vars_dtype
is
not
None
and
name
in
self
.
vars_dtype
.
keys
():
var_desc
.
set_dtype
(
convert_np_dtype_to_dtype_
(
self
.
vars_dtype
[
name
]))
for
op_config
in
self
.
ops
:
op_desc
=
block_desc
.
append_op
()
op_desc
.
set_type
(
op_config
.
type
)
for
name
,
values
in
op_config
.
inputs
.
items
():
op_desc
.
set_input
(
name
,
values
)
for
name
,
values
in
op_config
.
attrs
.
items
():
op_desc
.
_set_attr
(
name
,
values
)
for
name
,
values
in
op_config
.
outputs
.
items
():
op_desc
.
set_output
(
name
,
values
)
for
v
in
values
:
if
block_desc
.
has_var_recursive
(
cpt
.
to_bytes
(
v
)):
continue
var_desc
=
block_desc
.
var
(
cpt
.
to_bytes
(
v
))
var_desc
.
set_type
(
core
.
VarDesc
.
VarType
.
LOD_TENSOR
)
if
op_config
.
outputs_var_type
is
not
None
and
v
in
op_config
.
outputs_var_type
.
keys
(
):
if
op_config
.
outputs_var_type
[
v
]
==
VarType
.
LOD_TENSOR_ARRAY
:
var_desc
.
set_type
(
core
.
VarDesc
.
VarType
.
LOD_TENSOR_ARRAY
)
elif
op_config
.
outputs_var_type
[
v
]
==
VarType
.
STEP_SCOPES
:
var_desc
.
set_type
(
core
.
VarDesc
.
VarType
.
STEP_SCOPES
)
continue
var_desc
.
set_dtype
(
convert_np_dtype_to_dtype_
(
np
.
float32
))
if
op_config
.
outputs_dtype
is
not
None
and
v
in
op_config
.
outputs_dtype
.
keys
(
):
var_desc
.
set_dtype
(
convert_np_dtype_to_dtype_
(
op_config
.
outputs_dtype
[
v
]))
if
op_config
.
type
not
in
_OP_WITHOUT_KERNEL_SET
:
op_desc
.
infer_var_type
(
block_desc
)
op_desc
.
infer_shape
(
block_desc
)
op_desc
.
check_attrs
()
class
ProgramConfig
:
''' A config builder for generating a Program. '''
...
...
@@ -137,6 +230,8 @@ def create_fake_model(program_config):
var_desc
.
set_dtype
(
convert_np_dtype_to_dtype_
(
tensor_config
.
dtype
))
var_desc
.
set_shape
(
tensor_config
.
shape
)
var_desc
.
set_need_check_feed
(
True
)
if
tensor_config
.
lod
is
not
None
:
var_desc
.
set_lod_level
(
len
(
tensor_config
.
lod
))
op_desc
=
main_block_desc
.
_prepend_op
()
op_desc
.
set_type
(
"feed"
)
op_desc
.
set_input
(
'X'
,
[
"feed"
])
...
...
@@ -177,16 +272,36 @@ def create_fake_model(program_config):
for
name
,
values
in
op_config
.
inputs
.
items
():
op_desc
.
set_input
(
name
,
values
)
for
name
,
values
in
op_config
.
attrs
.
items
():
op_desc
.
_set_attr
(
name
,
values
)
if
name
==
'sub_block'
:
sub_block_desc
=
main_program_desc
.
append_block
(
main_block_desc
)
values
.
fill_block_desc
(
sub_block_desc
)
op_desc
.
_set_attr
(
name
,
sub_block_desc
)
else
:
op_desc
.
_set_attr
(
name
,
values
)
for
name
,
values
in
op_config
.
outputs
.
items
():
op_desc
.
set_output
(
name
,
values
)
for
v
in
values
:
if
main_block_desc
.
has_var_recursive
(
cpt
.
to_bytes
(
v
)):
continue
var_desc
=
main_block_desc
.
var
(
cpt
.
to_bytes
(
v
))
var_desc
.
set_type
(
core
.
VarDesc
.
VarType
.
LOD_TENSOR
)
var_desc
.
set_dtype
(
convert_np_dtype_to_dtype_
(
tensor_config
.
dtype
))
op_desc
.
infer_var_type
(
main_block_desc
)
op_desc
.
infer_shape
(
main_block_desc
)
if
op_config
.
outputs_var_type
is
not
None
and
v
in
op_config
.
outputs_var_type
.
keys
(
):
if
op_config
.
outputs_var_type
[
v
]
==
VarType
.
LOD_TENSOR_ARRAY
:
var_desc
.
set_type
(
core
.
VarDesc
.
VarType
.
LOD_TENSOR_ARRAY
)
elif
op_config
.
outputs_var_type
[
v
]
==
VarType
.
STEP_SCOPES
:
var_desc
.
set_type
(
core
.
VarDesc
.
VarType
.
STEP_SCOPES
)
continue
var_desc
.
set_dtype
(
convert_np_dtype_to_dtype_
(
np
.
float32
))
if
op_config
.
outputs_dtype
is
not
None
and
v
in
op_config
.
outputs_dtype
.
keys
(
):
var_desc
.
set_dtype
(
convert_np_dtype_to_dtype_
(
op_config
.
outputs_dtype
[
v
]))
if
op_config
.
type
not
in
_OP_WITHOUT_KERNEL_SET
:
op_desc
.
infer_var_type
(
main_block_desc
)
op_desc
.
infer_shape
(
main_block_desc
)
op_desc
.
check_attrs
()
for
index
,
name
in
enumerate
(
program_config
.
outputs
):
var_desc
=
main_block_desc
.
var
(
cpt
.
to_bytes
(
"fetch"
))
...
...
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