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3881b6cb
编写于
3月 14, 2022
作者:
Z
zhaoyingli
提交者:
GitHub
3月 14, 2022
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差异文件
[AutoParallel] Converter (#40434)
* [AutoParallel] Converter Converter API
上级
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变更
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5 changed file
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+610
-1
python/paddle/distributed/auto_parallel/converter.py
python/paddle/distributed/auto_parallel/converter.py
+455
-0
python/paddle/fluid/tests/unittests/auto_parallel/CMakeLists.txt
...paddle/fluid/tests/unittests/auto_parallel/CMakeLists.txt
+2
-0
python/paddle/fluid/tests/unittests/auto_parallel/converter.py
...n/paddle/fluid/tests/unittests/auto_parallel/converter.py
+83
-0
python/paddle/fluid/tests/unittests/auto_parallel/test_converter.py
...dle/fluid/tests/unittests/auto_parallel/test_converter.py
+69
-0
python/paddle/fluid/tests/unittests/auto_parallel/test_engine_api.py
...le/fluid/tests/unittests/auto_parallel/test_engine_api.py
+1
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未找到文件。
python/paddle/distributed/auto_parallel/converter.py
0 → 100644
浏览文件 @
3881b6cb
# Copyright (c) 2022 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
import
warnings
import
logging
import
numpy
as
np
from
..utils
import
get_logger
class
Converter
(
object
):
"""
Converter is a class object for auto parallel to convert tensors from
one parallel strategy to another one. Tensors will merge and slice value
with their strategy when strategies are different.
"""
def
__init__
(
self
,
tensors_dict
,
pre_strategy
,
cur_strategy
):
"""
Args:
tensors_dict(dict): tensors' value of all ranks that to be converted.
key is tensor's name(str), value is all ranks' data(list(numpy.ndarray))
pre_strategy(dict): tensors' distributed attribute of last training process.
key is tensor's name(str), value is tensor's distributed attribute in last
training process.
cur_strategy(dict): tensors' distributed attribute of current rank.
key is tensor's name(str), value is tensor's distributed attribute in current
rank.
"""
self
.
_tensors_dict
=
self
.
_check_tensor_dict
(
tensors_dict
)
self
.
_pre_strategy
=
self
.
_check_pre_strategy
(
pre_strategy
)
self
.
_cur_strategy
=
self
.
_check_cur_strategy
(
cur_strategy
)
self
.
_logger
=
get_logger
(
logging
.
INFO
)
def
_check_tensor_dict
(
self
,
tensors_dict
):
if
not
tensors_dict
:
raise
ValueError
(
"'tensors_dict' is None, "
"the tensors to be converted cannot be None."
)
if
not
isinstance
(
tensors_dict
,
dict
):
raise
TypeError
(
"The type of 'tensors_dict' should be 'dict', but got '{}'."
.
format
(
str
(
type
(
tensors_dict
))))
return
tensors_dict
def
_check_pre_strategy
(
self
,
pre_strategy
):
if
not
pre_strategy
:
raise
ValueError
(
"'pre_strategy' is None, "
"there are not tensors in pre process."
)
if
not
isinstance
(
pre_strategy
,
dict
):
raise
TypeError
(
"The type of 'pre_strategy' should be 'dict', "
"but got '{}'."
.
format
(
str
(
type
(
pre_strategy
))))
return
pre_strategy
def
_check_cur_strategy
(
self
,
cur_strategy
):
if
not
cur_strategy
:
warnings
.
warn
(
"'cur_strategy' is None, "
"there are not tensors in cur process"
)
if
not
isinstance
(
cur_strategy
,
dict
):
raise
TypeError
(
"The type of 'cur_strategy' should be 'dict', "
"but got '{}'."
.
format
(
str
(
type
(
cur_strategy
))))
return
cur_strategy
def
convert
(
self
,
strict
=
True
):
"""
Convert tensors
Args:
strict(bool): whether to strict convert tensor with tensor's name. If False, it will
convert tensors by prefix matching. Otherwise, tensors will be converted with
their name strictly.
Returns:
converted tensors(dict)
Examples:
.. code-block:: python
import numpy as np
complete_tensors = np.arange(4).reshape([2, 2])
partitial_tensors = np.split(complete_tensors, 2, axis=0)
name = "tmp_0"
tensors_dict = {name: partitial_tensors}
strategy_1 = {
name: {
"process_shape": [2],
"process_group": [0, 1],
"dims_mapping": [0, -1]
}
}
strategy_2 = {
name: {
"process_shape": [2],
"process_group": [0, 1],
"dims_mapping": [-1, -1]
}
}
converter = Converter(tensors_dict, strategy_1, strategy_2)
result = converter.convert()
# the result's value is equal to `complete_tensors`
"""
tensors_dict
=
{}
# the name which is in cur_process but not in pre_process
tensor_not_in_pre
=
[]
# the name which is in pre_process but not in cur_process
tensor_not_in_cur
=
[]
# the name which is in strategy but not in ckpt files
tensor_not_in_ckpt
=
[]
self
.
_logger
.
info
(
"Start to convert tensors."
)
for
tensor_name
in
self
.
_cur_strategy
:
if
tensor_name
not
in
self
.
_pre_strategy
:
tensor_not_in_pre
.
append
(
tensor_name
)
continue
if
tensor_name
not
in
self
.
_tensors_dict
:
tensor_not_in_ckpt
.
append
(
tensor_name
)
continue
self
.
_pre_name
=
tensor_name
self
.
_cur_name
=
tensor_name
tensor_list
=
self
.
_tensors_dict
[
tensor_name
]
pre_dist_attr
=
self
.
_pre_strategy
[
tensor_name
]
cur_dist_attr
=
self
.
_cur_strategy
[
tensor_name
]
try
:
tensors_dict
[
tensor_name
]
=
Converter
.
merge_and_slice
(
tensor_list
,
pre_dist_attr
,
cur_dist_attr
)
except
ValueError
as
err
:
raise
ValueError
(
"Fail to convert tensor '{}'. "
.
format
(
str
(
tensor_name
))
+
str
(
err
))
for
tensor_name
in
self
.
_pre_strategy
:
if
tensor_name
not
in
self
.
_cur_strategy
:
tensor_not_in_cur
.
append
(
tensor_name
)
if
not
strict
:
tensors_dict
,
tensor_match_with_pre
,
tensor_match_with_cur
=
self
.
convert_with_prefix_match
(
tensors_dict
,
tensor_not_in_pre
,
tensor_not_in_cur
)
else
:
tensors_dict
,
tensor_match_with_pre
,
tensor_match_with_cur
=
tensors_dict
,
[],
[]
tensor_not_in_pre
=
set
(
tensor_not_in_pre
)
-
set
(
tensor_match_with_pre
)
tensor_not_in_cur
=
set
(
tensor_not_in_cur
)
-
set
(
tensor_match_with_cur
)
if
tensor_not_in_pre
:
warnings
.
warn
(
"tensors [{}] are not found in last training strategy."
.
format
(
str
(
tensor_not_in_pre
)))
if
tensor_not_in_cur
:
warnings
.
warn
(
"tensors [{}] are not found in current training strategy."
.
format
(
str
(
tensor_not_in_cur
)))
if
tensor_not_in_ckpt
:
warnings
.
warn
(
"tensors [{}] are found in pre_strategy, but are not found"
"in checkpoint files, please check your checkpoint files."
.
format
(
str
(
tensor_not_in_ckpt
)))
return
tensors_dict
def
convert_with_prefix_match
(
self
,
tensors_dict
,
tensor_not_in_pre
,
tensor_not_in_cur
):
# the name which in cur_process and can match with pre_process
tensor_match_with_pre
=
[]
# the name which in pre_process and can match with cur_process
tensor_match_with_cur
=
[]
for
cur_name
in
tensor_not_in_pre
:
prefix_name
=
cur_name
while
prefix_name
.
find
(
"_"
)
!=
-
1
:
prefix_name
=
prefix_name
[:
prefix_name
.
rfind
(
"_"
)]
for
pre_name
in
tensor_not_in_cur
:
if
prefix_name
in
pre_name
:
# 'cur_name' of cur_process can match with 'pre_name' of pre_process
self
.
_pre_name
=
pre_name
self
.
_cur_name
=
cur_name
pre_tensor_list
=
self
.
_tensors_dict
[
pre_name
]
pre_dist_attr
=
self
.
_pre_strategy
[
pre_name
]
cur_dist_attr
=
self
.
_cur_strategy
[
cur_name
]
try
:
tensors_dict
[
cur_name
]
=
Converter
.
merge_and_slice
(
pre_tensor_list
,
pre_dist_attr
,
cur_dist_attr
)
except
ValueError
as
err
:
raise
ValueError
(
"Fail to convert tensor '{}' by '{}'. "
.
format
(
str
(
cur_name
),
str
(
pre_name
))
+
str
(
err
))
self
.
_logger
.
info
(
"tensor [{}] is matched with tensor [{}]"
.
format
(
cur_name
,
pre_name
))
tensor_match_with_pre
.
append
(
cur_name
)
tensor_match_with_cur
.
append
(
pre_name
)
break
break
return
tensors_dict
,
tensor_match_with_pre
,
tensor_match_with_cur
@
staticmethod
def
merge_and_slice
(
tensor_list
,
pre_dist_attr
,
cur_dist_attr
):
"""
Merge tensors with previous dist_attr and slice tensors with current dist_attr
Returns:
tensor(numpy.narray): a tensor's value of current rank.
"""
assert
isinstance
(
tensor_list
,
list
)
assert
all
(
isinstance
(
p
,
np
.
ndarray
)
for
p
in
tensor_list
)
if
pre_dist_attr
==
cur_dist_attr
:
# skip merge and slice tensor
rank_id
=
paddle
.
distributed
.
get_rank
()
index
=
cur_dist_attr
[
"process_group"
].
index
(
rank_id
)
tensor
=
tensor_list
[
index
]
else
:
pre_dims_mapping
=
pre_dist_attr
[
"dims_mapping"
]
cur_dims_mapping
=
cur_dist_attr
[
"dims_mapping"
]
if
len
(
set
(
pre_dims_mapping
))
>
1
or
-
1
not
in
pre_dims_mapping
:
# merge tensor
tensor
=
Converter
.
merge_with_dist_attr
(
tensor_list
,
pre_dist_attr
)
else
:
# skip merge tensor
tensor
=
tensor_list
[
0
]
if
len
(
set
(
cur_dims_mapping
))
>
1
or
-
1
not
in
cur_dims_mapping
:
# slice tensor
tensor
=
Converter
.
slice_with_dist_attr
(
tensor
,
cur_dist_attr
)
return
tensor
@
staticmethod
def
merge_with_dist_attr
(
tensor_list
,
dist_attr
):
""" Merge tensor with distributed attribute """
from
.reshard
import
_compute_complete_shape
,
_compute_partition_index
dims_mapping
=
dist_attr
[
"dims_mapping"
]
process_shape
=
dist_attr
[
"process_shape"
]
process_group
=
dist_attr
[
"process_group"
]
# get the complete shape of the tensor
complete_shape
=
_compute_complete_shape
(
tensor_list
[
0
].
shape
,
process_shape
,
dims_mapping
)
# merge the tensor with dist_attr
partition_tensor_list
=
[]
merged_partiton
=
[]
for
process
in
process_group
:
partition_index
=
_compute_partition_index
(
process
,
complete_shape
,
dims_mapping
,
process_shape
,
process_group
)
index
=
process_group
.
index
(
process
)
if
partition_index
not
in
merged_partiton
:
merged_partiton
.
append
(
partition_index
)
Converter
.
merge
(
partition_tensor_list
,
tensor_list
[
index
],
partition_index
,
complete_shape
)
if
len
(
partition_tensor_list
)
!=
1
:
raise
ValueError
(
"Fail to merge tensor with dist_attr '{}'."
.
format
(
str
(
dist_attr
)))
complete_tensor
=
partition_tensor_list
[
0
][
0
]
return
complete_tensor
@
staticmethod
def
slice_with_dist_attr
(
tensor
,
dist_attr
):
""" Slice tensor with distributed attribute """
dims_mapping
=
dist_attr
[
"dims_mapping"
]
process_shape
=
dist_attr
[
"process_shape"
]
process_group
=
dist_attr
[
"process_group"
]
# slice the tensor with dist_attr
partition_index_list
=
Converter
.
_get_split_indices
(
tensor
.
shape
,
dims_mapping
,
process_shape
,
process_group
)
sliced_tensor_list
=
Converter
.
split
(
tensor
,
partition_index_list
,
len
(
partition_index_list
))
# get the current tensor's index in sliced_tensor_list
rank_id
=
paddle
.
distributed
.
get_rank
()
sliced_tensor_index
=
Converter
.
_get_sliced_index
(
rank_id
,
tensor
.
shape
,
dims_mapping
,
process_shape
,
process_group
)
if
sliced_tensor_index
not
in
range
(
len
(
sliced_tensor_list
)):
raise
ValueError
(
"Fail to slice tensor with dist_attr '{}'."
.
format
(
str
(
dist_attr
)))
sliced_tensor
=
sliced_tensor_list
[
sliced_tensor_index
]
return
sliced_tensor
@
staticmethod
def
merge
(
partition_tensor_list
,
tensor
,
partition_index
,
complete_shape
):
"""
Merge partitial tensors to a complete.
Returns:
None
Examples:
.. code-block:: python
import numpy as np
partition_tensor_list = [(np.array([[[1.11, 1.12]]]), [[0,1],[0,1],[0,2]])]
tensor = np.array([[[1.13, 1.14]]])
partition_index = [[0,1],[0,1],[2,4]]
_merge_tensor(partition_tensor_list, tensor, partition_index)
# partition_tensor_list: [(np.array([[[1.11, 1.12, 1.13, 1.14]]]), [[0,1],[0,1],[0,4]])]
"""
from
.reshard
import
_compute_concat_info
if
len
(
partition_tensor_list
)
==
1
:
is_complete_data
=
True
for
idx
,
item
in
enumerate
(
partition_tensor_list
[
0
][
1
]):
if
item
[
0
]
!=
0
or
item
[
1
]
!=
complete_shape
[
idx
]:
is_complete_data
=
False
break
if
is_complete_data
:
return
if
not
partition_tensor_list
:
partition_tensor_list
.
append
((
tensor
,
partition_index
))
else
:
i
=
0
while
i
<
len
(
partition_tensor_list
):
concat_axis
,
first_order
,
new_partition
=
_compute_concat_info
(
partition_tensor_list
[
i
][
1
],
partition_index
)
if
concat_axis
!=
-
1
:
if
first_order
==
0
:
new_tensor
=
np
.
concatenate
(
(
partition_tensor_list
[
i
][
0
],
tensor
),
axis
=
concat_axis
)
else
:
new_tensor
=
np
.
concatenate
(
(
tensor
,
partition_tensor_list
[
i
][
0
]),
axis
=
concat_axis
)
partition_tensor_list
.
pop
(
i
)
Converter
.
merge
(
partition_tensor_list
,
new_tensor
,
new_partition
,
complete_shape
)
break
i
+=
1
@
staticmethod
def
split
(
complete_tensor
,
partition_index_list
,
length
):
"""
Slice a complete tensor.
Returns:
sliced_tensor_list(list): sliced tensors with 'partition_index_list'
Examples:
.. code-block:: python
import numpy as np
complete_tensor = np.array([[[1.11, 1.12, 1.13, 1.14, 1.15, 1.16]]])
rank = 2
complete_shape = [1, 1, 6]
dims_mapping = [-1, -1, 0]
process_shape = [3]
process_group = [0, 1, 2]
sliced_tensor_list = split(complete_tensor, [[], [], [2, 4]], 3)
# [array([[[1.11, 1.12]]]), array([[[1.13, 1.14]]]), array([[[1.15, 1.16]]])]
"""
sliced_tensor_list
=
[]
axis
=
len
(
complete_tensor
.
shape
)
-
length
sliced_tensor
=
np
.
split
(
complete_tensor
,
partition_index_list
[
axis
],
axis
=
axis
)
if
length
==
1
:
return
sliced_tensor
for
tensor
in
sliced_tensor
:
sliced_tensor_list
.
extend
(
Converter
.
split
(
tensor
,
partition_index_list
,
length
-
1
))
return
sliced_tensor_list
@
staticmethod
def
_get_split_indices
(
complete_shape
,
dims_mapping
,
process_shape
,
process_group
):
"""
Get split indices of every dimension.
Returns:
split_indices_list(list): the split indices of every dimension of the tensor
Examples:
.. code-block:: python
import numpy as np
complete_tensor = np.array([[[1.11, 1.12, 1.13, 1.14, 1.15, 1.16]]])
complete_shape = [1, 1, 6]
dims_mapping = [-1, -1, 0]
process_shape = [3]
process_group = [0, 1, 2]
index = _get_split_indices(complete_shape, dims_mapping, process_shape, process_group)
# index: [[], [], [2, 4]]
"""
from
.reshard
import
_compute_partition_index
split_indices_list
=
[]
for
process
in
process_group
:
partition_index
=
_compute_partition_index
(
process
,
complete_shape
,
dims_mapping
,
process_shape
,
process_group
)
if
split_indices_list
:
for
dim
in
range
(
len
(
partition_index
)):
split_indices_list
[
dim
].
extend
(
partition_index
[
dim
])
else
:
split_indices_list
=
partition_index
split_indices_list
=
list
(
map
(
lambda
x
,
y
:
list
(
set
(
x
)
-
set
([
y
])
-
set
([
0
])),
split_indices_list
,
complete_shape
))
split_indices_list
=
[
sorted
(
x
)
for
x
in
split_indices_list
]
return
split_indices_list
@
staticmethod
def
_get_sliced_index
(
rank_id
,
complete_shape
,
dims_mapping
,
process_shape
,
process_group
):
"""
Get sliced_tensor's index of current rank in all sliced tensors list.
Returns:
sliced_tensor_index(int): the index of sliced tensor in sliced_tensor_list
Examples:
.. code-block:: python
import numpy as np
complete_tensor = np.array([[[1.11, 1.12, 1.13, 1.14, 1.15, 1.16]]])
rank = 2
complete_shape = [1, 1, 6]
dims_mapping = [-1, -1, 0]
process_shape = [3]
process_group = [0, 1, 2]
slice_tensor = _slice_tensor(complete_tensor, [[], [], [2, 4]], 3)
# slice_tensor:
# [array([[[1.11, 1.12]]]), array([[[1.13, 1.14]]]), array([[[1.15, 1.16]]])]
index = _get_sliced_index(rank, complete_shape, dims_mapping
process_shape, process_group)
# index: 2
"""
from
.reshard
import
_compute_partition_index
partition_index
=
_compute_partition_index
(
rank_id
,
complete_shape
,
dims_mapping
,
process_shape
,
process_group
)
sliced_index
=
0
for
i
,
shape
in
enumerate
(
complete_shape
):
if
dims_mapping
[
i
]
==
-
1
:
slice_shape
=
shape
else
:
slice_shape
=
shape
//
process_shape
[
dims_mapping
[
i
]]
if
shape
==
1
:
index
=
0
else
:
index
=
(
partition_index
[
i
][
0
]
+
1
)
//
slice_shape
sliced_index
=
sliced_index
*
(
shape
//
slice_shape
)
+
index
return
sliced_index
python/paddle/fluid/tests/unittests/auto_parallel/CMakeLists.txt
浏览文件 @
3881b6cb
...
...
@@ -9,4 +9,6 @@ if(WITH_DISTRIBUTE AND WITH_GPU)
set_tests_properties
(
test_relaunch_with_gpt_planner PROPERTIES LABELS
"RUN_TYPE=EXCLUSIVE"
TIMEOUT 240
)
py_test_modules
(
test_engine_api MODULES test_engine_api ENVS
${
dist_ENVS
}
)
set_tests_properties
(
test_engine_api PROPERTIES LABELS
"RUN_TYPE=EXCLUSIVE"
TIMEOUT 80
)
py_test_modules
(
test_converter MODULES test_converter ENVS
${
dist_ENVS
}
)
set_tests_properties
(
test_converter PROPERTIES LABELS
"RUN_TYPE=EXCLUSIVE"
TIMEOUT 50
)
endif
()
python/paddle/fluid/tests/unittests/auto_parallel/converter.py
0 → 100644
浏览文件 @
3881b6cb
# Copyright (c) 2022 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
unittest
import
numpy
as
np
import
paddle
from
paddle.distributed.auto_parallel.converter
import
Converter
def
test_convert
():
rank_id
=
paddle
.
distributed
.
get_rank
()
complete_tensor
=
np
.
arange
(
64
).
reshape
([
8
,
8
])
tensor_row
=
np
.
split
(
complete_tensor
,
2
,
axis
=
0
)
tensor_col
=
np
.
split
(
complete_tensor
,
2
,
axis
=
1
)
tensor_name
=
"tensor_0"
complet_strategy
=
{
tensor_name
:
{
"process_shape"
:
[
2
],
"process_group"
:
[
0
,
1
],
"dims_mapping"
:
[
-
1
,
-
1
]
}
}
row_strategy
=
{
tensor_name
:
{
"process_shape"
:
[
2
],
"process_group"
:
[
0
,
1
],
"dims_mapping"
:
[
0
,
-
1
]
}
}
col_strategy
=
{
tensor_name
:
{
"process_shape"
:
[
2
],
"process_group"
:
[
0
,
1
],
"dims_mapping"
:
[
-
1
,
0
]
}
}
# test merge
tensor_dict
=
{
tensor_name
:
tensor_row
}
converter
=
Converter
(
tensor_dict
,
row_strategy
,
complet_strategy
)
convert_tensor_dict
=
converter
.
convert
()
assert
np
.
equal
(
convert_tensor_dict
[
tensor_name
],
complete_tensor
).
all
()
# test slice
tensor_dict
=
{
tensor_name
:
[
complete_tensor
]}
converter
=
Converter
(
tensor_dict
,
complet_strategy
,
col_strategy
)
convert_tensor_dict
=
converter
.
convert
()
assert
np
.
equal
(
convert_tensor_dict
[
tensor_name
],
tensor_col
[
rank_id
]).
all
()
# test merge and slice
tensor_dict
=
{
tensor_name
:
tensor_col
}
converter
=
Converter
(
tensor_dict
,
col_strategy
,
row_strategy
)
convert_tensor_dict
=
converter
.
convert
()
assert
np
.
equal
(
convert_tensor_dict
[
tensor_name
],
tensor_row
[
rank_id
]).
all
()
# test merge and slice with prefix match
new_name
=
"tensor_1"
row_strategy
=
{
new_name
:
{
"process_shape"
:
[
2
],
"process_group"
:
[
0
,
1
],
"dims_mapping"
:
[
0
,
-
1
]
}
}
converter
=
Converter
(
tensor_dict
,
col_strategy
,
row_strategy
)
convert_tensor_dict
=
converter
.
convert
(
strict
=
False
)
assert
np
.
equal
(
convert_tensor_dict
[
new_name
],
tensor_row
[
rank_id
]).
all
()
if
__name__
==
"__main__"
:
test_convert
()
python/paddle/fluid/tests/unittests/auto_parallel/test_converter.py
0 → 100644
浏览文件 @
3881b6cb
# Copyright (c) 2022 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
unittest
import
os
import
sys
import
shutil
import
subprocess
from
paddle.distributed.fleet.launch_utils
import
run_with_coverage
from
paddle.distributed.auto_parallel.converter
import
Converter
class
TestConverter
(
unittest
.
TestCase
):
def
test_converter
(
self
):
file_dir
=
os
.
path
.
dirname
(
os
.
path
.
abspath
(
__file__
))
launch_model_path
=
os
.
path
.
join
(
file_dir
,
"converter.py"
)
if
os
.
environ
.
get
(
"WITH_COVERAGE"
,
"OFF"
)
==
"ON"
:
coverage_args
=
[
"-m"
,
"coverage"
,
"run"
,
"--branch"
,
"-p"
]
else
:
coverage_args
=
[]
cmd
=
[
sys
.
executable
,
"-u"
]
+
coverage_args
+
[
"-m"
,
"launch"
,
"--gpus"
,
"0,1"
,
launch_model_path
]
process
=
subprocess
.
Popen
(
cmd
)
process
.
wait
()
self
.
assertEqual
(
process
.
returncode
,
0
)
# Remove unnecessary files
log_path
=
os
.
path
.
join
(
file_dir
,
"log"
)
if
os
.
path
.
exists
(
log_path
):
shutil
.
rmtree
(
log_path
)
def
test_input_invalid
(
self
):
with
self
.
assertRaises
(
ValueError
):
Converter
({},
[],
[])
with
self
.
assertRaises
(
TypeError
):
Converter
([
0
,
1
],
[],
[])
with
self
.
assertRaises
(
ValueError
):
Converter
({
"tmp_0"
:
[
0
]},
{},
[])
with
self
.
assertRaises
(
TypeError
):
Converter
({
"tmp_0"
:
[
0
]},
[
0
],
[])
strategy_1
=
{
'tmp_0'
:
{
"process_shape"
:
[
1
],
"process_group"
:
[
0
],
"dims_mapping"
:
[
-
1
]
}
}
with
self
.
assertRaises
(
TypeError
):
Converter
({
"tmp_0"
:
[
0
]},
strategy_1
,
[])
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/auto_parallel/test_engine_api.py
浏览文件 @
3881b6cb
# Copyright (c) 202
1
PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 202
2
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.
...
...
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