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4da841e0
编写于
3月 01, 2022
作者:
S
ShenLiang
提交者:
GitHub
3月 01, 2022
浏览文件
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浏览文件
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电子邮件补丁
差异文件
[DP] Construct reducer group (#39987)
* add reducer
上级
657dd5a9
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
265 addition
and
91 deletion
+265
-91
paddle/fluid/distributed/collective/CMakeLists.txt
paddle/fluid/distributed/collective/CMakeLists.txt
+1
-0
paddle/fluid/distributed/collective/reducer.cc
paddle/fluid/distributed/collective/reducer.cc
+131
-0
paddle/fluid/distributed/collective/reducer.h
paddle/fluid/distributed/collective/reducer.h
+32
-0
paddle/fluid/pybind/CMakeLists.txt
paddle/fluid/pybind/CMakeLists.txt
+1
-1
paddle/fluid/pybind/distributed_py.cc
paddle/fluid/pybind/distributed_py.cc
+14
-0
python/paddle/fluid/dygraph/parallel.py
python/paddle/fluid/dygraph/parallel.py
+7
-1
python/paddle/fluid/tests/unittests/test_imperative_group.py
python/paddle/fluid/tests/unittests/test_imperative_group.py
+79
-89
未找到文件。
paddle/fluid/distributed/collective/CMakeLists.txt
浏览文件 @
4da841e0
cc_library
(
processgroup SRCS ProcessGroup.cc DEPS phi phi_api eager_api
)
cc_library
(
processgroup SRCS ProcessGroup.cc DEPS phi phi_api eager_api
)
cc_library
(
eager_reducer SRCS reducer.cc DEPS eager_api processgroup
)
if
(
WITH_NCCL
)
if
(
WITH_NCCL
)
cc_library
(
processgroup_nccl SRCS ProcessGroupNCCL.cc DEPS place cuda_stream enforce collective_helper device_context phi phi_api eager_api
)
cc_library
(
processgroup_nccl SRCS ProcessGroupNCCL.cc DEPS place cuda_stream enforce collective_helper device_context phi phi_api eager_api
)
...
...
paddle/fluid/distributed/collective/reducer.cc
0 → 100644
浏览文件 @
4da841e0
// 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.
#include "paddle/fluid/distributed/collective/reducer.h"
#include "paddle/phi/common/data_type.h"
namespace
paddle
{
namespace
distributed
{
std
::
vector
<
std
::
vector
<
size_t
>>
Eager_AssignGroupBySize
(
const
std
::
vector
<
Tensor
>
tensors
,
const
std
::
vector
<
bool
>
&
is_sparse_gradient
,
const
std
::
vector
<
size_t
>
&
group_size_limits
,
const
std
::
vector
<
int64_t
>
&
tensor_indices
)
{
PADDLE_ENFORCE_EQ
(
tensors
.
size
(),
is_sparse_gradient
.
size
(),
platform
::
errors
::
PreconditionNotMet
(
"tensors len must be equal to is_sparse_gradient len, but "
"[%lu] != [%lu]"
,
tensors
.
size
(),
is_sparse_gradient
.
size
()));
auto
check_perm
=
[](
const
std
::
vector
<
int64_t
>
&
x
)
->
bool
{
size_t
len
=
x
.
size
();
std
::
vector
<
size_t
>
cnt
(
len
,
0
);
for
(
size_t
i
=
0
;
i
<
len
;
++
i
)
{
if
(
x
[
i
]
>=
static_cast
<
int64_t
>
(
len
)
||
x
[
i
]
<
0
||
cnt
[
x
[
i
]])
{
return
false
;
}
cnt
[
x
[
i
]]
++
;
}
return
true
;
};
PADDLE_ENFORCE_EQ
(
true
,
check_perm
(
tensor_indices
),
platform
::
errors
::
PreconditionNotMet
(
"tensor_indices must be a permutation from 0 to %lu"
,
tensor_indices
.
size
()));
// the return vector
std
::
vector
<
std
::
vector
<
size_t
>>
res
;
// Key: the var type
// Value: should use which index in group_size_limits for group size limit
std
::
map
<
experimental
::
DataType
,
size_t
>
group_limit_index
;
// Key: the var type
// Value: <the var index in input tensors, total numel in this group>
std
::
map
<
experimental
::
DataType
,
std
::
pair
<
std
::
vector
<
size_t
>
,
size_t
>>
next_group
;
for
(
size_t
i
=
0
;
i
<
tensors
.
size
();
++
i
)
{
const
auto
&
var
=
tensors
[
i
];
size_t
tensor_real_index
=
i
;
if
(
!
tensor_indices
.
empty
())
{
tensor_real_index
=
tensor_indices
[
i
];
}
if
(
is_sparse_gradient
[
tensor_real_index
])
{
// we keep sparse var a single group
res
.
push_back
({
tensor_real_index
});
continue
;
}
const
auto
&
var_dtype
=
var
.
dtype
();
VLOG
(
3
)
<<
"var["
<<
var
.
name
()
<<
"] 's type is "
<<
var_dtype
;
auto
&
group_info
=
next_group
[
var_dtype
];
int64_t
var_size
=
-
1
;
if
(
var
.
is_dense_tensor
())
{
var_size
=
std
::
dynamic_pointer_cast
<
phi
::
DenseTensor
>
(
var
.
impl
())
->
numel
();
}
else
{
VLOG
(
3
)
<<
"var "
<<
var
.
name
()
<<
" is not tensor or selected_rows, so skip it"
;
continue
;
}
group_info
.
first
.
push_back
(
tensor_real_index
);
group_info
.
second
+=
experimental
::
SizeOf
(
var_dtype
)
*
var_size
;
// group_info.second += framework::SizeOfType(var_dtype) * var_size;
if
(
group_limit_index
.
find
(
var_dtype
)
==
group_limit_index
.
end
())
{
// means it is the first var of var_dtype
group_limit_index
[
var_dtype
]
=
0
;
}
auto
&
cur_limit_index
=
group_limit_index
[
var_dtype
];
if
(
group_info
.
second
>=
group_size_limits
[
cur_limit_index
])
{
// exceed group capacity and create a new group
res
.
emplace_back
(
std
::
move
(
group_info
.
first
));
group_info
=
std
::
pair
<
std
::
vector
<
size_t
>
,
size_t
>
();
cur_limit_index
=
(
std
::
min
)(
cur_limit_index
+
1
,
group_size_limits
.
size
()
-
1
);
}
}
// add the final groups
for
(
auto
&
e
:
next_group
)
{
auto
&
group_info
=
e
.
second
;
if
(
!
group_info
.
first
.
empty
())
{
res
.
emplace_back
(
std
::
move
(
group_info
.
first
));
}
}
for
(
const
auto
&
group_index
:
res
)
{
PADDLE_ENFORCE_NE
(
group_index
.
empty
(),
true
,
platform
::
errors
::
PreconditionNotMet
(
"AssignGroupBySize construct empty group, please check."
));
}
if
(
tensor_indices
.
empty
())
{
std
::
sort
(
res
.
begin
(),
res
.
end
(),
[](
const
std
::
vector
<
size_t
>
&
x
,
const
std
::
vector
<
size_t
>
&
y
)
{
return
x
.
front
()
<
y
.
front
();
});
}
return
res
;
}
}
// namespace distributed
}
// namespace paddle
paddle/fluid/distributed/collective/reducer.h
0 → 100644
浏览文件 @
4da841e0
// 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.
#pragma once
#include <map>
#include <vector>
#include "paddle/fluid/distributed/collective/ProcessGroup.h"
#include "paddle/fluid/eager/api/utils/tensor_utils.h"
namespace
paddle
{
namespace
distributed
{
using
Tensor
=
paddle
::
experimental
::
Tensor
;
std
::
vector
<
std
::
vector
<
size_t
>>
Eager_AssignGroupBySize
(
const
std
::
vector
<
Tensor
>
,
const
std
::
vector
<
bool
>&
is_sparse_gradient
,
const
std
::
vector
<
size_t
>&
group_size_limits
,
const
std
::
vector
<
int64_t
>&
tensor_indices
=
{});
}
// namespace distributed
}
// namespace paddle
paddle/fluid/pybind/CMakeLists.txt
浏览文件 @
4da841e0
...
@@ -81,7 +81,7 @@ set(PYBIND_SRCS
...
@@ -81,7 +81,7 @@ set(PYBIND_SRCS
cuda_streams_py.cc
)
cuda_streams_py.cc
)
if
(
NOT ON_INFER
)
if
(
NOT ON_INFER
)
set
(
PYBIND_DEPS
${
PYBIND_DEPS
}
processgroup
)
set
(
PYBIND_DEPS
${
PYBIND_DEPS
}
processgroup
eager_reducer
)
if
(
WITH_NCCL
)
if
(
WITH_NCCL
)
set
(
PYBIND_DEPS
${
PYBIND_DEPS
}
processgroup_nccl
)
set
(
PYBIND_DEPS
${
PYBIND_DEPS
}
processgroup_nccl
)
endif
()
endif
()
...
...
paddle/fluid/pybind/distributed_py.cc
浏览文件 @
4da841e0
...
@@ -23,6 +23,7 @@ limitations under the License. */
...
@@ -23,6 +23,7 @@ limitations under the License. */
#include "paddle/fluid/distributed/collective/ProcessGroup.h"
#include "paddle/fluid/distributed/collective/ProcessGroup.h"
#include "paddle/fluid/distributed/collective/Types.h"
#include "paddle/fluid/distributed/collective/Types.h"
#include "paddle/fluid/distributed/collective/reducer.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/imperative/layer.h"
#include "paddle/fluid/imperative/layer.h"
...
@@ -143,6 +144,19 @@ void BindDistributed(py::module *m) {
...
@@ -143,6 +144,19 @@ void BindDistributed(py::module *m) {
[](
distributed
::
ProcessGroupStrategy
&
self
,
int
nrings
)
{
[](
distributed
::
ProcessGroupStrategy
&
self
,
int
nrings
)
{
self
.
nrings_
=
nrings
;
self
.
nrings_
=
nrings
;
});
});
m
->
def
(
"eager_assign_group_by_size"
,
[](
py
::
handle
py_tensors
,
std
::
vector
<
bool
>
is_sparse_gradient
,
std
::
vector
<
size_t
>
group_size_limits
,
std
::
vector
<
int64_t
>
tensor_indices
)
{
auto
tensors
=
CastPyArg2VectorOfTensor
(
py_tensors
.
ptr
(),
0
);
return
distributed
::
Eager_AssignGroupBySize
(
tensors
,
is_sparse_gradient
,
group_size_limits
,
tensor_indices
);
},
py
::
arg
(
"tensors"
),
py
::
arg
(
"is_sparse_gradient"
),
py
::
arg
(
"group_size_limits"
)
=
std
::
vector
<
size_t
>
{
25
*
1024
*
1024
},
py
::
arg
(
"tensor_indices"
)
=
std
::
vector
<
int64_t
>
{},
py
::
call_guard
<
py
::
gil_scoped_release
>
());
}
}
}
// end namespace pybind
}
// end namespace pybind
...
...
python/paddle/fluid/dygraph/parallel.py
浏览文件 @
4da841e0
...
@@ -560,13 +560,19 @@ class DataParallel(layers.Layer):
...
@@ -560,13 +560,19 @@ class DataParallel(layers.Layer):
strategy
=
None
,
strategy
=
None
,
comm_buffer_size
=
25
,
comm_buffer_size
=
25
,
last_comm_buffer_size
=
1
,
last_comm_buffer_size
=
1
,
find_unused_parameters
=
False
):
find_unused_parameters
=
False
,
process_group
=
None
,
gradient_as_buffer_view
=
False
,
static_graph
=
False
):
super
(
DataParallel
,
super
(
DataParallel
,
self
).
__init__
(
layers
.
full_name
()
+
"_data_parallel"
)
self
).
__init__
(
layers
.
full_name
()
+
"_data_parallel"
)
self
.
_layers
=
layers
self
.
_layers
=
layers
self
.
find_unused_parameters
=
find_unused_parameters
self
.
find_unused_parameters
=
find_unused_parameters
self
.
grad_need_sync
=
True
self
.
grad_need_sync
=
True
self
.
process_group
=
process_group
self
.
gradient_as_buffer_view
=
gradient_as_buffer_view
self
.
static_graph
=
static_graph
# NOTE(chenweihang): The ParallelStrategy here is not strictly a strategy.
# NOTE(chenweihang): The ParallelStrategy here is not strictly a strategy.
# It just stores some environment variables, which can be constructed by
# It just stores some environment variables, which can be constructed by
...
...
python/paddle/fluid/tests/unittests/test_imperative_group.py
浏览文件 @
4da841e0
...
@@ -26,159 +26,149 @@ import paddle.fluid.dygraph as dygraph
...
@@ -26,159 +26,149 @@ import paddle.fluid.dygraph as dygraph
from
paddle.fluid.dygraph.nn
import
Linear
from
paddle.fluid.dygraph.nn
import
Linear
import
paddle.fluid.core
as
core
import
paddle.fluid.core
as
core
from
paddle.fluid.optimizer
import
SGDOptimizer
from
paddle.fluid.optimizer
import
SGDOptimizer
from
paddle.fluid.framework
import
_test_eager_guard
class
MLP
(
fluid
.
Layer
):
def
__init__
(
self
,
param_attr
=
None
,
bias_attr
=
None
):
super
(
MLP
,
self
).
__init__
()
self
.
_linear1
=
Linear
(
784
,
10
)
self
.
_linear2
=
Linear
(
10
,
10
)
def
forward
(
self
,
inputs
):
y
=
self
.
_linear1
(
inputs
)
y
=
self
.
_linear2
(
y
)
return
y
class
TestDataParallelGroup
(
unittest
.
TestCase
):
class
TestDataParallelGroup
(
unittest
.
TestCase
):
def
create_varbase
(
self
,
dtype
,
shape
,
def
create_varbase
(
self
,
dtype
,
shape
):
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
):
return
paddle
.
rand
(
shape
=
shape
,
dtype
=
dtype
)
return
core
.
VarBase
(
dtype
,
shape
,
""
,
type
,
True
)
def
assign_group_by_size
(
self
,
*
args
):
return
core
.
assign_group_by_size
(
*
args
)
def
test_construct_group0
(
self
):
def
test_construct_group0
(
self
):
# one dtype & one limit capability
# one dtype & one limit capability
var_list
=
[]
var_list
=
[]
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
2
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
2
,
50
]))
var_list
.
append
(
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
2
,
100
]))
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
2
,
100
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
2
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
2
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
2
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
2
,
25
]))
res
=
self
.
assign_group_by_size
(
var_list
,
[
False
,
False
,
False
,
False
],
res
=
core
.
assign_group_by_size
(
var_list
,
[
False
,
False
,
False
,
False
],
[
400
])
[
400
])
self
.
assertEqual
([[
0
],
[
1
],
[
2
],
[
3
]],
res
)
self
.
assertEqual
([[
0
],
[
1
],
[
2
],
[
3
]],
res
)
def
test_construct_group1
(
self
):
def
test_construct_group1
(
self
):
# multi dtype & one limit capability
# multi dtype & one limit capability
var_list
=
[]
var_list
=
[]
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP64
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
"float64"
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP64
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
"float64"
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP64
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
"float64"
,
[
1
,
25
]))
res
=
core
.
assign_group_by_size
(
res
=
self
.
assign_group_by_size
(
var_list
,
[
False
,
False
,
False
,
False
,
False
,
False
],
[
400
])
var_list
,
[
False
,
False
,
False
,
False
,
False
,
False
],
[
400
])
self
.
assertEqual
([[
0
,
2
],
[
1
,
3
],
[
4
],
[
5
]],
res
)
self
.
assertEqual
([[
0
,
2
],
[
1
,
3
],
[
4
],
[
5
]],
res
)
def
test_construct_group2
(
self
):
def
test_construct_group2
(
self
):
# one dtype & multi limit capability
# one dtype & multi limit capability
var_list
=
[]
var_list
=
[]
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
2
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
2
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
2
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
2
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
2
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
2
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
2
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
2
,
50
]))
res
=
core
.
assign_group_by_size
(
var_list
,
[
False
,
False
,
False
,
False
],
res
=
self
.
assign_group_by_size
(
var_list
,
[
False
,
False
,
False
,
False
],
[
400
,
800
])
[
400
,
800
])
self
.
assertEqual
([[
0
],
[
1
,
2
],
[
3
]],
res
)
self
.
assertEqual
([[
0
],
[
1
,
2
],
[
3
]],
res
)
def
test_construct_group3
(
self
):
def
test_construct_group3
(
self
):
# multi dtype & multi limit capability
# multi dtype & multi limit capability
var_list
=
[]
var_list
=
[]
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP64
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
"float64"
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP64
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
"float64"
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP64
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
"float64"
,
[
1
,
25
]))
res
=
core
.
assign_group_by_size
(
res
=
self
.
assign_group_by_size
(
var_list
,
[
False
,
False
,
False
,
False
,
False
,
False
],
[
200
,
400
])
var_list
,
[
False
,
False
,
False
,
False
,
False
,
False
],
[
200
,
400
])
self
.
assertEqual
([[
0
],
[
1
],
[
2
,
4
],
[
3
,
5
]],
res
)
self
.
assertEqual
([[
0
],
[
1
],
[
2
,
4
],
[
3
,
5
]],
res
)
def
test_construct_group4
(
self
):
def
test_construct_group4
(
self
):
# multi dtype & zero limit capability
# multi dtype & zero limit capability
var_list
=
[]
var_list
=
[]
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP64
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
"float64"
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP64
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
"float64"
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP64
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
"float64"
,
[
1
,
25
]))
res
=
core
.
assign_group_by_size
(
res
=
self
.
assign_group_by_size
(
var_list
,
[
False
,
False
,
False
,
False
,
False
,
False
],
[
0
])
var_list
,
[
False
,
False
,
False
,
False
,
False
,
False
],
[
0
])
self
.
assertEqual
([[
0
],
[
1
],
[
2
],
[
3
],
[
4
],
[
5
]],
res
)
self
.
assertEqual
([[
0
],
[
1
],
[
2
],
[
3
],
[
4
],
[
5
]],
res
)
def
test_construct_group5
(
self
):
def
test_construct_group5
(
self
):
# multi dtype & infinite capability
# multi dtype & infinite capability
var_list
=
[]
var_list
=
[]
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP64
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
"float64"
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP64
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
"float64"
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP64
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
"float64"
,
[
1
,
25
]))
res
=
core
.
assign_group_by_size
(
res
=
self
.
assign_group_by_size
(
var_list
,
[
False
,
False
,
False
,
False
,
False
,
False
],
[
10000
])
var_list
,
[
False
,
False
,
False
,
False
,
False
,
False
],
[
10000
])
self
.
assertEqual
([[
0
,
2
,
4
],
[
1
,
3
,
5
]],
res
)
self
.
assertEqual
([[
0
,
2
,
4
],
[
1
,
3
,
5
]],
res
)
def
test_construct_group6
(
self
):
def
test_construct_group6
(
self
):
# multi dtype & limit capability & multi tensor type
# multi dtype & limit capability & multi tensor type
var_list
=
[]
var_list
=
[]
var_list
.
append
(
var_list
.
append
(
self
.
create_varbase
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
1
,
50
],
"float32"
,
core
.
VarDesc
.
VarType
.
SELECTED_ROWS
))
[
1
,
50
],
))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP64
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
"float64"
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP64
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
"float64"
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
1
,
50
]))
var_list
.
append
(
var_list
.
append
(
self
.
create_varbase
(
"float64"
,
[
1
,
25
]))
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP64
,
[
1
,
25
],
res
=
self
.
assign_group_by_size
(
core
.
VarDesc
.
VarType
.
SELECTED_ROWS
))
res
=
core
.
assign_group_by_size
(
var_list
,
[
True
,
False
,
False
,
False
,
False
,
True
],
[
400
])
var_list
,
[
True
,
False
,
False
,
False
,
False
,
True
],
[
400
])
self
.
assertEqual
([[
0
],
[
1
,
3
],
[
2
,
4
],
[
5
]],
res
)
self
.
assertEqual
([[
0
],
[
1
,
3
],
[
2
,
4
],
[
5
]],
res
)
def
test_construct_group7
(
self
):
def
test_construct_group7
(
self
):
# multi dtype & multi limit capability & multi tensor type
# multi dtype & multi limit capability & multi tensor type
var_list
=
[]
var_list
=
[]
var_list
.
append
(
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
1
,
50
]))
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
1
,
50
],
var_list
.
append
(
self
.
create_varbase
(
"float64"
,
[
1
,
25
]))
core
.
VarDesc
.
VarType
.
SELECTED_ROWS
))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP64
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
"float64"
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
1
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP64
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
"float64"
,
[
1
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
1
,
50
]))
res
=
self
.
assign_group_by_size
(
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP64
,
[
1
,
25
],
core
.
VarDesc
.
VarType
.
SELECTED_ROWS
))
res
=
core
.
assign_group_by_size
(
var_list
,
[
True
,
False
,
False
,
False
,
False
,
True
],
[
200
,
400
])
var_list
,
[
True
,
False
,
False
,
False
,
False
,
True
],
[
200
,
400
])
self
.
assertEqual
([[
0
],
[
1
],
[
2
],
[
3
],
[
4
],
[
5
]],
res
)
self
.
assertEqual
([[
0
],
[
1
],
[
2
],
[
3
],
[
4
],
[
5
]],
res
)
def
test_construct_group8
(
self
):
def
test_construct_group8
(
self
):
# one dtype & one limit capability & have tensor_indices
# one dtype & one limit capability & have tensor_indices
var_list
=
[]
var_list
=
[]
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
2
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
2
,
25
]))
var_list
.
append
(
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
2
,
100
]))
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
2
,
100
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
2
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
2
,
50
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
2
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
2
,
25
]))
res
=
self
.
assign_group_by_size
(
var_list
,
[
False
,
False
,
False
,
False
],
res
=
core
.
assign_group_by_size
(
var_list
,
[
False
,
False
,
False
,
False
],
[
400
],
[
3
,
0
,
1
,
2
])
[
400
],
[
3
,
0
,
1
,
2
])
self
.
assertEqual
([[
3
,
0
],
[
1
],
[
2
]],
res
)
self
.
assertEqual
([[
3
,
0
],
[
1
],
[
2
]],
res
)
def
test_construct_group9
(
self
):
def
test_construct_group9
(
self
):
# one dtype & one limit capability & have tensor_indices
# one dtype & one limit capability & have tensor_indices
var_list
=
[]
var_list
=
[]
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
2
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
2
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
2
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
2
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
2
,
25
]))
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
2
,
25
]))
var_list
.
append
(
var_list
.
append
(
self
.
create_varbase
(
"float32"
,
[
2
,
1000
]))
self
.
create_varbase
(
core
.
VarDesc
.
VarType
.
FP32
,
[
2
,
1000
]))
res
=
self
.
assign_group_by_size
(
var_list
,
[
False
,
False
,
False
,
True
],
res
=
core
.
assign_group_by_size
(
var_list
,
[
False
,
False
,
False
,
True
],
[
300
],
[
1
,
0
,
2
,
3
])
[
300
],
[
1
,
0
,
2
,
3
])
self
.
assertEqual
([[
1
,
0
],
[
3
],
[
2
]],
res
)
self
.
assertEqual
([[
1
,
0
],
[
3
],
[
2
]],
res
)
class
TestDataParallelGroupEager
(
TestDataParallelGroup
):
def
create_varbase
(
self
,
dtype
,
shape
):
with
_test_eager_guard
():
return
paddle
.
rand
(
shape
=
shape
,
dtype
=
dtype
)
def
assign_group_by_size
(
self
,
*
args
):
return
core
.
eager_assign_group_by_size
(
*
args
)
if
__name__
==
'__main__'
:
if
__name__
==
'__main__'
:
unittest
.
main
()
unittest
.
main
()
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