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mindspore
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5d4144de
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mindspore
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5d4144de
编写于
4月 14, 2020
作者:
G
gong chen
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
bugfix(side effect): fix adding wrong control depend between AllReduce and GetStatus.
上级
c9fba7f0
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
6 addition
and
8 deletion
+6
-8
mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py
mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py
+3
-4
mindspore/nn/wrap/loss_scale.py
mindspore/nn/wrap/loss_scale.py
+3
-4
未找到文件。
mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py
浏览文件 @
5d4144de
...
...
@@ -370,7 +370,7 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell):
self
.
parallel_mode
=
context
.
get_auto_parallel_context
(
"parallel_mode"
)
if
self
.
parallel_mode
in
[
ParallelMode
.
DATA_PARALLEL
,
ParallelMode
.
HYBRID_PARALLEL
]:
self
.
reducer_flag
=
True
self
.
grad_reducer
=
None
self
.
grad_reducer
=
F
.
identity
if
self
.
reducer_flag
:
mean
=
context
.
get_auto_parallel_context
(
"mirror_mean"
)
degree
=
get_group_size
()
...
...
@@ -428,9 +428,8 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell):
mstype
.
float32
))
grads
=
self
.
hyper_map
(
F
.
partial
(
grad_scale
,
scaling_sens
),
grads
)
grads
=
self
.
clip_gradients
(
grads
,
GRADIENT_CLIP_TYPE
,
GRADIENT_CLIP_VALUE
)
if
self
.
reducer_flag
:
# apply grad reducer on grads
grads
=
self
.
grad_reducer
(
grads
)
# apply grad reducer on grads
grads
=
self
.
grad_reducer
(
grads
)
self
.
get_status
(
init
)
flag_sum
=
self
.
reduce_sum
(
init
,
(
0
,))
if
self
.
is_distributed
:
...
...
mindspore/nn/wrap/loss_scale.py
浏览文件 @
5d4144de
...
...
@@ -220,7 +220,7 @@ class TrainOneStepWithLossScaleCell(Cell):
self
.
depend_parameter_use
=
ControlDepend
(
depend_mode
=
1
)
self
.
allreduce
=
P
.
AllReduce
()
self
.
parallel_mode
=
_get_parallel_mode
()
self
.
grad_reducer
=
None
self
.
grad_reducer
=
F
.
identity
self
.
reducer_flag
=
self
.
parallel_mode
in
[
ParallelMode
.
DATA_PARALLEL
,
ParallelMode
.
HYBRID_PARALLEL
]
if
self
.
reducer_flag
:
mean
=
_get_mirror_mean
()
...
...
@@ -250,9 +250,8 @@ class TrainOneStepWithLossScaleCell(Cell):
scaling_sens
=
sens
grads
=
self
.
grad
(
self
.
network
,
weights
)(
data
,
label
,
F
.
cast
(
scaling_sens
,
F
.
dtype
(
loss
)))
grads
=
self
.
hyper_map
(
F
.
partial
(
_grad_scale
,
scaling_sens
),
grads
)
if
self
.
reducer_flag
:
# apply grad reducer on grads
grads
=
self
.
grad_reducer
(
grads
)
# apply grad reducer on grads
grads
=
self
.
grad_reducer
(
grads
)
# get the overflow buffer
if
not
self
.
gpu_target
:
self
.
get_status
(
init
)
...
...
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