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Multi groups for broadcast of sharding stage 2 #46894

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merged 7 commits into from
Oct 12, 2022
Merged

Multi groups for broadcast of sharding stage 2 #46894

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030fa75
multi groups for broadcast
FeixLiu Oct 11, 2022
e4e91f9
multi stream for broadcast
FeixLiu Oct 11, 2022
40bf687
update default value
FeixLiu Oct 11, 2022
0c45132
update ut
FeixLiu Oct 11, 2022
3fb328f
default to none
FeixLiu Oct 11, 2022
7a1e5c5
update ut
FeixLiu Oct 11, 2022
83357ae
update group idx
FeixLiu Oct 11, 2022
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39 changes: 33 additions & 6 deletions python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_optimizer_stage2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -187,7 +187,10 @@ def _set_reduce_overlap(self, reduce_overlap):
# Enable gradients' reduces overlap with backward calculation.
self._reduce_overlap = reduce_overlap

def _set_broadcast_overlap(self, broadcast_overlap, layers=None):
def _set_broadcast_overlap(self,
broadcast_overlap,
layers=None,
num_groups=None):
# Enable post optimizer broadcasts overlap with the forward calculation of next batch.
self._broadcast_overlap = broadcast_overlap
if self._broadcast_overlap:
Expand All @@ -205,6 +208,27 @@ def _set_broadcast_overlap(self, broadcast_overlap, layers=None):
"overlap broadcast may harm the performance.")
self._broadcast_order_params = self._local_params

if num_groups is None or num_groups > len(self._broadcast_order_params):
warnings.warn(
"The num_groups for broadcast is larger than the number of params to be broadcast. "
"It will set to default value: 1 (use the default sharding group)."
)
num_groups = 1

assert isinstance(
num_groups,
int) and num_groups > 0, "num_groups should be a positive integer"

self._number_of_broadcast_groups = num_groups
self._broadcast_groups = [
None for _ in range(self._number_of_broadcast_groups)
]
self._broadcast_groups[0] = self._group

ranks = self._group.ranks
for i in range(1, self._number_of_broadcast_groups):
self._broadcast_groups[i] = new_group(ranks)

def _generate_master_params(self, trainable_params):
if self.offload:
for param in trainable_params:
Expand Down Expand Up @@ -487,14 +511,17 @@ def __impl__(x, y):
def _broadcast_params_overlap_forward(self):
# Exchange all the shards with the other ranks,
# but overlap the broadcast with next batch's calculation.
group_idx = 0

param2task = {}
for x in self._broadcast_order_params:
if x.trainable:
task = broadcast(
tensor=x,
src=self._group.ranks[self._param2rank[x.name]],
group=self._group,
sync_op=False)
group = self._broadcast_groups[group_idx]
group_idx = (group_idx + 1) % self._number_of_broadcast_groups
task = broadcast(tensor=x,
src=group.ranks[self._param2rank[x.name]],
group=group,
sync_op=False)
assert x.name not in param2task
param2task[x.name] = task

Expand Down