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fix bugs in HybridParallelClipGrad of hybrid_parallel_optimizer #36237

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merged 3 commits into from
Oct 7, 2021
Merged

fix bugs in HybridParallelClipGrad of hybrid_parallel_optimizer #36237

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1c6938f
fix bugs in HybridParallelClipGrad of hybrid_parallel_optimizer
haohongxiang Sep 30, 2021
3865e93
update
haohongxiang Oct 5, 2021
edc1988
update
haohongxiang Oct 5, 2021
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38 changes: 27 additions & 11 deletions ...n/paddle/distributed/fleet/meta_optimizers/dygraph_optimizer/hybrid_parallel_optimizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,7 +50,8 @@ def __init__(self, clip, hcg):
@imperative_base.no_grad
def _dygraph_clip(self, params_grads):
params_and_grads = []
sum_square_list = []
sum_square_list_dist = []
sum_square_list_not_dist = []
for p, g in params_grads:
if g is None:
continue
Expand All @@ -62,18 +63,33 @@ def _dygraph_clip(self, params_grads):
merge_grad = layers.get_tensor_from_selected_rows(merge_grad)
square = layers.square(merge_grad)
sum_square = layers.reduce_sum(square)
sum_square_list.append(sum_square)

if p.is_distributed:
sum_square_list_dist.append(sum_square)
else:
sum_square_list_not_dist.append(sum_square)

# all parameters have been filterd out
if len(sum_square_list) == 0:
if len(sum_square_list_dist) + len(sum_square_list_not_dist) == 0:
return params_grads

global_norm_var = layers.concat(sum_square_list)
global_norm_var = layers.reduce_sum(global_norm_var)
# add all reduce to get global norm in world size
paddle.distributed.all_reduce(global_norm_var,
self._hcg.get_check_parallel_group())
global_norm_var = layers.sqrt(global_norm_var)
global_norm_var_dist = layers.concat(sum_square_list_dist) if len(
sum_square_list_dist) != 0 else layers.concat(
[paddle.to_tensor([0.])])
global_norm_var_dist = layers.reduce_sum(global_norm_var_dist)
global_norm_var_not_dist = layers.concat(
sum_square_list_not_dist) if len(
sum_square_list_not_dist) != 0 else layers.concat(
[paddle.to_tensor([0.])])
global_norm_var_not_dist = layers.reduce_sum(global_norm_var_not_dist)

# add all reduce to get global norm of distributed params_and_grads in world size
# all reduce is not needed while getting global norm of non-distributed params_and_grads
paddle.distributed.all_reduce(
global_norm_var_dist, group=self._hcg.get_check_parallel_group())

global_norm_var = layers.sqrt(global_norm_var_dist +
global_norm_var_not_dist)

max_global_norm = layers.fill_constant(
shape=[1], dtype=global_norm_var.dtype, value=self.clip_norm)
Expand All @@ -96,7 +112,7 @@ def __getattr__(self, item):
return getattr(self._clip, item)

def __call__(self, params_grads):
return self._clip(params_grads)
return self._dygraph_clip(params_grads)


class HybridParallelOptimizer:
Expand All @@ -112,7 +128,7 @@ def __init__(self, optimizer, hcg, strategy):
self._need_dp = (self._hcg.get_data_parallel_world_size() > 1)

# NOTE(shenliang03): Because of the pure DataParallel mode, the gradient synchronization
# is achieved through reducer, so there is no need to call fuse_allreduce in oprimizer.
# is achieved through reducer, so there is no need to call fuse_allreduce in optimizer.
self._dp_enable = not self._use_dp_mode and self._need_dp

self._sharding_enable = (
Expand Down