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clip expert grad #11035

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11 changes: 11 additions & 0 deletions paddlenlp/trainer/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1220,6 +1220,17 @@ def fused_allreduce_gradients_no_sync(paramlist, hcg):
elif p.grad is not None:
p.grad.scale_(1.0 / self.args.gradient_accumulation_steps)

for p in model._layers.parameters():
if hasattr(p, "is_expert_weight") and p.is_expert_weight:
print(f"param {p.name} is expert weight")
with paddle.no_grad():
if hasattr(p, "main_grad") and p.main_grad is not None:
print("main grad scale 1/ep")
p.main_grad.scale_(1.0 / self.args.expert_parallel_degree)
elif p.grad is not None:
print("grad scale 1/ep")
p.grad.scale_(1.0 / self.args.expert_parallel_degree)

# Optimizer step
self.callback_handler.on_optimizer_begin(
args, self.state, self.control, scaler=self.scaler if self.do_grad_scaling else None
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5 changes: 5 additions & 0 deletions paddlenlp/transformers/deepseek_v2/modeling.py
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Expand Up @@ -1045,6 +1045,11 @@ def __init__(self, config: DeepseekV2Config, norm_weight=None, norm_eps=None):
using_post_norm_recompute=self.using_post_norm_recompute,
)

for expert in self.experts:
if expert is not None:
setattr(expert.w1, "is_expert_weight", True)
setattr(expert.w2, "is_expert_weight", True)

if config.offline_quant_expert_weight and config.clear_origin_weight_when_offline_quant:
moe_grad_group = fleet.get_hybrid_communicate_group().expert_grad_comm_group
expert_w1_list = [expert.w1 for expert in self.experts if expert is not None]
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