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fix: make mcore lr scheduler configuration consistent with dtensor #681

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Merged
terrykong merged 5 commits into from
Jul 17, 2025
Merged

fix: make mcore lr scheduler configuration consistent with dtensor #681

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0fbc0b2
take one scheduler step per rollout batch
ashors1 Jul 16, 2025
c0c7590
update configs
ashors1 Jul 16, 2025
cae0933
remove unused variable
ashors1 Jul 16, 2025
8310511
Remove old comment
ashors1 Jul 16, 2025
fd69656
increase learning rate for test
ashors1 Jul 17, 2025
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2 changes: 1 addition & 1 deletion examples/configs/grpo_math_1B_megatron.yaml
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Expand Up @@ -116,7 +116,7 @@ policy:
weight_decay_incr_style: "constant"
lr_decay_style: "constant"
lr_decay_iters: null
lr_warmup_iters: 50
lr_warmup_iters: 13
lr_warmup_init: 5.0e-7

distributed_data_parallel_config:
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2 changes: 1 addition & 1 deletion examples/configs/grpo_math_70B_megatron.yaml
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Expand Up @@ -49,7 +49,7 @@ policy:
weight_decay_incr_style: "constant"
lr_decay_style: "constant"
lr_decay_iters: null
lr_warmup_iters: 50
lr_warmup_iters: 13
lr_warmup_init: 3.0e-8

generation:
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2 changes: 1 addition & 1 deletion examples/configs/grpo_math_qwen30ba3b_megatron.yaml
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Expand Up @@ -52,7 +52,7 @@ policy:
weight_decay_incr_style: "constant"
lr_decay_style: "constant"
lr_decay_iters: null
lr_warmup_iters: 50
lr_warmup_iters: 13
lr_warmup_init: 3.0e-8

env_vars:
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10 changes: 6 additions & 4 deletions nemo_rl/models/policy/megatron_policy_worker.py
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Expand Up @@ -859,11 +859,7 @@ def train(

# Update learning rate.
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if update_successful:
increment = total_dataset_size.item()
self.scheduler.step(increment=increment)
skipped_iter = 0
curr_lr = self.scheduler.get_lr(self.optimizer.param_groups[0])
curr_wd = self.scheduler.get_wd()
else:
skipped_iter = 1

Expand All @@ -880,6 +876,8 @@ def train(
for k in x.keys():
loss_metrics[k] = x[k] / num_global_batches
gb_loss_metrics.append(loss_metrics)
curr_lr = self.scheduler.get_lr(self.optimizer.param_groups[0])
curr_wd = self.scheduler.get_wd()
loss_metrics["lr"] = curr_lr
loss_metrics["wd"] = curr_wd
loss_metrics["grad_norm"] = grad_norm
Expand All @@ -905,6 +903,10 @@ def train(
all_mb_metrics.extend(gb_loss_metrics)
losses.append(torch.tensor(mb_losses).sum().item())

if not eval_mode:
# take one LR step every rollout batch
self.scheduler.step(increment=1)

# Aggregate metrics across all microbatches
mb_metrics = defaultdict(list)
for m in all_mb_metrics:
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