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Update training.py #98

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de94a8f
Update training.py
mngom2 Jan 30, 2026
ade1d2a
Update muon.py
mngom2 Jan 30, 2026
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4 changes: 2 additions & 2 deletions megatron/optimizer/muon.py
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Original file line number Diff line number Diff line change
Expand Up @@ -125,8 +125,8 @@ def adjust_lr_for_muon(self, lr, param_shape):
"""
A, B = param_shape[:2]
# We adjust the learning rate based on the size of the parameter matrix
#adjusted_ratio = max(1.0, float(A) / float(B)) ** 0.5
adjusted_ratio = 0.2 * math.sqrt(max(A, B))
#adjusted_ratio = max(1.0, float(A) / float(B)) ** 0.5 #Muon implementation that enables MuP, Uncomment to have MuP enabled for Muon
adjusted_ratio = 0.2 * math.sqrt(max(A, B)) #comment if enabling above line is uncommented
adjusted_lr = lr * adjusted_ratio
return adjusted_lr

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34 changes: 0 additions & 34 deletions megatron/training.py
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Expand Up @@ -1182,40 +1182,6 @@ def train(
if smoothed_loss < best_loss or batch_num == 1:
best_loss = smoothed_loss

# explode = (batch_num > 1 and smoothed_loss > 4 * best_loss)


# if explode:
# if mpu.get_data_parallel_rank() == 0:
# log.info(f"Loss exploding at lr={curr_lr:.8f}, stopping LR finder")
# break

## Record the best loss (use the same global smoothed_loss)
#if smoothed_loss < best_loss or batch_num == 1:
# best_loss = smoothed_loss

# --- GLOBALIZE the "loss exploding" decision (any rank => all ranks) ---
#explode_local = (batch_num > 1 and smoothed_loss > 4 * best_loss)
#print(f"Rank {mpu.get_data_parallel_rank()}: explode_local={explode_local}")
#if mpu.get_data_parallel_rank() == 0:

# print(f"Iter {i}: batch_num={batch_num}, smoothed_loss={smoothed_loss:.8f}, best_loss={best_loss:.8f}, ratio={smoothed_loss/best_loss:.2f}")
#if tdist.is_available() and tdist.is_initialized():
# _exp = torch.tensor([1 if explode_local else 0], device=dev)
# tdist.all_reduce(_exp, op=tdist.ReduceOp.MAX, group=dp_group)
# explode = bool(_exp.item())
#else:
# explode = explode_local



#if explode:
# if mpu.get_data_parallel_rank() == 0:
# log.info(f"Loss exploding at lr={curr_lr:.8f}, stopping LR finder")
# Keep everyone in lockstep before breaking
# if tdist.is_available() and tdist.is_initialized():
# tdist.barrier(group=dp_group)
# break

# Record values for plotting
lr_finder_losses.append(smoothed_loss)
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