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-rw-r--r--ep_run/muon.py14
1 files changed, 11 insertions, 3 deletions
diff --git a/ep_run/muon.py b/ep_run/muon.py
index 5ac6fc6..3116030 100644
--- a/ep_run/muon.py
+++ b/ep_run/muon.py
@@ -52,13 +52,21 @@ class MultiSched:
for s in self.scheds: s.step()
-def build_hybrid(blocks, other_params, lr_adamw, lr_muon, warmup):
- """Muon(2D block matrices) + AdamW(everything else), with linear-warmup scheds for both."""
+def build_hybrid(blocks, other_params, lr_adamw, lr_muon, warmup, total_steps=0, lr_min_ratio=0.1):
+ """Muon(2D block matrices) + AdamW(everything else). Scheds: linear warmup, then cosine decay to
+ lr_min_ratio*peak if total_steps>0 (long runs), else constant after warmup (legacy)."""
+ import math as _m
mats = [p for p in blocks.parameters() if p.ndim == 2]
mat_ids = {id(p) for p in mats}
rest = [p for p in other_params if id(p) not in mat_ids]
om = Muon(mats, lr=lr_muon)
oa = torch.optim.AdamW(rest, lr=lr_adamw, weight_decay=1e-4)
- fn = lambda s: min(1.0, (s + 1) / max(warmup, 1))
+ if total_steps > 0:
+ def fn(s):
+ if s < warmup: return (s + 1) / max(warmup, 1)
+ p = min(1.0, (s - warmup) / max(1, total_steps - warmup))
+ return lr_min_ratio + 0.5 * (1 - lr_min_ratio) * (1 + _m.cos(_m.pi * p))
+ else:
+ fn = lambda s: min(1.0, (s + 1) / max(warmup, 1))
scheds = [torch.optim.lr_scheduler.LambdaLR(om, fn), torch.optim.lr_scheduler.LambdaLR(oa, fn)]
return MultiOpt([om, oa]), MultiSched(scheds)