Combine AdamW and Muon into single MuonAdamW optimizer, cleaner, ty @chrisjmccormick for idea/help

scripts/chat_rl.py

@@ -201,7 +201,7 @@ def run_gsm8k_eval(task, tokenizer, engine,
 # Training loop
 
 # Init the optimizer
-optimizers = model.setup_optimizers(
+optimizer = model.setup_optimizer(
     unembedding_lr=args.unembedding_lr,
     embedding_lr=args.embedding_lr,
     matrix_lr=args.matrix_lr,
@@ -209,10 +209,9 @@ optimizers = model.setup_optimizers(
 )
 
 # Set the initial learning rate as a fraction of the base learning rate
-for opt in optimizers:
-    for group in opt.param_groups:
-        group["lr"] = group["lr"] * args.init_lr_frac
-        group["initial_lr"] = group["lr"] # save the initial learning so we can decay easily later
+for group in optimizer.param_groups:
+    group["lr"] = group["lr"] * args.init_lr_frac
+    group["initial_lr"] = group["lr"]
 
 # Learning rate scheduler: simple rampdown to zero over num_steps
 def get_lr_multiplier(it):
@@ -305,11 +304,9 @@ for step in range(num_steps):
 
     # Update the model parameters
     lrm = get_lr_multiplier(step)
-    for opt in optimizers: # first set the learning rate
-        for group in opt.param_groups:
-            group["lr"] = group["initial_lr"] * lrm
-    for opt in optimizers: # then step the optimizers
-        opt.step()
+    for group in optimizer.param_groups:
+        group["lr"] = group["initial_lr"] * lrm
+    optimizer.step()
     model.zero_grad(set_to_none=True)
     wandb_run.log({
         "step": step,