added support for optional prodigy optimizer (https://github.com/konstmish/prodigy) although it consumes a lot more VRAM per parameter

vall_e/models/ar_nar.py

@@ -1,5 +1,6 @@
-from ..config import cfg
 from .base import Base, list_to_tensor, Categorical
+from ..utils import wrapper as ml
+from ..config import cfg
 
 import torch
 from torch.nn.utils.rnn import pad_sequence
@@ -169,7 +170,7 @@ def example_usage():
 
 	from einops import repeat
 
-	from ..emb.qnt import decode_to_file
+	from ..emb.qnt import decode_to_file, unload_model
 	from ..engines import Engine
 	from tqdm import tqdm
 
@@ -201,9 +202,9 @@ def example_usage():
 
 	kwargs = {
 		'n_tokens': 1024,
-		'd_model': 1024,
-		'n_heads': 16,
-		'n_layers': 24,
+		'd_model': 1536, # 1536
+		'n_heads': 24, # 24
+		'n_layers': 24, # 32
 	}
 	
 	"""
@@ -214,7 +215,9 @@ def example_usage():
 	"""
 
 	model = AR_NAR(**kwargs).to(device)
-	engine = Engine(model=model, optimizer=torch.optim.AdamW(model.parameters(), lr=0.001))
+	optimizer = ml.Prodigy(model.parameters(), lr=1.0)
+	#optimizer = ml.AdamW(model.parameters(), lr=0.0001)
+	engine = Engine(model=model, optimizer=optimizer)
 	
 	def sample( name, steps=600 ):
 		engine.eval()
@@ -229,9 +232,11 @@ def example_usage():
 		for i, o in enumerate(resps_list):
 			_ = decode_to_file(o, f"data/ar+nar.{i}.{name}.wav", device=device)
 
+		unload_model()
+
 	def train():
 		engine.train()
-		t = trange(1000)
+		t = trange(500)
 		for i in t:
 			stats = {"step": i}
 			stats |= engine.traverse(text_list=text_list, proms_list=proms_list, resps_list=resps_list)

vall_e/utils/trainer.py

@@ -84,6 +84,15 @@ def load_engines(invert=False):
 				model.parameters(),
 				**params,
 			)
+		elif (cfg.trainer.backend == "local" and cfg.hyperparameters.optimizer.lower() == "prodigy") or (cfg.trainer.backend == "deepspeed" and cfg.hyperparameters.optimizer.lower() == "prodigy-torch"):
+			params = {
+				"lr": cfg.hyperparameters.learning_rate,
+			}
+			params.update(cfg.hyperparameters.optimizer_params)
+			optimizer = ml.Prodigy(
+				model.parameters(),
+				**params,
+			)
 
 		if not model._cfg.training:
 			optimizer = None

vall_e/utils/wrapper.py

@@ -29,9 +29,9 @@ if cfg.bitsandbytes.enabled:
 if cfg.bitsandbytes.enabled:
 	import bitsandbytes as bnb
 
-	Adam = bnb.optim.Adam
-	AdamW = bnb.optim.AdamW
-	SGD = bnb.optim.SGD
+	Adam = bnb.optim.Adam8bit
+	AdamW = bnb.optim.AdamW8bit
+	SGD = bnb.optim.SGD8bit
 else:
 	Adam = torch.optim.Adam
 	AdamW = torch.optim.AdamW
@@ -76,4 +76,10 @@ if cfg.bitsandbytes.injects and cfg.bitsandbytes.enabled:
 
 	torch.optim.Adam = Adam
 	torch.optim.AdamW = AdamW
-	torch.optim.SGD = SGD
+	torch.optim.SGD = SGD
+
+# https://github.com/konstmish/prodigy
+try:
+	from prodigyopt import Prodigy
+except Exception as e:
+	pass