ugh

vall_e/data.py

@@ -429,7 +429,7 @@ class Dataset(_Dataset):
 		self.training = training
 		self.dataset_type = "training" if self.training else "validation"
 		self.dataset = cfg.dataset.training if self.training else cfg.dataset.validation
-		self.sampler_type = cfg.dataset.sample_type if self.dataset_type == "training" else "group"
+		self.sampler_type = cfg.dataset.sample_type # if self.dataset_type == "training" else "group"
 
 		# to-do: do not do validation if there's nothing in the validation
 		# this just makes it be happy

vall_e/engines/base.py

@@ -86,7 +86,7 @@ class Engine():
 		self._frozen_params.clear()
 
 	@property
-	def training(self):
+	def _training(self):
 		if not hasattr(self, "hyper_config"):
 			return True
 		return self.hyper_config.training
@@ -321,7 +321,7 @@ class Engines(dict[str, Engine]):
 
 		cfg.ckpt_dir.mkdir(parents=True, exist_ok=True)
 		for name, engine in self.items():
-			if not engine.training:
+			if not engine._training:
 				continue
 
 			save_dir = cfg.ckpt_dir / name

vall_e/engines/deepspeed.py

@@ -77,7 +77,7 @@ class Engine(DeepSpeedEngine):
 		self._frozen_params.clear()
 	
 	@property
-	def training(self):
+	def _training(self):
 		return self.hyper_config.training
 
 	@property

vall_e/train.py

@@ -57,7 +57,7 @@ def train_feeder(engine, batch):
 		else:
 			engine(
 				text_list=batch["text"],
-				proms_list=[prom[:, :engine._cfg.prom_levels] for prom in batch["proms"]], # reduce the input prompt to the target prom level
+				proms_list=batch["proms"],
 				resps_list=batch["resps"],
 				lang_list=batch["lang"],
 			)

vall_e/utils/trainer.py

@@ -157,9 +157,6 @@ def train(
 
 	# Training loop
 	for batch in _make_infinite_epochs(train_dl):
-		if not engine.training:
-			continue
-
 		if engines.global_step >= cfg.trainer.iterations:
 			break