fixes...

vall_e/data.py

@@ -751,10 +751,7 @@ def _load_paths_from_metadata(group_name, type="training", validate=False):
 	metadata = {}
 
 	if cfg.dataset.use_metadata and metadata_path.exists():
-		try:
-			metadata = json_read( metadata_path )
-		except Exception as e:
-			return {}
+		metadata = json_read( metadata_path )
 
 	if len(metadata) == 0:
 		return _fn( data_dir, type if cfg.dataset.use_hdf5 else _get_artifact_extension(), validate )
@@ -820,7 +817,7 @@ def _get_paths_of_extensions( path, extensions=_get_artifact_extension(), valida
 	if isinstance(path, str):
 		path = Path(path)
 	
-	return [ p for p in list(path.iterdir()) ] if path.exists() and path.is_dir() else []
+	return [ str(p) for p in list(path.iterdir()) ] if path.exists() and path.is_dir() else []
 
 def _load_artifact(path, return_metadata=False, return_artifact=False, validate=True) -> Tensor:
 	artifact = np.load(_get_artifact_path(path), allow_pickle=True)[()]

vall_e/engines/base.py

@@ -586,7 +586,7 @@ class Engines(dict[str, Engine]):
 			loss_scale = 1
 			if hasattr(engine.optimizer, "loss_scale") and engine.optimizer.loss_scale is not None:
 				loss_scale = engine.optimizer.loss_scale
-			elif engine.loss_scaler is not None:
+			elif hasattr(engine, "loss_scaler") and engine.loss_scaler is not None:
 				loss_scale = engine.loss_scaler.get_scale()
 
 			if grad_norm is not None:

vall_e/models/arch/llama.py

@@ -7,7 +7,7 @@ from typing import Literal, overload, Optional, Tuple, Union, List
 from torch import Tensor, nn
 
 # lazy
-from transformers.models.llama.configuration_llama import LlamaConfig as Config
+from transformers.models.llama.configuration_llama import LlamaConfig as BaseConfig
 from transformers.models.llama.modeling_llama import LlamaPreTrainedModel
 
 from transformers.modeling_utils import PreTrainedModel
@@ -19,6 +19,18 @@ from transformers.activations import ACT2FN
 
 from .attention import *
 
+class Config(BaseConfig):
+	def __init__(
+		self,
+		attn_mode = "sdpa",
+		output_norm = True,
+		*args, **kwargs
+	):
+		super().__init__(*args, **kwargs)
+
+		self.attn_mode = attn_mode
+		self.output_norm = output_norm
+
 def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
 	batch, num_key_value_heads, slen, head_dim = hidden_states.shape
 

vall_e/models/base_v2.py

@@ -450,9 +450,9 @@ class Base_V2(nn.Module):
 				is_encoder_decoder=False,
 				is_decoder=True,
 				#gradient_checkpointing=self.gradient_checkpointing,
+				output_norm = not per_level_normalization, # moves the LN out to the decoder
+				attn_mode = attention_backend,
 			)
-			self.model_config.output_norm = not per_level_normalization # moves the LN out to the decoder
-			self.model_config.attn_mode = attention_backend
 			self.model = LlamaModel(self.model_config)
 
 			if self.gradient_checkpointing and not self.model.gradient_checkpointing: