I think I made resp_parallel_training=True faster with loss factoring?

vall_e/engines/base.py

@@ -629,7 +629,10 @@ class Engines(dict[str, Engine]):
 			if cfg.lora is not None:			
 				key_name = cfg.lora.full_name
 
-			stats.update(flatten_dict({key_name.split("-")[0]: model_stats}))
+			if len(self) == 1:
+				stats.update(flatten_dict(model_stats))
+			else:
+				stats.update(flatten_dict({key_name.split("-")[0]: model_stats}))
 
 		self._update()
 

vall_e/models/base_v2.py

@@ -748,7 +748,7 @@ class Base_V2(nn.Module):
 			# filter tokens that exceed the vocab size
 			sequence = torch.where( sequence >= logit.shape[-1], self.ignore_index, sequence )
 			# drop if all tokens are ignored
-			if all(sequence == self.ignore_index):
+			if torch.all(sequence == self.ignore_index):
 				return None, None
 
 			# shift if causal
@@ -757,8 +757,14 @@ class Base_V2(nn.Module):
 				logit = logit[..., :-l, :] # shift the target so that token n...
 				sequence = sequence[..., l:] # ...predicts token n + 1
 
+			# flatten batch
+			if sequence.dim() > 1:
+				logit = logit.reshape(-1, logit.shape[-1])
+				sequence = sequence.reshape(-1)
+
 			nll = None
 			metrics = None
+
 			if compute_hard_loss:
 				nll = F.cross_entropy( logit, sequence, ignore_index=self.ignore_index )
 
@@ -868,41 +874,15 @@ class Base_V2(nn.Module):
 							if classifier_level.endswith(f':{i}:{i}'):
 								level = i
 								break
+						"""
 						if name == "resp":
 							name = f'{name}[{level}]'
+						"""
 						sequence = token if token.dim() <= 1 else token[:, level]
 						nll, metrics = _calc_loss( logits[batch_index][level][start:end], sequence.long(), causal )
 					else:
-						nlls = []
-						accs = []
-						
-						for level, logit in enumerate( logits[batch_index] ):
-							sequence = token if token.dim() <= 1 else token[:, level]
-							nll, metrics = _calc_loss( logit[start:end], sequence.long(), causal )
-
-							if name == "resp":
-								if nll is not None:
-									if f'{name}[{level}].nll' not in loss:
-										loss[f'{name}[{level}].nll'] = []
-									loss[f"{name}[{level}].nll"].append( nll * loss_factor )
-							
-								if metrics is not None:
-									if f'{name}[{level}].acc' not in stats:
-										stats[f'{name}[{level}].acc'] = []
-									stats[f"{name}[{level}].acc"].append( metrics )
-
-								nll = None
-								metrics = None
-							else:
-								if nll:
-									nlls.append( nll )
-								if metrics:
-									accs.append( metrics )
-						if nlls:
-							nll = sum(nlls) / len(nlls)
-						if accs:
-							accs = sum(accs) / len(accs)
-
+						sequence = token.t()						
+						nll, metrics = _calc_loss( logits[batch_index][:, start:end], sequence.long(), causal )
 					if nll is not None:
 						if f'{name}.nll' not in loss:
 							loss[f'{name}.nll'] = []