ugh

vall_e/models/ar_nar.py

@@ -245,7 +245,6 @@ class AR_NAR(Base):
 				)
 
 				resps_list = sampled[0]
-
 				prev_list = [ torch.cat([rs, r.unsqueeze(-1).to(device=device)], dim=-1) for rs, r in zip(prev_list, resps_list) ]
 
 			return prev_list
@@ -377,6 +376,7 @@ class AR_NAR(Base):
 		sequence_list = [self._prune(r, audio_stop_token if task_list[i] not in text_task else text_stop_token) for i, r in enumerate(sequence_list)]
 		# remove <bos>
 		sequence_list = [ sequence_list[i][start_slice[i]:] for i, task in enumerate( task_list ) ]
+
 		return sequence_list
 
 

vall_e/models/base.py

@@ -1507,23 +1507,6 @@ class Base(nn.Module):
 		elif self.causal:
 			logits = [ logit[-self.causal_size:] for logit in logits ]
 
-		# entropix sampling
-		if attentions is not None:
-			# move to CPU for speedups
-			logits = [ logit.to(device="cpu", dtype=logit.dtype if logit.dtype != torch.float16 else torch.float32) for logit in logits ]
-
-			res = [ sample_entropix(
-				logit,
-				attentions[-1], #torch.stack(attentions, dim=1),
-				temperature,
-				top_k,
-				top_p,
-				min_p,
-			) for logit in logits ]
-
-			if res:
-				return Sampled([ r[0] for r in res], scores, [ r[1] for r in res])
-
 		# (NAR) disable stop token
 		if quant_levels is not None and "ar" in self.capabilities:
 			logits = [ ban_tokens(logit, tokens=[self.stop_token]) for logit, l in zip( logits, map(len, prev_list) ) ]
@@ -1546,6 +1529,23 @@ class Base(nn.Module):
 		if quant_levels is None and self.causal and prev_list is not None and length_penalty != 0.0:
 			logits = [ length_penalize(logit, length=l + 1, factor=length_penalty, token=self.stop_token) for logit, l in zip( logits, map(len, prev_list) ) ]
 
+		# (AR) entropix sampling
+		if attentions is not None and quant_levels is None:
+			# move to CPU for speedups
+			logits = [ logit.to(device="cpu", dtype=logit.dtype if logit.dtype != torch.float16 else torch.float32) for logit in logits ]
+
+			res = [ sample_entropix(
+				logit,
+				attentions[-1], #torch.stack(attentions, dim=1),
+				temperature,
+				top_k,
+				top_p,
+				min_p,
+			) for logit in logits ]
+
+			if res:
+				return Sampled([ r[0] for r in res], scores, [ r[1] for r in res])
+
 		# perform min_p filtering of our logits
 		if min_p > 0.0:
 			logits = [ min_p_filtering(logit, min_p=min_p) for logit in logits ]

vall_e/samplers.py

@@ -356,7 +356,7 @@ def _sample_entropix(
 	probs_sort = probs_sort / torch.sum(probs_sort, dim=-1, keepdims=True)
 
 	next_token = torch.argmax(probs_sort / Exponential.sample(probs_sort.shape), dim=-1, keepdim=True)
-	return torch.take_along_dim(probs_idx, next_token, dim=-1)
+	return torch.take_along_dim(probs_idx, next_token, dim=-1)[0]
 
 def sample_entropix(
 	logits,
@@ -430,6 +430,12 @@ def sample_entropix(
 				agreement * cfg.ada_score_agree +
 				interaction_strength * cfg.ada_score_int
 			)
+
+			"""
+			if 1024 in sample:
+				return 1000
+			"""
+
 			return log_prob + confidence_score
 
 		sample_scores = [ score_sample(sample) for sample in samples ]