diff --git a/vall_e/models/arch/llama.py b/vall_e/models/arch/llama.py
index 59b7b83..03093a7 100644
--- a/vall_e/models/arch/llama.py
+++ b/vall_e/models/arch/llama.py
@@ -589,48 +589,28 @@ class LlamaModel_Adapted(LlamaModel):
 
 	# shamelessly borrowed from https://github.com/open-mmlab/Amphion/blob/main/models/tts/maskgct/llama_nar.py#L256 until I replace it with my own noncausal-mask maker
 	def _update_noncausal_mask(
-		self, attention_mask, inputs_embeds, past_key_values_length
+		self,
+		attention_mask,
+		inputs_embeds,
+		past_key_values_length,
 	):
 		# create noncausal mask
-		# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-		combined_attention_mask = None
+		# [bsz, seq_len] -> [bsz, 1, seq_len, seq_len]
 
-		input_shape = (inputs_embeds.shape[0], inputs_embeds.shape[1])
-
-		def _expand_mask(
-			mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None
-		):
-			"""
-			Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
-			"""
-			bsz, src_len = mask.size()
-			tgt_len = tgt_len if tgt_len is not None else src_len
-
-			expanded_mask = (
-				mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
-			)
-
-			inverted_mask = 1.0 - expanded_mask
-
-			return inverted_mask.masked_fill(
-				inverted_mask.to(torch.bool), torch.finfo(dtype).min
-			)
+		bsz, seq_len, _ = inputs_embeds.size()
 
+		# generate default mask based on input
 		if attention_mask is None:
-			attention_mask = torch.ones( input_shape, dtype=torch.bool, device=inputs_embeds.device )
+			attention_mask = torch.ones( (bsz, seq_len), dtype=torch.bool, device=inputs_embeds.device )
 
-		# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-		expanded_attn_mask = _expand_mask(
-			attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
-		).to(inputs_embeds.device)
-		combined_attention_mask = (
-			expanded_attn_mask
-			if combined_attention_mask is None
-			else expanded_attn_mask + combined_attention_mask
-		)
+		# make square
+		expanded_mask = attention_mask[:, None, None, :].expand( bsz, 1, seq_len, seq_len ).to( dtype=inputs_embeds.dtype )
 
-		return combined_attention_mask
+		# invert from 1.0 = attend, 0.0 = masked to 0.0 = valid, -inf = masked
+		inverted_mask = 1.0 - expanded_mask
+		return inverted_mask.masked_fill( inverted_mask.to(dtype=torch.bool), torch.finfo(inputs_embeds.dtype).min )
 
+	# gut out the things that just shoves responsibility on SDPA's is_causal generating a mask because this causes problems
 	def _update_causal_mask(
 		self,
 		attention_mask: torch.Tensor,