respect attention defined in the yaml for web UI (which might explain why theres been a discrepancy in outputs for me)

vall_e/models/arch/llama.py

@@ -184,6 +184,8 @@ class LlamaAttention_Adapted(LlamaAttention):
 			cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
 			key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
 
+		attn_scores = None
+
 		if mode in ["xformers", "flash_attn"]:
 			# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
 			# to be able to avoid many of these transpose/reshape/view.
@@ -241,9 +243,6 @@ class LlamaAttention_Adapted(LlamaAttention):
 		key_states = repeat_kv(key_states, self.num_key_value_groups)
 		value_states = repeat_kv(value_states, self.num_key_value_groups)
 
-		# to-do: actually find what is our attention scores, since these seem to not vary at all
-		attn_scores = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) if output_attentions else None
-
 		causal_mask = attention_mask
 		if attention_mask is not None:
 			causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]

vall_e/models/base.py

@@ -543,8 +543,7 @@ class Base(nn.Module):
 
 		if attention_backend == "flash_attn_v100":
 			self.l_padding = 32
-		
-		if attention_backend == "fused_attn":
+		elif attention_backend == "fused_attn":
 			self.l_padding = 128
 
 		if self.arch_type == "transformer":

vall_e/webui.py

@@ -107,7 +107,7 @@ def load_sample( speaker ):
 
 	return data, (sr, wav)
 
-def init_tts(yaml=None, restart=False, device="cuda", dtype="auto", attention="auto"):
+def init_tts(yaml=None, restart=False, device="cuda", dtype="auto", attention=None):
 	global tts
 
 	if tts is not None: