[Bugfix] Fix bug of xformer prefill for encoder-decoder (#9026)

vllm/attention/backends/xformers.py

@@ -559,25 +559,32 @@ class XFormersImpl(AttentionImpl[XFormersMetadata]):
                                                     self.kv_cache_dtype,
                                                     k_scale, v_scale)
 
-        if attn_type != AttentionType.ENCODER:
-            # Decoder self-attention supports chunked prefill.
-            # Encoder/decoder cross-attention requires no chunked
-            # prefill (100% prefill or 100% decode tokens, no mix)
-            num_prefill_tokens = attn_metadata.num_prefill_tokens
-            num_decode_tokens = attn_metadata.num_decode_tokens
-        else:
+        if attn_type == AttentionType.ENCODER:
             # Encoder attention - chunked prefill is not applicable;
             # derive token-count from query shape & and treat them
             # as 100% prefill tokens
             assert attn_metadata.num_encoder_tokens is not None
             num_prefill_tokens = attn_metadata.num_encoder_tokens
+            num_encoder_tokens = attn_metadata.num_encoder_tokens
             num_decode_tokens = 0
-
-        if attn_type == AttentionType.DECODER:
+        elif attn_type == AttentionType.DECODER:
+            # Decoder self-attention supports chunked prefill.
+            num_prefill_tokens = attn_metadata.num_prefill_tokens
+            num_encoder_tokens = attn_metadata.num_prefill_tokens
+            num_decode_tokens = attn_metadata.num_decode_tokens
             # Only enforce this shape-constraint for decoder
             # self-attention
             assert key.shape[0] == num_prefill_tokens + num_decode_tokens
             assert value.shape[0] == num_prefill_tokens + num_decode_tokens
+        else:  # attn_type == AttentionType.ENCODER_DECODER
+            # Encoder/decoder cross-attention requires no chunked
+            # prefill (100% prefill or 100% decode tokens, no mix)
+            num_prefill_tokens = attn_metadata.num_prefill_tokens
+            if attn_metadata.num_encoder_tokens is not None:
+                num_encoder_tokens = attn_metadata.num_encoder_tokens
+            else:
+                num_encoder_tokens = attn_metadata.num_prefill_tokens
+            num_decode_tokens = attn_metadata.num_decode_tokens
 
         output = torch.empty_like(query)
         # Query for decode. KV is not needed because it is already cached.
@@ -585,8 +592,8 @@ class XFormersImpl(AttentionImpl[XFormersMetadata]):
         # QKV for prefill.
         query = query[:num_prefill_tokens]
         if key is not None and value is not None:
-            key = key[:num_prefill_tokens]
-            value = value[:num_prefill_tokens]
+            key = key[:num_encoder_tokens]
+            value = value[:num_encoder_tokens]
 
         assert query.shape[0] == num_prefill_tokens
         assert decode_query.shape[0] == num_decode_tokens