fa3 & trtllm_mha spec overlap (#11874)

python/sglang/srt/layers/attention/flashattention_backend.py

@@ -584,7 +584,9 @@ class FlashAttentionBackend(AttentionBackend):
                         metadata, metadata_expand
                     )
 
-        elif forward_batch.forward_mode.is_extend_or_draft_extend_or_mixed():
+        elif forward_batch.forward_mode.is_extend_or_draft_extend_or_mixed(
+            include_draft_extend_v2=True
+        ):
             metadata.cache_seqlens_int32 = seqlens_in_batch.to(torch.int32)
             metadata.max_seq_len_k = forward_batch.seq_lens_cpu.max().item()
             metadata.cu_seqlens_k = torch.nn.functional.pad(
@@ -594,10 +596,9 @@ class FlashAttentionBackend(AttentionBackend):
                 forward_batch.req_pool_indices, : metadata.max_seq_len_k
             ]
 
-            if (
-                any(forward_batch.extend_prefix_lens_cpu)
-                or forward_batch.forward_mode == ForwardMode.DRAFT_EXTEND
-            ):
+            if any(
+                forward_batch.extend_prefix_lens_cpu
+            ) or forward_batch.forward_mode.is_draft_extend(include_v2=True):
                 extend_seq_lens = forward_batch.extend_seq_lens
                 metadata.max_seq_len_q = max(forward_batch.extend_seq_lens_cpu)
                 metadata.cu_seqlens_q = torch.nn.functional.pad(
@@ -826,7 +827,7 @@ class FlashAttentionBackend(AttentionBackend):
             if (
                 forward_batch.attn_attend_prefix_cache is not None
                 and not forward_batch.forward_mode.is_target_verify()
-                and not forward_batch.forward_mode.is_draft_extend()
+                and not forward_batch.forward_mode.is_draft_extend(include_v2=True)
             ):
                 # Do multi-head attention with chunked prefix cache
                 if forward_batch.attn_attend_prefix_cache:

python/sglang/srt/layers/attention/trtllm_mha_backend.py

@@ -488,10 +488,9 @@ class TRTLLMHAAttnBackend(FlashInferAttnBackend):
                 forward_batch.req_pool_indices, : metadata.max_seq_len_k
             ]
 
-            if (
-                any(forward_batch.extend_prefix_lens_cpu)
-                or forward_batch.forward_mode == ForwardMode.DRAFT_EXTEND
-            ):
+            if any(
+                forward_batch.extend_prefix_lens_cpu
+            ) or forward_batch.forward_mode.is_draft_extend(include_v2=True):
                 extend_seq_lens = forward_batch.extend_seq_lens
                 metadata.max_seq_len_q = max(forward_batch.extend_seq_lens_cpu)
                 metadata.cu_seqlens_q = torch.nn.functional.pad(

python/sglang/srt/model_executor/forward_batch_info.py

@@ -90,11 +90,7 @@ class ForwardMode(IntEnum):
             self == ForwardMode.EXTEND
             or self == ForwardMode.MIXED
             or self == ForwardMode.DRAFT_EXTEND
-            or (
-                self == ForwardMode.DRAFT_EXTEND_V2
-                if include_draft_extend_v2
-                else False
-            )
+            or (include_draft_extend_v2 and self == ForwardMode.DRAFT_EXTEND_V2)
             or self == ForwardMode.TARGET_VERIFY
             or self == ForwardMode.SPLIT_PREFILL
         )
@@ -115,22 +111,21 @@ class ForwardMode(IntEnum):
         return self == ForwardMode.TARGET_VERIFY
 
     def is_draft_extend(self, include_v2: bool = False):
-        if include_v2:
-            return (
-                self == ForwardMode.DRAFT_EXTEND_V2 or self == ForwardMode.DRAFT_EXTEND
+        return self == ForwardMode.DRAFT_EXTEND or (
+            include_v2 and self == ForwardMode.DRAFT_EXTEND_V2
         )
-        return self == ForwardMode.DRAFT_EXTEND
 
     def is_draft_extend_v2(self):
         # For fixed shape logits output in v2 eagle worker
         return self == ForwardMode.DRAFT_EXTEND_V2
 
-    def is_extend_or_draft_extend_or_mixed(self):
+    def is_extend_or_draft_extend_or_mixed(self, include_draft_extend_v2: bool = False):
         return (
             self == ForwardMode.EXTEND
             or self == ForwardMode.DRAFT_EXTEND
             or self == ForwardMode.MIXED
             or self == ForwardMode.SPLIT_PREFILL
+            or (include_draft_extend_v2 and self == ForwardMode.DRAFT_EXTEND_V2)
         )
 
     def is_cuda_graph(self):