diff --git a/vllm/attention/backends/abstract.py b/vllm/attention/backends/abstract.py
index 97b13917ccfaa..23c7830cd6264 100644
--- a/vllm/attention/backends/abstract.py
+++ b/vllm/attention/backends/abstract.py
@@ -75,6 +75,9 @@ def copy_blocks(
     ) -> None:
         raise NotImplementedError
 
+    def advance_step(self, num_seqs: int, num_queries: int):
+        raise NotImplementedError
+
 
 @dataclass
 class AttentionMetadata:
diff --git a/vllm/attention/backends/flash_attn.py b/vllm/attention/backends/flash_attn.py
index f230bb57e3177..f146285bfc9e2 100644
--- a/vllm/attention/backends/flash_attn.py
+++ b/vllm/attention/backends/flash_attn.py
@@ -297,6 +297,51 @@ def decode_metadata(self) -> Optional["FlashAttentionMetadata"]:
         )
         return self._cached_decode_metadata
 
+    def advance_step(self, num_seqs: int, num_queries: int):
+        """
+        Update metadata in-place to advance one decode step.
+        """
+        # GPU in-place update is currently called separately through
+        # custom_ops.advance_step(). See draft_model_runner. TODO(will): Move
+        # this logic to the backend.
+
+        # When using cudagraph, the num_seqs is padded to the next captured
+        # batch sized, but num_queries tracks the actual number of requests in
+        # the batch. For --enforce-eager mode, num_seqs == num_queries
+        if num_seqs != num_queries:
+            assert num_seqs > num_queries
+            assert self.use_cuda_graph
+
+        assert self.num_prefills == 0
+        assert self.num_prefill_tokens == 0
+        assert self.num_decode_tokens == num_seqs
+        assert self.slot_mapping.shape == (num_seqs, )
+
+        assert self.seq_lens is not None
+        assert len(self.seq_lens) == num_seqs
+        assert self.seq_lens_tensor is not None
+        assert self.seq_lens_tensor.shape == (num_seqs, )
+        assert self.max_query_len == 1
+        assert self.max_prefill_seq_len == 0
+        assert self.max_decode_seq_len == max(self.seq_lens)
+
+        assert self.query_start_loc is not None
+        assert self.query_start_loc.shape == (num_queries + 1, )
+        assert self.seq_start_loc is not None
+        assert self.seq_start_loc.shape == (num_seqs + 1, )
+
+        assert self.context_lens_tensor is not None
+        assert self.context_lens_tensor.shape == (num_queries, )
+
+        assert self.block_tables is not None
+        assert self.block_tables.shape[0] == num_seqs
+
+        # Update query lengths. Note that we update only queries and not seqs,
+        # since tensors may be padded due to captured cuda graph batch size
+        for i in range(num_queries):
+            self.seq_lens[i] += 1
+        self.max_decode_seq_len = max(self.seq_lens)
+
 
 class FlashAttentionMetadataBuilder(
         AttentionMetadataBuilder[FlashAttentionMetadata]):
diff --git a/vllm/spec_decode/draft_model_runner.py b/vllm/spec_decode/draft_model_runner.py
index 7707d38a0f666..324044c96d994 100644
--- a/vllm/spec_decode/draft_model_runner.py
+++ b/vllm/spec_decode/draft_model_runner.py
@@ -97,38 +97,6 @@ def __init__(
         self.flashinfer_prefill_workspace_buffer = None
         self.flashinfer_prefill_wrapper = None
 
-    def _update_flash_attn_metadata(self, attn_metadata, num_seqs,
-                                    num_queries):
-        assert isinstance(attn_metadata, FlashAttentionMetadata)
-
-        if num_seqs != num_queries:
-            assert num_seqs > num_queries
-            assert attn_metadata.use_cuda_graph
-
-        assert attn_metadata.num_prefills == 0
-        assert attn_metadata.num_prefill_tokens == 0
-        assert attn_metadata.num_decode_tokens == num_seqs
-        assert attn_metadata.slot_mapping.shape == (num_seqs, )
-
-        assert len(attn_metadata.seq_lens) == num_seqs
-        assert attn_metadata.seq_lens_tensor.shape == (num_seqs, )
-        assert attn_metadata.max_query_len == 1
-        assert attn_metadata.max_prefill_seq_len == 0
-        assert attn_metadata.max_decode_seq_len == max(attn_metadata.seq_lens)
-
-        assert attn_metadata.query_start_loc.shape == (num_queries + 1, )
-        assert attn_metadata.seq_start_loc.shape == (num_seqs + 1, )
-
-        assert attn_metadata.context_lens_tensor.shape == (num_queries, )
-
-        assert attn_metadata.block_tables.shape[0] == num_seqs
-
-        # Update query lengths. Note that we update only queries and not seqs,
-        # since tensors may be padded due to captured cuda graph batch size
-        for i in range(num_queries):
-            attn_metadata.seq_lens[i] += 1
-        attn_metadata.max_decode_seq_len = max(attn_metadata.seq_lens)
-
     def _update_sampling_metadata(self, sampling_metadata, num_seqs,
                                   num_queries):
 
@@ -166,7 +134,7 @@ def _gpu_advance_step(
         # Update attn_metadata
         attn_metadata = model_input.attn_metadata
         assert isinstance(attn_metadata, FlashAttentionMetadata)
-        self._update_flash_attn_metadata(attn_metadata, num_seqs, num_queries)
+        attn_metadata.advance_step(num_seqs, num_queries)
 
         # Update GPU tensors
         ops.advance_step(num_seqs=num_seqs,