[Attention][1/n] Remove usage of deprecated seq_lens_cpu and num_computed_tokens_cpu CommonAttentionMetadata properties

tests/v1/attention/test_attention_backends.py

@@ -126,12 +126,12 @@ def create_and_prepopulate_kv_cache(
         Tuple of (kv_cache, updated_block_table)
     """
     batch_size = len(k_contexts)
-    seq_lens = common_attn_metadata.seq_lens_cpu
+    seq_lens = common_attn_metadata.seq_lens.cpu()
     query_lens = (
         common_attn_metadata.query_start_loc_cpu[1:]
         - common_attn_metadata.query_start_loc_cpu[:-1]
     )
-    context_lens = common_attn_metadata.num_computed_tokens_cpu
+    context_lens = seq_lens - query_lens
     block_table = common_attn_metadata.block_table_tensor
     slot_mapping = common_attn_metadata.slot_mapping
 

tests/v1/attention/test_mla_backends.py

@@ -154,12 +154,12 @@ def create_and_prepopulate_kv_cache(
         MLA KV cache tensor
     """
     batch_size = len(kv_c_contexts)
-    seq_lens = common_attn_metadata.seq_lens_cpu
+    seq_lens = common_attn_metadata.seq_lens.cpu()
     query_lens = (
         common_attn_metadata.query_start_loc_cpu[1:]
         - common_attn_metadata.query_start_loc_cpu[:-1]
     )
-    context_lens = common_attn_metadata.num_computed_tokens_cpu
+    context_lens = seq_lens - query_lens
     block_table = common_attn_metadata.block_table_tensor
     slot_mapping = common_attn_metadata.slot_mapping
 

tests/v1/attention/test_sparse_mla_backends.py

@@ -297,7 +297,7 @@ def test_sparse_backend_decode_correctness(
     positions = np.arange(starts[-1], dtype=np.int32) - np.repeat(
         starts[:-1], seg_lengths
     )
-    seq_lengths = np.asarray(common_attn_metadata.seq_lens_cpu, dtype=np.int32)
+    seq_lengths = np.asarray(common_attn_metadata.seq_lens.cpu(), dtype=np.int32)
     prefix_lengths = seq_lengths - seg_lengths
     positions += np.repeat(prefix_lengths, seg_lengths)
 

vllm/v1/attention/backends/flashinfer.py

@@ -870,7 +870,9 @@ def build(
         # Guard access to seq_lens_cpu, which may not always be needed
         # and can be expensive to retrieve in async mode.
         needs_seq_lens_cpu = self.use_dcp or use_cascade or not is_only_trtllm_decode
-        seq_lens_cpu = common_attn_metadata.seq_lens_cpu if needs_seq_lens_cpu else None
+        seq_lens_cpu = (
+            common_attn_metadata.seq_lens.cpu() if needs_seq_lens_cpu else None
+        )
         seq_lens_np = seq_lens_cpu.numpy() if seq_lens_cpu is not None else None
         num_blocks_np = (
             (seq_lens_np + (page_size - 1)) // page_size

vllm/v1/attention/backends/flex_attention.py

@@ -727,9 +727,7 @@ def build(
             block_table_tensor, seq_lens, block_size, num_gpu_blocks
         )
 
-        offset_tensor = common_attn_metadata.num_computed_tokens_cpu.to(
-            self.device, non_blocking=True
-        )
+        offset_tensor = common_attn_metadata.compute_num_computed_tokens()
 
         out = FlexAttentionMetadata(
             causal=common_attn_metadata.causal,

vllm/v1/attention/backends/mla/common.py

@@ -791,7 +791,9 @@ def build(
 
         prefill_metadata = None
         if num_prefills > 0:
-            num_computed_tokens_cpu = common_attn_metadata.num_computed_tokens_cpu
+            num_computed_tokens_cpu = (
+                common_attn_metadata.compute_num_computed_tokens().cpu()
+            )
 
             reqs_start = num_decodes  # prefill_start
 

vllm/v1/attention/backends/mla/flashmla_sparse.py

@@ -511,7 +511,7 @@ def _build_fp8_separate_prefill_decode(
         # For pure decode batches, prefill_request_id will be None
         # For mixed batches, it will have -1 for decode and request_id for prefill
         if num_prefills > 0:
-            seq_lens_cpu = common_attn_metadata.seq_lens_cpu
+            seq_lens_cpu = common_attn_metadata.seq_lens.cpu()
             seq_lens = common_attn_metadata.seq_lens
             query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
 

vllm/v1/attention/backends/triton_attn.py

@@ -221,7 +221,7 @@ def build(
             prefix_kv_lens = torch.tensor(
                 [common_prefix_len], dtype=torch.int32, device=self.device
             )
-            suffix_kv_lens = common_attn_metadata.seq_lens_cpu - common_prefix_len
+            suffix_kv_lens = common_attn_metadata.seq_lens.cpu() - common_prefix_len
             suffix_kv_lens = suffix_kv_lens.to(self.device)
         else:
             cu_prefix_query_lens = None

vllm/v1/attention/backends/utils.py

@@ -100,6 +100,8 @@ class CommonAttentionMetadata:
     _seq_lens_cpu: torch.Tensor | None = None
     _num_computed_tokens_cpu: torch.Tensor | None = None
 
+    _num_computed_tokens_cache: torch.Tensor | None = None
+
     @property
     @deprecated(
         """
@@ -130,6 +132,13 @@ def num_computed_tokens_cpu(self) -> torch.Tensor:
             self._num_computed_tokens_cpu = self.seq_lens_cpu - query_seq_lens
         return self._num_computed_tokens_cpu
 
+    def compute_num_computed_tokens(self) -> torch.Tensor:
+        """Compute num_computed_tokens on device (seq_lens - query_lens)."""
+        if self._num_computed_tokens_cache is None:
+            query_lens = self.query_start_loc[1:] - self.query_start_loc[:-1]
+            self._num_computed_tokens_cache = self.seq_lens - query_lens
+        return self._num_computed_tokens_cache
+
     # TODO(lucas): remove once we have FULL-CG spec-decode support
     def unpadded(
         self, num_actual_tokens: int, num_actual_reqs: int