[V0 Deprecation] Refactor kv cache from list to element (#37487)

Signed-off-by: yewentao256 <zhyanwentao@126.com>

vllm/model_executor/models/olmo_hybrid.py

@@ -428,7 +428,7 @@ class OlmoHybridGatedDeltaNet(nn.Module, MambaBase):
         non_spec_token_indx = attn_metadata.non_spec_token_indx
         spec_state_indices_tensor = attn_metadata.spec_state_indices_tensor
         non_spec_state_indices_tensor = attn_metadata.non_spec_state_indices_tensor
-        self_kv_cache = self.kv_cache[0]
+        self_kv_cache = self.kv_cache
         conv_state = self_kv_cache[0].transpose(-1, -2)
         ssm_state = self_kv_cache[1]
         num_actual_tokens = attn_metadata.num_actual_tokens

vllm/model_executor/models/plamo2.py

@@ -262,7 +262,7 @@ class Plamo2MambaMixer(MambaBase, PluggableLayer):
             assert isinstance(attn_metadata, dict)
             attn_metadata = attn_metadata[self.prefix]
             assert isinstance(attn_metadata, Mamba2AttentionMetadata)
-            self_kv_cache = self.kv_cache[0]
+            self_kv_cache = self.kv_cache
             # conv_state = (..., dim, width-1) yet contiguous along 'dim'
             conv_state = self_kv_cache[0].transpose(-1, -2)
             ssm_state = self_kv_cache[1]

vllm/model_executor/models/qwen3_next.py

@@ -858,7 +858,7 @@ class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
         non_spec_token_indx = attn_metadata.non_spec_token_indx
         spec_state_indices_tensor = attn_metadata.spec_state_indices_tensor  # noqa: E501
         non_spec_state_indices_tensor = attn_metadata.non_spec_state_indices_tensor  # noqa: E501
-        self_kv_cache = self.kv_cache[0]
+        self_kv_cache = self.kv_cache
         conv_state = self_kv_cache[0].transpose(-1, -2)
         ssm_state = self_kv_cache[1]
         num_actual_tokens = attn_metadata.num_actual_tokens
@@ -1046,7 +1046,7 @@ class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
         Core attention computation with a packed non-spec decode fast path.
         """
         non_spec_state_indices_tensor = attn_metadata.non_spec_state_indices_tensor  # noqa: E501
-        self_kv_cache = self.kv_cache[0]
+        self_kv_cache = self.kv_cache
         conv_state = self_kv_cache[0].transpose(-1, -2)
         ssm_state = self_kv_cache[1]
         num_actual_tokens = attn_metadata.num_actual_tokens

vllm/v1/attention/backends/rocm_aiter_fa.py

@@ -481,13 +481,9 @@ class AiterFlashAttentionMetadataBuilder(
         ):
             layers = get_layers_from_vllm_config(self.vllm_config, Attention)
             first_layer_name = [k for k in layers][0]
-            kv_cache_shape = (
-                self.vllm_config.compilation_config.static_forward_context[
+            kv_cache_shape = self.vllm_config.compilation_config.static_forward_context[
                 first_layer_name
-                ]
-                .kv_cache[0]
-                .shape
-            )
+            ].kv_cache.shape
             num_blocks = kv_cache_shape[1]
             self.scale = torch.ones(
                 [num_blocks, self.num_heads_kv, self.block_size],

vllm/v1/worker/gpu_model_runner.py

@@ -5830,7 +5830,10 @@ class GPUModelRunner(
 
         for layer in self.compilation_config.static_forward_context.values():
             if hasattr(layer, "kv_cache"):
-                layer.kv_cache = []
+                kv_cache = layer.kv_cache
+                layer.kv_cache = (
+                    torch.tensor([]) if isinstance(kv_cache, torch.Tensor) else []
+                )
 
         gc.collect()
         torch.accelerator.empty_cache()

vllm/v1/worker/mamba_utils.py

@@ -119,7 +119,7 @@ def collect_mamba_copy_meta(
         layer_names = kv_cache_config.kv_cache_groups[mamba_group_id].layer_names
         for layer_name in layer_names:
             attention = forward_context[layer_name]
-            kv_caches: list[torch.Tensor] = attention.kv_cache[0]
+            kv_caches: list[torch.Tensor] = attention.kv_cache
             for state, state_copy_func in zip(kv_caches, mamba_state_copy_funcs):
                 copy_spec = state_copy_func(
                     state, block_ids, src_block_idx, accept_token_bias + 1

vllm/v1/worker/utils.py

@@ -136,8 +136,8 @@ class KVBlockZeroer:
             for layer_name in group.layer_names:
                 if layer_name in runner_only_attn_layers:
                     continue
-                kv = static_forward_context[layer_name].kv_cache[0]
-                if isinstance(kv, list):
+                kv = static_forward_context[layer_name].kv_cache
+                if not isinstance(kv, torch.Tensor):
                     continue
                 dp = kv.data_ptr()
                 if dp in seen_ptrs:
@@ -510,8 +510,7 @@ def bind_kv_cache(
 
     # Bind kv_caches to forward context
     for layer_name, kv_cache in kv_caches.items():
-        # NOTE: Keep list wrapper for layers that index kv_cache by engine slot.
-        forward_context[layer_name].kv_cache = [kv_cache]
+        forward_context[layer_name].kv_cache = kv_cache
 
 
 def is_residual_scattered_for_sp(