[Bugfix][V1] Handle MLA in kv_cache_interface

vllm/v1/kv_cache_interface.py

@@ -23,9 +23,9 @@ class KVCacheSpecBase:
     def type_id(self) -> str:
         """
         The type identifier of this KV cache.
-        Return different strings for layers with different KV cache type (e.g., 
-        different number of tokens like full attention vs sliding window 
-        attention, different KV cache size per token like layers with different 
+        Return different strings for layers with different KV cache type (e.g.,
+        different number of tokens like full attention vs sliding window
+        attention, different KV cache size per token like layers with different
         number of heads)
 
         Returns:
@@ -59,14 +59,17 @@ class FullAttentionSpec(KVCacheSpecBase):
     num_kv_heads: int
     head_size: int
     dtype: torch.dtype
+    use_mla: bool
 
     @property
     def type_id(self) -> str:
         return f"full_attention_{self.block_size}_{self.page_size_bytes}"
 
     @property
     def page_size_bytes(self) -> int:
-        return  2 * self.block_size * self.num_kv_heads * self.head_size \
+        # For MLA we only store a single latent vector
+        coef = 1 if self.use_mla else 2
+        return coef * self.block_size * self.num_kv_heads * self.head_size \
                 * get_dtype_size(self.dtype)
 
     def bytes_for_tokens(self, num_tokens: int) -> int:
@@ -104,7 +107,7 @@ class KVCacheConfig:
     2. (not implemented yet) A model with the same number of full attention
     layers and sliding window attention layers: two groups, one for full
     attention layers and one for sliding window attention layers.
-    3. (not implemented yet) A model with 2 full attention layers and 4 sliding 
+    3. (not implemented yet) A model with 2 full attention layers and 4 sliding
     window attention layers: three groups, (full * 2), (sw * 2), (sw * 2).
     """
     groups: list[list[str]]

vllm/v1/worker/gpu_model_runner.py

@@ -1460,21 +1460,22 @@ def get_kv_cache_spec(self) -> KVCacheSpec:
 
         forward_ctx = self.vllm_config.compilation_config.static_forward_context
         block_size = self.vllm_config.cache_config.block_size
+        use_mla = self.vllm_config.model_config.use_mla
         kv_cache_spec: KVCacheSpec = {}
         for layer_name, attn_module in forward_ctx.items():
             if isinstance(attn_module, FusedMoE):
                 continue
 
             # TODO: Support other attention modules, e.g., sliding window,
-            # cross-attention, MLA.
+            # cross-attention
             assert isinstance(attn_module, Attention)
             if attn_module.attn_type == AttentionType.DECODER:
                 kv_cache_spec[layer_name] = FullAttentionSpec(
                     block_size=block_size,
                     num_kv_heads=attn_module.num_kv_heads,
                     head_size=attn_module.head_size,
                     dtype=attn_module.dtype,
-                )
+                    use_mla=use_mla)
             elif attn_module.attn_type in (AttentionType.ENCODER,
                                            AttentionType.ENCODER_ONLY):
                 # encoder-only attention does not need KV cache.

vllm/v1/worker/tpu_model_runner.py

@@ -303,10 +303,10 @@ def get_model(self) -> nn.Module:
 
     def get_kv_cache_spec(self) -> KVCacheSpec:
         """
-        Generates the KVCacheSpec by parsing the kv cache format from each 
+        Generates the KVCacheSpec by parsing the kv cache format from each
         Attention module in the static forward context.
         Returns:
-            KVCacheSpec: A dictionary mapping layer names to their KV cache 
+            KVCacheSpec: A dictionary mapping layer names to their KV cache
             format. Layers that do not need KV cache are not included.
         """
 
@@ -323,6 +323,7 @@ def get_kv_cache_spec(self) -> KVCacheSpec:
                     num_kv_heads=attn_module.num_kv_heads,
                     head_size=attn_module.head_size,
                     dtype=attn_module.dtype,
+                    use_mla=False,
                 )
             elif attn_module.attn_type in (AttentionType.ENCODER,
                                            AttentionType.ENCODER_ONLY):
@@ -764,7 +765,7 @@ def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
         """
         Initialize KV cache based on `kv_cache_config`.
         Args:
-            kv_cache_config: Configuration for the KV cache, including the KV 
+            kv_cache_config: Configuration for the KV cache, including the KV
             cache size of each layer
         """
         if len(kv_cache_config.groups) > 1: