review comments

Signed-off-by: Lucas Wilkinson <lwilkinson@neuralmagic.com>

vllm/attention/layer.py

@@ -44,7 +44,7 @@ def __init__(
         use_mla: bool = False,
         prefix: str = "",
         attn_type: str = AttentionType.DECODER,
-        **kwargs,
+        **extra_impl_args,
     ) -> None:
         super().__init__()
         if per_layer_sliding_window is not None:
@@ -114,7 +114,7 @@ def __init__(
         self.impl = impl_cls(num_heads, head_size, scale, num_kv_heads,
                              alibi_slopes, sliding_window, kv_cache_dtype,
                              blocksparse_params, logits_soft_cap, attn_type,
-                             **kwargs)
+                             **extra_impl_args)
         self.num_heads = num_heads
         self.head_size = head_size
         self.num_kv_heads = num_kv_heads

vllm/config.py

@@ -165,7 +165,6 @@ class ModelConfig:
             `logits_processors` extra completion argument. Defaults to None,
             which allows no processors.
         generation_config: Configuration parameter file for generation.
-        disable_mla: Whether to disable MLA for DeepSeek models.
         override_generation_config: Override the generation config with the
             given config.
     """
@@ -227,7 +226,6 @@ def __init__(
         override_pooler_config: Optional["PoolerConfig"] = None,
         logits_processor_pattern: Optional[str] = None,
         generation_config: Optional[str] = None,
-        disable_mla: bool = False,
         enable_sleep_mode: bool = False,
         override_generation_config: Optional[Dict[str, Any]] = None,
     ) -> None:
@@ -278,7 +276,6 @@ def __init__(
         self.max_logprobs = max_logprobs
         self.disable_sliding_window = disable_sliding_window
         self.skip_tokenizer_init = skip_tokenizer_init
-        self.disable_mla = disable_mla
         self.enable_sleep_mode = enable_sleep_mode
 
         from vllm.platforms import current_platform
@@ -748,7 +745,7 @@ def is_deepseek_mla(self) -> bool:
     def get_head_size(self) -> int:
         # TODO remove hard code
         if self.is_deepseek_mla:
-            if self.should_use_mla:
+            if self.use_mla:
                 return self.hf_text_config.kv_lora_rank
             else:
                 qk_rope_head_dim = getattr(self.hf_text_config,
@@ -815,7 +812,7 @@ def get_total_num_kv_heads(self) -> int:
 
     def get_num_kv_heads(self, parallel_config: "ParallelConfig") -> int:
         """Returns the number of KV heads per GPU."""
-        if self.should_use_mla:
+        if self.use_mla:
             # When using MLA during decode it becomes MQA
             return 1
 
@@ -971,8 +968,7 @@ def is_cross_encoder(self) -> bool:
 
     @property
     def use_mla(self) -> bool:
-        use_mla = (self.is_deepseek_mla and not self.disable_mla
-                   and not envs.VLLM_MLA_DISABLE)
+        use_mla = (self.is_deepseek_mla and not envs.VLLM_MLA_DISABLE)
         return use_mla
 
     def supported_runner_types(self) -> Set[RunnerType]:

vllm/engine/arg_utils.py

@@ -100,7 +100,6 @@ class EngineArgs:
     kv_cache_dtype: str = 'auto'
     seed: int = 0
     max_model_len: Optional[int] = None
-    disable_mla: bool = False
     # Note: Specifying a custom executor backend by passing a class
     # is intended for expert use only. The API may change without
     # notice.
@@ -932,9 +931,6 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             type=str,
             default="auto",
             help='The worker class to use for distributed execution.')
-        parser.add_argument('--disable-mla',
-                            action='store_true',
-                            help='Disable MLA for DeepSeek models.')
         parser.add_argument(
             "--generation-config",
             type=nullable_str,
@@ -1015,7 +1011,6 @@ def create_model_config(self) -> ModelConfig:
             disable_mm_preprocessor_cache=self.disable_mm_preprocessor_cache,
             override_neuron_config=self.override_neuron_config,
             override_pooler_config=self.override_pooler_config,
-            disable_mla=self.disable_mla,
             logits_processor_pattern=self.logits_processor_pattern,
             generation_config=self.generation_config,
             override_generation_config=self.override_generation_config,

vllm/model_executor/models/deepseek_v2.py

@@ -488,7 +488,7 @@ def __init__(
         # DecoderLayers are created with `make_layers` which passes the prefix
         # with the layer's index.
         layer_idx = int(prefix.split(sep='.')[-1])
-        if model_config.should_use_mla:
+        if model_config.use_mla:
             attn_cls = DeepseekV2MLAAttention
         else:
             attn_cls = DeepseekV2Attention

vllm/worker/cache_engine.py

@@ -52,13 +52,12 @@ def __init__(
             self.dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]
 
         # Get attention backend.
-        self.attn_backend = get_attn_backend(
-            self.head_size,
-            model_config.dtype,
-            cache_config.cache_dtype,
-            self.block_size,
-            model_config.is_attention_free,
-            use_mla=model_config.should_use_mla)
+        self.attn_backend = get_attn_backend(self.head_size,
+                                             model_config.dtype,
+                                             cache_config.cache_dtype,
+                                             self.block_size,
+                                             model_config.is_attention_free,
+                                             use_mla=model_config.use_mla)
 
         # Initialize the cache.
         self.gpu_cache = self._allocate_kv_cache(

vllm/worker/model_runner.py

@@ -1066,7 +1066,7 @@ def __init__(
             self.kv_cache_dtype,
             self.block_size,
             self.model_config.is_attention_free,
-            use_mla=self.model_config.should_use_mla,
+            use_mla=self.model_config.use_mla,
         ) if needs_attn_backend else None
         if self.attn_backend:
             self.attn_state = self.attn_backend.get_state_cls()(