[MyPy] Enable mypy for vllm/model_executor/layers/

tools/pre_commit/mypy.py

@@ -29,7 +29,6 @@
     "tests",
     # v0 related
     "vllm/lora",
-    "vllm/model_executor/layers",
 ]
 
 # TODO(woosuk): Include the code from Megatron and HuggingFace.

vllm/model_executor/layers/activation.py

@@ -666,16 +666,7 @@ def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor):
         "gelu": lambda: GELU(),
         "gelu_fast": lambda: FastGELU(),
         "gelu_new": lambda: NewGELU(),
-        "gelu_pytorch_tanh": lambda: (
-            # TODO:[ROCm] PyTorch native GELU with tanh is unstable with torch.compile
-            logger.warning_once(
-                "[ROCm] PyTorch's native GELU with tanh approximation is unstable. "
-                "Falling back to GELU(approximate='none')."
-            ),
-            nn.GELU(approximate="none"),
-        )[1]
-        if current_platform.is_rocm()
-        else nn.GELU(approximate="tanh"),
+        "gelu_pytorch_tanh": lambda: _get_gelu_pytorch_tanh(),
         "relu": lambda: nn.ReLU(),
         "relu2": lambda: ReLUSquaredActivation(),
         "silu": lambda: nn.SiLU(),
@@ -687,6 +678,18 @@ def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor):
 )
 
 
+def _get_gelu_pytorch_tanh() -> nn.Module:
+    """Get PyTorch GELU with tanh approximation, with ROCm fallback."""
+    if current_platform.is_rocm():
+        # TODO:[ROCm] PyTorch native GELU with tanh is unstable with torch.compile
+        logger.warning_once(
+            "[ROCm] PyTorch's native GELU with tanh approximation is unstable. "
+            "Falling back to GELU(approximate='none')."
+        )
+        return nn.GELU(approximate="none")
+    return nn.GELU(approximate="tanh")
+
+
 def get_act_fn(act_fn_name: str) -> nn.Module:
     """Get an activation function by name."""
     act_fn_name = act_fn_name.lower()
@@ -703,12 +706,12 @@ def get_act_fn(act_fn_name: str) -> nn.Module:
     return _ACTIVATION_REGISTRY[act_fn_name]
 
 
-_ACTIVATION_AND_MUL_REGISTRY = LazyDict(
+_ACTIVATION_AND_MUL_REGISTRY: LazyDict[nn.Module] = LazyDict(
     {
         "gelu": lambda: GeluAndMul(),
         "silu": lambda: SiluAndMul(),
         "geglu": lambda: GeluAndMul(),
-        "swigluoai": lambda *args, **kwargs: SwigluOAIAndMul(*args, **kwargs),
+        "swigluoai": lambda: SwigluOAIAndMul(),
     }
 )
 

vllm/model_executor/layers/attention/attention.py

@@ -33,6 +33,7 @@
 )
 from vllm.v1.attention.backend import (
     AttentionBackend,
+    AttentionMetadata,
     AttentionType,
 )
 from vllm.v1.attention.backends.registry import AttentionBackendEnum
@@ -209,6 +210,7 @@ def __init__(
         `self.kv_cache`.
         """
         super().__init__()
+        sliding_window: int | None
         if per_layer_sliding_window is not None:
             # per-layer sliding window
             sliding_window = per_layer_sliding_window
@@ -335,7 +337,7 @@ def __init__(
             cache_config.enable_prefix_caching = False
 
         impl_cls = self.attn_backend.get_impl_cls()
-        self.impl = impl_cls(
+        self.impl = impl_cls(  # type: ignore[assignment]  # impl_cls always returns an AttentionImpl subclass
             num_heads,
             head_size,
             scale,
@@ -576,7 +578,7 @@ def process_weights_after_loading(self, act_dtype: torch.dtype):
     def get_attn_backend(self) -> type[AttentionBackend]:
         return self.attn_backend
 
-    def get_kv_cache_spec(self, vllm_config: VllmConfig) -> KVCacheSpec:
+    def get_kv_cache_spec(self, vllm_config: VllmConfig) -> KVCacheSpec | None:
         # Block size may get updated after model loading, refresh it
         block_size = vllm_config.cache_config.block_size
         # Should not be called for enc-dec or encoder-only attention.
@@ -680,9 +682,15 @@ def get_attention_context(
         extracted from the forward context.
     """
     forward_context: ForwardContext = get_forward_context()
-    attn_metadata = forward_context.attn_metadata
-    if isinstance(attn_metadata, dict):
-        attn_metadata = attn_metadata[layer_name]
+    attn_metadata_raw = forward_context.attn_metadata
+    attn_metadata: AttentionMetadata
+    if isinstance(attn_metadata_raw, dict):
+        attn_metadata = attn_metadata_raw[layer_name]
+    else:
+        # list[dict[str, AttentionMetadata]]: used in speculative decoding
+        # where [0] is the base-model (non-speculative) metadata dict.
+        assert isinstance(attn_metadata_raw, list)
+        attn_metadata = attn_metadata_raw[0][layer_name]
     attn_layer: Attention | MLAAttention = forward_context.no_compile_layers[layer_name]
     kv_cache = attn_layer.kv_cache
     slot_mapping = forward_context.slot_mapping
@@ -708,7 +716,7 @@ def unified_kv_cache_update(
         assert hasattr(attn_layer.impl, "do_kv_cache_update"), (
             f"{attn_layer.impl.__class__.__name__} does not support kv cache update"
         )
-        attn_layer.impl.do_kv_cache_update(
+        attn_layer.impl.do_kv_cache_update(  # type: ignore[attr-defined]
             attn_layer,
             key,
             value,

vllm/model_executor/layers/attention/chunked_local_attention.py

@@ -29,7 +29,7 @@
 
 @functools.lru_cache
 def create_chunked_local_attention_backend(
-    underlying_attn_backend: AttentionBackend,
+    underlying_attn_backend: type[AttentionBackend],
     attention_chunk_size: int,
 ) -> type[AttentionBackend]:
     prefix = f"ChunkedLocalAttention_{attention_chunk_size}_"

vllm/model_executor/layers/attention/cross_attention.py

@@ -72,7 +72,7 @@ def _get_cross_slot_mapping(
 
 @functools.lru_cache
 def create_cross_attention_backend(
-    underlying_attn_backend: AttentionBackend,
+    underlying_attn_backend: type[AttentionBackend],
 ) -> type[AttentionBackend]:
     prefix = "CrossAttention_"
     underlying_builder = underlying_attn_backend.get_builder_cls()
@@ -87,6 +87,7 @@ def build(
         ) -> AttentionMetadata:
             new_metadata = copy(common_attn_metadata)
             new_metadata.causal = False
+            assert new_metadata.encoder_seq_lens_cpu is not None
             max_encoder_len = int(new_metadata.encoder_seq_lens_cpu.max())
             new_metadata.max_seq_len = max_encoder_len
             # Any computed tokens indicated decode step>1 (no chunked prefill)
@@ -118,7 +119,7 @@ def build(
                 self.device,
             )
             attn_metadata = super().build(common_prefix_len, new_metadata, fast_build)
-            attn_metadata.slot_mapping = slot_mapping
+            attn_metadata.slot_mapping = slot_mapping  # type: ignore[attr-defined]
             return attn_metadata
 
     # NOTE(Lucas): we need a custom impl so we can use the slot-mapping computed by
@@ -144,8 +145,12 @@ def forward(
                 and key is not None
                 and value is not None
             ):
-                self.do_kv_cache_update(
-                    layer, key, value, kv_cache, attn_metadata.slot_mapping
+                self.do_kv_cache_update(  # type: ignore[attr-defined]
+                    layer,
+                    key,
+                    value,
+                    kv_cache,
+                    attn_metadata.slot_mapping,  # type: ignore[attr-defined]
                 )
 
             return super().forward(

vllm/model_executor/layers/attention/encoder_only_attention.py

@@ -21,7 +21,7 @@
 
 @functools.lru_cache
 def create_encoder_only_attention_backend(
-    underlying_attn_backend: AttentionBackend,
+    underlying_attn_backend: type[AttentionBackend],
 ) -> type[AttentionBackend]:
     prefix = "EncoderOnlyAttention_"
     underlying_builder = underlying_attn_backend.get_builder_cls()
@@ -93,6 +93,6 @@ def __init__(
             **kwargs,
         )
 
-    def get_kv_cache_spec(self, vllm_config: VllmConfig) -> KVCacheSpec:
+    def get_kv_cache_spec(self, vllm_config: VllmConfig) -> KVCacheSpec | None:
         # Does not need KV cache
         return None

vllm/model_executor/layers/attention/mla_attention.py

@@ -389,7 +389,7 @@ def __init__(
             cache_config.enable_prefix_caching = False
 
         impl_cls = cast(type[MLAAttentionImpl], self.attn_backend.get_impl_cls())
-        self.impl = impl_cls(
+        self.impl = impl_cls(  # type: ignore[assignment]  # impl_cls always returns an MLAAttentionImpl subclass
             num_heads=self.num_heads,
             head_size=self.head_size,
             scale=self.scale,
@@ -485,16 +485,22 @@ def forward(
 
         if self.use_direct_call:
             forward_context: ForwardContext = get_forward_context()
-            attn_metadata = forward_context.attn_metadata
-            if isinstance(attn_metadata, dict):
-                attn_metadata = attn_metadata[self.layer_name]
+            attn_metadata_raw = forward_context.attn_metadata
+            attn_metadata: MLACommonMetadata
+            if isinstance(attn_metadata_raw, dict):
+                attn_metadata = attn_metadata_raw[self.layer_name]  # type: ignore[assignment]
+            else:
+                # list[dict[str, AttentionMetadata]]: used in speculative decoding
+                # where [0] is the base-model (non-speculative) metadata dict.
+                assert isinstance(attn_metadata_raw, list)
+                attn_metadata = attn_metadata_raw[0][self.layer_name]  # type: ignore[assignment]
             self_kv_cache = self.kv_cache
             slot_mapping = forward_context.slot_mapping
 
             assert isinstance(slot_mapping, dict), (
                 f"Expected slot_mapping to be a dict, got {type(slot_mapping)}. "
             )
-            self.impl.do_kv_cache_update(
+            self.impl.do_kv_cache_update(  # type: ignore[attr-defined]
                 kv_c_normed,
                 k_pe,
                 self_kv_cache,
@@ -612,7 +618,7 @@ def forward_impl(
             num_mha_tokens = q.size(0) - num_mqa_tokens
 
         if num_mha_tokens > 0:
-            self.impl.forward_mha(
+            self.impl.forward_mha(  # type: ignore[attr-defined]
                 q[num_mqa_tokens:],
                 k_c_normed[num_mqa_tokens:],
                 k_pe[num_mqa_tokens:],
@@ -695,7 +701,7 @@ def forward_impl(
             # call decode attn
             if not is_sparse_impl:
                 assert attn_metadata.decode is not None
-            attn_out, lse = self.impl.forward_mqa(mqa_q, kv_cache, attn_metadata, self)
+            attn_out, lse = self.impl.forward_mqa(mqa_q, kv_cache, attn_metadata, self)  # type: ignore[attr-defined]
 
             # correct dcp attn_out with lse.
             if self.impl.dcp_world_size > 1:
@@ -1053,9 +1059,9 @@ class QueryLenSupport(Enum):
                 "AITER_MLA backends use aiter kernels instead."
             )
     elif current_platform.is_xpu():
-        from vllm._xpu_ops import xpu_ops as ops
+        from vllm import _xpu_ops
 
-        flash_attn_varlen_func = ops.flash_attn_varlen_func  # type: ignore[no-redef]
+        flash_attn_varlen_func = _xpu_ops.xpu_ops.flash_attn_varlen_func  # type: ignore[no-redef,attr-defined,assignment]
 
 
 def dynamic_per_batched_tensor_quant(
@@ -1988,7 +1994,7 @@ def build(
             assert isinstance(attn_metadata.prefill, FlashInferPrefillMetadata)
             self._build_fi_prefill_wrappers(attn_metadata.prefill)
 
-        return attn_metadata
+        return attn_metadata  # type: ignore[return-value]
 
 
 def reorg_kvcache(

vllm/model_executor/layers/fused_moe/all2all_utils.py

@@ -117,17 +117,20 @@ def maybe_make_prepare_finalize(
                 "Detected DP deployment with no --enable-expert-parallel. "
                 "Falling back to AllGather+ReduceScatter dispatch/combine."
             )
+            device_communicator = get_ep_group().device_communicator
+            assert device_communicator is not None
+            assert device_communicator.all2all_manager is not None
             return make_moe_prepare_and_finalize_naive_dp_ep(
                 is_sequence_parallel=moe.moe_parallel_config.is_sequence_parallel,
-                num_dispatchers=(
-                    get_ep_group().device_communicator.all2all_manager.world_size
-                ),
+                num_dispatchers=(device_communicator.all2all_manager.world_size),
                 use_monolithic=use_monolithic,
             )
         else:
             return make_moe_prepare_and_finalize_no_dp_ep(use_monolithic)
 
-    all2all_manager = get_ep_group().device_communicator.all2all_manager
+    device_communicator = get_ep_group().device_communicator
+    assert device_communicator is not None
+    all2all_manager = device_communicator.all2all_manager
     assert all2all_manager is not None
 
     prepare_finalize: FusedMoEPrepareAndFinalize | None = None

vllm/model_executor/layers/fused_moe/config.py

@@ -7,6 +7,7 @@
 import torch
 
 from vllm.config import ParallelConfig, SchedulerConfig
+from vllm.config.kernel import MoEBackend
 from vllm.distributed import get_dp_group, get_pcp_group, get_tensor_model_parallel_rank
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe.activation import MoEActivation
@@ -1192,7 +1193,7 @@ class FusedMoEConfig:
     # Defaults to intermediate_size_per_partition if not specified.
     intermediate_size_per_partition_unpadded: int | None = None
 
-    moe_backend: str = "auto"
+    moe_backend: MoEBackend = "auto"
     max_num_tokens: int = SchedulerConfig.DEFAULT_MAX_NUM_BATCHED_TOKENS_FOR_BATCHED_DP
     has_bias: bool = False
     is_act_and_mul: bool = True

vllm/model_executor/layers/fused_moe/experts/batched_deep_gemm_moe.py

@@ -210,9 +210,9 @@ def persistent_masked_m_silu_mul_quant(
         DeepGemmQuantScaleFMT.UE8M0,
     ]
 
-    cuda_arch = current_platform.get_device_capability(
-        device_id=y.device.index
-    ).to_int()
+    device_capability = current_platform.get_device_capability(device_id=y.device.index)
+    assert device_capability is not None
+    cuda_arch = device_capability.to_int()
 
     if current_platform.is_cuda() and cuda_arch >= 80:
         torch.ops._C.persistent_masked_m_silu_mul_quant(

vllm/model_executor/layers/fused_moe/oracle/mxfp4.py

@@ -7,6 +7,7 @@
 
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm import envs
+from vllm.config.kernel import MoEBackend
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe import (
     FusedMoEConfig,
@@ -146,7 +147,7 @@ def backend_to_kernel_cls(
         raise ValueError(f"Unknown MXFP4 MoE backend: {backend.value}")
 
 
-def map_mxfp4_backend(runner_backend: str) -> Mxfp4MoeBackend:
+def map_mxfp4_backend(runner_backend: MoEBackend) -> Mxfp4MoeBackend:
     """Map user's moe_backend string to Mxfp4MoeBackend."""
     mapping: dict[str, Mxfp4MoeBackend] = {
         "flashinfer_trtllm": Mxfp4MoeBackend.FLASHINFER_TRTLLM_MXFP4_BF16,
@@ -201,10 +202,12 @@ def select_gpt_oss_mxfp4_moe_backend(
     Select the primary MXFP4 MoE backend.
     Note: Shape-specific fallbacks may still occur at runtime.
     """
-    triton_kernels_supported = has_triton_kernels() and (
-        9,
-        0,
-    ) <= current_platform.get_device_capability() < (11, 0)
+    device_capability = current_platform.get_device_capability()
+    triton_kernels_supported = (
+        has_triton_kernels()
+        and device_capability is not None
+        and (9, 0) <= device_capability < (11, 0)
+    )
 
     # LoRA: separate experts backend path
     if config.is_lora_enabled: