fixed mypy warnings for files vllm/v1/attention with TEMPORARY workaround (#31465)

Signed-off-by: Zhuohao Yang <zy242@cornell.edu>
Co-authored-by: Zhuohao Yang <zy242@cornell.edu>
Co-authored-by: Wentao Ye <44945378+yewentao256@users.noreply.github.com>

tools/pre_commit/mypy.py

@@ -41,6 +41,7 @@ FILES = [
     "vllm/usage",
     "vllm/utils",
     "vllm/worker",
+    "vllm/v1/attention",
     "vllm/v1/core",
     "vllm/v1/engine",
     "vllm/v1/executor",
@@ -60,7 +61,6 @@ SEPARATE_GROUPS = [
     "vllm/lora",
     "vllm/model_executor",
     # v1 related
-    "vllm/v1/attention",
     "vllm/v1/kv_offload",
     "vllm/v1/spec_decode",
     "vllm/v1/structured_output",

vllm/attention/backends/abstract.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from abc import ABC, abstractmethod
+from enum import Enum
 from typing import TYPE_CHECKING, ClassVar, Generic, Protocol, TypeVar, get_args
 
 import torch
@@ -14,7 +15,7 @@ if TYPE_CHECKING:
     from vllm.v1.attention.backends.utils import KVCacheLayoutType
 
 
-class AttentionType:
+class AttentionType(str, Enum):
     """
     Attention type.
     Use string to be compatible with `torch.compile`.
@@ -193,7 +194,7 @@ class AttentionBackend(ABC):
         head_size: int,
         dtype: torch.dtype,
         kv_cache_dtype: "CacheDType | None",
-        block_size: int | None,
+        block_size: int,
         use_mla: bool,
         has_sink: bool,
         use_sparse: bool,
@@ -207,7 +208,7 @@ class AttentionBackend(ABC):
         head_size: int,
         dtype: torch.dtype,
         kv_cache_dtype: "CacheDType | None",
-        block_size: int | None,
+        block_size: int,
         use_mla: bool,
         has_sink: bool,
         use_sparse: bool,
@@ -290,6 +291,11 @@ class AttentionLayer(Protocol):
 
 
 class AttentionImpl(ABC, Generic[T]):
+    # Required attributes that all impls should have
+    num_heads: int
+    head_size: int
+    scale: float
+
     # Whether the attention impl can return the softmax lse for decode.
     # Some features like decode context parallelism require the softmax lse.
     can_return_lse_for_decode: bool = False

vllm/model_executor/layers/attention_layer_base.py

@@ -4,7 +4,7 @@
 
 from abc import ABC, abstractmethod
 
-from vllm.attention.backends.abstract import AttentionBackend
+from vllm.attention.backends.abstract import AttentionBackend, AttentionImpl
 from vllm.config import VllmConfig
 from vllm.v1.kv_cache_interface import KVCacheSpec
 
@@ -18,6 +18,8 @@ class AttentionLayerBase(ABC):
     from different layer types.
     """
 
+    impl: "AttentionImpl"
+
     @abstractmethod
     def get_attn_backend(self) -> type[AttentionBackend]:
         """Get the attention backend class for this layer."""

vllm/v1/attention/backends/flash_attn.py

@@ -167,7 +167,7 @@ class FlashAttentionBackend(AttentionBackend):
         head_size: int,
         dtype: torch.dtype,
         kv_cache_dtype: CacheDType | None,
-        block_size: int,
+        block_size: int | None,
         use_mla: bool,
         has_sink: bool,
         use_sparse: bool,
@@ -354,7 +354,11 @@ class FlashAttentionMetadataBuilder(AttentionMetadataBuilder[FlashAttentionMetad
                     aot_schedule = False
 
         max_num_splits = 0  # 0 means use FA3's heuristics, not CG compatible
-        if self.use_full_cuda_graph and num_actual_tokens <= self.max_cudagraph_size:
+        if (
+            self.use_full_cuda_graph
+            and self.max_cudagraph_size is not None
+            and num_actual_tokens <= self.max_cudagraph_size
+        ):
             # NOTE(woosuk): Setting num_splits > 1 may increase the memory
             # usage, because the intermediate buffers of size [num_splits,
             # num_heads, num_tokens, head_size] are allocated. Therefore,
@@ -599,6 +603,9 @@ class FlashAttentionImpl(AttentionImpl):
               We use torch's .expand() to avoid duplicating values
         """
         assert output is not None, "Output tensor must be provided."
+        assert self.vllm_flash_attn_version is not None, (
+            "FlashAttention version not detected."
+        )
 
         if output_scale is not None or output_block_scale is not None:
             raise NotImplementedError(
@@ -697,6 +704,11 @@ class FlashAttentionImpl(AttentionImpl):
                 )
                 return output
             else:
+                sliding_window_size = (
+                    list(self.sliding_window)
+                    if self.sliding_window is not None
+                    else None
+                )
                 flash_attn_varlen_func(
                     q=query[:num_actual_tokens],
                     k=key_cache,
@@ -709,7 +721,7 @@ class FlashAttentionImpl(AttentionImpl):
                     softmax_scale=self.scale,
                     causal=attn_metadata.causal,
                     alibi_slopes=self.alibi_slopes,
-                    window_size=self.sliding_window,
+                    window_size=sliding_window_size,
                     block_table=block_table,
                     softcap=self.logits_soft_cap,
                     scheduler_metadata=scheduler_metadata,
@@ -764,12 +776,19 @@ class FlashAttentionImpl(AttentionImpl):
         k_descale: torch.Tensor | None = None,
         v_descale: torch.Tensor | None = None,
     ) -> torch.Tensor:
+        assert self.vllm_flash_attn_version is not None, (
+            "FlashAttention version not detected."
+        )
+
         cu_seqlens_q = attn_metadata.query_start_loc
         max_seqlen_q = attn_metadata.max_query_len
         block_table = attn_metadata.block_table
 
         query = query.contiguous()
         query_across_dcp = get_dcp_group().all_gather(query, dim=1)
+        sliding_window_size = (
+            list(self.sliding_window) if self.sliding_window is not None else None
+        )
         context_attn_out, context_lse = flash_attn_varlen_func(
             q=query_across_dcp,
             k=key_cache,
@@ -782,7 +801,7 @@ class FlashAttentionImpl(AttentionImpl):
             softmax_scale=self.scale,
             causal=False,
             alibi_slopes=self.alibi_slopes,
-            window_size=self.sliding_window,
+            window_size=sliding_window_size,
             block_table=block_table,
             softcap=self.logits_soft_cap,
             return_softmax_lse=True,
@@ -813,7 +832,7 @@ class FlashAttentionImpl(AttentionImpl):
             softmax_scale=self.scale,
             causal=attn_metadata.causal,
             alibi_slopes=self.alibi_slopes,
-            window_size=self.sliding_window,
+            window_size=sliding_window_size,
             softcap=self.logits_soft_cap,
             return_softmax_lse=True,
             fa_version=self.vllm_flash_attn_version,
@@ -850,6 +869,10 @@ class FlashAttentionImpl(AttentionImpl):
             attn_metadata: Encoder attention metadata
             layer: The attention layer
         """
+        assert self.vllm_flash_attn_version is not None, (
+            "FlashAttention version not detected."
+        )
+
         # For encoder attention, process FP8 quantization if needed
         if self.kv_cache_dtype.startswith("fp8"):
             raise NotImplementedError(
@@ -868,6 +891,9 @@ class FlashAttentionImpl(AttentionImpl):
         )
 
         # Call flash attention directly on Q, K, V tensors
+        sliding_window_size = (
+            list(self.sliding_window) if self.sliding_window is not None else None
+        )
         flash_attn_varlen_func(
             q=query,
             k=key,
@@ -880,7 +906,7 @@ class FlashAttentionImpl(AttentionImpl):
             softmax_scale=self.scale,
             causal=False,  # Encoder attention is bidirectional
             alibi_slopes=self.alibi_slopes,
-            window_size=self.sliding_window,
+            window_size=sliding_window_size,
             softcap=self.logits_soft_cap,
             fa_version=self.vllm_flash_attn_version,
             q_descale=layer._q_scale.expand(descale_shape),
@@ -1020,7 +1046,7 @@ def cascade_attention(
         max_seqlen_k=common_prefix_len,
         softmax_scale=softmax_scale,
         causal=False,
-        window_size=sliding_window,
+        window_size=list(sliding_window),
         block_table=block_table[:1],
         softcap=logits_soft_cap,
         return_softmax_lse=True,
@@ -1048,7 +1074,7 @@ def cascade_attention(
         max_seqlen_k=max_kv_len - common_prefix_len,
         softmax_scale=softmax_scale,
         causal=True,
-        window_size=sliding_window,
+        window_size=list(sliding_window),
         block_table=block_table[:, num_common_kv_blocks:],
         softcap=logits_soft_cap,
         return_softmax_lse=True,

vllm/v1/attention/backends/flash_attn_diffkv.py

@@ -113,6 +113,9 @@ class FlashAttentionDiffKVImpl(FlashAttentionImpl):
               We use torch's .expand() to avoid duplicating values
         """
         assert output is not None, "Output tensor must be provided."
+        assert self.vllm_flash_attn_version is not None, (
+            "FlashAttention version not detected."
+        )
 
         if output_scale is not None or output_block_scale is not None:
             raise NotImplementedError(
@@ -214,6 +217,11 @@ class FlashAttentionDiffKVImpl(FlashAttentionImpl):
                 )
                 return output
             else:
+                sliding_window_size = (
+                    list(self.sliding_window)
+                    if self.sliding_window is not None
+                    else None
+                )
                 flash_attn_varlen_func(
                     q=query[:num_actual_tokens],
                     k=key_cache,
@@ -226,7 +234,7 @@ class FlashAttentionDiffKVImpl(FlashAttentionImpl):
                     softmax_scale=self.scale,
                     causal=attn_metadata.causal,
                     alibi_slopes=self.alibi_slopes,
-                    window_size=self.sliding_window,
+                    window_size=sliding_window_size,
                     block_table=block_table,
                     softcap=self.logits_soft_cap,
                     scheduler_metadata=scheduler_metadata,

vllm/v1/attention/backends/flashinfer.py

@@ -530,11 +530,12 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
             self._decode_wrappers_cudagraph: dict[
                 int, BatchDecodeWithPagedKVCacheWrapper
             ] = {}
+            self._decode_cudagraph_max_bs = (1 + num_spec_tokens) * max_num_reqs
+            if self.compilation_config.max_cudagraph_capture_size is not None:
                 self._decode_cudagraph_max_bs = min(
-                (1 + num_spec_tokens) * max_num_reqs,
+                    self._decode_cudagraph_max_bs,
                     self.compilation_config.max_cudagraph_capture_size,
                 )
-
         try:
             self.dcp_world_size = get_dcp_group().world_size
             self.dcp_rank = get_dcp_group().rank_in_group

vllm/v1/attention/backends/flex_attention.py

@@ -215,7 +215,7 @@ def physical_to_logical_mapping(
     )
 
     # Only process valid blocks to avoid garbage values
-    num_blocks_per_seq = cdiv(seq_lens, block_size)
+    num_blocks_per_seq: torch.Tensor = cdiv(seq_lens, block_size)
     mask = (
         torch.arange(max_num_blocks, device=device)[None, :]
         < num_blocks_per_seq[:, None]

vllm/v1/attention/backends/gdn_attn.py

@@ -75,8 +75,10 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
         self.compilation_config = vllm_config.compilation_config
         self.speculative_config = vllm_config.speculative_config
         self.kv_cache_spec = kv_cache_spec
+
         if self.speculative_config:
-            self.num_spec = self.speculative_config.num_speculative_tokens
+            assert self.speculative_config.num_speculative_tokens is not None
+            self.num_spec: int = self.speculative_config.num_speculative_tokens
         else:
             self.num_spec = 0
         self.use_spec_decode = self.num_spec > 0
@@ -85,8 +87,13 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
         self.use_full_cuda_graph = (
             self.compilation_config.cudagraph_mode.has_full_cudagraphs()
         )
+
+        self.decode_cudagraph_max_bs = (
+            self.vllm_config.scheduler_config.max_num_seqs * (self.num_spec + 1)
+        )
+        if self.compilation_config.max_cudagraph_capture_size is not None:
             self.decode_cudagraph_max_bs = min(
-            self.vllm_config.scheduler_config.max_num_seqs * (self.num_spec + 1),
+                self.decode_cudagraph_max_bs,
                 self.compilation_config.max_cudagraph_capture_size,
             )
 

vllm/v1/attention/backends/mamba2_attn.py

@@ -123,10 +123,11 @@ class Mamba2AttentionMetadataBuilder(
         device: torch.device,
     ):
         super().__init__(kv_cache_spec, layer_names, vllm_config, device)
-        self.chunk_size = vllm_config.model_config.get_mamba_chunk_size()
-        assert self.chunk_size is not None, (
+        chunk_size = vllm_config.model_config.get_mamba_chunk_size()
+        assert chunk_size is not None, (
             "chunk_size needs to be set in the model config for Mamba2 models"
         )
+        self.chunk_size: int = chunk_size
 
     def _compute_chunk_metadata(
         self,

vllm/v1/attention/backends/mamba_attn.py

@@ -69,8 +69,10 @@ class BaseMambaAttentionMetadataBuilder(AttentionMetadataBuilder[M], abc.ABC):
 
         assert isinstance(kv_cache_spec, MambaSpec)
         self.compilation_config = vllm_config.compilation_config
+        self.decode_cudagraph_max_bs = self.vllm_config.scheduler_config.max_num_seqs
+        if self.compilation_config.max_cudagraph_capture_size is not None:
             self.decode_cudagraph_max_bs = min(
-            self.vllm_config.scheduler_config.max_num_seqs,
+                self.decode_cudagraph_max_bs,
                 self.compilation_config.max_cudagraph_capture_size,
             )
 
@@ -150,9 +152,13 @@ class BaseMambaAttentionMetadataBuilder(AttentionMetadataBuilder[M], abc.ABC):
             cdiv(common_attn_metadata.seq_lens, mamba_block_size) - 1
         )
         # -1 in case it's non-computed and causes later issues with indexing
-        block_idx_last_computed_token = block_idx_last_computed_token.clamp(min=0)
+        block_idx_last_computed_token = torch.clamp(
+            block_idx_last_computed_token, min=0
+        )
         # -1 in the case we have a padded request (0 seq-len)
-        block_idx_last_scheduled_token = block_idx_last_scheduled_token.clamp(min=0)
+        block_idx_last_scheduled_token = torch.clamp(
+            block_idx_last_scheduled_token, min=0
+        )
 
         return (
             block_idx_last_computed_token,

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

@@ -62,7 +62,7 @@ class AiterTritonMLAImpl(AiterMLAImpl):
             k,
             v,
             softmax_scale=softmax_scale,
-            return_lse=return_softmax_lse,
+            return_softmax_lse=return_softmax_lse,
             **kwargs,
         )
         # Transpose the LSE if Triton MHA is used:

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

@@ -202,6 +202,7 @@ from vllm._aiter_ops import rocm_aiter_ops
 from vllm.attention.backends.abstract import (
     AttentionBackend,
     AttentionLayer,
+    AttentionMetadata,
     MLAAttentionImpl,
 )
 from vllm.attention.backends.utils import get_mla_dims
@@ -251,13 +252,15 @@ class QueryLenSupport(Enum):
 
 
 try:
-    from vllm.vllm_flash_attn import flash_attn_varlen_func
+    from vllm.vllm_flash_attn import (  # type: ignore[attr-defined]
+        flash_attn_varlen_func,
+    )
 
     is_vllm_fa = True
 except ImportError:
     # For rocm use upstream flash attention
     if current_platform.is_rocm():
-        from flash_attn import flash_attn_varlen_func
+        from flash_attn import flash_attn_varlen_func  # type: ignore[no-redef]
     is_vllm_fa = False
 
 try:
@@ -386,7 +389,7 @@ D = TypeVar("D", bound=MLACommonDecodeMetadata)
 
 
 @dataclass
-class MLACommonMetadata(Generic[D]):
+class MLACommonMetadata(AttentionMetadata, Generic[D]):
     """Metadata for MLACommon.
 
     NOTE: Please read the comment at the top of the file before trying to
@@ -434,7 +437,7 @@ class MLACommonMetadata(Generic[D]):
 
 
 M = TypeVar("M", bound=MLACommonMetadata)
-A = TypeVar("A")
+A = TypeVar("A", bound=AttentionMetadata)
 
 
 def use_flashinfer_prefill() -> bool:
@@ -617,7 +620,7 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
             self._fi_prefill_chunks: list[BatchPrefillWithRaggedKVCacheWrapper] = []
 
             self._global_hyperparameters = infer_global_hyperparameters(
-                get_per_layer_parameters(vllm_config, layer_names, MLACommonImpl)
+                get_per_layer_parameters(vllm_config, layer_names, MLACommonImpl)  # type: ignore[type-abstract]
             )
 
         if self._use_trtllm_ragged_prefill:
@@ -874,7 +877,7 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
                     )
                     # Note(qcs): The max local context lengths
                     # padded to `dcp_local_block_size`.
-                    padded_local_context_lens_cpu = (
+                    padded_local_context_lens_cpu: torch.Tensor = (
                         cdiv(
                             context_lens_cpu,
                             self.dcp_virtual_block_size,
@@ -1171,7 +1174,9 @@ class MLACommonBaseImpl(MLAAttentionImpl[A], Generic[A]):
             )
 
         def get_and_maybe_dequant_weights(layer: LinearBase):
-            if not isinstance(layer.quant_method, UnquantizedLinearMethod):
+            if layer.quant_method is not None and not isinstance(
+                layer.quant_method, UnquantizedLinearMethod
+            ):
                 # NOTE: This should only be used offline, since it's O(N^3)
                 eye = torch.eye(
                     layer.input_size_per_partition,
@@ -1327,12 +1332,14 @@ class MLACommonImpl(MLACommonBaseImpl[M], Generic[M]):
             # v with 0s to match the qk head dim for attention backends that do
             # not support different headdims
             # We don't need to pad V if we are on a hopper system with FA3
+            device_capability = current_platform.get_device_capability()
             self._pad_v = self.vllm_flash_attn_version is None or not (
                 self.vllm_flash_attn_version == 3
-                and current_platform.get_device_capability()[0] == 9
+                and device_capability is not None
+                and device_capability[0] == 9
             )
 
-        self.dcp_world_size: int | None = None
+        self.dcp_world_size: int = -1
 
         self.chunked_prefill_workspace_size = (
             MLACommonMetadataBuilder.determine_chunked_prefill_workspace_size(
@@ -1583,7 +1590,9 @@ class MLACommonImpl(MLACommonBaseImpl[M], Generic[M]):
             )
 
         def get_and_maybe_dequant_weights(layer: LinearBase):
-            if not isinstance(layer.quant_method, UnquantizedLinearMethod):
+            if layer.quant_method is not None and not isinstance(
+                layer.quant_method, UnquantizedLinearMethod
+            ):
                 # NOTE: This should only be used offline, since it's O(N^3)
                 eye = torch.eye(
                     layer.input_size_per_partition,
@@ -1875,7 +1884,7 @@ class MLACommonImpl(MLACommonBaseImpl[M], Generic[M]):
     ) -> None:
         # TODO (zyongye): Prefill function here
         assert attn_metadata.prefill is not None
-        assert self.dcp_world_size is not None
+        assert self.dcp_world_size != -1
 
         has_context = attn_metadata.prefill.chunked_context is not None
         kv_nope = self.kv_b_proj(kv_c_normed)[0].view(
@@ -1975,7 +1984,7 @@ class MLACommonImpl(MLACommonBaseImpl[M], Generic[M]):
             # same expert outputs.
             return output.fill_(0)
 
-        if self.dcp_world_size is None:
+        if self.dcp_world_size == -1:
             self.dcp_world_size = get_dcp_group().world_size
 
         fp8_attention = self.kv_cache_dtype.startswith("fp8")

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

@@ -33,7 +33,10 @@ from vllm.v1.attention.backends.mla.common import (
 )
 from vllm.v1.attention.backends.utils import AttentionCGSupport
 from vllm.v1.kv_cache_interface import AttentionSpec
-from vllm.vllm_flash_attn import flash_attn_varlen_func, get_scheduler_metadata
+from vllm.vllm_flash_attn import (  # type: ignore[attr-defined]
+    flash_attn_varlen_func,
+    get_scheduler_metadata,
+)
 
 logger = init_logger(__name__)
 
@@ -181,7 +184,11 @@ class FlashAttnMLAMetadataBuilder(MLACommonMetadataBuilder[FlashAttnMLAMetadata]
 
         # For Flash Attention MLA + full cudagraph
         max_num_splits = 0
-        if self.use_full_cuda_graph and num_decode_tokens <= self.max_cudagraph_size:
+        if (
+            self.use_full_cuda_graph
+            and self.max_cudagraph_size is not None
+            and num_decode_tokens <= self.max_cudagraph_size
+        ):
             # NOTE(woosuk): Setting num_splits > 1 may increase the memory
             # usage, because the intermediate buffers of size [num_splits,
             # num_heads, num_tokens, head_size] are allocated. Therefore,

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

@@ -10,6 +10,7 @@ from vllm import _custom_ops as ops
 from vllm.attention.backends.abstract import (
     AttentionBackend,
     AttentionLayer,
+    AttentionMetadata,
     MultipleOf,
 )
 from vllm.attention.backends.utils import get_mla_dims
@@ -124,7 +125,7 @@ class FlashMLASparseBackend(AttentionBackend):
 
 
 @dataclass
-class FlashMLASparseMetadata:
+class FlashMLASparseMetadata(AttentionMetadata):
     num_reqs: int
     max_query_len: int
     max_seq_len: int
@@ -718,7 +719,7 @@ class FlashMLASparseImpl(MLACommonBaseImpl[FlashMLASparseMetadata]):
         )
         self.softmax_scale = scale
         assert indexer is not None
-        self.topk_indices_buffer = indexer.topk_indices_buffer
+        self.topk_indices_buffer: torch.Tensor | None = indexer.topk_indices_buffer
         self.padding = 128 if current_platform.is_device_capability_family(100) else 64
 
         if kv_cache_dtype == "fp8_ds_mla":
@@ -980,6 +981,7 @@ class FlashMLASparseImpl(MLACommonBaseImpl[FlashMLASparseMetadata]):
         q = q[:num_actual_toks, ...]
         k_c_normed = k_c_normed[:num_actual_toks, ...]
         k_pe = k_pe[:num_actual_toks, ...]
+        assert self.topk_indices_buffer is not None
         topk_indices = self.topk_indices_buffer[:num_actual_toks]
 
         q_nope, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)

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

@@ -236,7 +236,7 @@ class AiterMLAImpl(MLACommonImpl[AiterMLAMetadata]):
             k=k,
             v=v,
             softmax_scale=softmax_scale,
-            return_lse=return_softmax_lse,
+            return_softmax_lse=return_softmax_lse,
             **kwargs,
         )
 
@@ -251,6 +251,7 @@ class AiterMLAImpl(MLACommonImpl[AiterMLAMetadata]):
     ) -> tuple[torch.Tensor, torch.Tensor | None]:
         assert kv_c_and_k_pe_cache.numel() > 0
         assert attn_metadata.decode is not None
+        assert attn_metadata.decode.max_qo_len is not None
 
         if type(q) is tuple:
             q = torch.cat(q, dim=-1)

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

@@ -43,7 +43,7 @@ class ROCMAiterMLASparseBackend(AttentionBackend):
         return "ROCM_AITER_MLA_SPARSE"
 
     @staticmethod
-    def get_metadata_cls() -> type[AttentionMetadata]:
+    def get_metadata_cls() -> type["ROCMAiterMLASparseMetadata"]:
         return ROCMAiterMLASparseMetadata
 
     @staticmethod
@@ -74,7 +74,7 @@ class ROCMAiterMLASparseBackend(AttentionBackend):
 
 
 @dataclass
-class ROCMAiterMLASparseMetadata:
+class ROCMAiterMLASparseMetadata(AttentionMetadata):
     num_reqs: int
     max_query_len: int
     max_seq_len: int
@@ -223,7 +223,7 @@ class ROCMAiterMLASparseImpl(MLACommonBaseImpl[ROCMAiterMLASparseMetadata]):
         )
         self.softmax_scale = scale
         assert indexer is not None
-        self.topk_indices_buffer = indexer.topk_indices_buffer
+        self.topk_indices_buffer: torch.Tensor | None = indexer.topk_indices_buffer
         self.is_fp8bmm_enabled = rocm_aiter_ops.is_fp8bmm_enabled()
 
     def _forward_bf16_kv(
@@ -294,6 +294,7 @@ class ROCMAiterMLASparseImpl(MLACommonBaseImpl[ROCMAiterMLASparseMetadata]):
             # Convert from (N, B, L) to (B, N, L)
             ql_nope = ql_nope.transpose(0, 1)
 
+        assert self.topk_indices_buffer is not None
         topk_indices = self.topk_indices_buffer[:num_actual_toks]
 
         topk_indices_global = triton_convert_req_index_to_global_index(

vllm/v1/attention/backends/tree_attn.py

@@ -155,7 +155,9 @@ class TreeAttentionMetadataBuilder(AttentionMetadataBuilder[TreeAttentionMetadat
         self.block_size = kv_cache_spec.block_size
 
         spec_config = vllm_config.speculative_config
-        spec_token_tree = (spec := spec_config) and spec.speculative_token_tree
+        spec_token_tree: str | None = None
+        if spec := spec_config:
+            spec_token_tree = spec.speculative_token_tree
         tree_choices: list[tuple[int, ...]] = (
             ast.literal_eval(spec_token_tree) if spec_token_tree is not None else [(0,)]
         )

vllm/v1/attention/backends/utils.py

@@ -469,6 +469,7 @@ def get_kv_cache_layout():
     # Format specified by the code.
     global _KV_CACHE_LAYOUT_OVERRIDE
 
+    cache_layout: Literal["NHD", "HND"] | None = None
     if _KV_CACHE_LAYOUT_OVERRIDE is not None:
         cache_layout = _KV_CACHE_LAYOUT_OVERRIDE
         logger.info_once(
@@ -524,7 +525,11 @@ def get_per_layer_parameters(
     to use during `plan`.
     """
 
-    layers = get_layers_from_vllm_config(vllm_config, AttentionLayerBase, layer_names)
+    layers = get_layers_from_vllm_config(
+        vllm_config,
+        AttentionLayerBase,  # type: ignore[type-abstract]
+        layer_names,
+    )
     per_layer_params: dict[str, PerLayerParameters] = {}
 
     for key, layer in layers.items():
@@ -1125,7 +1130,7 @@ class KVSharingFastPrefillMetadata(Protocol):
 
 def create_fast_prefill_custom_backend(
     prefix: str,
-    underlying_attn_backend: AttentionBackend,
+    underlying_attn_backend: type[AttentionBackend],
 ) -> type[AttentionBackend]:
     underlying_builder = underlying_attn_backend.get_builder_cls()