biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

[CI] Bump mypy version (#34950)

Browse Source 
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2026-03-04 20:55:11 +00:00
committed by GitHub
parent 7eca859110
commit 17dc9c7fc9
13 changed files with 90 additions and 61 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
vllm/v1/attention/backends/flashinfer.py
View File

@@ -972,6 +972,7 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
# Early-out for cascade attention
if use_cascade:
assert num_blocks_np is not None
# Grab the blocks of the shared prefix from the first request.
num_common_kv_blocks = common_prefix_len // page_size
@@ -1117,6 +1118,7 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
max_seq_len=max_seq_len,
)
else:
assert seq_lens_cpu is not None
pure_decode = num_prefills == 0
use_cudagraph = (
self.enable_cuda_graph

23
vllm/v1/attention/backends/gdn_attn.py
View File

@@ -88,14 +88,14 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
self.num_spec: int = self.speculative_config.num_speculative_tokens
else:
self.num_spec = 0
self.use_spec_decode = self.num_spec > 0
self.use_spec_decode: bool = self.num_spec > 0
self._init_reorder_batch_threshold(1, self.use_spec_decode)
self.use_full_cuda_graph = (
self.use_full_cuda_graph: bool = (
self.compilation_config.cudagraph_mode.has_full_cudagraphs()
)
self.decode_cudagraph_max_bs = (
self.decode_cudagraph_max_bs: int = (
self.vllm_config.scheduler_config.max_num_seqs * (self.num_spec + 1)
)
if self.compilation_config.max_cudagraph_capture_size is not None:
@@ -104,42 +104,42 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
self.compilation_config.max_cudagraph_capture_size,
)
self.spec_state_indices_tensor = torch.empty(
self.spec_state_indices_tensor: torch.Tensor = torch.empty(
(self.decode_cudagraph_max_bs, self.num_spec + 1),
dtype=torch.int32,
device=device,
)
self.non_spec_state_indices_tensor = torch.empty(
self.non_spec_state_indices_tensor: torch.Tensor = torch.empty(
(self.decode_cudagraph_max_bs,),
dtype=torch.int32,
device=device,
)
self.spec_sequence_masks = torch.empty(
self.spec_sequence_masks: torch.Tensor = torch.empty(
(self.decode_cudagraph_max_bs,),
dtype=torch.bool,
device=device,
)
self.spec_token_indx = torch.empty(
self.spec_token_indx: torch.Tensor = torch.empty(
(self.decode_cudagraph_max_bs * (self.num_spec + 1),),
dtype=torch.int32,
device=device,
)
self.non_spec_token_indx = torch.empty(
self.non_spec_token_indx: torch.Tensor = torch.empty(
(self.decode_cudagraph_max_bs * (self.num_spec + 1),),
dtype=torch.int32,
device=device,
)
self.spec_query_start_loc = torch.empty(
self.spec_query_start_loc: torch.Tensor = torch.empty(
(self.decode_cudagraph_max_bs + 1,),
dtype=torch.int32,
device=device,
)
self.non_spec_query_start_loc = torch.empty(
self.non_spec_query_start_loc: torch.Tensor = torch.empty(
(self.decode_cudagraph_max_bs + 1,),
dtype=torch.int32,
device=device,
)
self.num_accepted_tokens = torch.empty(
self.num_accepted_tokens: torch.Tensor = torch.empty(
(self.decode_cudagraph_max_bs,),
dtype=torch.int32,
device=device,
@@ -322,6 +322,7 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
and num_spec_decodes <= self.decode_cudagraph_max_bs
and num_spec_decode_tokens <= self.decode_cudagraph_max_bs
):
assert spec_sequence_masks is not None
self.spec_state_indices_tensor[:num_spec_decodes].copy_(
spec_state_indices_tensor, non_blocking=True
)

12
vllm/v1/attention/backends/mamba_attn.py
View File

@@ -98,8 +98,8 @@ class BaseMambaAttentionMetadataBuilder(AttentionMetadataBuilder[M], abc.ABC):
self.use_spec_decode = self.num_spec_tokens > 0
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
scheduler_config = vllm_config.scheduler_config
self.decode_cudagraph_max_bs: int = scheduler_config.max_num_seqs
if self.compilation_config.max_cudagraph_capture_size is not None:
self.decode_cudagraph_max_bs = min(
self.decode_cudagraph_max_bs,
@@ -114,7 +114,7 @@ class BaseMambaAttentionMetadataBuilder(AttentionMetadataBuilder[M], abc.ABC):
# Speculative decoding not supported with prefix caching,
# so keep shape consistent with prefill buffer
# TODO: reduce this size as needed for decode-only cudagraph capture
self.state_indices_tensor_d = torch.empty(
self.state_indices_tensor_d: torch.Tensor = torch.empty(
(
self.decode_cudagraph_max_bs,
max_num_blocks,
@@ -122,12 +122,12 @@ class BaseMambaAttentionMetadataBuilder(AttentionMetadataBuilder[M], abc.ABC):
dtype=torch.int32,
device=device,
)
self.block_idx_last_scheduled_token = torch.empty(
self.block_idx_last_scheduled_token: torch.Tensor = torch.empty(
(self.decode_cudagraph_max_bs,),
dtype=torch.int32,
device=device,
)
self.block_idx_last_computed_token = torch.empty(
self.block_idx_last_computed_token: torch.Tensor = torch.empty(
(self.decode_cudagraph_max_bs,),
dtype=torch.int32,
device=device,
@@ -142,7 +142,7 @@ class BaseMambaAttentionMetadataBuilder(AttentionMetadataBuilder[M], abc.ABC):
# For speculative decoding, we need to store the following buffers
# for CUDA graph capture during decode
if self.num_spec_tokens > 0:
self.decode_num_accepted_tokens = torch.empty(
self.decode_num_accepted_tokens: torch.Tensor = torch.empty(
(self.decode_cudagraph_max_bs,),
dtype=torch.int32,
device=device,