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[CI] Revert PRs 34818 and 33600 (#34979)

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This commit is contained in:
Lucas Wilkinson
2026-02-20 16:25:50 -05:00
committed by GitHub
parent f24b2de3d3
commit aaefc58ee0
16 changed files with 249 additions and 301 deletions
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11
vllm/model_executor/layers/attention/chunked_local_attention.py
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@@ -30,8 +30,9 @@ from vllm.v1.kv_cache_interface import (
def create_chunked_local_attention_backend(
underlying_attn_backend: AttentionBackend,
attention_chunk_size: int,
block_size: int,
) -> type[AttentionBackend]:
prefix = f"ChunkedLocalAttention_{attention_chunk_size}_"
prefix = f"ChunkedLocalAttention_{attention_chunk_size}_{block_size}_"
underlying_builder = underlying_attn_backend.get_builder_cls()
assert issubclass(underlying_builder, AttentionMetadataBuilder)
@@ -54,9 +55,7 @@ def create_chunked_local_attention_backend(
fast_build: bool = False,
):
cm, make_virtual_batches_block_table = make_local_attention_virtual_batches(
attention_chunk_size,
common_attn_metadata,
self.kv_cache_spec.block_size,
attention_chunk_size, common_attn_metadata, block_size
)
metadata = super().build(common_prefix_len, cm, fast_build)
metadata.make_virtual_batches_block_table = make_virtual_batches_block_table
@@ -98,13 +97,13 @@ class ChunkedLocalAttention(Attention):
block_size = cache_config.block_size
else:
kv_cache_dtype = "auto"
block_size = None
block_size = 16
underlying_attn_backend = get_attn_backend(
head_size, dtype, kv_cache_dtype, block_size
)
attn_backend = create_chunked_local_attention_backend(
underlying_attn_backend, attention_chunk_size
underlying_attn_backend, attention_chunk_size, block_size
)
super().__init__(

17
vllm/model_executor/layers/attention/mla_attention.py
View File

@@ -407,24 +407,17 @@ class MLAAttention(nn.Module, AttentionLayerBase):
)
# Attributes for forward_impl method
self._vllm_config = get_current_vllm_config()
self._chunked_prefill_workspace_size: int | None = None
self.chunked_prefill_workspace_size = (
MLACommonMetadataBuilder.determine_chunked_prefill_workspace_size(
get_current_vllm_config()
)
)
self._decode_concat_quant_fp8_op = _DecodeConcatQuantFP8(
static=True,
group_shape=GroupShape.PER_TENSOR,
compile_native=True,
)
@property
def chunked_prefill_workspace_size(self) -> int:
if self._chunked_prefill_workspace_size is None:
self._chunked_prefill_workspace_size = (
MLACommonMetadataBuilder.determine_chunked_prefill_workspace_size(
self._vllm_config
)
)
return self._chunked_prefill_workspace_size
def forward(
self,
q: torch.Tensor,