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vllm.attention.layers.chunked_local_attention

ChunkedLocalAttention

Bases: Attention

Source code in vllm/attention/layers/chunked_local_attention.py 
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class ChunkedLocalAttention(Attention):
    def __init__(
        self,
        num_heads: int,
        head_size: int,
        scale: float,
        attention_chunk_size: int,
        num_kv_heads: int | None = None,
        alibi_slopes: list[float] | None = None,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        kv_sharing_target_layer_name: str | None = None,
        prefix: str = "",
    ):
        dtype = torch.get_default_dtype()
        if cache_config is not None:
            kv_cache_dtype = cache_config.cache_dtype
            block_size = cache_config.block_size
        else:
            kv_cache_dtype = "auto"
            block_size = 16

        if envs.VLLM_USE_V1:
            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, block_size
            )
        else:
            # in v0 the local attention is handled inside the backends
            attn_backend = None

        super().__init__(
            num_heads=num_heads,
            head_size=head_size,
            scale=scale,
            num_kv_heads=num_kv_heads,
            alibi_slopes=alibi_slopes,
            cache_config=cache_config,
            quant_config=quant_config,
            prefix=prefix,
            kv_sharing_target_layer_name=kv_sharing_target_layer_name,
            attn_backend=attn_backend,
        )

__init__ 

__init__(
    num_heads: int,
    head_size: int,
    scale: float,
    attention_chunk_size: int,
    num_kv_heads: int | None = None,
    alibi_slopes: list[float] | None = None,
    cache_config: CacheConfig | None = None,
    quant_config: QuantizationConfig | None = None,
    kv_sharing_target_layer_name: str | None = None,
    prefix: str = "",
)
Source code in vllm/attention/layers/chunked_local_attention.py 
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def __init__(
    self,
    num_heads: int,
    head_size: int,
    scale: float,
    attention_chunk_size: int,
    num_kv_heads: int | None = None,
    alibi_slopes: list[float] | None = None,
    cache_config: CacheConfig | None = None,
    quant_config: QuantizationConfig | None = None,
    kv_sharing_target_layer_name: str | None = None,
    prefix: str = "",
):
    dtype = torch.get_default_dtype()
    if cache_config is not None:
        kv_cache_dtype = cache_config.cache_dtype
        block_size = cache_config.block_size
    else:
        kv_cache_dtype = "auto"
        block_size = 16

    if envs.VLLM_USE_V1:
        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, block_size
        )
    else:
        # in v0 the local attention is handled inside the backends
        attn_backend = None

    super().__init__(
        num_heads=num_heads,
        head_size=head_size,
        scale=scale,
        num_kv_heads=num_kv_heads,
        alibi_slopes=alibi_slopes,
        cache_config=cache_config,
        quant_config=quant_config,
        prefix=prefix,
        kv_sharing_target_layer_name=kv_sharing_target_layer_name,
        attn_backend=attn_backend,
    )

create_chunked_local_attention_backend cached 

create_chunked_local_attention_backend(
    underlying_attn_backend: AttentionBackend,
    attention_chunk_size: int,
    block_size: int,
) -> type[AttentionBackend]
Source code in vllm/attention/layers/chunked_local_attention.py 
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@functools.lru_cache
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}_{block_size}_"

    underlying_builder = underlying_attn_backend.get_builder_cls()

    class ChunkedLocalAttentionBuilder(underlying_builder):  # type: ignore
        cudagraph_support: ClassVar[AttentionCGSupport] = AttentionCGSupport.NEVER

        def build(
            self,
            common_prefix_len: int,
            common_attn_metadata: CommonAttentionMetadata,
            fast_build: bool = False,
        ) -> AttentionMetadata:
            common_attn_metadata = make_local_attention_virtual_batches(
                attention_chunk_size, common_attn_metadata, block_size
            )
            return super().build(common_prefix_len, common_attn_metadata, fast_build)

    attn_backend = subclass_attention_backend(
        name_prefix=prefix,
        attention_backend_cls=underlying_attn_backend,
        builder_cls=ChunkedLocalAttentionBuilder,
    )

    return attn_backend