Skip to content

vllm.attention.layers.chunked_local_attention

ChunkedLocalAttention

Bases: Attention

Source code in vllm/attention/layers/chunked_local_attention.py 
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
class ChunkedLocalAttention(Attention):

    def __init__(self,
                 num_heads: int,
                 head_size: int,
                 scale: float,
                 attention_chunk_size: int,
                 num_kv_heads: Optional[int] = None,
                 alibi_slopes: Optional[List[float]] = None,
                 cache_config: Optional[CacheConfig] = None,
                 quant_config: Optional[QuantizationConfig] = None,
                 kv_sharing_target_layer_name: Optional[str] = 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: Optional[int] = None,
    alibi_slopes: Optional[List[float]] = None,
    cache_config: Optional[CacheConfig] = None,
    quant_config: Optional[QuantizationConfig] = None,
    kv_sharing_target_layer_name: Optional[str] = None,
    prefix: str = "",
)
Source code in vllm/attention/layers/chunked_local_attention.py 
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
def __init__(self,
             num_heads: int,
             head_size: int,
             scale: float,
             attention_chunk_size: int,
             num_kv_heads: Optional[int] = None,
             alibi_slopes: Optional[List[float]] = None,
             cache_config: Optional[CacheConfig] = None,
             quant_config: Optional[QuantizationConfig] = None,
             kv_sharing_target_layer_name: Optional[str] = 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 
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
@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

        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