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vllm.v1.attention.backends.short_conv_attn

ShortConvAttentionBackend

Bases: AttentionBackend

Source code in vllm/v1/attention/backends/short_conv_attn.py 
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class ShortConvAttentionBackend(AttentionBackend):
    @staticmethod
    def get_builder_cls() -> type["ShortConvAttentionMetadataBuilder"]:
        return ShortConvAttentionMetadataBuilder

get_builder_cls staticmethod 

get_builder_cls() -> type[
    ShortConvAttentionMetadataBuilder
]
Source code in vllm/v1/attention/backends/short_conv_attn.py 
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@staticmethod
def get_builder_cls() -> type["ShortConvAttentionMetadataBuilder"]:
    return ShortConvAttentionMetadataBuilder

ShortConvAttentionMetadata dataclass

Source code in vllm/v1/attention/backends/short_conv_attn.py 
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@dataclass
class ShortConvAttentionMetadata:
    num_prefills: int
    num_prefill_tokens: int
    num_decodes: int
    num_decode_tokens: int

    query_start_loc: torch.Tensor
    state_indices_tensor: torch.Tensor
    has_initial_states_p: Optional[torch.Tensor]

    # For causal_conv1d
    nums_dict: Optional[dict] = None
    batch_ptr: Optional[torch.Tensor] = None
    token_chunk_offset_ptr: Optional[torch.Tensor] = None

batch_ptr class-attribute instance-attribute 

batch_ptr: Optional[Tensor] = None

has_initial_states_p instance-attribute 

has_initial_states_p: Optional[Tensor]

num_decode_tokens instance-attribute 

num_decode_tokens: int

num_decodes instance-attribute 

num_decodes: int

num_prefill_tokens instance-attribute 

num_prefill_tokens: int

num_prefills instance-attribute 

num_prefills: int

nums_dict class-attribute instance-attribute 

nums_dict: Optional[dict] = None

query_start_loc instance-attribute 

query_start_loc: Tensor

state_indices_tensor instance-attribute 

state_indices_tensor: Tensor

token_chunk_offset_ptr class-attribute instance-attribute 

token_chunk_offset_ptr: Optional[Tensor] = None

__init__ 

__init__(
    num_prefills: int,
    num_prefill_tokens: int,
    num_decodes: int,
    num_decode_tokens: int,
    query_start_loc: Tensor,
    state_indices_tensor: Tensor,
    has_initial_states_p: Optional[Tensor],
    nums_dict: Optional[dict] = None,
    batch_ptr: Optional[Tensor] = None,
    token_chunk_offset_ptr: Optional[Tensor] = None,
) -> None

ShortConvAttentionMetadataBuilder

Bases: BaseMambaAttentionMetadataBuilder[ShortConvAttentionMetadata]

Source code in vllm/v1/attention/backends/short_conv_attn.py 
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class ShortConvAttentionMetadataBuilder(
    BaseMambaAttentionMetadataBuilder[ShortConvAttentionMetadata]
):
    def build(
        self,
        common_prefix_len: int,
        common_attn_metadata: CommonAttentionMetadata,
        fast_build: bool = False,
    ) -> ShortConvAttentionMetadata:
        num_reqs = common_attn_metadata.num_reqs
        query_start_loc = common_attn_metadata.query_start_loc
        state_indices_tensor = common_attn_metadata.block_table_tensor[:, 0]

        # for causal_conv1d
        nums_dict, batch_ptr, token_chunk_offset_ptr = None, None, None

        num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
            split_decodes_and_prefills(
                common_attn_metadata, decode_threshold=self.reorder_batch_threshold
            )
        )

        has_initial_states_p = None
        if num_prefills > 0:
            has_initial_states_cpu = (
                common_attn_metadata.num_computed_tokens_cpu[
                    num_reqs - num_prefills : num_reqs
                ]
                > 0
            )
            has_initial_states_p = has_initial_states_cpu.to(query_start_loc.device)

            query_start_loc_p = (
                common_attn_metadata.query_start_loc[-num_prefills - 1 :]
                - num_decode_tokens
            )

            nums_dict, batch_ptr, token_chunk_offset_ptr = (
                compute_causal_conv1d_metadata(query_start_loc_p)
            )

        elif (
            num_decodes > 0
            and num_decodes <= self.decode_cudagraph_max_bs
            and self.compilation_config.full_cuda_graph
        ):
            num_input_tokens = self.vllm_config.pad_for_cudagraph(num_decodes)
            self.state_indices_tensor[:num_decodes].copy_(
                state_indices_tensor, non_blocking=True
            )
            state_indices_tensor = self.state_indices_tensor[:num_input_tokens]
            state_indices_tensor[num_decodes:] = PAD_SLOT_ID

        attn_metadata = ShortConvAttentionMetadata(
            query_start_loc=query_start_loc,
            state_indices_tensor=state_indices_tensor,
            has_initial_states_p=has_initial_states_p,
            num_prefills=num_prefills,
            num_prefill_tokens=num_prefill_tokens,
            num_decodes=num_decodes,
            num_decode_tokens=num_decode_tokens,
            nums_dict=nums_dict,
            batch_ptr=batch_ptr,
            token_chunk_offset_ptr=token_chunk_offset_ptr,
        )
        return attn_metadata

build 

build(
    common_prefix_len: int,
    common_attn_metadata: CommonAttentionMetadata,
    fast_build: bool = False,
) -> ShortConvAttentionMetadata
Source code in vllm/v1/attention/backends/short_conv_attn.py 
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def build(
    self,
    common_prefix_len: int,
    common_attn_metadata: CommonAttentionMetadata,
    fast_build: bool = False,
) -> ShortConvAttentionMetadata:
    num_reqs = common_attn_metadata.num_reqs
    query_start_loc = common_attn_metadata.query_start_loc
    state_indices_tensor = common_attn_metadata.block_table_tensor[:, 0]

    # for causal_conv1d
    nums_dict, batch_ptr, token_chunk_offset_ptr = None, None, None

    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
        split_decodes_and_prefills(
            common_attn_metadata, decode_threshold=self.reorder_batch_threshold
        )
    )

    has_initial_states_p = None
    if num_prefills > 0:
        has_initial_states_cpu = (
            common_attn_metadata.num_computed_tokens_cpu[
                num_reqs - num_prefills : num_reqs
            ]
            > 0
        )
        has_initial_states_p = has_initial_states_cpu.to(query_start_loc.device)

        query_start_loc_p = (
            common_attn_metadata.query_start_loc[-num_prefills - 1 :]
            - num_decode_tokens
        )

        nums_dict, batch_ptr, token_chunk_offset_ptr = (
            compute_causal_conv1d_metadata(query_start_loc_p)
        )

    elif (
        num_decodes > 0
        and num_decodes <= self.decode_cudagraph_max_bs
        and self.compilation_config.full_cuda_graph
    ):
        num_input_tokens = self.vllm_config.pad_for_cudagraph(num_decodes)
        self.state_indices_tensor[:num_decodes].copy_(
            state_indices_tensor, non_blocking=True
        )
        state_indices_tensor = self.state_indices_tensor[:num_input_tokens]
        state_indices_tensor[num_decodes:] = PAD_SLOT_ID

    attn_metadata = ShortConvAttentionMetadata(
        query_start_loc=query_start_loc,
        state_indices_tensor=state_indices_tensor,
        has_initial_states_p=has_initial_states_p,
        num_prefills=num_prefills,
        num_prefill_tokens=num_prefill_tokens,
        num_decodes=num_decodes,
        num_decode_tokens=num_decode_tokens,
        nums_dict=nums_dict,
        batch_ptr=batch_ptr,
        token_chunk_offset_ptr=token_chunk_offset_ptr,
    )
    return attn_metadata