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vllm.model_executor.layers.mamba.mamba_utils ¶

MambaStateCopyFunc `module-attribute` ¶

MambaStateCopyFunc: TypeAlias = Callable[
    [Tensor, list[int], int, int], MambaCopySpec
]

Type alias for a function that computes a MambaCopySpec for copying state slices. Parameters: state: torch.Tensor - the Mamba state tensor (e.g., conv or temporal states). block_ids: list[int] - the list of block indices for the state to copy. cur_block_idx: int - current block index within block_ids to copy from. num_accepted_tokens: int - number of accepted tokens used to compute the copy offset. Range: 1 .. 1 + num_speculative_tokens (inclusive).

MambaCopySpec `dataclass` ¶

Data class specifying the memory-copy parameters for Mamba states used for prefix caching in align mode.

Attributes:

Name	Type	Description
`start_addr`	`int`	Starting address for the memory copy operation.
`num_elements`	`int`	Number of elements to copy from the starting address.

Source code in vllm/model_executor/layers/mamba/mamba_utils.py

@dataclass
class MambaCopySpec:
    """
    Data class specifying the memory-copy parameters for Mamba states used for
    prefix caching in align mode.

    Attributes:
        start_addr (int): Starting address for the memory copy operation.
        num_elements (int): Number of elements to copy from the starting address.
    """

    start_addr: int
    num_elements: int

MambaStateShapeCalculator ¶

Source code in vllm/model_executor/layers/mamba/mamba_utils.py

class MambaStateShapeCalculator:
    @classmethod
    def linear_attention_state_shape(
        cls,
        num_heads: int,
        tp_size: int,
        head_dim: int,
    ) -> tuple[tuple[int, int, int], ...]:
        state_shape = (num_heads // tp_size, head_dim, head_dim)
        return (state_shape,)

    @classmethod
    def mamba1_state_shape(
        cls,
        tp_world_size: int,
        intermediate_size: int,
        state_size: int,
        conv_kernel: int,
    ) -> tuple[tuple[int, int], tuple[int, int]]:
        conv_state_shape = (divide(intermediate_size, tp_world_size), conv_kernel - 1)

        temporal_state_shape = (divide(intermediate_size, tp_world_size), state_size)

        conv_state_shape = conv_state_shape[1], conv_state_shape[0]

        return conv_state_shape, temporal_state_shape

    @classmethod
    def mamba2_state_shape(
        cls,
        tp_world_size: int,
        intermediate_size: int,
        n_groups: int,
        num_heads: int,
        head_dim: int,
        state_size: int,
        conv_kernel: int,
    ) -> tuple[tuple[int, int], tuple[int, int, int]]:
        # if n_groups is not divisible by world_size, need to extend the shards
        # to ensure all groups needed by a head is sharded along with it
        n_groups = n_groups + cls.extra_groups_for_head_shards(n_groups, tp_world_size)
        # heads and n_groups are TP-ed
        conv_dim = intermediate_size + 2 * n_groups * state_size

        # contiguous along 'dim' axis
        conv_state_shape = (conv_kernel - 1, divide(conv_dim, tp_world_size))

        # These are not TP-ed as they depend on A, dt_bias, D
        # - they are typically small
        #   e.g., (h_heads, head_dim, state_size) = (128, 64, 128)
        temporal_state_shape = (divide(num_heads, tp_world_size), head_dim, state_size)
        return conv_state_shape, temporal_state_shape

    @classmethod
    def short_conv_state_shape(
        cls,
        tp_world_size: int,
        intermediate_size: int,
        conv_kernel: int,
    ) -> tuple[tuple[int, int]]:
        conv_dim = divide(intermediate_size, tp_world_size)
        conv_state_shape = (conv_kernel - 1, conv_dim)
        return (conv_state_shape,)

    @classmethod
    def extra_groups_for_head_shards(cls, ngroups: int, tp_size: int):
        """Compute the increase in group numbers to account for
        replication in order to accompany the head shards."""

        # in the case ngoups % tp_size == 0, this will be zero
        if ngroups % tp_size == 0:
            return 0

        # for n_groups == 1, this is exactly tp_size - n_groups
        return tp_size - ngroups

    @classmethod
    def gated_delta_net_state_shape(
        cls,
        tp_world_size: int,
        num_k_heads: int,
        num_v_heads: int,
        head_k_dim: int,
        head_v_dim: int,
        conv_kernel_size: int,
        num_spec: int = 0,
    ):
        conv_dim = head_k_dim * num_k_heads * 2 + head_v_dim * num_v_heads
        conv_state_shape = (
            divide(conv_dim, tp_world_size),
            conv_kernel_size - 1 + num_spec,
        )

        conv_state_shape = conv_state_shape[1], conv_state_shape[0]

        temporal_state_shape = (
            divide(num_v_heads, tp_world_size),
            head_v_dim,
            head_k_dim,
        )
        return conv_state_shape, temporal_state_shape

    @classmethod
    def kda_state_shape(
        cls,
        tp_world_size: int,
        num_heads: int,
        head_dim: int,
        num_k_heads: int | None = None,
        head_k_dim: int | None = None,
        conv_kernel_size: int = 4,
        num_spec: int = 0,
    ) -> tuple[tuple[int, int], tuple[int, int], tuple[int, int], tuple[int, int, int]]:
        if num_k_heads is None:
            num_k_heads = num_heads
        if head_k_dim is None:
            head_k_dim = head_dim

        proj_size = num_heads * head_dim
        proj_k_size = num_k_heads * head_k_dim

        conv_state_shape = (divide(proj_size, tp_world_size), conv_kernel_size - 1)
        conv_state_k_shape = (divide(proj_k_size, tp_world_size), conv_kernel_size - 1)
        recurrent_state_shape = (divide(num_heads, tp_world_size), head_dim, head_dim)

        conv_state_shape = conv_state_shape[1], conv_state_shape[0]
        conv_state_k_shape = conv_state_k_shape[1], conv_state_k_shape[0]
        return (
            conv_state_shape,
            conv_state_k_shape,
            conv_state_k_shape,
            recurrent_state_shape,
        )

extra_groups_for_head_shards `classmethod` ¶

extra_groups_for_head_shards(ngroups: int, tp_size: int)

Compute the increase in group numbers to account for replication in order to accompany the head shards.

Source code in vllm/model_executor/layers/mamba/mamba_utils.py

@classmethod
def extra_groups_for_head_shards(cls, ngroups: int, tp_size: int):
    """Compute the increase in group numbers to account for
    replication in order to accompany the head shards."""

    # in the case ngoups % tp_size == 0, this will be zero
    if ngroups % tp_size == 0:
        return 0

    # for n_groups == 1, this is exactly tp_size - n_groups
    return tp_size - ngroups

get_conv_copy_spec ¶

get_conv_copy_spec(
    state: Tensor,
    block_ids: list[int],
    cur_block_idx: int,
    num_accepted_tokens: int,
) -> MambaCopySpec

Return a MambaCopySpec for copying a convolutional state slice.

Source code in vllm/model_executor/layers/mamba/mamba_utils.py

def get_conv_copy_spec(
    state: torch.Tensor,
    block_ids: list[int],
    cur_block_idx: int,
    num_accepted_tokens: int,
) -> MambaCopySpec:
    """Return a MambaCopySpec for copying a convolutional state slice."""
    src_block_id = block_ids[cur_block_idx]
    src_state = state[src_block_id, num_accepted_tokens - 1 :]
    return MambaCopySpec(
        start_addr=src_state.data_ptr(), num_elements=src_state.numel()
    )

get_temporal_copy_spec ¶

get_temporal_copy_spec(
    state: Tensor,
    block_ids: list[int],
    cur_block_idx: int,
    num_accepted_tokens: int,
) -> MambaCopySpec

Return a MambaCopySpec for copying a temporal state slice.

Source code in vllm/model_executor/layers/mamba/mamba_utils.py

def get_temporal_copy_spec(
    state: torch.Tensor,
    block_ids: list[int],
    cur_block_idx: int,
    num_accepted_tokens: int,
) -> MambaCopySpec:
    """Return a MambaCopySpec for copying a temporal state slice."""
    src_block_id = block_ids[cur_block_idx + num_accepted_tokens - 1]
    src_state = state[src_block_id]
    return MambaCopySpec(
        start_addr=src_state.data_ptr(), num_elements=src_state.numel()
    )