vllm.model_executor.layers.mamba.ops.ssd_state_passing ¶

def _state_passing_fwd(
    states,
    dA_cumsum,
    cu_chunk_seqlens,
    seq_idx,
    initial_states=None,
    out_dtype=None,
):
    nchunks, nheads, dim = states.shape
    chunk_size = dA_cumsum.shape[-1]
    assert dA_cumsum.shape == (nheads, nchunks, chunk_size)
    seqlen = seq_idx.shape[-1]
    out_dtype = states.dtype if out_dtype is None else out_dtype
    out = torch.empty((nchunks, nheads, dim), device=states.device, dtype=out_dtype)

    initial_states_strides = (
        (initial_states.stride(0), initial_states.stride(1), initial_states.stride(2))
        if initial_states is not None
        else (0, 0, 0)
    )

    grid = lambda META: (triton.cdiv(dim, META["BLOCK_SIZE"]), nheads)
    with torch.cuda.device(states.device.index):
        _state_passing_fwd_kernel[grid](
            states_ptr=states,
            out_ptr=out,
            dA_cs_ptr=dA_cumsum,
            initstates_ptr=initial_states,
            seq_idx_ptr=seq_idx,
            cu_chunk_seqlens_ptr=cu_chunk_seqlens,
            dim=dim,
            nchunks=nchunks,
            seqlen=seqlen if seq_idx is not None else 0,
            chunk_size=chunk_size if seq_idx is not None else 0,
            stride_states_chunk=states.stride(0),
            stride_states_head=states.stride(1),
            stride_states_dim=states.stride(2),
            stride_out_chunk=out.stride(0),
            stride_out_head=out.stride(1),
            stride_out_dim=out.stride(2),
            stride_dA_cs_head=dA_cumsum.stride(0),
            stride_dA_cs_chunk=dA_cumsum.stride(1),
            stride_dA_cs_csize=dA_cumsum.stride(2),
            stride_initstates_batch=initial_states_strides[0],
            stride_initstates_head=initial_states_strides[1],
            stride_initstates_dim=initial_states_strides[2],
            stride_seq_idx_chunk=seq_idx.stride(0),
            HAS_INITSTATES=initial_states is not None,
        )
    return out

@triton.autotune(
    configs=[
        triton.Config({"BLOCK_SIZE": 64}),
        triton.Config({"BLOCK_SIZE": 128}),
        triton.Config({"BLOCK_SIZE": 256}),
        triton.Config({"BLOCK_SIZE": 512}),
        triton.Config({"BLOCK_SIZE": 1024}),
        triton.Config({"BLOCK_SIZE": 2048}),
    ],
    key=["dim"],
)
@triton.jit
def _state_passing_fwd_kernel(
    # Pointers to matrices
    states_ptr,
    out_ptr,
    dA_cs_ptr,
    initstates_ptr,
    seq_idx_ptr,
    cu_chunk_seqlens_ptr,
    # Matrix dimensions
    dim: tl.constexpr,
    nchunks,
    seqlen,
    chunk_size: tl.constexpr,
    # Strides
    stride_states_chunk: tl.int64,
    stride_states_head: tl.int64,
    stride_states_dim: tl.constexpr,
    stride_out_chunk: tl.int64,
    stride_out_head: tl.int64,
    stride_out_dim: tl.constexpr,
    stride_dA_cs_head: tl.int64,
    stride_dA_cs_chunk: tl.int64,
    stride_dA_cs_csize: tl.constexpr,
    stride_initstates_batch: tl.int64,
    stride_initstates_head: tl.int64,
    stride_initstates_dim: tl.constexpr,
    stride_seq_idx_chunk: tl.constexpr,
    # Meta-parameters
    HAS_INITSTATES: tl.constexpr,
    BLOCK_SIZE: tl.constexpr,
):
    pid_h = tl.program_id(axis=1)
    pid_m = tl.program_id(axis=0)

    states_ptr += pid_h * stride_states_head
    dA_cs_ptr += pid_h * stride_dA_cs_head + (chunk_size - 1) * stride_dA_cs_csize
    out_ptr += pid_h * stride_out_head

    offs_m = pid_m * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    states_ptrs = states_ptr + offs_m * stride_states_dim
    out_ptrs = out_ptr + offs_m * stride_out_dim

    if HAS_INITSTATES:
        initstates_ptrs = (
            initstates_ptr
            + pid_h * stride_initstates_head
            + offs_m * stride_initstates_dim
        )

        states = tl.load(initstates_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32)
    else:
        states = tl.zeros((BLOCK_SIZE,), dtype=tl.float32)

    prev_seq_idx = 0
    for c in range(nchunks):
        new_states = tl.load(states_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32)
        dA_cs = tl.load(dA_cs_ptr).to(tl.float32)
        seq_idx = tl.load(seq_idx_ptr + c * stride_seq_idx_chunk)
        # we have started a new sequence
        if prev_seq_idx != seq_idx:
            if HAS_INITSTATES:
                initstates_ptrs = (
                    initstates_ptr
                    + seq_idx * stride_initstates_batch
                    + pid_h * stride_initstates_head
                    + offs_m * stride_initstates_dim
                )
                states = tl.load(initstates_ptrs, mask=offs_m < dim, other=0.0).to(
                    tl.float32
                )
            else:
                states = tl.zeros((BLOCK_SIZE,), dtype=tl.float32)

        prev_seq_idx = seq_idx
        states = tl.exp(dA_cs) * states + new_states
        tl.store(out_ptrs, states, mask=offs_m < dim)

        states_ptrs += stride_states_chunk
        dA_cs_ptr += stride_dA_cs_chunk
        out_ptrs += stride_out_chunk