vllm.model_executor.layers.fla.ops.cumsum ¶

@input_guard
def chunk_local_cumsum(g: torch.Tensor,
                       chunk_size: int,
                       reverse: bool = False,
                       cu_seqlens: Optional[torch.Tensor] = None,
                       head_first: bool = False,
                       output_dtype: Optional[torch.dtype] = torch.float,
                       **kwargs) -> torch.Tensor:
    if not head_first and g.shape[1] < g.shape[2]:
        warnings.warn(
            f"Input tensor shape suggests potential format mismatch: seq_len ({g.shape[1]}) < num_heads ({g.shape[2]}). "
            "This may indicate the inputs were passed in head-first format [B, H, T, ...] "
            "when head_first=False was specified. "
            "Please verify your input tensor format matches the expected shape [B, T, H, ...].",
            stacklevel=2)
    if cu_seqlens is not None:
        assert g.shape[
            0] == 1, "Only batch size 1 is supported when cu_seqlens are provided"
    if len(g.shape) == 3:
        return chunk_local_cumsum_scalar(g, chunk_size, reverse, cu_seqlens,
                                         head_first, output_dtype)
    elif len(g.shape) == 4:
        return chunk_local_cumsum_vector(g, chunk_size, reverse, cu_seqlens,
                                         head_first, output_dtype)
    else:
        raise ValueError(f"Unsupported input shape {g.shape}. "
                         f"which should be (B, T, H, D) if `head_first=False` "
                         f"or (B, H, T, D) otherwise")

def chunk_local_cumsum_scalar(
        g: torch.Tensor,
        chunk_size: int,
        reverse: bool = False,
        cu_seqlens: Optional[torch.Tensor] = None,
        head_first: bool = False,
        output_dtype: Optional[torch.dtype] = torch.float) -> torch.Tensor:
    if head_first:
        B, H, T = g.shape
    else:
        B, T, H = g.shape
    assert chunk_size == 2**(chunk_size.bit_length() -
                             1), "chunk_size must be a power of 2"
    BT = chunk_size
    chunk_indices = prepare_chunk_indices(
        cu_seqlens, BT) if cu_seqlens is not None else None
    NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
    g_org, g = g, torch.empty_like(g, dtype=output_dtype or g.dtype)
    grid = (NT, B * H)
    chunk_local_cumsum_scalar_kernel[grid](g_org,
                                           g,
                                           cu_seqlens,
                                           chunk_indices,
                                           T=T,
                                           B=B,
                                           H=H,
                                           BT=BT,
                                           HEAD_FIRST=head_first,
                                           REVERSE=reverse)
    return g

@triton.heuristics({'IS_VARLEN': lambda args: args['cu_seqlens'] is not None})
@triton.autotune(configs=[
    triton.Config({}, num_warps=num_warps) for num_warps in [1, 2, 4, 8]
],
                 key=['B', 'H', 'BT', 'IS_VARLEN', 'REVERSE'])
@triton.jit(do_not_specialize=['T'])
def chunk_local_cumsum_scalar_kernel(
    s,
    o,
    cu_seqlens,
    chunk_indices,
    T,
    B: tl.constexpr,
    H: tl.constexpr,
    BT: tl.constexpr,
    REVERSE: tl.constexpr,
    IS_VARLEN: tl.constexpr,
    HEAD_FIRST: tl.constexpr,
):
    i_t, i_bh = tl.program_id(0), tl.program_id(1)
    i_b, i_h = i_bh // H, i_bh % H
    if IS_VARLEN:
        i_n, i_t = tl.load(chunk_indices + i_t * 2).to(
            tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int32)
        bos, eos = tl.load(cu_seqlens + i_n).to(
            tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32)
        T = eos - bos
    else:
        bos, eos = i_b * T, i_b * T + T

    if HEAD_FIRST:
        p_s = tl.make_block_ptr(s + bos * H + i_h * T, (T, ), (1, ),
                                (i_t * BT, ), (BT, ), (0, ))
        p_o = tl.make_block_ptr(o + bos * H + i_h * T, (T, ), (1, ),
                                (i_t * BT, ), (BT, ), (0, ))
    else:
        p_s = tl.make_block_ptr(s + bos * H + i_h, (T, ), (H, ), (i_t * BT, ),
                                (BT, ), (0, ))
        p_o = tl.make_block_ptr(o + bos * H + i_h, (T, ), (H, ), (i_t * BT, ),
                                (BT, ), (0, ))
    # [BT]
    b_s = tl.load(p_s, boundary_check=(0, )).to(tl.float32)
    b_o = tl.cumsum(b_s, axis=0)
    if REVERSE:
        b_z = tl.sum(b_s, axis=0)
        b_o = -b_o + b_z[None] + b_s
    tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0, ))

def chunk_local_cumsum_vector(
        g: torch.Tensor,
        chunk_size: int,
        reverse: bool = False,
        cu_seqlens: Optional[torch.Tensor] = None,
        head_first: bool = False,
        output_dtype: Optional[torch.dtype] = torch.float) -> torch.Tensor:
    if head_first:
        B, H, T, S = g.shape
    else:
        B, T, H, S = g.shape
    BT = chunk_size
    chunk_indices = prepare_chunk_indices(
        cu_seqlens, chunk_size) if cu_seqlens is not None else None
    NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
    assert chunk_size == 2**(chunk_size.bit_length() -
                             1), "chunk_size must be a power of 2"

    g_org, g = g, torch.empty_like(g, dtype=output_dtype or g.dtype)

    def grid(meta):
        return (triton.cdiv(meta['S'], meta['BS']), NT, B * H)

    # keep cumulative normalizer in fp32
    # this kernel is equivalent to
    # g = g.view(B, H, NT, BT, -1).cumsum(-2).view(B, H, T, -1)
    chunk_local_cumsum_vector_kernel[grid](g_org,
                                           g,
                                           cu_seqlens,
                                           chunk_indices,
                                           T=T,
                                           B=B,
                                           H=H,
                                           S=S,
                                           BT=BT,
                                           HEAD_FIRST=head_first,
                                           REVERSE=reverse)
    return g

@triton.heuristics({'IS_VARLEN': lambda args: args['cu_seqlens'] is not None})
@triton.autotune(configs=[
    triton.Config({'BS': BS}, num_warps=num_warps) for BS in BS_LIST
    for num_warps in [2, 4, 8]
],
                 key=['B', 'H', 'S', 'BT', 'IS_VARLEN', 'REVERSE'])
@triton.jit(do_not_specialize=['T'])
def chunk_local_cumsum_vector_kernel(
    s,
    o,
    cu_seqlens,
    chunk_indices,
    T,
    B: tl.constexpr,
    H: tl.constexpr,
    S: tl.constexpr,
    BT: tl.constexpr,
    BS: tl.constexpr,
    REVERSE: tl.constexpr,
    IS_VARLEN: tl.constexpr,
    HEAD_FIRST: tl.constexpr,
):
    i_s, i_t, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
    i_b, i_h = i_bh // H, i_bh % H
    if IS_VARLEN:
        i_n, i_t = tl.load(chunk_indices + i_t * 2).to(
            tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int32)
        bos, eos = tl.load(cu_seqlens + i_n).to(
            tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32)
        T = eos - bos
    else:
        bos, eos = i_b * T, i_b * T + T

    o_i = tl.arange(0, BT)
    if REVERSE:
        m_s = tl.where(o_i[:, None] <= o_i[None, :], 1., 0.)
    else:
        m_s = tl.where(o_i[:, None] >= o_i[None, :], 1., 0.)

    if HEAD_FIRST:
        p_s = tl.make_block_ptr(s + (bos * H + i_h * T) * S, (T, S), (S, 1),
                                (i_t * BT, i_s * BS), (BT, BS), (1, 0))
        p_o = tl.make_block_ptr(o + (bos * H + i_h * T) * S, (T, S), (S, 1),
                                (i_t * BT, i_s * BS), (BT, BS), (1, 0))
    else:
        p_s = tl.make_block_ptr(s + (bos * H + i_h) * S, (T, S), (H * S, 1),
                                (i_t * BT, i_s * BS), (BT, BS), (1, 0))
        p_o = tl.make_block_ptr(o + (bos * H + i_h) * S, (T, S), (H * S, 1),
                                (i_t * BT, i_s * BS), (BT, BS), (1, 0))
    # [BT, BS]
    b_s = tl.load(p_s, boundary_check=(0, 1)).to(tl.float32)
    b_o = tl.dot(m_s, b_s, allow_tf32=False)
    tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0, 1))