vllm.model_executor.layers.fla.ops.kda ¶

BT_LIST_AUTOTUNE `module-attribute` ¶

BT_LIST_AUTOTUNE = [32, 64, 128]

NUM_WARPS_AUTOTUNE `module-attribute` ¶

NUM_WARPS_AUTOTUNE = (
    [2, 4, 8, 16] if is_amd else [4, 8, 16, 32]
)

FusedRMSNormGated ¶

Bases: Module

Source code in vllm/model_executor/layers/fla/ops/kda.py

class FusedRMSNormGated(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        elementwise_affine: bool = True,
        eps: float = 1e-5,
        activation: str = "swish",
        device: torch.device | None = None,
        dtype: torch.dtype | None = None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        super().__init__()

        self.hidden_size = hidden_size
        self.elementwise_affine = elementwise_affine
        self.eps = eps
        self.activation = activation

        if self.activation not in ["swish", "silu", "sigmoid"]:
            raise ValueError(f"Unsupported activation: {self.activation}")

        if elementwise_affine:
            self.weight = nn.Parameter(torch.empty(hidden_size, **factory_kwargs))
        else:
            self.register_parameter("weight", None)
        self.register_parameter("bias", None)

    def forward(
        self,
        x: torch.Tensor,
        g: torch.Tensor,
        residual: torch.Tensor | None = None,
        prenorm: bool = False,
        residual_in_fp32: bool = False,
    ) -> torch.Tensor:
        return rms_norm_gated(
            x,
            g,
            self.weight,
            self.bias,
            self.activation,
            residual=residual,
            eps=self.eps,
            prenorm=prenorm,
            residual_in_fp32=residual_in_fp32,
        )

activation `instance-attribute` ¶

activation = activation

elementwise_affine `instance-attribute` ¶

elementwise_affine = elementwise_affine

eps `instance-attribute` ¶

eps = eps

hidden_size `instance-attribute` ¶

hidden_size = hidden_size

weight `instance-attribute` ¶

weight = Parameter(empty(hidden_size, **factory_kwargs))

init ¶

__init__(
    hidden_size: int,
    elementwise_affine: bool = True,
    eps: float = 1e-05,
    activation: str = "swish",
    device: device | None = None,
    dtype: dtype | None = None,
) -> None

Source code in vllm/model_executor/layers/fla/ops/kda.py

def __init__(
    self,
    hidden_size: int,
    elementwise_affine: bool = True,
    eps: float = 1e-5,
    activation: str = "swish",
    device: torch.device | None = None,
    dtype: torch.dtype | None = None,
) -> None:
    factory_kwargs = {"device": device, "dtype": dtype}
    super().__init__()

    self.hidden_size = hidden_size
    self.elementwise_affine = elementwise_affine
    self.eps = eps
    self.activation = activation

    if self.activation not in ["swish", "silu", "sigmoid"]:
        raise ValueError(f"Unsupported activation: {self.activation}")

    if elementwise_affine:
        self.weight = nn.Parameter(torch.empty(hidden_size, **factory_kwargs))
    else:
        self.register_parameter("weight", None)
    self.register_parameter("bias", None)

forward ¶

forward(
    x: Tensor,
    g: Tensor,
    residual: Tensor | None = None,
    prenorm: bool = False,
    residual_in_fp32: bool = False,
) -> Tensor

Source code in vllm/model_executor/layers/fla/ops/kda.py

def forward(
    self,
    x: torch.Tensor,
    g: torch.Tensor,
    residual: torch.Tensor | None = None,
    prenorm: bool = False,
    residual_in_fp32: bool = False,
) -> torch.Tensor:
    return rms_norm_gated(
        x,
        g,
        self.weight,
        self.bias,
        self.activation,
        residual=residual,
        eps=self.eps,
        prenorm=prenorm,
        residual_in_fp32=residual_in_fp32,
    )

chunk_gla_fwd_kernel_o ¶

chunk_gla_fwd_kernel_o(
    q,
    v,
    g,
    h,
    o,
    A,
    cu_seqlens,
    chunk_indices,
    scale,
    T,
    H: constexpr,
    K: constexpr,
    V: constexpr,
    BT: constexpr,
    BK: constexpr,
    BV: constexpr,
    IS_VARLEN: constexpr,
)

Source code in vllm/model_executor/layers/fla/ops/kda.py

@triton.heuristics({"IS_VARLEN": lambda args: args["cu_seqlens"] is not None})
@triton.autotune(
    configs=[
        triton.Config({"BK": BK, "BV": BV}, num_warps=num_warps, num_stages=num_stages)
        for BK in [32, 64]
        for BV in [64, 128]
        for num_warps in [2, 4, 8]
        for num_stages in [2, 3, 4]
    ],
    key=["BT"],
)
@triton.jit(do_not_specialize=["T"])
def chunk_gla_fwd_kernel_o(
    q,
    v,
    g,
    h,
    o,
    A,
    cu_seqlens,
    chunk_indices,
    scale,
    T,
    H: tl.constexpr,
    K: tl.constexpr,
    V: tl.constexpr,
    BT: tl.constexpr,
    BK: tl.constexpr,
    BV: tl.constexpr,
    IS_VARLEN: tl.constexpr,
):
    i_v, 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_tg = i_t
        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
        NT = tl.cdiv(T, BT)
    else:
        NT = tl.cdiv(T, BT)
        i_tg = i_b * NT + i_t
        bos, eos = i_b * T, i_b * T + T

    m_s = tl.arange(0, BT)[:, None] >= tl.arange(0, BT)[None, :]

    b_o = tl.zeros([BT, BV], dtype=tl.float32)
    for i_k in range(tl.cdiv(K, BK)):
        p_q = tl.make_block_ptr(
            q + (bos * H + i_h) * K,
            (T, K),
            (H * K, 1),
            (i_t * BT, i_k * BK),
            (BT, BK),
            (1, 0),
        )
        p_g = tl.make_block_ptr(
            g + (bos * H + i_h) * K,
            (T, K),
            (H * K, 1),
            (i_t * BT, i_k * BK),
            (BT, BK),
            (1, 0),
        )
        p_h = tl.make_block_ptr(
            h + (i_tg * H + i_h) * K * V,
            (K, V),
            (V, 1),
            (i_k * BK, i_v * BV),
            (BK, BV),
            (1, 0),
        )

        # [BT, BK]
        b_q = tl.load(p_q, boundary_check=(0, 1))
        b_q = (b_q * scale).to(b_q.dtype)
        # [BT, BK]
        b_g = tl.load(p_g, boundary_check=(0, 1))
        # [BT, BK]
        b_qg = (b_q * exp(b_g)).to(b_q.dtype)
        # [BK, BV]
        b_h = tl.load(p_h, boundary_check=(0, 1))
        # works but dkw, owing to divine benevolence
        # [BT, BV]
        if i_k >= 0:
            b_o += tl.dot(b_qg, b_h.to(b_qg.dtype))
    p_v = tl.make_block_ptr(
        v + (bos * H + i_h) * V,
        (T, V),
        (H * V, 1),
        (i_t * BT, i_v * BV),
        (BT, BV),
        (1, 0),
    )
    p_o = tl.make_block_ptr(
        o + (bos * H + i_h) * V,
        (T, V),
        (H * V, 1),
        (i_t * BT, i_v * BV),
        (BT, BV),
        (1, 0),
    )
    p_A = tl.make_block_ptr(
        A + (bos * H + i_h) * BT, (T, BT), (H * BT, 1), (i_t * BT, 0), (BT, BT), (1, 0)
    )
    # [BT, BV]
    b_v = tl.load(p_v, boundary_check=(0, 1))
    # [BT, BT]
    b_A = tl.load(p_A, boundary_check=(0, 1))
    b_A = tl.where(m_s, b_A, 0.0).to(b_v.dtype)
    b_o += tl.dot(b_A, b_v, allow_tf32=False)
    tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0, 1))

chunk_gla_fwd_o_gk ¶

chunk_gla_fwd_o_gk(
    q: Tensor,
    v: Tensor,
    g: Tensor,
    A: Tensor,
    h: Tensor,
    o: Tensor,
    scale: float,
    cu_seqlens: LongTensor | None = None,
    chunk_size: int = 64,
)

Source code in vllm/model_executor/layers/fla/ops/kda.py

def chunk_gla_fwd_o_gk(
    q: torch.Tensor,
    v: torch.Tensor,
    g: torch.Tensor,
    A: torch.Tensor,
    h: torch.Tensor,
    o: torch.Tensor,
    scale: float,
    cu_seqlens: torch.LongTensor | None = None,
    chunk_size: int = 64,
):
    B, T, H, K, V = *q.shape, v.shape[-1]
    BT = chunk_size

    chunk_indices = (
        prepare_chunk_indices(cu_seqlens, chunk_size)
        if cu_seqlens is not None
        else None
    )
    NT = cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)

    def grid(meta):
        return (cdiv(V, meta["BV"]), NT, B * H)

    chunk_gla_fwd_kernel_o[grid](
        q=q,
        v=v,
        g=g,
        h=h,
        o=o,
        A=A,
        cu_seqlens=cu_seqlens,
        chunk_indices=chunk_indices,
        scale=scale,
        T=T,
        H=H,
        K=K,
        V=V,
        BT=BT,
    )
    return o

chunk_kda ¶

chunk_kda(
    q: Tensor,
    k: Tensor,
    v: Tensor,
    g: Tensor,
    beta: Tensor,
    scale: float = None,
    initial_state: Tensor = None,
    output_final_state: bool = False,
    use_qk_l2norm_in_kernel: bool = False,
    cu_seqlens: LongTensor | None = None,
    **kwargs,
)

Source code in vllm/model_executor/layers/fla/ops/kda.py

def chunk_kda(
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    g: torch.Tensor,
    beta: torch.Tensor,
    scale: float = None,
    initial_state: torch.Tensor = None,
    output_final_state: bool = False,
    use_qk_l2norm_in_kernel: bool = False,
    cu_seqlens: torch.LongTensor | None = None,
    **kwargs,
):
    if scale is None:
        scale = k.shape[-1] ** -0.5

    if use_qk_l2norm_in_kernel:
        q = l2norm_fwd(q.contiguous())
        k = l2norm_fwd(k.contiguous())

    o, final_state = chunk_kda_fwd(
        q=q,
        k=k,
        v=v.contiguous(),
        g=g.contiguous(),
        beta=beta.contiguous(),
        scale=scale,
        initial_state=initial_state.contiguous(),
        output_final_state=output_final_state,
        cu_seqlens=cu_seqlens,
    )
    return o, final_state

chunk_kda_fwd ¶

chunk_kda_fwd(
    q: Tensor,
    k: Tensor,
    v: Tensor,
    g: Tensor,
    beta: Tensor,
    scale: float,
    initial_state: Tensor,
    output_final_state: bool,
    cu_seqlens: LongTensor | None = None,
)

Source code in vllm/model_executor/layers/fla/ops/kda.py

def chunk_kda_fwd(
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    g: torch.Tensor,
    beta: torch.Tensor,
    scale: float,
    initial_state: torch.Tensor,
    output_final_state: bool,
    cu_seqlens: torch.LongTensor | None = None,
):
    chunk_size = 64
    g = chunk_local_cumsum(g, chunk_size=chunk_size, cu_seqlens=cu_seqlens)
    # the intra Aqk is kept in fp32
    # the computation has very marginal effect on the entire throughput
    A, Aqk = chunk_kda_scaled_dot_kkt_fwd(
        q=q,
        k=k,
        gk=g,
        beta=beta,
        scale=scale,
        cu_seqlens=cu_seqlens,
        output_dtype=torch.float32,
    )
    A = solve_tril(A=A, cu_seqlens=cu_seqlens, output_dtype=k.dtype)
    w, u, _, kg = recompute_w_u_fwd(
        k=k,
        v=v,
        beta=beta,
        A=A,
        gk=g,
        cu_seqlens=cu_seqlens,
    )
    del A
    h, v_new, final_state = chunk_gated_delta_rule_fwd_h(
        k=kg,
        w=w,
        u=u,
        gk=g,
        initial_state=initial_state,
        output_final_state=output_final_state,
        cu_seqlens=cu_seqlens,
    )
    del w, u, kg
    o = chunk_gla_fwd_o_gk(
        q=q,
        v=v_new,
        g=g,
        A=Aqk,
        h=h,
        o=v,
        scale=scale,
        cu_seqlens=cu_seqlens,
        chunk_size=chunk_size,
    )
    del Aqk, v_new, h
    return o, final_state

chunk_kda_scaled_dot_kkt_fwd ¶

chunk_kda_scaled_dot_kkt_fwd(
    q: Tensor,
    k: Tensor,
    gk: Tensor | None = None,
    beta: Tensor | None = None,
    scale: float | None = None,
    cu_seqlens: LongTensor | None = None,
    chunk_size: int = 64,
    output_dtype: dtype = float32,
) -> tuple[Tensor, Tensor]

Compute beta * K * K^T.

Parameters:

Name	Type	Description	Default
`k`	`Tensor`	The key tensor of shape `[B, T, H, K]`.	required
`beta`	`Tensor`	The beta tensor of shape `[B, T, H]`.	`None`
`gk`	`Tensor`	The cumulative sum of the gate tensor of shape `[B, T, H, K]` applied to the key tensor. Default: `None`.	`None`
`cu_seqlens`	`LongTensor`	The cumulative sequence lengths of the input tensor. Default: None	`None`
`chunk_size`	`int`	The chunk size. Default: 64.	`64`
`output_dtype`	`dtype`	The dtype of the output tensor. Default: `torch.float32`	`float32`

Returns:

Type	Description
`tuple[Tensor, Tensor]`	beta * K * K^T of shape `[B, T, H, BT]` where `BT` is the chunk size.

Source code in vllm/model_executor/layers/fla/ops/kda.py

def chunk_kda_scaled_dot_kkt_fwd(
    q: torch.Tensor,
    k: torch.Tensor,
    gk: torch.Tensor | None = None,
    beta: torch.Tensor | None = None,
    scale: float | None = None,
    cu_seqlens: torch.LongTensor | None = None,
    chunk_size: int = 64,
    output_dtype: torch.dtype = torch.float32,
) -> tuple[torch.Tensor, torch.Tensor]:
    r"""
    Compute beta * K * K^T.

    Args:
        k (torch.Tensor):
            The key tensor of shape `[B, T, H, K]`.
        beta (torch.Tensor):
            The beta tensor of shape `[B, T, H]`.
        gk (torch.Tensor):
            The cumulative sum of the gate tensor of shape `[B, T, H, K]` applied to the key tensor. Default: `None`.
        cu_seqlens (torch.LongTensor):
            The cumulative sequence lengths of the input tensor.
            Default: None
        chunk_size (int):
            The chunk size. Default: 64.
        output_dtype (torch.dtype):
            The dtype of the output tensor. Default: `torch.float32`

    Returns:
        beta * K * K^T of shape `[B, T, H, BT]` where `BT` is the chunk size.
    """
    B, T, H, K = k.shape
    assert K <= 256
    BT = chunk_size
    chunk_indices = (
        prepare_chunk_indices(cu_seqlens, BT) if cu_seqlens is not None else None
    )
    NT = cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)

    BC = min(16, BT)
    NC = cdiv(BT, BC)
    BK = max(next_power_of_2(K), 16)
    A = torch.zeros(B, T, H, BT, device=k.device, dtype=output_dtype)
    Aqk = torch.zeros(B, T, H, BT, device=k.device, dtype=output_dtype)
    grid = (NT, NC * NC, B * H)
    chunk_kda_scaled_dot_kkt_fwd_kernel_intra_sub_inter[grid](
        q=q,
        k=k,
        g=gk,
        beta=beta,
        A=A,
        Aqk=Aqk,
        scale=scale,
        cu_seqlens=cu_seqlens,
        chunk_indices=chunk_indices,
        T=T,
        H=H,
        K=K,
        BT=BT,
        BC=BC,
        NC=NC,
    )

    grid = (NT, NC, B * H)
    chunk_kda_scaled_dot_kkt_fwd_kernel_intra_sub_intra[grid](
        q=q,
        k=k,
        g=gk,
        beta=beta,
        A=A,
        Aqk=Aqk,
        scale=scale,
        cu_seqlens=cu_seqlens,
        chunk_indices=chunk_indices,
        T=T,
        H=H,
        K=K,
        BT=BT,
        BC=BC,
        BK=BK,
    )
    return A, Aqk

chunk_kda_scaled_dot_kkt_fwd_kernel_intra_sub_inter ¶

chunk_kda_scaled_dot_kkt_fwd_kernel_intra_sub_inter(
    q,
    k,
    g,
    beta,
    A,
    Aqk,
    scale,
    cu_seqlens,
    chunk_indices,
    T,
    H: constexpr,
    K: constexpr,
    BT: constexpr,
    BC: constexpr,
    BK: constexpr,
    NC: constexpr,
    IS_VARLEN: constexpr,
)

Source code in vllm/model_executor/layers/fla/ops/kda.py

@triton.heuristics({"IS_VARLEN": lambda args: args["cu_seqlens"] is not None})
@triton.autotune(
    configs=[
        triton.Config({"BK": BK}, num_warps=num_warps, num_stages=num_stages)
        for BK in [32, 64]
        for num_warps in [1, 2, 4, 8]
        for num_stages in [2, 3, 4]
    ],
    key=["BC"],
)
@triton.jit(do_not_specialize=["T"])
def chunk_kda_scaled_dot_kkt_fwd_kernel_intra_sub_inter(
    q,
    k,
    g,
    beta,
    A,
    Aqk,
    scale,
    cu_seqlens,
    chunk_indices,
    T,
    H: tl.constexpr,
    K: tl.constexpr,
    BT: tl.constexpr,
    BC: tl.constexpr,
    BK: tl.constexpr,
    NC: tl.constexpr,
    IS_VARLEN: tl.constexpr,
):
    i_t, i_c, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
    i_b, i_h = i_bh // H, i_bh % H
    i_i, i_j = i_c // NC, i_c % NC
    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 i_t * BT + i_i * BC >= T:
        return
    if i_i <= i_j:
        return

    q += (bos * H + i_h) * K
    k += (bos * H + i_h) * K
    g += (bos * H + i_h) * K
    A += (bos * H + i_h) * BT
    Aqk += (bos * H + i_h) * BT

    p_b = tl.make_block_ptr(
        beta + bos * H + i_h, (T,), (H,), (i_t * BT + i_i * BC,), (BC,), (0,)
    )
    b_b = tl.load(p_b, boundary_check=(0,))

    b_A = tl.zeros([BC, BC], dtype=tl.float32)
    b_Aqk = tl.zeros([BC, BC], dtype=tl.float32)
    for i_k in range(tl.cdiv(K, BK)):
        p_q = tl.make_block_ptr(
            q, (T, K), (H * K, 1), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0)
        )
        p_k = tl.make_block_ptr(
            k, (T, K), (H * K, 1), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0)
        )
        p_g = tl.make_block_ptr(
            g, (T, K), (H * K, 1), (i_t * BT + i_i * BC, i_k * BK), (BC, BK), (1, 0)
        )
        b_kt = tl.make_block_ptr(
            k, (K, T), (1, H * K), (i_k * BK, i_t * BT + i_j * BC), (BK, BC), (0, 1)
        )
        p_gk = tl.make_block_ptr(
            g, (K, T), (1, H * K), (i_k * BK, i_t * BT + i_j * BC), (BK, BC), (0, 1)
        )

        o_k = i_k * BK + tl.arange(0, BK)
        m_k = o_k < K
        # [BK,]
        b_gn = tl.load(g + (i_t * BT + i_i * BC) * H * K + o_k, mask=m_k, other=0)
        # [BC, BK]
        b_g = tl.load(p_g, boundary_check=(0, 1))
        b_k = tl.load(p_k, boundary_check=(0, 1)) * exp(b_g - b_gn[None, :])
        # [BK, BC]
        b_gk = tl.load(p_gk, boundary_check=(0, 1))
        b_kt = tl.load(b_kt, boundary_check=(0, 1))
        # [BC, BC]
        b_ktg = b_kt * exp(b_gn[:, None] - b_gk)
        b_A += tl.dot(b_k, b_ktg)

        b_q = tl.load(p_q, boundary_check=(0, 1))
        b_qg = b_q * exp(b_g - b_gn[None, :]) * scale
        b_Aqk += tl.dot(b_qg, b_ktg)

    b_A *= b_b[:, None]

    p_A = tl.make_block_ptr(
        A, (T, BT), (H * BT, 1), (i_t * BT + i_i * BC, i_j * BC), (BC, BC), (1, 0)
    )
    tl.store(p_A, b_A.to(A.dtype.element_ty), boundary_check=(0, 1))
    p_Aqk = tl.make_block_ptr(
        Aqk, (T, BT), (H * BT, 1), (i_t * BT + i_i * BC, i_j * BC), (BC, BC), (1, 0)
    )
    tl.store(p_Aqk, b_Aqk.to(Aqk.dtype.element_ty), boundary_check=(0, 1))

chunk_kda_scaled_dot_kkt_fwd_kernel_intra_sub_intra ¶

chunk_kda_scaled_dot_kkt_fwd_kernel_intra_sub_intra(
    q,
    k,
    g,
    beta,
    A,
    Aqk,
    scale,
    cu_seqlens,
    chunk_indices,
    T,
    H: constexpr,
    K: constexpr,
    BT: constexpr,
    BC: constexpr,
    BK: constexpr,
    IS_VARLEN: constexpr,
)

Source code in vllm/model_executor/layers/fla/ops/kda.py

@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=["BK", "BT"],
)
@triton.jit(do_not_specialize=["T"])
def chunk_kda_scaled_dot_kkt_fwd_kernel_intra_sub_intra(
    q,
    k,
    g,
    beta,
    A,
    Aqk,
    scale,
    cu_seqlens,
    chunk_indices,
    T,
    H: tl.constexpr,
    K: tl.constexpr,
    BT: tl.constexpr,
    BC: tl.constexpr,
    BK: tl.constexpr,
    IS_VARLEN: tl.constexpr,
):
    i_t, i_i, 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

    if i_t * BT + i_i * BC >= T:
        return

    o_i = tl.arange(0, BC)
    o_k = tl.arange(0, BK)
    m_k = o_k < K
    m_A = (i_t * BT + i_i * BC + o_i) < T
    o_A = (bos + i_t * BT + i_i * BC + o_i) * H * BT + i_h * BT + i_i * BC

    p_q = tl.make_block_ptr(
        q + (bos * H + i_h) * K,
        (T, K),
        (H * K, 1),
        (i_t * BT + i_i * BC, 0),
        (BC, BK),
        (1, 0),
    )
    p_k = tl.make_block_ptr(
        k + (bos * H + i_h) * K,
        (T, K),
        (H * K, 1),
        (i_t * BT + i_i * BC, 0),
        (BC, BK),
        (1, 0),
    )
    p_g = tl.make_block_ptr(
        g + (bos * H + i_h) * K,
        (T, K),
        (H * K, 1),
        (i_t * BT + i_i * BC, 0),
        (BC, BK),
        (1, 0),
    )
    b_q = tl.load(p_q, boundary_check=(0, 1))
    b_k = tl.load(p_k, boundary_check=(0, 1))
    b_g = tl.load(p_g, boundary_check=(0, 1))

    p_b = beta + (bos + i_t * BT + i_i * BC + o_i) * H + i_h
    b_k = b_k * tl.load(p_b, mask=m_A, other=0)[:, None]

    p_kt = k + (bos + i_t * BT + i_i * BC) * H * K + i_h * K + o_k
    p_gk = g + (bos + i_t * BT + i_i * BC) * H * K + i_h * K + o_k

    for j in range(0, min(BC, T - i_t * BT - i_i * BC)):
        b_kt = tl.load(p_kt, mask=m_k, other=0).to(tl.float32)
        b_gk = tl.load(p_gk, mask=m_k, other=0).to(tl.float32)
        b_ktg = b_kt[None, :] * exp(b_g - b_gk[None, :])
        b_A = tl.sum(b_k * b_ktg, 1)
        b_A = tl.where(o_i > j, b_A, 0.0)
        b_Aqk = tl.sum(b_q * b_ktg, 1)
        b_Aqk = tl.where(o_i >= j, b_Aqk * scale, 0.0)
        tl.store(A + o_A + j, b_A, mask=m_A)
        tl.store(Aqk + o_A + j, b_Aqk, mask=m_A)
        p_kt += H * K
        p_gk += H * K

fused_recurrent_kda ¶

fused_recurrent_kda(
    q: Tensor,
    k: Tensor,
    v: Tensor,
    g: Tensor,
    beta: Tensor = None,
    scale: float = None,
    initial_state: Tensor = None,
    inplace_final_state: bool = True,
    use_qk_l2norm_in_kernel: bool = True,
    cu_seqlens: LongTensor | None = None,
    ssm_state_indices: LongTensor | None = None,
    **kwargs,
) -> tuple[Tensor, Tensor]

Source code in vllm/model_executor/layers/fla/ops/kda.py

def fused_recurrent_kda(
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    g: torch.Tensor,
    beta: torch.Tensor = None,
    scale: float = None,
    initial_state: torch.Tensor = None,
    inplace_final_state: bool = True,
    use_qk_l2norm_in_kernel: bool = True,
    cu_seqlens: torch.LongTensor | None = None,
    ssm_state_indices: torch.LongTensor | None = None,
    **kwargs,
) -> tuple[torch.Tensor, torch.Tensor]:
    if cu_seqlens is not None and q.shape[0] != 1:
        raise ValueError(
            f"The batch size is expected to be 1 rather than {q.shape[0]} when using `cu_seqlens`."
            f"Please flatten variable-length inputs before processing."
        )
    if scale is None:
        scale = k.shape[-1] ** -0.5

    o, final_state = fused_recurrent_kda_fwd(
        q=q.contiguous(),
        k=k.contiguous(),
        v=v.contiguous(),
        g=g.contiguous(),
        beta=beta.contiguous(),
        scale=scale,
        initial_state=initial_state,
        inplace_final_state=inplace_final_state,
        cu_seqlens=cu_seqlens,
        ssm_state_indices=ssm_state_indices,
        num_accepted_tokens=None,
        use_qk_l2norm_in_kernel=use_qk_l2norm_in_kernel,
    )
    return o, final_state

fused_recurrent_kda_fwd ¶

fused_recurrent_kda_fwd(
    q: Tensor,
    k: Tensor,
    v: Tensor,
    g: Tensor,
    beta: Tensor,
    scale: float,
    initial_state: Tensor,
    inplace_final_state: bool = True,
    cu_seqlens: LongTensor | None = None,
    ssm_state_indices: Tensor | None = None,
    num_accepted_tokens: Tensor | None = None,
    use_qk_l2norm_in_kernel: bool = False,
) -> tuple[Tensor, Tensor]

Source code in vllm/model_executor/layers/fla/ops/kda.py

def fused_recurrent_kda_fwd(
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    g: torch.Tensor,
    beta: torch.Tensor,
    scale: float,
    initial_state: torch.Tensor,
    inplace_final_state: bool = True,
    cu_seqlens: torch.LongTensor | None = None,
    ssm_state_indices: torch.Tensor | None = None,
    num_accepted_tokens: torch.Tensor | None = None,
    use_qk_l2norm_in_kernel: bool = False,
) -> tuple[torch.Tensor, torch.Tensor]:
    B, T, H, K, V = *k.shape, v.shape[-1]
    HV = v.shape[2]
    N = B if cu_seqlens is None else len(cu_seqlens) - 1
    BK, BV = next_power_of_2(K), min(next_power_of_2(V), 8)
    NK, NV = cdiv(K, BK), cdiv(V, BV)
    assert NK == 1, "NK > 1 is not supported yet"
    num_stages = 3
    num_warps = 1

    o = torch.empty_like(k)
    if inplace_final_state:
        final_state = initial_state
    else:
        final_state = q.new_empty(T, HV, K, V, dtype=initial_state.dtype)

    stride_init_state_token = initial_state.stride(0)
    stride_final_state_token = final_state.stride(0)

    if ssm_state_indices is None:
        stride_indices_seq, stride_indices_tok = 1, 1
    elif ssm_state_indices.ndim == 1:
        stride_indices_seq, stride_indices_tok = ssm_state_indices.stride(0), 1
    else:
        stride_indices_seq, stride_indices_tok = ssm_state_indices.stride()

    grid = (NK, NV, N * HV)
    fused_recurrent_gated_delta_rule_fwd_kernel[grid](
        q=q,
        k=k,
        v=v,
        g=g,
        beta=beta,
        o=o,
        h0=initial_state,
        ht=final_state,
        cu_seqlens=cu_seqlens,
        ssm_state_indices=ssm_state_indices,
        num_accepted_tokens=num_accepted_tokens,
        scale=scale,
        N=N,
        T=T,
        B=B,
        H=H,
        HV=HV,
        K=K,
        V=V,
        BK=BK,
        BV=BV,
        stride_init_state_token=stride_init_state_token,
        stride_final_state_token=stride_final_state_token,
        stride_indices_seq=stride_indices_seq,
        stride_indices_tok=stride_indices_tok,
        IS_BETA_HEADWISE=beta.ndim == v.ndim,
        USE_QK_L2NORM_IN_KERNEL=use_qk_l2norm_in_kernel,
        INPLACE_FINAL_STATE=inplace_final_state,
        IS_KDA=True,
        num_warps=num_warps,
        num_stages=num_stages,
    )

    return o, final_state

kda_gate_fwd ¶

kda_gate_fwd(
    g: Tensor,
    A: Tensor,
    head_k_dim: int,
    g_bias: Tensor | None = None,
    beta: float = 1.0,
    threshold: float = 20.0,
) -> Tensor

Forward pass for KDA gate

input g: [..., H*D] param A: [H] or [1, 1, H, 1] beta: softplus beta parameter threshold: softplus threshold parameter return : [..., H, D]

Source code in vllm/model_executor/layers/fla/ops/kda.py

def kda_gate_fwd(
    g: torch.Tensor,
    A: torch.Tensor,
    head_k_dim: int,
    g_bias: torch.Tensor | None = None,
    beta: float = 1.0,
    threshold: float = 20.0,
) -> torch.Tensor:
    """
    Forward pass for KDA gate:
      input g: [..., H*D]
      param A: [H] or [1, 1, H, 1]
      beta: softplus beta parameter
      threshold: softplus threshold parameter
      return  : [..., H, D]
    """
    orig_shape = g.shape[:-1]

    g = g.view(-1, g.shape[-1])
    T = g.shape[0]
    HD = g.shape[1]
    H = A.numel()
    assert H * head_k_dim == HD

    y = torch.empty_like(g, dtype=torch.float32)

    def grid(meta):
        return (cdiv(T, meta["BT"]), H)

    kda_gate_fwd_kernel[grid](
        g,
        A,
        y,
        g_bias,
        beta,
        threshold,
        T,
        H,
        head_k_dim,
        BD=next_power_of_2(head_k_dim),
        HAS_BIAS=g_bias is not None,
    )

    y = y.view(*orig_shape, H, head_k_dim)
    return y

kda_gate_fwd_kernel ¶

kda_gate_fwd_kernel(
    g,
    A,
    y,
    g_bias,
    beta: constexpr,
    threshold: constexpr,
    T,
    H,
    D: constexpr,
    BT: constexpr,
    BD: constexpr,
    HAS_BIAS: constexpr,
)

Source code in vllm/model_executor/layers/fla/ops/kda.py

@triton.autotune(
    configs=[
        triton.Config({"BT": bt}, num_warps=nw, num_stages=ns)
        for bt in BT_LIST_AUTOTUNE
        for nw in NUM_WARPS_AUTOTUNE
        for ns in [2, 3]
    ],
    key=["H", "D"],
)
@triton.jit
def kda_gate_fwd_kernel(
    g,
    A,
    y,
    g_bias,
    beta: tl.constexpr,
    threshold: tl.constexpr,
    T,
    H,
    D: tl.constexpr,
    BT: tl.constexpr,
    BD: tl.constexpr,
    HAS_BIAS: tl.constexpr,
):
    i_t, i_h = tl.program_id(0), tl.program_id(1)
    n_t = i_t * BT

    b_a = tl.load(A + i_h).to(tl.float32)
    b_a = -tl.exp(b_a)

    stride_row = H * D
    stride_col = 1

    g_ptr = tl.make_block_ptr(
        base=g + i_h * D,
        shape=(T, D),
        strides=(stride_row, stride_col),
        offsets=(n_t, 0),
        block_shape=(BT, BD),
        order=(1, 0),
    )

    y_ptr = tl.make_block_ptr(
        base=y + i_h * D,
        shape=(T, D),
        strides=(stride_row, stride_col),
        offsets=(n_t, 0),
        block_shape=(BT, BD),
        order=(1, 0),
    )

    b_g = tl.load(g_ptr, boundary_check=(0, 1)).to(tl.float32)

    if HAS_BIAS:
        n_d = tl.arange(0, BD)
        bias_mask = n_d < D
        b_bias = tl.load(g_bias + i_h * D + n_d, mask=bias_mask, other=0.0).to(
            tl.float32
        )
        b_g = b_g + b_bias[None, :]

    # softplus(x, beta) = (1/beta) * log(1 + exp(beta * x))
    # When beta * x > threshold, use linear approximation x
    # Use threshold to switch to linear when beta*x > threshold
    g_scaled = b_g * beta
    use_linear = g_scaled > threshold
    sp = tl.where(use_linear, b_g, (1.0 / beta) * log(1.0 + tl.exp(g_scaled)))
    b_y = b_a * sp

    tl.store(y_ptr, b_y.to(y.dtype.element_ty), boundary_check=(0, 1))

layer_norm_gated_fwd ¶

layer_norm_gated_fwd(
    x: Tensor,
    g: Tensor,
    weight: Tensor,
    bias: Tensor,
    activation: str = "swish",
    eps: float = 1e-05,
    residual: Tensor = None,
    out_dtype: dtype = None,
    residual_dtype: dtype = None,
    is_rms_norm: bool = False,
)

Source code in vllm/model_executor/layers/fla/ops/kda.py

def layer_norm_gated_fwd(
    x: torch.Tensor,
    g: torch.Tensor,
    weight: torch.Tensor,
    bias: torch.Tensor,
    activation: str = "swish",
    eps: float = 1e-5,
    residual: torch.Tensor = None,
    out_dtype: torch.dtype = None,
    residual_dtype: torch.dtype = None,
    is_rms_norm: bool = False,
):
    if residual is not None:
        residual_dtype = residual.dtype
    T, D = x.shape
    if residual is not None:
        assert residual.shape == (T, D)
    if weight is not None:
        assert weight.shape == (D,)
    if bias is not None:
        assert bias.shape == (D,)
    # allocate output
    y = x if out_dtype is None else torch.empty_like(x, dtype=out_dtype)
    if residual is not None or (
        residual_dtype is not None and residual_dtype != x.dtype
    ):
        residual_out = torch.empty(T, D, device=x.device, dtype=residual_dtype)
    else:
        residual_out = None
    mean = (
        torch.empty((T,), dtype=torch.float, device=x.device)
        if not is_rms_norm
        else None
    )
    rstd = torch.empty((T,), dtype=torch.float, device=x.device)
    # Less than 64KB per feature: enqueue fused kernel
    MAX_FUSED_SIZE = 65536 // x.element_size()
    BD = min(MAX_FUSED_SIZE, next_power_of_2(D))
    if D > BD:
        raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.")
    # heuristics for number of warps

    if D <= 512:
        BT = 32
        layer_norm_gated_fwd_kernel[(cdiv(T, BT),)](
            x=x,
            g=g,
            y=y,
            w=weight,
            b=bias,
            residual=residual,
            residual_out=residual_out,
            mean=mean,
            rstd=rstd,
            eps=eps,
            T=T,
            D=D,
            BD=BD,
            BT=BT,
            ACTIVATION=activation,
            IS_RMS_NORM=is_rms_norm,
            num_warps=4,
        )
    else:
        layer_norm_gated_fwd_kernel1[(T,)](
            x=x,
            g=g,
            y=y,
            w=weight,
            b=bias,
            residual=residual,
            residual_out=residual_out,
            mean=mean,
            rstd=rstd,
            eps=eps,
            D=D,
            BD=BD,
            ACTIVATION=activation,
            IS_RMS_NORM=is_rms_norm,
            num_warps=4,
        )
    # residual_out is None if residual is None and residual_dtype == input_dtype
    return y, mean, rstd, residual_out if residual_out is not None else x

layer_norm_gated_fwd_kernel ¶

layer_norm_gated_fwd_kernel(
    x,
    g,
    y,
    w,
    b,
    residual,
    residual_out,
    mean,
    rstd,
    eps,
    T,
    D: constexpr,
    BT: constexpr,
    BD: constexpr,
    ACTIVATION: constexpr,
    IS_RMS_NORM: constexpr,
    STORE_RESIDUAL_OUT: constexpr,
    HAS_RESIDUAL: constexpr,
    HAS_WEIGHT: constexpr,
    HAS_BIAS: constexpr,
)

Source code in vllm/model_executor/layers/fla/ops/kda.py

@triton.heuristics(
    {
        "STORE_RESIDUAL_OUT": lambda args: args["residual_out"] is not None,
        "HAS_RESIDUAL": lambda args: args["residual"] is not None,
        "HAS_WEIGHT": lambda args: args["w"] is not None,
        "HAS_BIAS": lambda args: args["b"] is not None,
    }
)
@triton.jit
def layer_norm_gated_fwd_kernel(
    x,  # pointer to the input
    g,  # pointer to the gate
    y,  # pointer to the output
    w,  # pointer to the weights
    b,  # pointer to the biases
    residual,  # pointer to the residual
    residual_out,  # pointer to the residual
    mean,  # pointer to the mean
    rstd,  # pointer to the 1/std
    eps,  # epsilon to avoid division by zero
    T,  # number of rows in x
    D: tl.constexpr,  # number of columns in x
    BT: tl.constexpr,
    BD: tl.constexpr,
    ACTIVATION: tl.constexpr,
    IS_RMS_NORM: tl.constexpr,
    STORE_RESIDUAL_OUT: tl.constexpr,
    HAS_RESIDUAL: tl.constexpr,
    HAS_WEIGHT: tl.constexpr,
    HAS_BIAS: tl.constexpr,
):
    i_t = tl.program_id(0)

    o_d = tl.arange(0, BD)
    m_d = o_d < D

    p_x = tl.make_block_ptr(x, (T, D), (D, 1), (i_t * BT, 0), (BT, BD), (1, 0))
    b_x = tl.load(p_x, boundary_check=(0, 1)).to(tl.float32)
    if HAS_RESIDUAL:
        p_res = tl.make_block_ptr(
            residual, (T, D), (D, 1), (i_t * BT, 0), (BT, BD), (1, 0)
        )
        b_x += tl.load(p_res, boundary_check=(0, 1)).to(tl.float32)
    if STORE_RESIDUAL_OUT:
        p_res_out = tl.make_block_ptr(
            residual_out, (T, D), (D, 1), (i_t * BT, 0), (BT, BD), (1, 0)
        )
        tl.store(p_res_out, b_x.to(p_res_out.dtype.element_ty), boundary_check=(0, 1))
    if not IS_RMS_NORM:
        b_mean = tl.sum(b_x, axis=1) / D
        p_mean = tl.make_block_ptr(mean, (T,), (1,), (i_t * BT,), (BT,), (0,))
        tl.store(p_mean, b_mean.to(p_mean.dtype.element_ty), boundary_check=(0,))
        b_xbar = tl.where(m_d[None, :], b_x - b_mean[:, None], 0.0)
        b_var = tl.sum(b_xbar * b_xbar, axis=1) / D
    else:
        b_xbar = tl.where(m_d[None, :], b_x, 0.0)
        b_var = tl.sum(b_xbar * b_xbar, axis=1) / D
    b_rstd = 1 / tl.sqrt(b_var + eps)

    p_rstd = tl.make_block_ptr(rstd, (T,), (1,), (i_t * BT,), (BT,), (0,))
    tl.store(p_rstd, b_rstd.to(p_rstd.dtype.element_ty), boundary_check=(0,))

    if HAS_WEIGHT:
        b_w = tl.load(w + o_d, mask=m_d).to(tl.float32)
    if HAS_BIAS:
        b_b = tl.load(b + o_d, mask=m_d).to(tl.float32)
    b_x_hat = (
        (b_x - b_mean[:, None]) * b_rstd[:, None]
        if not IS_RMS_NORM
        else b_x * b_rstd[:, None]
    )
    b_y = b_x_hat * b_w[None, :] if HAS_WEIGHT else b_x_hat
    if HAS_BIAS:
        b_y = b_y + b_b[None, :]

    # swish/sigmoid output gate
    p_g = tl.make_block_ptr(g, (T, D), (D, 1), (i_t * BT, 0), (BT, BD), (1, 0))
    b_g = tl.load(p_g, boundary_check=(0, 1)).to(tl.float32)
    if ACTIVATION == "swish" or ACTIVATION == "silu":
        b_y = b_y * b_g * tl.sigmoid(b_g)
    elif ACTIVATION == "sigmoid":
        b_y = b_y * tl.sigmoid(b_g)

    # Write output
    p_y = tl.make_block_ptr(y, (T, D), (D, 1), (i_t * BT, 0), (BT, BD), (1, 0))
    tl.store(p_y, b_y.to(p_y.dtype.element_ty), boundary_check=(0, 1))

layer_norm_gated_fwd_kernel1 ¶

layer_norm_gated_fwd_kernel1(
    x,
    g,
    y,
    w,
    b,
    residual,
    residual_out,
    mean,
    rstd,
    eps,
    D: constexpr,
    BD: constexpr,
    ACTIVATION: constexpr,
    IS_RMS_NORM: constexpr,
    STORE_RESIDUAL_OUT: constexpr,
    HAS_RESIDUAL: constexpr,
    HAS_WEIGHT: constexpr,
    HAS_BIAS: constexpr,
)

Source code in vllm/model_executor/layers/fla/ops/kda.py

@triton.heuristics(
    {
        "STORE_RESIDUAL_OUT": lambda args: args["residual_out"] is not None,
        "HAS_RESIDUAL": lambda args: args["residual"] is not None,
        "HAS_WEIGHT": lambda args: args["w"] is not None,
        "HAS_BIAS": lambda args: args["b"] is not None,
    }
)
@triton.jit
def layer_norm_gated_fwd_kernel1(
    x,  # pointer to the input
    g,  # pointer to the gate
    y,  # pointer to the output
    w,  # pointer to the weights
    b,  # pointer to the biases
    residual,  # pointer to the residual
    residual_out,  # pointer to the residual
    mean,  # pointer to the mean
    rstd,  # pointer to the 1/std
    eps,  # epsilon to avoid division by zero
    D: tl.constexpr,  # number of columns in x
    BD: tl.constexpr,
    ACTIVATION: tl.constexpr,
    IS_RMS_NORM: tl.constexpr,
    STORE_RESIDUAL_OUT: tl.constexpr,
    HAS_RESIDUAL: tl.constexpr,
    HAS_WEIGHT: tl.constexpr,
    HAS_BIAS: tl.constexpr,
):
    i_t = tl.program_id(0)
    x += i_t * D
    y += i_t * D
    g += i_t * D
    if HAS_RESIDUAL:
        residual += i_t * D
    if STORE_RESIDUAL_OUT:
        residual_out += i_t * D

    o_d = tl.arange(0, BD)
    m_d = o_d < D
    b_x = tl.load(x + o_d, mask=m_d, other=0.0).to(tl.float32)
    if HAS_RESIDUAL:
        b_x += tl.load(residual + o_d, mask=m_d, other=0.0).to(tl.float32)
    if STORE_RESIDUAL_OUT:
        tl.store(residual_out + o_d, b_x, mask=m_d)
    if not IS_RMS_NORM:
        b_mean = tl.sum(b_x, axis=0) / D
        tl.store(mean + i_t, b_mean)
        b_xbar = tl.where(m_d, b_x - b_mean, 0.0)
        b_var = tl.sum(b_xbar * b_xbar, axis=0) / D
    else:
        b_xbar = tl.where(m_d, b_x, 0.0)
        b_var = tl.sum(b_xbar * b_xbar, axis=0) / D
    b_rstd = 1 / tl.sqrt(b_var + eps)
    tl.store(rstd + i_t, b_rstd)

    if HAS_WEIGHT:
        b_w = tl.load(w + o_d, mask=m_d).to(tl.float32)
    if HAS_BIAS:
        b_b = tl.load(b + o_d, mask=m_d).to(tl.float32)
    b_x_hat = (b_x - b_mean) * b_rstd if not IS_RMS_NORM else b_x * b_rstd
    b_y = b_x_hat * b_w if HAS_WEIGHT else b_x_hat
    if HAS_BIAS:
        b_y = b_y + b_b

    # swish/sigmoid output gate
    b_g = tl.load(g + o_d, mask=m_d, other=0.0).to(tl.float32)
    if ACTIVATION == "swish" or ACTIVATION == "silu":
        b_y = b_y * b_g * tl.sigmoid(b_g)
    elif ACTIVATION == "sigmoid":
        b_y = b_y * tl.sigmoid(b_g)

    # Write output
    tl.store(y + o_d, b_y, mask=m_d)

recompute_w_u_fwd ¶

recompute_w_u_fwd(
    k: Tensor,
    v: Tensor,
    beta: Tensor,
    A: Tensor,
    q: Tensor | None = None,
    gk: Tensor | None = None,
    cu_seqlens: LongTensor | None = None,
) -> tuple[Tensor, Tensor]

Source code in vllm/model_executor/layers/fla/ops/kda.py

def recompute_w_u_fwd(
    k: torch.Tensor,
    v: torch.Tensor,
    beta: torch.Tensor,
    A: torch.Tensor,
    q: torch.Tensor | None = None,
    gk: torch.Tensor | None = None,
    cu_seqlens: torch.LongTensor | None = None,
) -> tuple[torch.Tensor, torch.Tensor]:
    B, T, H, K, V = *k.shape, v.shape[-1]
    BT = A.shape[-1]
    BK = 64
    BV = 64

    chunk_indices = (
        prepare_chunk_indices(cu_seqlens, BT) if cu_seqlens is not None else None
    )
    NT = cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)

    w = torch.empty_like(k)
    u = torch.empty_like(v)
    kg = torch.empty_like(k) if gk is not None else None
    recompute_w_u_fwd_kernel[(NT, B * H)](
        q=q,
        k=k,
        qg=None,
        kg=kg,
        v=v,
        beta=beta,
        w=w,
        u=u,
        A=A,
        gk=gk,
        cu_seqlens=cu_seqlens,
        chunk_indices=chunk_indices,
        T=T,
        H=H,
        K=K,
        V=V,
        BT=BT,
        BK=BK,
        BV=BV,
        DOT_PRECISION="ieee",
    )
    return w, u, None, kg

recompute_w_u_fwd_kernel ¶

recompute_w_u_fwd_kernel(
    q,
    k,
    qg,
    kg,
    v,
    beta,
    w,
    u,
    A,
    gk,
    cu_seqlens,
    chunk_indices,
    T,
    H: constexpr,
    K: constexpr,
    V: constexpr,
    BT: constexpr,
    BK: constexpr,
    BV: constexpr,
    STORE_QG: constexpr,
    STORE_KG: constexpr,
    IS_VARLEN: constexpr,
    DOT_PRECISION: constexpr,
)

Source code in vllm/model_executor/layers/fla/ops/kda.py

@triton.heuristics(
    {
        "STORE_QG": lambda args: args["qg"] is not None,
        "STORE_KG": lambda args: args["kg"] is not None,
        "IS_VARLEN": lambda args: args["cu_seqlens"] is not None,
    }
)
@triton.autotune(
    configs=[
        triton.Config({}, num_warps=num_warps, num_stages=num_stages)
        for num_warps in [2, 4, 8]
        for num_stages in [2, 3, 4]
    ],
    key=["H", "K", "V", "BT", "BK", "BV", "IS_VARLEN"],
)
@triton.jit(do_not_specialize=["T"])
def recompute_w_u_fwd_kernel(
    q,
    k,
    qg,
    kg,
    v,
    beta,
    w,
    u,
    A,
    gk,
    cu_seqlens,
    chunk_indices,
    T,
    H: tl.constexpr,
    K: tl.constexpr,
    V: tl.constexpr,
    BT: tl.constexpr,
    BK: tl.constexpr,
    BV: tl.constexpr,
    STORE_QG: tl.constexpr,
    STORE_KG: tl.constexpr,
    IS_VARLEN: tl.constexpr,
    DOT_PRECISION: 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
    p_b = tl.make_block_ptr(beta + bos * H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
    b_b = tl.load(p_b, boundary_check=(0,))

    p_A = tl.make_block_ptr(
        A + (bos * H + i_h) * BT, (T, BT), (H * BT, 1), (i_t * BT, 0), (BT, BT), (1, 0)
    )
    b_A = tl.load(p_A, boundary_check=(0, 1))

    for i_v in range(tl.cdiv(V, BV)):
        p_v = tl.make_block_ptr(
            v + (bos * H + i_h) * V,
            (T, V),
            (H * V, 1),
            (i_t * BT, i_v * BV),
            (BT, BV),
            (1, 0),
        )
        p_u = tl.make_block_ptr(
            u + (bos * H + i_h) * V,
            (T, V),
            (H * V, 1),
            (i_t * BT, i_v * BV),
            (BT, BV),
            (1, 0),
        )
        b_v = tl.load(p_v, boundary_check=(0, 1))
        b_vb = (b_v * b_b[:, None]).to(b_v.dtype)
        b_u = tl.dot(b_A, b_vb, input_precision=DOT_PRECISION)
        tl.store(p_u, b_u.to(p_u.dtype.element_ty), boundary_check=(0, 1))

    for i_k in range(tl.cdiv(K, BK)):
        p_w = tl.make_block_ptr(
            w + (bos * H + i_h) * K,
            (T, K),
            (H * K, 1),
            (i_t * BT, i_k * BK),
            (BT, BK),
            (1, 0),
        )
        p_k = tl.make_block_ptr(
            k + (bos * H + i_h) * K,
            (T, K),
            (H * K, 1),
            (i_t * BT, i_k * BK),
            (BT, BK),
            (1, 0),
        )
        b_k = tl.load(p_k, boundary_check=(0, 1))
        b_kb = b_k * b_b[:, None]

        p_gk = tl.make_block_ptr(
            gk + (bos * H + i_h) * K,
            (T, K),
            (H * K, 1),
            (i_t * BT, i_k * BK),
            (BT, BK),
            (1, 0),
        )
        b_gk = tl.load(p_gk, boundary_check=(0, 1))
        b_kb *= exp(b_gk)
        if STORE_QG:
            p_q = tl.make_block_ptr(
                q + (bos * H + i_h) * K,
                (T, K),
                (H * K, 1),
                (i_t * BT, i_k * BK),
                (BT, BK),
                (1, 0),
            )
            p_qg = tl.make_block_ptr(
                qg + (bos * H + i_h) * K,
                (T, K),
                (H * K, 1),
                (i_t * BT, i_k * BK),
                (BT, BK),
                (1, 0),
            )
            b_q = tl.load(p_q, boundary_check=(0, 1))
            b_qg = b_q * exp(b_gk)
            tl.store(p_qg, b_qg.to(p_qg.dtype.element_ty), boundary_check=(0, 1))
        if STORE_KG:
            last_idx = min(i_t * BT + BT, T) - 1

            o_k = i_k * BK + tl.arange(0, BK)
            m_k = o_k < K
            b_gn = tl.load(
                gk + ((bos + last_idx) * H + i_h) * K + o_k, mask=m_k, other=0.0
            )
            b_kg = b_k * exp(b_gn - b_gk)

            p_kg = tl.make_block_ptr(
                kg + (bos * H + i_h) * K,
                (T, K),
                (H * K, 1),
                (i_t * BT, i_k * BK),
                (BT, BK),
                (1, 0),
            )
            tl.store(p_kg, b_kg.to(p_kg.dtype.element_ty), boundary_check=(0, 1))

        b_w = tl.dot(b_A, b_kb.to(b_k.dtype))
        tl.store(p_w, b_w.to(p_w.dtype.element_ty), boundary_check=(0, 1))

rms_norm_gated ¶

rms_norm_gated(
    x: Tensor,
    g: Tensor,
    weight: Tensor,
    bias: Tensor,
    activation: str = "swish",
    residual: Tensor | None = None,
    prenorm: bool = False,
    residual_in_fp32: bool = False,
    eps: float = 1e-06,
)

Source code in vllm/model_executor/layers/fla/ops/kda.py

def rms_norm_gated(
    x: torch.Tensor,
    g: torch.Tensor,
    weight: torch.Tensor,
    bias: torch.Tensor,
    activation: str = "swish",
    residual: torch.Tensor | None = None,
    prenorm: bool = False,
    residual_in_fp32: bool = False,
    eps: float = 1e-6,
):
    x_shape_og = x.shape
    # reshape input data into 2D tensor
    x = x.contiguous().reshape(-1, x.shape[-1])
    g = g.contiguous().reshape(-1, g.shape[-1])
    if residual is not None:
        assert residual.shape == x_shape_og
        residual = residual.contiguous().reshape(-1, residual.shape[-1])
    residual_dtype = (
        residual.dtype
        if residual is not None
        else (torch.float if residual_in_fp32 else None)
    )
    y, _, _, residual_out = layer_norm_gated_fwd(
        x=x,
        g=g,
        weight=weight,
        bias=bias,
        activation=activation,
        eps=eps,
        residual=residual,
        residual_dtype=residual_dtype,
        is_rms_norm=True,
    )
    y = y.reshape(x_shape_og)
    return y if not prenorm else (y, residual_out.reshape(x_shape_og))

vllm.model_executor.layers.fla.ops.kda ¶

BT_LIST_AUTOTUNE module-attribute ¶

NUM_WARPS_AUTOTUNE module-attribute ¶

FusedRMSNormGated ¶

activation instance-attribute ¶

elementwise_affine instance-attribute ¶

eps instance-attribute ¶

hidden_size instance-attribute ¶

weight instance-attribute ¶

__init__ ¶

forward ¶

chunk_gla_fwd_kernel_o ¶

chunk_gla_fwd_o_gk ¶

chunk_kda ¶

chunk_kda_fwd ¶

chunk_kda_scaled_dot_kkt_fwd ¶

chunk_kda_scaled_dot_kkt_fwd_kernel_intra_sub_inter ¶

chunk_kda_scaled_dot_kkt_fwd_kernel_intra_sub_intra ¶

fused_recurrent_kda ¶

fused_recurrent_kda_fwd ¶

kda_gate_fwd ¶

kda_gate_fwd_kernel ¶

layer_norm_gated_fwd ¶

layer_norm_gated_fwd_kernel ¶

layer_norm_gated_fwd_kernel1 ¶

recompute_w_u_fwd ¶

recompute_w_u_fwd_kernel ¶

rms_norm_gated ¶

BT_LIST_AUTOTUNE `module-attribute` ¶

NUM_WARPS_AUTOTUNE `module-attribute` ¶

activation `instance-attribute` ¶

elementwise_affine `instance-attribute` ¶

eps `instance-attribute` ¶

hidden_size `instance-attribute` ¶

weight `instance-attribute` ¶

init ¶