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fla / build /torch-cuda /ops /kda /gate.py
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# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# For a list of all contributors, visit:
# https://github.com/fla-org/flash-linear-attention/graphs/contributors
# This file is modified and supported by the Moonshot AI Team
import torch
import torch.nn.functional as F
import triton
import triton.language as tl
from ...ops.utils.cache import fla_cache_autotune
from ...ops.utils.index import prepare_chunk_indices
from ...ops.utils.op import exp
from ...ops.utils.softplus import softplus
from ...utils import IS_AMD, autocast_custom_bwd, autocast_custom_fwd, autotune_cache_kwargs, check_shared_mem, input_guard
BS_LIST = [32, 64] if check_shared_mem() else [16, 32]
BT_LIST_AUTOTUNE = [32, 64, 128]
NUM_WARPS_AUTOTUNE = [2, 4, 8, 16] if IS_AMD else [4, 8, 16, 32]
def naive_kda_gate(
 g: torch.Tensor,
 A_log: torch.Tensor,
 dt_bias: torch.Tensor | None = None,
 output_dtype: torch.dtype = torch.float32,
) -> torch.Tensor:
 """
 Torch reference implementation for KDA gate computation.
 Computes: g = -A_log.exp().unsqueeze(-1) * softplus(g + dt_bias.view(g.shape[-2:]))
 Args:
 g (torch.Tensor):
 Input tensor of shape `[..., H, K]`.
 A_log (torch.Tensor):
 Parameter tensor with `H` elements.
 dt_bias (torch.Tensor | None):
 Optional bias tensor added to `g` before activation, shape `[H * K]`.
 Returns:
 Output tensor of shape `[..., H, K]` .
 """
 H, _ = g.shape[-2:]
 g = g.float()
 if dt_bias is not None:
 g = g + dt_bias.view(H, -1)
g = (-A_log.view(H, 1).float().exp() * F.softplus(g.float())).to(output_dtype)
 return g
def naive_kda_lowerbound_gate(
 g: torch.Tensor,
 A_log: torch.Tensor,
 dt_bias: torch.Tensor | None = None,
 lower_bound: float = -5.0,
 output_dtype: torch.dtype = torch.float32,
) -> torch.Tensor:
 H, _ = g.shape[-2:]
 g = g.float()
 if dt_bias is not None:
 g = g + dt_bias.view(H, -1)
 g = lower_bound * F.sigmoid(A_log.view(H, 1).exp() * g)
 return g.to(output_dtype)
@triton.heuristics({
 "HAS_BIAS": lambda args: args["dt_bias"] is not None,
 "HAS_BETA": lambda args: args["beta"] is not None,
 'USE_LOWER_BOUND': lambda args: args['lower_bound'] is not None,
})
@fla_cache_autotune(
 configs=[
 triton.Config({"BT": BT}, num_warps=num_warps, num_stages=num_stages)
 for BT in BT_LIST_AUTOTUNE
 for num_warps in NUM_WARPS_AUTOTUNE
 for num_stages in [2, 3]
 ],
 key=["H", "D"],
 **autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=['T'])
def kda_gate_fwd_kernel(
 g,
 A_log,
 dt_bias,
 beta,
 yg,
 yb,
 lower_bound,
 T,
 H: tl.constexpr,
 D: tl.constexpr,
 BT: tl.constexpr,
 BD: tl.constexpr,
 HAS_BIAS: tl.constexpr,
 HAS_BETA: tl.constexpr,
 USE_LOWER_BOUND: tl.constexpr,
):
 i_t, i_h = tl.program_id(0), tl.program_id(1)
b_A = tl.load(A_log + i_h).to(tl.float32)
p_g = tl.make_block_ptr(g + i_h * D, (T, D), (H * D, 1), (i_t * BT, 0), (BT, BD), (1, 0))
 p_yg = tl.make_block_ptr(yg + i_h * D, (T, D), (H * D, 1), (i_t * BT, 0), (BT, BD), (1, 0))
 # [BT, BD]
 b_g = tl.load(p_g, boundary_check=(0, 1)).to(tl.float32)
 if HAS_BIAS:
 p_b = tl.make_block_ptr(dt_bias, (H * D,), (1,), (i_h * D,), (BD,), (0,))
 b_g = b_g + tl.load(p_b, boundary_check=(0,)).to(tl.float32)
 if not USE_LOWER_BOUND:
 b_yg = -exp(b_A) * softplus(b_g)
 else:
 b_yg = lower_bound * tl.sigmoid(exp(b_A) * b_g)
 tl.store(p_yg, b_yg.to(p_yg.dtype.element_ty), boundary_check=(0, 1))
if HAS_BETA:
 p_b = tl.make_block_ptr(beta + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
 p_yb = tl.make_block_ptr(yb + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
 b_yb = tl.sigmoid(tl.load(p_b, boundary_check=(0,)).to(tl.float32))
 tl.store(p_yb, b_yb.to(p_yb.dtype.element_ty), boundary_check=(0,))
@triton.heuristics({
 "HAS_BIAS": lambda args: args["dt_bias"] is not None,
 "HAS_BETA": lambda args: args["beta"] is not None,
 'USE_LOWER_BOUND': lambda args: args['lower_bound'] is not None,
})
@fla_cache_autotune(
 configs=[
 triton.Config({}, num_warps=num_warps, num_stages=num_stages)
 for num_warps in NUM_WARPS_AUTOTUNE
 for num_stages in [2, 3]
 ],
 key=["H", "D"],
 **autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=['T'])
def kda_gate_bwd_kernel(
 g,
 A_log,
 dt_bias,
 beta,
 dyg,
 dyb,
 dg,
 dA,
 dbeta,
 lower_bound,
 T,
 H: tl.constexpr,
 D: tl.constexpr,
 BT: tl.constexpr,
 BD: tl.constexpr,
 HAS_BIAS: tl.constexpr,
 HAS_BETA: tl.constexpr,
 USE_LOWER_BOUND: tl.constexpr,
):
 i_t, i_h = tl.program_id(0), tl.program_id(1)
b_A = tl.load(A_log + i_h).to(tl.float32)
p_g = tl.make_block_ptr(g + i_h * D, (T, D), (H * D, 1), (i_t * BT, 0), (BT, BD), (1, 0))
 p_dg = tl.make_block_ptr(dg + i_h * D, (T, D), (H * D, 1), (i_t * BT, 0), (BT, BD), (1, 0))
 p_dyg = tl.make_block_ptr(dyg + i_h * D, (T, D), (H * D, 1), (i_t * BT, 0), (BT, BD), (1, 0))
# [BT, BD]
 b_g = tl.load(p_g, boundary_check=(0, 1)).to(tl.float32)
 b_dyg = tl.load(p_dyg, boundary_check=(0, 1)).to(tl.float32)
if HAS_BIAS:
 p_b = tl.make_block_ptr(dt_bias, (H * D,), (1,), (i_h * D,), (BD,), (0,))
 b_g = b_g + tl.load(p_b, boundary_check=(0,)).to(tl.float32)
# [BT, BD]
 if not USE_LOWER_BOUND:
 b_A = -exp(b_A)
 b_yg = b_A * softplus(b_g)
 b_dg = b_A * (b_dyg * tl.sigmoid(b_g))
 b_dA = tl.sum(tl.sum(b_dyg * b_yg, 1), 0)
 else:
 b_A = exp(b_A)
 b_inner = b_A * b_g
 b_sig = tl.sigmoid(b_inner)
 b_dsig = b_sig * (1.0 - b_sig)
 # Common term: dy * (LB * dsig)
 b_d_inner_term = b_dyg * (lower_bound * b_dsig)
 # dg = d_inner_term * A
 b_dg = b_d_inner_term * b_A
 b_dA = tl.sum(tl.sum(b_dg * b_g, 1), 0)
tl.store(p_dg, b_dg.to(p_dg.dtype.element_ty), boundary_check=(0, 1))
 tl.store(dA + i_t * H + i_h, b_dA)
if HAS_BETA:
 p_b = tl.make_block_ptr(beta + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
 p_db = tl.make_block_ptr(dbeta + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
 p_dyb = tl.make_block_ptr(dyb + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
b_b = tl.load(p_b, boundary_check=(0,)).to(tl.float32)
 b_db = tl.load(p_dyb, boundary_check=(0,)).to(tl.float32) * b_b * (1.0 - b_b)
 tl.store(p_db, b_db.to(p_db.dtype.element_ty), boundary_check=(0,))
def kda_gate_fwd(
 g: torch.Tensor,
 A_log: torch.Tensor,
 dt_bias: torch.Tensor | None = None,
 lower_bound: float | None = None,
 output_dtype: torch.dtype = torch.float32,
) -> torch.Tensor:
 H, K = g.shape[-2:]
 T = g.numel() // (H * K)
yg = torch.empty_like(g, dtype=output_dtype)
def grid(meta):
 return (triton.cdiv(T, meta["BT"]), H)
kda_gate_fwd_kernel[grid](
 g=g,
 A_log=A_log,
 dt_bias=dt_bias,
 beta=None,
 yg=yg,
 yb=None,
 T=T,
 H=H,
 D=K,
 BD=triton.next_power_of_2(K),
 lower_bound=lower_bound,
 )
 return yg
def kda_gate_bwd(
 g: torch.Tensor,
 A_log: torch.Tensor,
 dt_bias: torch.Tensor | None = None,
 dyg: torch.Tensor | None = None,
 lower_bound: float | None = None,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None]:
 H, K = g.shape[-2:]
 T = g.numel() // (H * K)
 BT = 32
 NT = triton.cdiv(T, BT)
dg = torch.empty_like(g, dtype=torch.float32)
 dA = A_log.new_empty(NT, H, dtype=torch.float32)
grid = (triton.cdiv(T, BT), H)
 kda_gate_bwd_kernel[grid](
 g=g,
 A_log=A_log,
 dt_bias=dt_bias,
 beta=None,
 dyg=dyg,
 dyb=None,
 dg=dg,
 dA=dA,
 dbeta=None,
 T=T,
 H=H,
 D=K,
 BT=BT,
 BD=triton.next_power_of_2(K),
 lower_bound=lower_bound,
 )
dg = dg.view_as(g).type_as(g)
 dA = dA.sum(0).view_as(A_log).type_as(A_log)
 dbias = dg.view(-1, H * K).sum(0).to(dt_bias) if dt_bias is not None else None
return dg, dA, dbias
class KDAGateFunction(torch.autograd.Function):
 @staticmethod
 @input_guard
 @autocast_custom_fwd
 def forward(
 ctx,
 g: torch.Tensor,
 A_log: torch.Tensor,
 dt_bias: torch.Tensor | None = None,
 lower_bound: float | None = None,
 output_dtype: torch.dtype = torch.float32,
 ) -> torch.Tensor:
 yg = kda_gate_fwd(
 g=g,
 A_log=A_log,
 dt_bias=dt_bias,
 lower_bound=lower_bound,
 output_dtype=output_dtype
 )
 ctx.save_for_backward(g, A_log, dt_bias)
 ctx.lower_bound = lower_bound
 return yg
@staticmethod
 @input_guard
 @autocast_custom_bwd
 def backward(ctx, dyg: torch.Tensor):
 g, A_log, dt_bias = ctx.saved_tensors
 dg, dA, dbias = kda_gate_bwd(
 g=g,
 A_log=A_log,
 dt_bias=dt_bias,
 dyg=dyg,
 lower_bound=ctx.lower_bound
 )
 return dg, dA, dbias, None, None
@torch.compiler.disable
def fused_kda_gate(
 g: torch.Tensor,
 A_log: torch.Tensor,
 dt_bias: torch.Tensor | None = None,
 lower_bound: float | None = None,
 output_dtype: torch.dtype = torch.float32,
) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
 """
 Fused KDA gate computation with autograd support.
 Computes: g = -A_log.exp().unsqueeze(-1) * softplus(g + dt_bias.view(g.shape[-2:]))
 Args:
 g (torch.Tensor):
 Input tensor of shape `[..., H, K]`.
 A_log (torch.Tensor):
 Parameter tensor with `H` elements.
 dt_bias (torch.Tensor | None):
 Optional bias tensor added to `g` before activation, shape `[H * K]`.
 Returns:
 Output tensor of shape `[..., H, K]`.
 """
 return KDAGateFunction.apply(g, A_log, dt_bias, lower_bound, output_dtype)
@triton.heuristics({
 "HAS_BIAS": lambda args: args["dt_bias"] is not None,
 'HAS_SCALE': lambda args: args['scale'] is not None,
 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None,
 'USE_LOWER_BOUND': lambda args: args['lower_bound'] is not None,
})
@fla_cache_autotune(
 configs=[
 triton.Config({'BS': BS}, num_warps=num_warps)
 for BS in BS_LIST
 for num_warps in [2, 4, 8]
 ],
 key=['H', 'S', 'BT', 'IS_VARLEN', 'REVERSE'],
 **autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=['T'])
def kda_gate_chunk_cumsum_vector_kernel(
 s,
 A_log,
 dt_bias,
 o,
 scale,
 cu_seqlens,
 chunk_indices,
 lower_bound,
 T,
 H: tl.constexpr,
 S: tl.constexpr,
 BT: tl.constexpr,
 BS: tl.constexpr,
 REVERSE: tl.constexpr,
 HAS_BIAS: tl.constexpr,
 HAS_SCALE: tl.constexpr,
 IS_VARLEN: tl.constexpr,
 USE_LOWER_BOUND: 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
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)
# Apply dt_bias if exists
 if HAS_BIAS:
 p_b = tl.make_block_ptr(dt_bias + i_h * S, (S,), (1,), (i_s * BS,), (BS,), (0,))
 b_bias = tl.load(p_b, boundary_check=(0,)).to(tl.float32)
 b_s = b_s + b_bias[None, :]
b_A = tl.load(A_log + i_h).to(tl.float32)
 if not USE_LOWER_BOUND:
 # Apply gate: -exp(A_log) * softplus(g + bias)
 b_gate = -exp(b_A) * softplus(b_s)
 else:
 b_gate = lower_bound * tl.sigmoid(exp(b_A) * b_s)
# Apply chunk local cumsum
 if REVERSE:
 b_o = tl.cumsum(b_gate, axis=0, reverse=True)
 else:
 b_o = tl.cumsum(b_gate, axis=0)
if HAS_SCALE:
 b_o *= scale
 tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0, 1))
@input_guard
def kda_gate_chunk_cumsum(
 g: torch.Tensor,
 A_log: torch.Tensor,
 chunk_size: int,
 scale: float = None,
 dt_bias: torch.Tensor | None = None,
 cu_seqlens: torch.Tensor | None = None,
 output_dtype: torch.dtype | None = torch.float,
 chunk_indices: torch.LongTensor | None = None,
 lower_bound: float | None = None,
 **kwargs,
) -> torch.Tensor:
 if cu_seqlens is not None:
 assert g.shape[0] == 1, "Only batch size 1 is supported when cu_seqlens are provided"
 assert len(g.shape) == 4
 B, T, H, S = g.shape
 BT = chunk_size
 if chunk_indices is None and cu_seqlens is not None:
 chunk_indices = prepare_chunk_indices(cu_seqlens, BT)
 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)
 kda_gate_chunk_cumsum_vector_kernel[grid](
 s=g_org,
 A_log=A_log,
 dt_bias=dt_bias,
 o=g,
 scale=scale,
 cu_seqlens=cu_seqlens,
 chunk_indices=chunk_indices,
 lower_bound=lower_bound,
 T=T,
 H=H,
 S=S,
 BT=BT,
 REVERSE=False,
 )
 return g