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b66f552 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | # Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
import torch
import triton
import triton.language as tl
from fla.ops.utils import prepare_chunk_indices
from fla.ops.utils.op import exp
from fla.utils import autotune_cache_kwargs
@triton.heuristics({
'USE_G': lambda args: args['g'] is not None,
'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, 128]
for num_warps in [2, 4, 8]
for num_stages in [2, 3, 4]
],
key=['H', 'K', 'BT', 'IS_VARLEN'],
**autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=['T'])
def chunk_scaled_dot_kkt_fwd_kernel(
k,
g,
beta,
A,
cu_seqlens,
chunk_indices,
T,
H: tl.constexpr,
K: tl.constexpr,
BT: tl.constexpr,
BK: tl.constexpr,
IS_VARLEN: tl.constexpr,
USE_G: 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
o_t = i_t * BT + tl.arange(0, BT)
m_t = o_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,))
b_A = tl.zeros([BT, BT], dtype=tl.float32)
for i_k in range(tl.cdiv(K, BK)):
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_A += tl.dot(b_k, tl.trans(b_k))
if USE_G:
p_g = tl.make_block_ptr(g + bos*H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
b_g = tl.load(p_g, boundary_check=(0,))
b_g_diff = b_g[:, None] - b_g[None, :]
b_A *= exp(b_g_diff)
b_A *= b_b[:, None]
m_A = (o_t[:, None] > o_t[None, :]) & (m_t[:, None] & m_t)
b_A = tl.where(m_A, b_A, 0)
p_A = tl.make_block_ptr(A + (bos*H + i_h) * BT, (T, BT), (BT*H, 1), (i_t * BT, 0), (BT, BT), (1, 0))
tl.store(p_A, b_A.to(p_A.dtype.element_ty), boundary_check=(0, 1))
def chunk_scaled_dot_kkt_fwd(
k: torch.Tensor,
g: torch.Tensor | None = None,
beta: torch.Tensor | None = None,
cu_seqlens: torch.LongTensor | None = None,
chunk_size: int = 64,
output_dtype: torch.dtype = torch.float32,
) -> 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]`.
g (torch.Tensor):
The cumulative sum of the gate tensor of shape `[B, T, H]`. Default: `None`.
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
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)
A = torch.empty(B, T, H, BT, device=k.device, dtype=output_dtype)
chunk_scaled_dot_kkt_fwd_kernel[(NT, B * H)](
k=k,
g=g,
beta=beta,
A=A,
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
T=T,
H=H,
K=K,
BT=BT,
)
return A
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