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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 IS_NVIDIA_HOPPER, autotune_cache_kwargs
NUM_WARPS = [2, 4] if IS_NVIDIA_HOPPER else [2, 4, 8]
@triton.heuristics({
'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 NUM_WARPS
for num_stages in [2, 3, 4]
],
key=['H', 'K', 'V', 'BT', 'BK', 'BV'],
**autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=['T'])
def chunk_mesa_net_h_kv_bwd_intra_kernel_dkv(
q_star,
k,
v,
beta,
h_kv,
g,
do,
dh_kv,
dk_beta,
dg,
dv,
cu_seqlens,
chunk_indices,
B: tl.constexpr,
T,
H: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
BT: tl.constexpr,
BK: tl.constexpr,
BV: tl.constexpr,
IS_VARLEN: 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_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
o_t = i_t * BT + tl.arange(0, BT)
m_t = o_t < T
# offset calculation
v += (bos * H + i_h) * V
do += (bos * H + i_h) * V
h_kv += (i_tg * H + i_h).to(tl.int64) * K*V
dh_kv += (i_tg * H + i_h).to(tl.int64) * K*V
q_star += (bos * H + i_h) * K
k += (bos * H + i_h) * K
beta += (bos * H + i_h)
g += bos * H + i_h
dg += bos * H + i_h
dk_beta += (bos * H + i_h) * K
dv += (bos * H + i_h) * V
b_dk = tl.zeros([BT, BK], dtype=tl.float32)
b_ds = tl.zeros([BT, BT], dtype=tl.float32)
b_dv = tl.zeros([BT, BK], dtype=tl.float32)
b_dg_last = tl.zeros([1], dtype=tl.float32)
b_dg = tl.zeros([BT], dtype=tl.float32)
p_v = tl.make_block_ptr(v, (T, V), (H*V, 1), (i_t * BT, 0), (BT, BV), (1, 0))
p_k = tl.make_block_ptr(k, (T, K), (H*K, 1), (i_t * BT, 0), (BT, BK), (1, 0))
p_beta = tl.make_block_ptr(beta, (T, ), (H, ), (i_t * BT,), (BT,), (0,))
p_do = tl.make_block_ptr(do, (T, V), (H*V, 1), (i_t * BT, 0), (BT, BV), (1, 0))
p_h = tl.make_block_ptr(h_kv, (V, K), (1, V), (0, 0), (BV, BK), (0, 1))
p_dh = tl.make_block_ptr(dh_kv, (V, K), (1, V), (0, 0), (BV, BK), (0, 1))
p_q = tl.make_block_ptr(q_star, (T, K), (H*K, 1), (i_t * BT, 0), (BT, BK), (1, 0))
p_g = tl.make_block_ptr(g, (T,), (H,), (i_t * BT,), (BT,), (0,))
b_q = tl.load(p_q, boundary_check=(0, 1))
b_v = tl.load(p_v, boundary_check=(0, 1))
b_k = tl.load(p_k, boundary_check=(0, 1))
b_beta = tl.load(p_beta, boundary_check=(0, ))
b_g = tl.load(p_g, boundary_check=(0,))
b_do = tl.load(p_do, boundary_check=(0, 1))
b_h = tl.load(p_h, boundary_check=(0, 1))
b_dh = tl.load(p_dh, boundary_check=(0, 1))
b_g_last = tl.load(g + (min(i_t * BT + BT, T) - 1) * H)
# calculation
b_dg_last += tl.sum(b_h * b_dh)
b_dg_last *= exp(b_g_last)
b_m = tl.where((o_t[:, None] >= o_t[None, :]) & (m_t[:, None] & m_t[None, :]), exp(b_g[:, None] - b_g[None, :]), 0)
b_k = (b_k * b_beta[:, None]).to(b_k.dtype)
b_s = tl.dot(b_q, tl.trans(b_k)) * b_m
b_ds = tl.dot(b_do, tl.trans(b_v))
b_dm = b_s * b_ds
b_dm = tl.where(tl.arange(0, BT)[:, None] >= tl.arange(0, BT)[None, :], b_dm, 0)
b_dg += tl.sum(b_dm, axis=1)
b_dg -= tl.sum(b_dm, axis=0)
b_g_exp_k = tl.where(m_t, exp(-b_g + b_g_last), 0)
b_ds = b_ds * b_m
b_dk += tl.dot(b_v, b_dh.to(b_v.dtype)) * b_g_exp_k[:, None]
b_dg_last += tl.sum(b_dk * b_k)
b_dg -= tl.sum(b_dk * b_k, axis=1)
b_dv += tl.dot(b_k, tl.trans(b_dh).to(b_k.dtype)) * b_g_exp_k[:, None] + tl.dot(tl.trans(b_s.to(b_do.dtype)), b_do)
b_dk += tl.dot(tl.trans(b_ds.to(b_q.dtype)), b_q)
b_dg = tl.where(o_t < min(i_t * BT + BT, T) - 1, b_dg, b_dg + b_dg_last)
p_dk = tl.make_block_ptr(dk_beta, (T, K), (H*K, 1), (i_t * BT, 0), (BT, BK), (1, 0))
p_dv = tl.make_block_ptr(dv, (T, V), (H*V, 1), (i_t * BT, 0), (BT, BV), (1, 0))
p_dg = tl.make_block_ptr(dg, (T,), (H,), (i_t * BT,), (BT,), (0,))
tl.store(p_dk, b_dk.to(p_dk.dtype.element_ty), boundary_check=(0, 1))
tl.store(p_dv, b_dv.to(p_dv.dtype.element_ty), boundary_check=(0, 1))
tl.store(p_dg, b_dg.to(p_dg.dtype.element_ty), boundary_check=(0,))
@triton.heuristics({
'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 NUM_WARPS
for num_stages in [2, 3, 4]
],
key=['H', 'K', 'V', 'BT', 'BK', 'BV'],
**autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=['T'])
def chunk_mesa_net_h_kv_bwd_intra_kernel_dq(
q_star,
k,
v,
beta,
h_kv,
g,
do,
dq,
dg_prev,
dg,
cu_seqlens,
chunk_indices,
B: tl.constexpr,
T,
H: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
BT: tl.constexpr,
BK: tl.constexpr,
BV: tl.constexpr,
IS_VARLEN: 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_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
o_t = i_t * BT + tl.arange(0, BT)
m_t = o_t < T
# offset calculation
v += (bos * H + i_h) * V
do += (bos * H + i_h) * V
h_kv += (i_tg * H + i_h).to(tl.int64) * K*V
q_star += (bos * H + i_h) * K
k += (bos * H + i_h) * K
beta += (bos * H + i_h)
g += bos * H + i_h
dg_prev += bos * H + i_h
dg += bos * H + i_h
dq += (bos * H + i_h) * K
b_dq = tl.zeros([BT, BK], dtype=tl.float32)
p_v = tl.make_block_ptr(v, (T, V), (H*V, 1), (i_t * BT, 0), (BT, BV), (1, 0))
p_k = tl.make_block_ptr(k, (T, K), (H*K, 1), (i_t * BT, 0), (BT, BK), (1, 0))
p_beta = tl.make_block_ptr(beta, (T, ), (H, ), (i_t * BT,), (BT,), (0,))
p_do = tl.make_block_ptr(do, (T, V), (H*V, 1), (i_t * BT, 0), (BT, BV), (1, 0))
p_h = tl.make_block_ptr(h_kv, (V, K), (1, V), (0, 0), (BV, BK), (0, 1))
p_q = tl.make_block_ptr(q_star, (T, K), (H*K, 1), (i_t * BT, 0), (BT, BK), (1, 0))
p_g = tl.make_block_ptr(g, (T,), (H,), (i_t * BT,), (BT,), (0,))
p_dg_prev = tl.make_block_ptr(dg_prev, (T,), (H,), (i_t * BT,), (BT,), (0,))
p_dg = tl.make_block_ptr(dg, (T,), (H,), (i_t * BT,), (BT,), (0,))
p_dq = tl.make_block_ptr(dq, (T, K), (H*K, 1), (i_t * BT, 0), (BT, BK), (1, 0))
b_q = tl.load(p_q, boundary_check=(0, 1))
b_v = tl.load(p_v, boundary_check=(0, 1))
b_k = tl.load(p_k, boundary_check=(0, 1))
b_beta = tl.load(p_beta, boundary_check=(0, ))
b_g = tl.load(p_g, boundary_check=(0,))
b_do = tl.load(p_do, boundary_check=(0, 1))
b_h = tl.load(p_h, boundary_check=(0, 1))
b_m = tl.where((o_t[:, None] >= o_t[None, :]) & (m_t[:, None] & m_t[None, :]), exp(b_g[:, None] - b_g[None, :]), 0)
b_k = (b_k * b_beta[:, None]).to(b_k.dtype)
b_ds = tl.dot(b_do, tl.trans(b_v)) * b_m
b_g_exp_q = exp(b_g)
b_dq = tl.dot(b_do, b_h.to(b_do.dtype)) * b_g_exp_q[:, None]
b_dg = tl.sum(b_dq * b_q, axis=1) + tl.load(p_dg_prev, boundary_check=(0,))
b_dq += tl.dot(b_ds.to(b_k.dtype), b_k)
tl.store(p_dq, b_dq.to(p_dq.dtype.element_ty), boundary_check=(0, 1))
tl.store(p_dg, b_dg.to(p_dg.dtype.element_ty), boundary_check=(0,))
def chunk_mesa_net_h_kv_bwd_intra_separate_fn(
q_star,
k,
v,
beta,
h_kv,
dh_kv,
g,
do,
cu_seqlens,
chunk_size=64,
chunk_indices: torch.LongTensor | None = None,
):
B, T, H, K, V = *k.shape, v.shape[-1]
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)
BK = max(triton.next_power_of_2(K), 16)
BV = max(triton.next_power_of_2(V), 16)
dq = torch.empty_like(q_star, dtype=torch.float32)
dk = torch.empty_like(k)
dv = torch.empty_like(v)
dg = torch.empty_like(g)
grid = (NT, B * H)
chunk_mesa_net_h_kv_bwd_intra_kernel_dkv[grid](
q_star=q_star,
k=k,
v=v,
beta=beta,
h_kv=h_kv,
g=g,
do=do,
dh_kv=dh_kv,
dk_beta=dk,
dg=dg,
dv=dv,
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
B=B,
T=T,
H=H,
K=K,
V=V,
BT=BT,
BK=BK,
BV=BV,
)
dg_final = torch.empty_like(dg)
chunk_mesa_net_h_kv_bwd_intra_kernel_dq[grid](
q_star=q_star,
k=k,
v=v,
beta=beta,
h_kv=h_kv,
g=g,
do=do,
dg=dg_final,
dg_prev=dg,
dq=dq,
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
B=B,
T=T,
H=H,
K=K,
V=V,
BT=BT,
BK=BK,
BV=BV,
)
return dq, dk, dv, dg_final
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