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import triton
import triton.language as tl
import torch
@triton.autotune(
configs=[
triton.Config({}, num_warps=1),
triton.Config({}, num_warps=2),
triton.Config({}, num_warps=4),
triton.Config({}, num_warps=8),
],
key=["BT", "BK", "BV", "USE_G", 'USE_GK', 'USE_GV'],
)
@triton.jit
def chunk_fwd_kernel_h(
k,
v,
h,
g,
gk,
gv,
h0,
ht,
s_qk_h,
s_qk_t,
s_qk_d,
s_vo_h,
s_vo_t,
s_vo_d,
s_h_h,
s_h_t,
T: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
BT: tl.constexpr,
BK: tl.constexpr,
BV: tl.constexpr,
NT: tl.constexpr,
USE_INITIAL_STATE: tl.constexpr,
STORE_FINAL_STATE: tl.constexpr,
USE_G: tl.constexpr,
USE_GK: tl.constexpr,
USE_GV: tl.constexpr
):
i_k, i_v, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
# [BK, BV]
b_h = tl.zeros([BK, BV], dtype=tl.float32)
if USE_INITIAL_STATE:
p_h0 = tl.make_block_ptr(h0 + i_bh * K * V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
b_h = tl.load(p_h0, boundary_check=(0, 1)).to(tl.float32)
for i_t in range(NT):
p_k = tl.make_block_ptr(k + i_bh * s_qk_h, (K, T), (s_qk_d, s_qk_t), (i_k * BK, i_t * BT), (BK, BT), (0, 1))
p_v = tl.make_block_ptr(v + i_bh * s_vo_h, (T, V), (s_vo_t, s_vo_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
p_h = tl.make_block_ptr(h + i_bh * s_h_h + i_t * K * V, (K, V), (s_h_t, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
tl.store(p_h, b_h.to(p_h.dtype.element_ty), boundary_check=(0, 1))
# [BK, BT]
b_k = tl.load(p_k, boundary_check=(0, 1))
# [BT, BV]
b_v = tl.load(p_v, boundary_check=(0, 1))
last_idx = min((i_t + 1) * BT, T) - 1
# scalar decay
if USE_G:
b_g_last = tl.load(g + i_bh * T + last_idx)
b_h *= tl.exp(b_g_last)
p_g = tl.make_block_ptr(g + i_bh * T, (T,), (1,), (i_t * BT,), (BT,), (0,))
b_g = tl.load(p_g, boundary_check=(0,))
b_v = (b_v * tl.exp(b_g_last - b_g)[:, None]).to(b_v.dtype)
# vector decay, h = Diag(gk) @ h
if USE_GK:
p_gk_last = tl.make_block_ptr(gk + i_bh * s_qk_h, (T * K,), (s_qk_d,), (last_idx * K + i_k * BK,), (BK,), (0,))
b_gk_last = tl.load(p_gk_last, boundary_check=(0,))
b_h *= tl.exp(b_gk_last)[:, None]
p_gk = tl.make_block_ptr(gk + i_bh * s_qk_h, (K, T), (s_qk_d, s_qk_t), (i_k * BK, i_t * BT), (BK, BT), (0, 1))
b_gk = tl.load(p_gk, boundary_check=(0, 1))
b_k = (b_k * tl.exp(b_gk_last[:, None] - b_gk)).to(b_k.dtype)
# vector decay, h = h @ Diag(gv)
if USE_GV:
p_gv_last = tl.make_block_ptr(gv + i_bh * s_vo_h, (T * V,), (s_vo_d,), (last_idx * V + i_v * BV,), (BV,), (0,))
b_gv_last = tl.load(p_gv, boundary_check=(0,))
b_h *= tl.exp(b_gv_last)[None, :]
p_gv = tl.make_block_ptr(gv + i_bh * s_vo_h, (T, V), (s_vo_t, s_vo_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
b_gv = tl.load(p_gv, boundary_check=(0, 1))
b_v = (b_v * tl.exp(b_gv_last[None, :] - b_gv)).to(b_v.dtype)
b_h += tl.dot(b_k, b_v, allow_tf32=False)
if STORE_FINAL_STATE:
p_ht = tl.make_block_ptr(ht + i_bh * K * V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), boundary_check=(0, 1))
@triton.autotune(
configs=[
triton.Config({}, num_warps=1),
triton.Config({}, num_warps=2),
triton.Config({}, num_warps=4),
triton.Config({}, num_warps=8),
],
key=["BT", "BK", "BV", "USE_G", 'USE_GK', 'USE_GV'],
)
@triton.jit
def chunk_bwd_kernel_dh(
q,
g,
gk,
gv,
do,
dh,
dht,
dh0,
s_qk_h,
s_qk_t,
s_qk_d,
s_vo_h,
s_vo_t,
s_vo_d,
s_h_h,
s_h_t,
scale,
T: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
BT: tl.constexpr,
BK: tl.constexpr,
BV: tl.constexpr,
NT: tl.constexpr,
USE_G: tl.constexpr,
USE_GK: tl.constexpr,
USE_GV: tl.constexpr,
STORE_INITIAL_STATE_GRADIENT: tl.constexpr,
LOAD_FINAL_STATE_GRADIENT: tl.constexpr
):
i_k, i_v, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
# [BK, BV]
b_dh = tl.zeros([BK, BV], dtype=tl.float32)
if LOAD_FINAL_STATE_GRADIENT:
p_dht = tl.make_block_ptr(dht + i_bh * K * V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
b_dh += tl.load(p_dht, boundary_check=(0, 1)).to(tl.float32)
for i_t in range(NT - 1, -1, -1):
p_dh = tl.make_block_ptr(dh + i_bh * s_h_h + i_t * K * V, (K, V), (s_h_t, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
tl.store(p_dh, b_dh.to(p_dh.dtype.element_ty), boundary_check=(0, 1))
last_idx = min(i_t * BT + BT, T) - 1
# [BK, BT]
p_q = tl.make_block_ptr(q + i_bh * s_qk_h, (K, T), (s_qk_d, s_qk_t), (i_k * BK, i_t * BT), (BK, BT), (0, 1))
b_q = tl.load(p_q, boundary_check=(0, 1))
b_q = (b_q * scale).to(b_q.dtype)
# [BT, BV]
p_do = tl.make_block_ptr(do + i_bh * s_vo_h, (T, V), (s_vo_t, s_vo_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
b_do = tl.load(p_do, boundary_check=(0, 1))
if USE_G:
p_g = tl.make_block_ptr(g + i_bh * T, (T,), (1,), (i_t * BT,), (BT,), (0,))
b_g = tl.load(p_g, boundary_check=(0,))
b_q = (b_q * tl.exp(b_g)[None, :]).to(b_q.dtype)
b_g_last = tl.load(g + i_bh * T + last_idx)
b_dh *= tl.exp(b_g_last)
if USE_GK:
p_gk = tl.make_block_ptr(gk + i_bh * s_qk_h, (K, T), (s_qk_d, s_qk_t), (i_k * BK, i_t * BT), (BK, BT), (0, 1))
b_gk = tl.load(p_gk, boundary_check=(0, 1))
b_q = (b_q * tl.exp(b_gk)).to(b_q.dtype)
p_gk_last = tl.make_block_ptr(gk + i_bh * s_qk_h, (T * K,), (s_qk_d,), (last_idx * K + i_k * BK,), (BK,), (0,))
b_gk_last = tl.load(p_gk_last, boundary_check=(0,))
b_dh *= tl.exp(b_gk_last)[:, None]
if USE_GV:
p_gv = tl.make_block_ptr(gv + i_bh * s_vo_h, (T, V), (s_vo_t, s_vo_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
b_gv = tl.load(p_gv, boundary_check=(0, 1))
b_do = (b_do * tl.exp(b_gv)).to(b_do.dtype)
p_gv_last = tl.make_block_ptr(gv + i_bh * s_vo_h, (T * V,), (s_vo_d,), (last_idx * V + i_v * BV,), (BV,), (0,))
b_gv_last = tl.load(p_gv, boundary_check=(0,))
b_dh *= tl.exp(b_gv_last)[None, :]
b_dh += tl.dot(b_q, b_do, allow_tf32=False)
if STORE_INITIAL_STATE_GRADIENT:
p_dh0 = tl.make_block_ptr(dh0 + i_bh * K * V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
tl.store(p_dh0, b_dh.to(p_dh0.dtype.element_ty), boundary_check=(0, 1))
def chunk_fwd_h_fn(k, v, g, gk, gv, BT, h0, output_final_state):
B, H, T, K, V = *k.shape, v.shape[-1]
ht = None
if output_final_state:
ht = k.new_empty(B, H, K, V, dtype=torch.float32)
BK, BV = min(64, triton.next_power_of_2(K)), min(64, triton.next_power_of_2(V))
NT, NK, NV = triton.cdiv(T, BT), triton.cdiv(K, BK), triton.cdiv(V, BV)
h = k.new_empty(B, H, NT * K, V)
grid = (NK, NV, B * H)
USE_G, USE_GK, USE_GV = g is not None, gk is not None, gv is not None
chunk_fwd_kernel_h[grid](
k, v, h, g, gk, gv, h0, ht,
k.stride(1), k.stride(2), k.stride(3),
v.stride(1), v.stride(2), v.stride(3),
h.stride(1), h.stride(2),
T=T, K=K, V=V, BT=BT, BK=BK, BV=BV, NT=NT,
USE_INITIAL_STATE=h0 is not None,
STORE_FINAL_STATE=output_final_state,
USE_G=USE_G, USE_GK=USE_GK, USE_GV=USE_GV
)
return h, ht
def chunk_bwd_dh_fn(q, k, v, g, gk, gv, do, h0, dht, BT, scale):
B, H, T, K, V = *k.shape, v.shape[-1]
BT = 64
BK = min(triton.next_power_of_2(K), 64)
BV = min(triton.next_power_of_2(V), 64)
NT, NK, NV = triton.cdiv(T, BT), triton.cdiv(K, BK), triton.cdiv(V, BV)
dh = k.new_empty(B, H, NT * K, V)
grid = (NK, NV, B * H)
if h0 is not None:
dh0 = torch.empty_like(h0, dtype=torch.float32)
else:
dh0 = None
USE_GATE = (g is not None) or (gk is not None) or (gv is not None)
assert not (USE_GATE and dht is not None), "Cannot load final state gradient and use gates at the same time"
chunk_bwd_kernel_dh[grid](
q, g, gk, gv, do, dh, dht, dh0,
q.stride(1), q.stride(2), q.stride(3),
v.stride(1), v.stride(2), v.stride(3),
dh.stride(1), dh.stride(2),
scale,
T=T, K=K, V=V, BT=BT, BK=BK, BV=BV, NT=NT,
USE_G=g is not None, USE_GK=gk is not None, USE_GV=gv is not None,
STORE_INITIAL_STATE_GRADIENT=dh0 is not None,
LOAD_FINAL_STATE_GRADIENT=dht is not None
)
return dh, dh0