from typing import Optional, Tuple
import torch
import triton
import triton.language as tl
from .utils import prepare_chunk_indices, exp
@triton.heuristics({
'IS_VARLEN': lambda args: args['cu_seqlens'] is not None
})
@triton.jit(do_not_specialize=['T'])
def prepare_wy_repr_bwd_kernel(
k,
v,
beta,
g,
A,
dw,
du,
dk,
dv,
dbeta,
dg,
cu_seqlens,
chunk_indices,
T,
B,
H: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
NT: tl.constexpr,
BT: tl.constexpr,
BK: tl.constexpr,
BV: tl.constexpr,
IS_VARLEN: tl.constexpr
):
core_id = tl.program_id(0)
total_cores = tl.num_programs(0)
T_max = T
base_chunks_per_pid = NT // total_cores
remainder_chunks = NT % total_cores
if core_id < remainder_chunks:
chunks_this_pid = base_chunks_per_pid + 1
start_idx = core_id * chunks_this_pid
else:
chunks_this_pid = base_chunks_per_pid
start_idx = core_id * chunks_this_pid + remainder_chunks
for idx in range(start_idx, start_idx + chunks_this_pid):
for i_b in range(B):
if IS_VARLEN:
i_n, i_t = tl.load(chunk_indices + idx * 2).to(tl.int32), tl.load(chunk_indices + idx * 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:
i_t = idx
bos, eos = i_b * T, i_b * T + T
o_t = i_t * BT + tl.arange(0, BT)
m_t = o_t < T
m_A = (o_t[:, None] > o_t[None, :]) & (m_t[:, None] & m_t)
for i_h in range(0, H):
if IS_VARLEN:
offset = bos + i_h * T_max
else:
offset = bos * H + i_h * T_max
p_beta = tl.make_block_ptr(beta + offset, (T,), (1,), (i_t * BT,), (BT,), (0,))
p_g = tl.make_block_ptr(g + offset, (T,), (1,), (i_t * BT,), (BT,), (0,))
p_A = tl.make_block_ptr(A + (bos * H + i_h) * BT, (BT, T), (1, H * BT), (0, i_t * BT), (BT, BT), (0, 1))
b_A = tl.load(p_A, boundary_check=(0, 1))
b_beta = tl.load(p_beta, boundary_check=(0,))
b_g = tl.load(p_g, boundary_check=(0,))
b_g_exp = tl.exp(b_g)
b_dbeta = tl.zeros([BT], dtype=tl.float32)
b_dA = tl.zeros([BT, BT], dtype=tl.float32)
b_dg = tl.zeros([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))
p_dk = tl.make_block_ptr(dk + (bos * H + i_h) * K, (T, K), (H * K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0))
p_dw = tl.make_block_ptr(dw + (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_k_beta_g = (b_k * b_beta[:, None] * b_g_exp[:, None]).to(b_k.dtype)
b_dw = tl.load(p_dw, boundary_check=(0, 1))
b_dA += tl.dot(b_dw, tl.trans(b_k_beta_g))
b_dk_beta_g = tl.dot(b_A, b_dw)
b_dk = b_dk_beta_g * b_beta[:, None] * b_g_exp[:, None]
b_dbeta += tl.sum(b_dk_beta_g * b_k * b_g_exp[:, None], 1)
b_dg += tl.sum(b_dk_beta_g * b_k * b_g_exp[:, None] * b_beta[:, None], 1)
tl.store(p_dk, b_dk.to(p_dk.dtype.element_ty), 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_dv = tl.make_block_ptr(dv + (bos * H + i_h) * V, (T, V), (H * V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
p_du = tl.make_block_ptr(du + (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_v_beta = (b_v * b_beta[:, None]).to(b_v.dtype)
b_du = tl.load(p_du, boundary_check=(0, 1))
b_dA += tl.dot(b_du, tl.trans(b_v_beta))
b_dv_beta = tl.dot(b_A, b_du)
b_dv = b_dv_beta * b_beta[:, None]
b_dbeta += tl.sum(b_dv_beta * b_v, 1)
tl.store(p_dv, b_dv.to(p_dv.dtype.element_ty), boundary_check=(0, 1))
b_dA = tl.where(m_A, b_dA, 0)
b_dA = tl.dot(b_dA.to(b_A.dtype), b_A)
b_dA = tl.dot(b_A, b_dA.to(b_A.dtype))
b_dA = tl.where(m_A, -b_dA * exp(b_g[:, None] - b_g[None, :]), 0)
b_dA = b_dA.to(k.dtype.element_ty)
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))
p_dk = tl.make_block_ptr(dk + (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_dk = tl.load(p_dk, boundary_check=(0, 1))
b_k_beta = (b_k * b_beta[:, None]).to(b_k.dtype)
b_A += tl.dot(b_k_beta, tl.trans(b_k))
b_dk_beta = tl.dot(b_dA, b_k)
b_dbeta += tl.sum(b_dk_beta * b_k, 1)
b_dk += tl.dot(tl.trans(b_dA), b_k_beta)
b_dk += b_dk_beta * b_beta[:, None]
tl.store(p_dk, b_dk.to(p_dk.dtype.element_ty), boundary_check=(0, 1))
b_dA_A = b_dA * b_A
b_dg += tl.sum(b_dA_A, axis=1) - tl.sum(b_dA_A, axis=0)
p_dg = tl.make_block_ptr(dg + offset, (T,), (1,), (i_t * BT,), (BT,), (0,))
p_dbeta = tl.make_block_ptr(dbeta + offset, (T,), (1,), (i_t * BT,), (BT,), (0,))
tl.store(p_dg, b_dg.to(p_dg.dtype.element_ty), boundary_check=(0,))
tl.store(p_dbeta, b_dbeta.to(p_dbeta.dtype.element_ty), boundary_check=(0,))
@triton.heuristics({
'USE_G': lambda args: args['g'] is not None,
'USE_GK': lambda args: args['gk'] is not None,
'IS_VARLEN': lambda args: args['cu_seqlens'] is not None
})
@triton.jit(do_not_specialize=['T'])
def recompute_w_u_fwd_kernel(
k,
v,
beta,
w,
u,
A,
g,
gk,
cu_seqlens,
chunk_indices,
T_tmp,
B,
H: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
NT: tl.constexpr,
BT: tl.constexpr,
BK: tl.constexpr,
BV: tl.constexpr,
USE_G: tl.constexpr,
USE_GK: tl.constexpr,
IS_VARLEN: tl.constexpr
):
core_id = tl.program_id(0)
total_cores = tl.num_programs(0)
T_max = T_tmp
base_chunks_per_pid = NT // total_cores
remainder_chunks = NT % total_cores
if core_id < remainder_chunks:
chunks_this_pid = base_chunks_per_pid + 1
start_idx = core_id * chunks_this_pid
else:
chunks_this_pid = base_chunks_per_pid
start_idx = core_id * chunks_this_pid + remainder_chunks
for idx in range(start_idx, start_idx + chunks_this_pid):
for i_b in range(B):
for i_h in range(0, H):
if IS_VARLEN:
i_n, i_t = tl.load(chunk_indices + idx * 2).to(tl.int32), tl.load(chunk_indices + idx * 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)
offset = bos + i_h * T_max
T = eos - bos
else:
T = T_tmp
i_t = idx
bos, eos = i_b * T, i_b * T + T
offset = bos * H + i_h * T_max
p_beta = tl.make_block_ptr(beta + offset, (T,), (1,), (i_t * BT,), (BT,), (0,))
b_beta = tl.load(p_beta, 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_beta[:, None]).to(b_v.dtype)
b_u = tl.dot(b_A, b_vb, allow_tf32=False)
tl.store(p_u, b_u.to(p_u.dtype.element_ty), boundary_check=(0, 1))
if USE_G:
p_g = tl.make_block_ptr(g + offset, (T,), (1,), (i_t * BT,), (BT,), (0,))
b_g = tl.exp(tl.load(p_g, boundary_check=(0,)))
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))
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))
b_k = tl.load(p_k, boundary_check=(0, 1))
b_kb = b_k * b_beta[:, None]
if USE_G:
b_kb *= b_g[:, None]
if USE_GK:
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_kb *= tl.exp(tl.load(p_gk, 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))
def recompute_w_u_fwd(
k: torch.Tensor,
v: torch.Tensor,
beta: torch.Tensor,
A: torch.Tensor,
g: Optional[torch.Tensor] = None,
gk: Optional[torch.Tensor] = None,
cu_seqlens: Optional[torch.LongTensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
B, T, H, K, V = *k.shape, v.shape[-1]
BT = A.shape[-1]
BK = 128
BV = 128
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)
g = g.transpose(1, 2).contiguous() if g is not None else None
beta = beta.transpose(1, 2).contiguous()
w = torch.empty_like(k)
u = torch.empty_like(v)
cv_kernel_num = 24
recompute_w_u_fwd_kernel[(cv_kernel_num,)](
k=k,
v=v,
beta=beta,
w=w,
u=u,
A=A,
g=g,
gk=gk,
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
T_tmp=T,
B=B,
H=H,
K=K,
V=V,
NT=NT,
BT=BT,
BK=BK,
BV=BV,
)
return w, u
def prepare_wy_repr_bwd(
k: torch.Tensor,
v: torch.Tensor,
g: torch.Tensor,
beta: torch.Tensor,
A: torch.Tensor,
dw: torch.Tensor,
du: torch.Tensor,
cu_seqlens: Optional[torch.LongTensor],
chunk_size: int = 64,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
B, T, H, K, V = *k.shape, v.shape[-1]
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)
BK = 128
BV = 128
beta = beta.transpose(1, 2).contiguous()
g = g.transpose(1, 2).contiguous()
dk = torch.empty_like(k)
dv = torch.empty_like(v)
dbeta = torch.empty_like(beta)
dg = torch.empty_like(g)
cv_kernel_num = 24
prepare_wy_repr_bwd_kernel[(cv_kernel_num,)](
k=k,
v=v,
beta=beta,
g=g,
A=A,
dw=dw,
du=du,
dk=dk,
dv=dv,
dbeta=dbeta,
dg=dg,
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
T=T,
B=B,
H=H,
K=K,
V=V,
NT=NT,
BT=BT,
BK=BK,
BV=BV,
)
dbeta = dbeta.transpose(1, 2).contiguous()
dg = dg.transpose(1, 2).contiguous()
return dk, dv, dbeta, dg
bwd_prepare_wy_repr = prepare_wy_repr_bwd
fwd_recompute_w_u = recompute_w_u_fwd
