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import torch
import math
import triton
import triton.language as tl
# We don't run auto-tuning every time to keep the tutorial fast. Uncommenting
# the code below and commenting out the equivalent parameters is convenient for
# re-tuning.
#@triton.autotune(
# configs=[
# triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64}, num_stages=4, num_warps=8),
# triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64}, num_stages=3, num_warps=8),
# triton.Config({'BLOCK_M': 256, 'BLOCK_N': 32}, num_stages=3, num_warps=8),
# triton.Config({'BLOCK_M': 256, 'BLOCK_N': 32}, num_stages=3, num_warps=4),
# triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32}, num_stages=3, num_warps=4),
# triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32}, num_stages=4, num_warps=4),
# triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64}, num_stages=3, num_warps=4),
# triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64}, num_stages=4, num_warps=4),
# triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64}, num_stages=3, num_warps=8),
# triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64}, num_stages=7, num_warps=8),
# triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32}, num_stages=7, num_warps=8),
# triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32}, num_stages=6, num_warps=8),
# triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32}, num_stages=5, num_warps=8),
# triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32}, num_stages=4, num_warps=8),
# triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64}, num_stages=6, num_warps=4),
# ],
# key=['N_CTX'],
#)
@triton.jit
def _attn_fwd_prefill(Q1, K1, Q2, K2, V, sm_scale, M, Out, #
stride_qz, stride_qh, stride_qm, stride_qk, #
stride_kz, stride_kh, stride_kn, stride_kk, #
stride_vz, stride_vh, stride_vk, stride_vn, #
stride_oz, stride_oh, stride_om, stride_on, #
Z, H, #
Q_CTX: tl.constexpr, #
N_CTX: tl.constexpr, #
WINDOW: tl.constexpr, #
BLOCK_M: tl.constexpr, #
BLOCK_DMODEL: tl.constexpr, #
BLOCK_N: tl.constexpr, #
):
start_m = tl.program_id(0)
off_hz = tl.program_id(1)
off_z = off_hz // H
off_h = off_hz % H
qvk_offset = off_z.to(tl.int64) * stride_qz + off_h.to(tl.int64) * stride_qh
# block pointers
Q1_block_ptr = tl.make_block_ptr(
base=Q1 + qvk_offset,
shape=(Q_CTX, BLOCK_DMODEL),
strides=(stride_qm, stride_qk),
offsets=(start_m * BLOCK_M, 0),
block_shape=(BLOCK_M, BLOCK_DMODEL),
order=(1, 0),
)
Q2_block_ptr = tl.make_block_ptr(
base=Q2 + qvk_offset,
shape=(Q_CTX, BLOCK_DMODEL),
strides=(stride_qm, stride_qk),
offsets=(start_m * BLOCK_M, 0),
block_shape=(BLOCK_M, BLOCK_DMODEL),
order=(1, 0),
)
V_block_ptr = tl.make_block_ptr(
base=V + qvk_offset,
shape=(N_CTX, BLOCK_DMODEL),
strides=(stride_vk, stride_vn),
offsets=(0, 0),
block_shape=(BLOCK_N, BLOCK_DMODEL),
order=(1, 0),
)
K1_block_ptr = tl.make_block_ptr(
base=K1 + qvk_offset,
shape=(BLOCK_DMODEL, N_CTX),
strides=(stride_kk, stride_kn),
offsets=(0, 0),
block_shape=(BLOCK_DMODEL, BLOCK_N),
order=(0, 1),
)
K2_block_ptr = tl.make_block_ptr(
base=K2 + qvk_offset,
shape=(BLOCK_DMODEL, N_CTX),
strides=(stride_kk, stride_kn),
offsets=(0, 0),
block_shape=(BLOCK_DMODEL, BLOCK_N),
order=(0, 1),
)
O_block_ptr = tl.make_block_ptr(
base=Out + qvk_offset,
shape=(Q_CTX, BLOCK_DMODEL),
strides=(stride_om, stride_on),
offsets=(start_m * BLOCK_M, 0),
block_shape=(BLOCK_M, BLOCK_DMODEL),
order=(1, 0),
)
# initialize offsets
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
offs_n = tl.arange(0, BLOCK_N)
# initialize pointer to m and l
m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
l_i = tl.zeros([BLOCK_M], dtype=tl.float32) + 1.0
acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
# load scales
qk_scale = sm_scale
qk_scale *= 1.442695040888963#1.44269504 # 1/log(2)
# load q: it will stay in SRAM throughout
#q = tl.load(Q_block_ptr)
if start_m * BLOCK_M + BLOCK_M > Q_CTX:
q1 = tl.load(Q1_block_ptr, boundary_check=(0,), padding_option='zero')
q2 = tl.load(Q2_block_ptr, boundary_check=(0,), padding_option='zero')
else:
q1 = tl.load(Q1_block_ptr)
q2 = tl.load(Q2_block_ptr)
#q1 = (q1 * qk_scale).to(tl.float16)
#q2 = (q2 * qk_scale).to(tl.float16)
lo = 0
hi = (start_m + 1) * BLOCK_M
# loop over k, v and update accumulator
for start_n in range(lo, hi, BLOCK_N):
start_n = tl.multiple_of(start_n, BLOCK_N)
#qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) #?
#qk = qk.to(tl.float16)
# if use condition, qk has to be float32, then convert to float16...
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
if start_n + BLOCK_N - 1 > start_m * BLOCK_M - 1:
qk += tl.where(offs_m[:, None] >= (start_n + offs_n[None, :]), 0, -1.0e6)#float("-inf"))
#qk = tl.where(offs_m[:, None] >= (start_n + offs_n[None, :]), qk, float("-inf"))
# -- compute qk ----
#k = tl.load(K_block_ptr)
# case 1: only need group attention: q2, k2
if BLOCK_N + start_n <= (start_m * BLOCK_M - WINDOW + 1):
if BLOCK_N + start_n >= N_CTX:
k2 = tl.load(K2_block_ptr, boundary_check=(1,), padding_option='zero')
v = tl.load(V_block_ptr, boundary_check=(0,), padding_option='zero')
else:
k2 = tl.load(K2_block_ptr)
v = tl.load(V_block_ptr)
#qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
#qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float16)
qk += tl.dot(q2, k2)#, out_dtype=tl.float16)
else:
#case 2: only need neighbor attention: q1, k1
if start_n >= (start_m+1) * BLOCK_M - WINDOW:
if BLOCK_N + start_n >= N_CTX:
k1 = tl.load(K1_block_ptr, boundary_check=(1,), padding_option='zero')
v = tl.load(V_block_ptr, boundary_check=(0,), padding_option='zero')
else:
k1 = tl.load(K1_block_ptr)
v = tl.load(V_block_ptr)
#qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
#qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float16)
qk += tl.dot(q1, k1)#, out_dtype=tl.float16)
else:
#case 3: need both q1, k1 and q2, k2
if BLOCK_N + start_n >= N_CTX:
k1 = tl.load(K1_block_ptr, boundary_check=(1,), padding_option='zero')
k2 = tl.load(K2_block_ptr, boundary_check=(1,), padding_option='zero')
v = tl.load(V_block_ptr, boundary_check=(0,), padding_option='zero')
else:
k1 = tl.load(K1_block_ptr)
k2 = tl.load(K2_block_ptr)
v = tl.load(V_block_ptr)
#qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
#qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float16)
qk1 = tl.dot(q1, k1)#, out_dtype=tl.float16)
qk2 = tl.dot(q2, k2)#, out_dtype=tl.float16)
#merge_mask = tl.abs((offs_m[:, None] - (start_n + offs_n[None, :]))) >= WINDOW
#qk += tl.where(merge_mask, qk2, qk1)
qk += tl.where(tl.abs(offs_m[:, None] - (start_n + offs_n[None, :])) < WINDOW, qk1, qk2)
qk *= qk_scale
m_ij = tl.maximum(m_i, tl.max(qk, 1))
qk = qk - m_ij[:, None]
p = tl.math.exp2(qk)
l_ij = tl.sum(p, 1)
# -- update m_i and l_i
alpha = tl.math.exp2(m_i - m_ij)
l_i = l_i * alpha + l_ij
# -- update output accumulator --
acc = acc * alpha[:, None]
# update acc
#v = tl.load(V_block_ptr)
#v = tl.load(V_block_ptr, boundary_check=(0,), padding_option='zero')
acc += tl.dot(p.to(tl.float16), v)
# update m_i and l_i
m_i = m_ij
V_block_ptr = tl.advance(V_block_ptr, (BLOCK_N, 0))
K1_block_ptr = tl.advance(K1_block_ptr, (0, BLOCK_N))
K2_block_ptr = tl.advance(K2_block_ptr, (0, BLOCK_N))
# epilogue
m_i += tl.math.log2(l_i)
acc = acc / l_i[:, None]
m_ptrs = M + off_hz * Q_CTX + offs_m
if start_m * BLOCK_M + BLOCK_M >= Q_CTX:
tl.store(m_ptrs, m_i, mask=offs_m < Q_CTX)
tl.store(O_block_ptr, acc.to(Out.type.element_ty), boundary_check=(0,))
else:
tl.store(m_ptrs, m_i)
tl.store(O_block_ptr, acc.to(Out.type.element_ty))
def prefill_flash_forward(q1, k1, q2, k2, v, q_len, seq_len, window, sm_scale=None):
# shape constraints
Lq, Lk, Lv = q1.shape[-1], k1.shape[-1], v.shape[-1]
assert Lq == Lk and Lk == Lv
assert Lk in {16, 32, 64, 128}
assert q_len == seq_len or q_len == 1
if sm_scale is None:
sm_scale = 1.0 / math.sqrt(Lq) # the default scale factor.
o = torch.empty_like(q1, device=q1.device)
block_m = 128
block_n = 64 # if Lk <= 64 else 32
num_stages = 4 if Lk <= 64 else 3
num_warps = 4
# Tuning for H100
if torch.cuda.get_device_capability()[0] == 9:
num_warps = 8
num_stages = 7 if Lk >= 64 else 3
grid = (triton.cdiv(q1.shape[2], block_m), q1.shape[0] * q1.shape[1], 1)
M = torch.empty((q1.shape[0], q1.shape[1], q1.shape[2]), device=q1.device, dtype=torch.float32)
with torch.cuda.device(v.device.index):
# https://github.com/Dao-AILab/flash-attention/commit/9795159082f6e6c847db2bf4284fd17326c31fbd
# to avoid the device issue .
_attn_fwd_prefill[grid](
q1, k1, q2, k2, v, sm_scale, M, o, #
q1.stride(0), q1.stride(1), q1.stride(2), q1.stride(3), #
k1.stride(0), k1.stride(1), k1.stride(2), k1.stride(3), #
v.stride(0), v.stride(1), v.stride(2), v.stride(3), #
o.stride(0), o.stride(1), o.stride(2), o.stride(3), #
q1.shape[0], q1.shape[1], #
Q_CTX=q_len,
N_CTX=seq_len, #
BLOCK_M=block_m, #
BLOCK_N=block_n, #
WINDOW=window,
BLOCK_DMODEL=Lk, #
num_warps=num_warps, #
num_stages=num_stages #
)
return o
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