import math
import torch
try:
import triton
import triton.language as tl
HAS_TRITON = True
except ImportError:
HAS_TRITON = False
print("please install triton from https://github.com/openai/triton")
if HAS_TRITON:
"""
this function is modified from
https://github.com/ModelTC/lightllm/blob/f093edc20683ac3ea1bca3fb5d8320a0dd36cf7b/lightllm/models/llama/triton_kernel/context_flashattention_nopad.py#L10
"""
if triton.__version__ < "2.1.0":
@triton.jit
def _context_flash_attention_kernel(
Q,
K,
V,
sm_scale,
B_Start_Loc,
B_Seqlen,
TMP,
alibi_ptr,
Out,
stride_qbs,
stride_qh,
stride_qd,
stride_kbs,
stride_kh,
stride_kd,
stride_vbs,
stride_vh,
stride_vd,
stride_obs,
stride_oh,
stride_od,
stride_tmp_b,
stride_tmp_h,
stride_tmp_s,
# suggtest set-up 64, 128, 256, 512
BLOCK_M: tl.constexpr,
BLOCK_DMODEL: tl.constexpr,
BLOCK_N: tl.constexpr,
):
batch_id = tl.program_id(0)
cur_head = tl.program_id(1)
start_m = tl.program_id(2)
# initialize offsets
offs_n = tl.arange(0, BLOCK_N)
offs_d = tl.arange(0, BLOCK_DMODEL)
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
# get batch info
cur_batch_seq_len = tl.load(B_Seqlen + batch_id)
cur_batch_start_index = tl.load(B_Start_Loc + batch_id)
block_start_loc = BLOCK_M * start_m
load_p_ptrs = (
Q
+ (cur_batch_start_index + offs_m[:, None]) * stride_qbs
+ cur_head * stride_qh
+ offs_d[None, :] * stride_qd
)
q = tl.load(load_p_ptrs, mask=offs_m[:, None] < cur_batch_seq_len, other=0.0)
k_ptrs = K + offs_n[None, :] * stride_kbs + cur_head * stride_kh + offs_d[:, None] * stride_kd
v_ptrs = V + offs_n[:, None] * stride_vbs + cur_head * stride_vh + offs_d[None, :] * stride_vd
t_ptrs = TMP + batch_id * stride_tmp_b + cur_head * stride_tmp_h + offs_m * stride_tmp_s
m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
if alibi_ptr is not None:
alibi_m = tl.load(alibi_ptr + cur_head)
block_mask = tl.where(block_start_loc < cur_batch_seq_len, 1, 0)
for start_n in range(0, block_mask * (start_m + 1) * BLOCK_M, BLOCK_N):
start_n = tl.multiple_of(start_n, BLOCK_N)
k = tl.load(
k_ptrs + (cur_batch_start_index + start_n) * stride_kbs,
mask=(start_n + offs_n[None, :]) < cur_batch_seq_len,
other=0.0,
)
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
qk += tl.dot(q, k)
qk *= sm_scale
if alibi_ptr is not None:
alibi_loc = offs_m[:, None] - (start_n + offs_n[None, :])
qk -= alibi_loc * alibi_m
qk = tl.where(offs_m[:, None] >= (start_n + offs_n[None, :]), qk, float("-inf"))
m_ij = tl.max(qk, 1)
p = tl.exp(qk - m_ij[:, None])
l_ij = tl.sum(p, 1)
# -- update m_i and l_i
m_i_new = tl.maximum(m_i, m_ij)
alpha = tl.exp(m_i - m_i_new)
beta = tl.exp(m_ij - m_i_new)
l_i_new = alpha * l_i + beta * l_ij
# -- update output accumulator --
# scale p
p_scale = beta / l_i_new
p = p * p_scale[:, None]
# scale acc
acc_scale = l_i / l_i_new * alpha
tl.store(t_ptrs, acc_scale)
acc_scale = tl.load(t_ptrs)
acc = acc * acc_scale[:, None]
# update acc
v = tl.load(
v_ptrs + (cur_batch_start_index + start_n) * stride_vbs,
mask=(start_n + offs_n[:, None]) < cur_batch_seq_len,
other=0.0,
)
p = p.to(v.dtype)
acc += tl.dot(p, v)
# update m_i and l_i
l_i = l_i_new
m_i = m_i_new
off_o = (
(cur_batch_start_index + offs_m[:, None]) * stride_obs + cur_head * stride_oh + offs_d[None, :] * stride_od
)
out_ptrs = Out + off_o
tl.store(out_ptrs, acc, mask=offs_m[:, None] < cur_batch_seq_len)
return
else:
# this function is modified from https://github.com/ModelTC/lightllm/blob/main/lightllm/models/llama/triton_kernel/context_flashattention_nopad.py#L11
@triton.jit
def _context_flash_attention_kernel_2(
Q, K, V, sm_scale, Alibi, B_Start_Loc, B_Seqlen,
Out,
kv_group_num,
stride_qbs, stride_qh, stride_qd,
stride_kbs, stride_kh, stride_kd,
stride_vbs, stride_vh, stride_vd,
stride_obs, stride_oh, stride_od,
BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr,
BLOCK_N: tl.constexpr,
):
cur_batch = tl.program_id(0)
cur_head = tl.program_id(1)
start_m = tl.program_id(2)
if kv_group_num is not None:
cur_kv_head = cur_head // kv_group_num
cur_batch_seq_len = tl.load(B_Seqlen + cur_batch)
cur_batch_in_all_start_index = tl.load(B_Start_Loc + cur_batch)
block_start_loc = BLOCK_M * start_m
# initialize offsets
offs_n = tl.arange(0, BLOCK_N)
offs_d = tl.arange(0, BLOCK_DMODEL)
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
off_q = (cur_batch_in_all_start_index + offs_m[:, None]) * stride_qbs + cur_head * stride_qh + offs_d[None, :] * stride_qd
if kv_group_num is None or kv_group_num == 1:
off_k = offs_n[None, :] * stride_kbs + cur_head * stride_kh + offs_d[:, None] * stride_kd
off_v = offs_n[:, None] * stride_vbs + cur_head * stride_vh + offs_d[None, :] * stride_vd
else:
off_k = offs_n[None, :] * stride_kbs + cur_kv_head * stride_kh + offs_d[:, None] * stride_kd
off_v = offs_n[:, None] * stride_vbs + cur_kv_head * stride_vh + offs_d[None, :] * stride_vd
q = tl.load(Q + off_q, mask=offs_m[:, None] < cur_batch_seq_len, other=0.0)
k_ptrs = K + off_k
v_ptrs = V + off_v
m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
if Alibi is not None:
alibi_m = tl.load(Alibi + cur_head)
block_mask = tl.where(block_start_loc < cur_batch_seq_len, 1, 0)
for start_n in range(0, block_mask * (start_m + 1) * BLOCK_M, BLOCK_N):
start_n = tl.multiple_of(start_n, BLOCK_N)
# -- compute qk ----
k = tl.load(k_ptrs + (cur_batch_in_all_start_index + start_n) * stride_kbs,
mask=(start_n + offs_n[None, :]) < cur_batch_seq_len, other=0.0)
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
qk += tl.dot(q, k)
qk *= sm_scale
if Alibi is not None:
alibi_loc = offs_m[:, None] - (start_n + offs_n[None, :])
qk -= alibi_loc * alibi_m
qk = tl.where(offs_m[:, None] >= (start_n + offs_n[None, :]), qk, float("-inf"))
m_ij = tl.max(qk, 1)
p = tl.exp(qk - m_ij[:, None])
l_ij = tl.sum(p, 1)
# -- update m_i and l_i
m_i_new = tl.maximum(m_i, m_ij)
alpha = tl.exp(m_i - m_i_new)
beta = tl.exp(m_ij - m_i_new)
l_i_new = alpha * l_i + beta * l_ij
# -- update output accumulator --
# scale p
p_scale = beta / l_i_new
p = p * p_scale[:, None]
# scale acc
acc_scale = l_i / l_i_new * alpha
acc = acc * acc_scale[:, None]
# update acc
v = tl.load(v_ptrs + (cur_batch_in_all_start_index + start_n) * stride_vbs,
mask=(start_n + offs_n[:, None]) < cur_batch_seq_len, other=0.0)
p = p.to(v.dtype)
acc += tl.dot(p, v)
# update m_i and l_i
l_i = l_i_new
m_i = m_i_new
# initialize pointers to output
off_o = (cur_batch_in_all_start_index + offs_m[:, None]) * stride_obs + cur_head * stride_oh + offs_d[None, :] * stride_od
out_ptrs = Out + off_o
tl.store(out_ptrs, acc, mask=offs_m[:, None] < cur_batch_seq_len)
return
@torch.no_grad()
def bloom_context_attn_fwd(q, k, v, o, b_start_loc, b_seq_len, max_input_len, alibi=None):
BLOCK = 128
# shape constraints
Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1]
assert Lq == Lk, "context process only supports equal query, key, value length"
assert Lk == Lv, "context process only supports equal query, key, value length"
assert Lk in {16, 32, 64, 128}
sm_scale = 1.0 / math.sqrt(Lk)
batch, head = b_seq_len.shape[0], q.shape[1]
grid = (batch, head, triton.cdiv(max_input_len, BLOCK))
num_warps = 4 if Lk <= 64 else 8
if triton.__version__ < "2.1.0":
tmp = torch.empty((batch, head, max_input_len + 256), device=q.device, dtype=torch.float32)
_context_flash_attention_kernel[grid](
q,
k,
v,
sm_scale,
b_start_loc,
b_seq_len,
tmp,
alibi,
o,
q.stride(0),
q.stride(1),
q.stride(2),
k.stride(0),
k.stride(1),
k.stride(2),
v.stride(0),
v.stride(1),
v.stride(2),
o.stride(0),
o.stride(1),
o.stride(2),
tmp.stride(0),
tmp.stride(1),
tmp.stride(2),
# manually setting this blcok num, we can use tuning config to futher speed-up
BLOCK_M=BLOCK,
BLOCK_DMODEL=Lk,
BLOCK_N=BLOCK,
num_warps=num_warps,
num_stages=1,
)
else:
_context_flash_attention_kernel_2[grid](
q, k, v, sm_scale, alibi, b_start_loc, b_seq_len,
o,
None,
q.stride(0),
q.stride(1),
q.stride(2),
k.stride(0),
k.stride(1),
k.stride(2),
v.stride(0),
v.stride(1),
v.stride(2),
o.stride(0),
o.stride(1),
o.stride(2),
BLOCK_M=BLOCK,
BLOCK_DMODEL=Lk,
BLOCK_N=BLOCK,
num_warps=num_warps,
num_stages=1,
)
return
@torch.no_grad()
def llama_context_attn_fwd(q, k, v, o, b_start_loc, b_seq_len, max_input_len):
BLOCK = 128
# shape constraints
Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1]
assert Lq == Lk, "context process only supports equal query, key, value length"
assert Lk == Lv, "context process only supports equal query, key, value length"
assert Lk in {16, 32, 64, 128}
sm_scale = 1.0 / math.sqrt(Lk)
batch, head = b_seq_len.shape[0], q.shape[1]
grid = (batch, head, triton.cdiv(max_input_len, BLOCK))
tmp = torch.empty((batch, head, max_input_len + 256), device=q.device, dtype=torch.float32)
num_warps = 4 if Lk <= 64 else 8
# num_warps = 4
if triton.__version__ < "2.1.0":
_context_flash_attention_kernel[grid](
q,
k,
v,
sm_scale,
b_start_loc,
b_seq_len,
tmp,
None,
o,
q.stride(0),
q.stride(1),
q.stride(2),
k.stride(0),
k.stride(1),
k.stride(2),
v.stride(0),
v.stride(1),
v.stride(2),
o.stride(0),
o.stride(1),
o.stride(2),
tmp.stride(0),
tmp.stride(1),
tmp.stride(2),
BLOCK_M=BLOCK,
BLOCK_DMODEL=Lk,
BLOCK_N=BLOCK,
num_warps=num_warps,
num_stages=1,
)
else:
kv_group_num = q.shape[1] // k.shape[1]
_context_flash_attention_kernel_2[grid](
q,
k,
v,
sm_scale,
None,
b_start_loc,
b_seq_len,
o,
kv_group_num,
q.stride(0),
q.stride(1),
q.stride(2),
k.stride(0),
k.stride(1),
k.stride(2),
v.stride(0),
v.stride(1),
v.stride(2),
o.stride(0),
o.stride(1),
o.stride(2),
BLOCK_M=BLOCK,
BLOCK_DMODEL=Lk,
BLOCK_N=BLOCK,
num_warps=num_warps,
num_stages=1,)
return