ColossalAI/colossalai/kernel/triton/token_attention_kernel.py

239 lines
7.7 KiB
Python

# Adapted from ModelTC https://github.com/ModelTC/lightllm
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")
try:
from lightllm.models.llama.triton_kernel.token_attention_nopad_reduceV import token_att_fwd2 as lightllm_llama_token_att_fwd2
from lightllm.models.llama.triton_kernel.token_attention_nopad_att1 import token_att_fwd as lightllm_llama_token_att_fwd
from lightllm.models.llama.triton_kernel.token_attention_nopad_softmax import token_softmax_fwd as lightllm_llama_token_softmax_fwd
from lightllm.models.bloom.triton_kernel.token_attention_nopad_att1 import token_att_fwd as lightllm_bloom_token_att_fwd
HAS_TRITON_TOKEN_ATTENTION = True
except ImportError:
print("unable to import lightllm kernels")
HAS_TRITON_TOKEN_ATTENTION = False
if HAS_TRITON:
@torch.no_grad()
def token_attention_fwd(
q, k, v, attn_out, kv_cache_loc, kv_cache_start_loc, kv_cache_seq_len, max_len_in_batch, alibi=None
):
head_num = k.shape[1]
batch_size = kv_cache_seq_len.shape[0]
calcu_shape1 = (batch_size, head_num, k.shape[2])
total_token_num = k.shape[0]
att_m_tensor = torch.empty((head_num, total_token_num), dtype=q.dtype, device="cuda")
if alibi is None:
lightllm_llama_token_att_fwd(
q.view(calcu_shape1),
k,
att_m_tensor,
kv_cache_loc,
kv_cache_start_loc,
kv_cache_seq_len,
max_len_in_batch,
)
else:
lightllm_bloom_token_att_fwd(
q.view(calcu_shape1),
k,
att_m_tensor,
alibi,
kv_cache_loc,
kv_cache_start_loc,
kv_cache_seq_len,
max_len_in_batch,
)
prob = torch.empty_like(att_m_tensor)
lightllm_llama_token_softmax_fwd(att_m_tensor, kv_cache_start_loc, kv_cache_seq_len, prob, max_len_in_batch)
att_m_tensor = None
lightllm_llama_token_att_fwd2(
prob, v, attn_out.view(calcu_shape1), kv_cache_loc, kv_cache_start_loc, kv_cache_seq_len, max_len_in_batch
)
prob = None
return
class Llama2TokenAttentionForwards:
@staticmethod
@triton.jit
# this function is adapted from https://github.com/ModelTC/lightllm/blob/5c559dd7981ed67679a08a1e09a88fb4c1550b3a/lightllm/models/llama2/triton_kernel/token_attention_nopad_softmax.py#L8
def _fwd_kernel(
Logics,
V,
Out,
B_Loc,
B_Start_Loc,
B_Seqlen,
max_input_len,
stride_logic_h,
stride_logic_bs,
stride_vbs,
stride_vh,
stride_vd,
stride_obs,
stride_oh,
stride_od,
stride_b_loc_b,
stride_b_loc_s,
other_kv_index, # avoid nan information
kv_group_num,
BLOCK_DMODEL: tl.constexpr,
BLOCK_N: tl.constexpr,
):
cur_batch = tl.program_id(0)
cur_head = tl.program_id(1)
cur_kv_head = cur_head // kv_group_num
cur_batch_seq_len = tl.load(B_Seqlen + cur_batch)
cur_batch_start_loc = tl.load(B_Start_Loc + cur_batch)
offs_n = tl.arange(0, BLOCK_N)
offs_d = tl.arange(0, BLOCK_DMODEL)
off_v = cur_kv_head * stride_vh + offs_d[None, :] * stride_vd
off_b_loc = cur_batch * stride_b_loc_b + (max_input_len - cur_batch_seq_len) * stride_b_loc_s
v_ptrs = V + off_v
e_max = float("-inf")
e_sum = 0.0
acc = tl.zeros([BLOCK_DMODEL], dtype=tl.float32)
for start_n in range(0, cur_batch_seq_len, BLOCK_N):
start_n = tl.multiple_of(start_n, BLOCK_N)
v_index = tl.load(
B_Loc + off_b_loc + (start_n + offs_n) * stride_b_loc_s,
mask=(start_n + offs_n) < cur_batch_seq_len,
other=other_kv_index,
)
qk = tl.load(
Logics + cur_head * stride_logic_h + (cur_batch_start_loc + start_n + offs_n) * stride_logic_bs,
mask=start_n + offs_n < cur_batch_seq_len,
other=float("-inf"),
)
n_e_max = tl.maximum(tl.max(qk, 0), e_max)
old_scale = tl.exp(e_max - n_e_max)
p = tl.exp(qk - n_e_max)
e_sum = e_sum * old_scale + tl.sum(p, 0)
v = tl.load(v_ptrs + v_index[:, None] * stride_vbs)
acc = acc * old_scale + tl.sum(p[:, None] * v, 0)
e_max = n_e_max
acc = acc / e_sum
off_o = cur_batch * stride_obs + cur_head * stride_oh + offs_d * stride_od
out_ptrs = Out + off_o
tl.store(out_ptrs, acc)
return
# this function is adapted from https://github.com/ModelTC/lightllm/blob/5c559dd7981ed67679a08a1e09a88fb4c1550b3a/lightllm/models/llama2/triton_kernel/token_attention_nopad_softmax.py#L36
@staticmethod
@torch.no_grad()
def token_softmax_reducev_fwd(logics, v, o, b_loc, b_start_loc, b_seq_len, max_input_len, other_kv_index):
BLOCK = 64
batch, head = b_seq_len.shape[0], logics.shape[0]
grid = (batch, head)
kv_group_num = logics.shape[0] // v.shape[1]
num_warps = 1
Llama2TokenAttentionForwards._fwd_kernel[grid](
logics,
v,
o,
b_loc,
b_start_loc,
b_seq_len,
max_input_len,
logics.stride(0),
logics.stride(1),
v.stride(0),
v.stride(1),
v.stride(2),
o.stride(0),
o.stride(1),
o.stride(2),
b_loc.stride(0),
b_loc.stride(1),
other_kv_index,
kv_group_num,
BLOCK_DMODEL=v.shape[-1],
BLOCK_N=BLOCK,
num_warps=num_warps,
num_stages=3,
)
return
# this is the interface of llama2 attn forward
@staticmethod
@torch.no_grad()
def token_attn(
q, k, v, attn_out, kv_cache_loc, kv_cache_start_loc, kv_cache_seq_len, max_len_in_batch, other_kv_index
):
total_token_num = k.shape[0]
batch_size, head_num, head_dim = q.shape
calcu_shape1 = (batch_size, head_num, head_dim)
att_m_tensor = torch.empty((head_num, total_token_num), dtype=q.dtype, device="cuda")
lightllm_llama_token_att_fwd(
q,
k,
att_m_tensor,
kv_cache_loc,
kv_cache_start_loc,
kv_cache_seq_len,
max_len_in_batch,
)
if triton.__version__ == "2.0.0":
prob = torch.empty_like(att_m_tensor)
lightllm_llama_token_softmax_fwd(
att_m_tensor, kv_cache_start_loc, kv_cache_seq_len, prob, max_len_in_batch
)
att_m_tensor = None
lightllm_llama_token_att_fwd2(
prob,
v,
attn_out.view(calcu_shape1),
kv_cache_loc,
kv_cache_start_loc,
kv_cache_seq_len,
max_len_in_batch,
)
prob = None
return
elif triton.__version__ >= "2.1.0":
Llama2TokenAttentionForwards.token_softmax_reducev_fwd(
att_m_tensor,
v,
attn_out.view(calcu_shape1),
kv_cache_loc,
kv_cache_start_loc,
kv_cache_seq_len,
max_len_in_batch,
other_kv_index,
)
else:
raise Exception("not support triton version")