import numpy as np
import pycuda.autoprimaryctx
import torch
import triton
import triton.language as tl
from flash_attn import flash_attn_varlen_func
from pycuda.compiler import SourceModule
@triton.autotune(
configs=[
triton.Config({}, num_stages=1, num_warps=4),
triton.Config({}, num_stages=1, num_warps=8),
triton.Config({}, num_stages=2, num_warps=4),
triton.Config({}, num_stages=2, num_warps=8),
triton.Config({}, num_stages=3, num_warps=4),
triton.Config({}, num_stages=3, num_warps=8),
triton.Config({}, num_stages=4, num_warps=4),
triton.Config({}, num_stages=4, num_warps=8),
triton.Config({}, num_stages=5, num_warps=4),
triton.Config({}, num_stages=5, num_warps=8),
],
key=['N_CTX'],
)
@triton.jit
def triton_sparse_fwd_kernel(
Q, K, V, seqlens, sm_scale,
col_count, col_index,
Out,
stride_qz, stride_qh, stride_qm, stride_qk,
stride_kz, stride_kh, stride_kn, stride_kk,
stride_vz, stride_vh, stride_vn, stride_vk,
stride_oz, stride_oh, stride_om, stride_ok,
Z, H, N_CTX,
NUM_ROWS, MAX_COLS_PRE_ROW,
BLOCK_M: tl.constexpr,
BLOCK_N: tl.constexpr,
BLOCK_DMODEL: tl.constexpr,
dtype: tl.constexpr,
):
start_m = tl.program_id(0)
off_hz = tl.program_id(1)
seqlen = tl.load(seqlens + off_hz // H)
if start_m * BLOCK_M >= seqlen:
return
# initialize offsets
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
offs_n = tl.arange(0, BLOCK_N)
offs_d = tl.arange(0, BLOCK_DMODEL)
qo_offset = (off_hz // H) * stride_qz + (off_hz % H) * stride_qh
kv_offset = (off_hz // H) * stride_kz + (off_hz % H) * stride_kh
q_ptrs = Q + qo_offset + offs_m[:, None] * stride_qm + offs_d[None, :] * stride_qk
k_ptrs = K + kv_offset + offs_d[:, None] * stride_kk
v_ptrs = V + kv_offset + offs_d[None, :] * stride_vk
o_ptrs = Out + qo_offset + offs_m[:, None] * stride_om + offs_d[None, :] * stride_ok
num_cols = tl.load(col_count + off_hz * NUM_ROWS + start_m)
cols_ptr = col_index + (off_hz * NUM_ROWS + start_m) * MAX_COLS_PRE_ROW
# 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)
acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
# scale sm_scale by log_2(e) and use
# 2^x instead of exp in the loop because CSE and LICM
# don't work as expected with `exp` in the loop
qk_scale = sm_scale * 1.44269504
# load q: it will stay in SRAM throughout
q = tl.load(q_ptrs)
q = (q * qk_scale).to(dtype)
# loop over k, v and update accumulator
m_mask = offs_m[:, None] < seqlen
split = tl.maximum(num_cols - BLOCK_N, 0) & ~(BLOCK_N - 1)
for start_n in range(0, split, BLOCK_N):
cols = tl.load(cols_ptr + start_n + offs_n)
# -- load k, v --
k = tl.load(k_ptrs + cols[None, :] * stride_kn)
v = tl.load(v_ptrs + cols[:, None] * stride_vn)
# -- compute qk --
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
qk = tl.where(m_mask, qk, float("-inf"))
qk += tl.dot(q, k)
# -- compute scaling constant --
m_i_new = tl.maximum(m_i, tl.max(qk, 1))
alpha = tl.math.exp2(m_i - m_i_new)
p = tl.math.exp2(qk - m_i_new[:, None])
# -- scale and update acc --
acc_scale = l_i * 0 + alpha # workaround some compiler bug
acc *= acc_scale[:, None]
acc += tl.dot(p.to(dtype), v)
# -- update m_i and l_i --
l_i = l_i * alpha + tl.sum(p, 1)
m_i = m_i_new
for start_n in range(split, num_cols, BLOCK_N):
n_mask = start_n + offs_n < num_cols
cols = tl.load(cols_ptr + start_n + offs_n, mask=n_mask, other=N_CTX - 1)
causal_mask = cols[None, :] <= offs_m[:, None]
# -- load k, v --
k = tl.load(k_ptrs + cols[None, :] * stride_kn)
v = tl.load(v_ptrs + cols[:, None] * stride_vn)
# -- compute qk --
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
qk = tl.where(m_mask & causal_mask, qk, float("-inf"))
qk += tl.dot(q, k)
# -- compute scaling constant --
m_i_new = tl.maximum(m_i, tl.max(qk, 1))
alpha = tl.math.exp2(m_i - m_i_new)
p = tl.math.exp2(qk - m_i_new[:, None])
# -- scale and update acc --
acc_scale = l_i * 0 + alpha # workaround some compiler bug
acc *= acc_scale[:, None]
acc += tl.dot(p.to(dtype), v)
# -- update m_i and l_i --
l_i = l_i * alpha + tl.sum(p, 1)
m_i = m_i_new
# write back O
acc = tl.where(m_mask, acc / l_i[:, None], 0.0)
tl.store(o_ptrs, acc.to(dtype), mask=m_mask)
def triton_sparse_forward(
q, # [BATCH, N_HEADS, N_CTX, D_HEAD]
k, # [BATCH, N_HEADS, N_CTX, D_HEAD]
v, # [BATCH, N_HEADS, N_CTX, D_HEAD]
seqlens, # [BATCH, ]
col_count, # [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M)]
col_index, # [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M), MAX_COLS_PRE_ROW]
sm_scale,
block_size_M=64,
block_size_N=64,
) -> torch.Tensor:
# shape constraints
Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1]
assert Lq == Lk and Lk == Lv
assert Lk in {16, 32, 64, 128}
o = torch.zeros_like(q)
grid = (triton.cdiv(q.shape[2], block_size_M), q.shape[0] * q.shape[1], 1)
num_warps = 4 if (Lk <= 64 or block_size_M <= 64) else 8 # 4
dtype = tl.bfloat16 if q.dtype == torch.bfloat16 else tl.float16
triton_sparse_fwd_kernel[grid](
q, k, v, seqlens, sm_scale,
col_count, col_index,
o,
q.stride(0), q.stride(1), q.stride(2), q.stride(3),
k.stride(0), k.stride(1), k.stride(2), k.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),
q.shape[0], q.shape[1], q.shape[2],
col_index.shape[-2], col_index.shape[-1],
BLOCK_M=block_size_M, BLOCK_N=block_size_N,
BLOCK_DMODEL=Lk,
dtype=dtype,
# num_warps=num_warps, num_stages=4,
)
return o
def torch_build_index(seqlens, vertical_indexes, slash_indexes, block_size_M=64):
max_cols_per_row = (seqlens.max().item() + 3) & (-4)
batch_size, num_heads, NNZ_S = slash_indexes.shape
NNZ_V = vertical_indexes.shape[-1]
num_rows = triton.cdiv(max_cols_per_row, block_size_M)
max_cols_per_row = max_cols_per_row
col_count = torch.zeros((batch_size, num_heads, num_rows), dtype=torch.int32)
col_index = torch.zeros((batch_size, num_heads, num_rows, max_cols_per_row), dtype=torch.int32)
for b in range(batch_size):
seqlen = seqlens[b]
for h in range(num_heads):
for m, start_m in enumerate(range(0, seqlen, block_size_M)):
end_m = start_m + block_size_M
tmp_col_count = 0
cursor, s, v = -1, 0, 0
v_idx = vertical_indexes[b, h, v].item()
while s < NNZ_S and slash_indexes[b, h, s] >= end_m:
s += 1
if s < NNZ_S:
s_idx = end_m - slash_indexes[b, h, s].item()
s_range = min(s_idx, block_size_M)
else:
s_idx = seqlen
s_range = 0
while s_idx <= end_m and v_idx < end_m:
if v_idx < s_idx:
if v_idx < s_idx - s_range:
col_index[b, h, m, tmp_col_count] = v_idx
tmp_col_count += 1
v += 1
if v < NNZ_V:
v_idx = vertical_indexes[b, h, v].item()
else:
break
else:
for idx in range(max(cursor, s_idx - s_range), min(s_idx, seqlen)):
col_index[b, h, m, tmp_col_count] = idx
tmp_col_count += 1
cursor = s_idx
s += 1
if s < NNZ_S:
s_idx = end_m - slash_indexes[b, h, s].item()
s_range = min(s_idx, block_size_M)
else:
break
while s_idx <= end_m and s < NNZ_S:
for idx in range(max(cursor, s_idx - s_range), min(s_idx, seqlen)):
col_index[b, h, m, tmp_col_count] = idx
tmp_col_count += 1
cursor = s_idx
s += 1
if s < NNZ_S:
s_idx = end_m - slash_indexes[b, h, s].item()
s_range = min(s_idx, block_size_M)
else:
break
while v_idx < end_m and v < NNZ_V:
if v_idx < s_idx - s_range:
col_index[b, h, m, tmp_col_count] = v_idx
tmp_col_count += 1
v += 1
if v < NNZ_V:
v_idx = vertical_indexes[b, h, v].item()
else:
break
col_count[b, h, m] = tmp_col_count
return col_count.to(seqlens.device), col_index.to(seqlens.device)
PYCUDA_BUILD_INDEX_KERNEL_CODE = '''\
__device__ int min(int x, int y) {
return x < y ? x : y;
}
__device__ int max(int x, int y) {
return x > y ? x : y;
}
__device__ void save_list(int* output, int loop_start, int loop_end, int& offset) {
if (loop_start + 4 >= loop_end) {
for (int idx = loop_start; idx < loop_end; idx++, offset++) {
output[offset] = idx;
}
return;
}
int4 tmp_int4;
int int4_start = ((offset + 3) & (-4)) - offset + loop_start;
int int4_end = ((offset + loop_end - loop_start) & (-4)) - offset + loop_start;
for (int idx = loop_start; idx < int4_start; idx++, offset++) {
output[offset] = idx;
}
for (int idx = int4_start; idx < int4_end; idx += 4, offset += 4) {
tmp_int4.x = idx + 0;
tmp_int4.y = idx + 1;
tmp_int4.z = idx + 2;
tmp_int4.w = idx + 3;
(reinterpret_cast<int4*>(&output[offset]))[0] = tmp_int4;
}
for (int idx = int4_end; idx < loop_end; idx++, offset++) {
output[offset] = idx;
}
}
__global__ void PYCUDA_BUILD_INDEX_KERNEL(
const int* seqlens, // [BATCH, ]
const int* vertical_indexes, // [BATCH, N_HEADS, NNZ_V]
const int* slash_indexes, // [BATCH, N_HEADS, NNZ_S]
int* col_count, // [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M)]
int* col_index, // [BATCH, N_HEADS, cdiv(N_CTX, BLOCK_SIZE_M), N_CTX]
int N_HEADS,
int N_CTX,
int BLOCK_SIZE_M,
int N_ROWS,
int NNZ_V,
int NNZ_S
) {
const int batch_idx = blockIdx.y;
const int head_idx = blockIdx.x;
const int group_idx = blockIdx.z;
int seqlen = seqlens[batch_idx];
int block_idx_m = group_idx * blockDim.x + threadIdx.x;
int start_m = block_idx_m * BLOCK_SIZE_M;
if (start_m >= seqlen) {
return;
}
int end_m = start_m + BLOCK_SIZE_M;
vertical_indexes += (batch_idx * N_HEADS + head_idx) * NNZ_V;
slash_indexes += (batch_idx * N_HEADS + head_idx) * NNZ_S;
int row_offset = (batch_idx * N_HEADS + head_idx) * N_ROWS + block_idx_m;
col_count += row_offset;
col_index += row_offset * N_CTX;
int tmp_col_count = 0, cursor = -1, s = 0, v = 0;
int v_idx = vertical_indexes[v];
/*
int left = 0, right = NNZ_S - 1;
int tmp_s_idx = 0, target = end_m - 1;
s = (left + right) >> 1;
while (left + 1 < right) {
tmp_s_idx = slash_indexes[s];
if (tmp_s_idx > target) {
left = s;
} else if (tmp_s_idx < target) {
right = s;
} else {
break;
}
s = (left + right) >> 1;
}
*/
while (s < NNZ_S && slash_indexes[s] >= end_m) s++;
int s_idx = (s < NNZ_S) ? (end_m - slash_indexes[s]) : seqlen;
int s_range = (s < NNZ_S) ? min(s_idx, BLOCK_SIZE_M) : 0;
while (s_idx <= end_m && v_idx < end_m) {
if (v_idx < s_idx) {
if (v_idx < s_idx - s_range) {
col_index[tmp_col_count] = v_idx;
tmp_col_count++;
}
v++;
if (v < NNZ_V) {
v_idx = vertical_indexes[v];
} else {
break;
}
} else {
save_list(col_index, max(cursor, s_idx - s_range), min(s_idx, seqlen), tmp_col_count);
cursor = s_idx;
s++;
if (s < NNZ_S) {
s_idx = end_m - slash_indexes[s];
s_range = min(s_idx, BLOCK_SIZE_M);
} else {
break;
}
}
}
while (s_idx <= end_m && s < NNZ_S) {
save_list(col_index, max(cursor, s_idx - s_range), min(s_idx, seqlen), tmp_col_count);
cursor = s_idx;
s++;
if (s < NNZ_S) {
s_idx = end_m - slash_indexes[s];
s_range = min(s_idx, BLOCK_SIZE_M);
} else {
break;
}
}
while (v_idx < end_m && v < NNZ_V) {
if (v_idx < s_idx - s_range) {
col_index[tmp_col_count] = v_idx;
tmp_col_count++;
}
v++;
if (v < NNZ_V) {
v_idx = vertical_indexes[v];
} else {
break;
}
}
col_count[0] = tmp_col_count;
}
'''
PYCUDA_BUILD_INDEX_KERNEL = SourceModule(
PYCUDA_BUILD_INDEX_KERNEL_CODE,
options=['-std=c++14', '-O3'],
).get_function(f'PYCUDA_BUILD_INDEX_KERNEL')
def pycuda_build_index(seqlens, vertical_indexes, slash_indexes, block_size_M=64):
max_cols_per_row = (seqlens.max().item() + 3) & (-4)
batch_size, num_heads, NNZ_S = slash_indexes.shape
NNZ_V = vertical_indexes.shape[-1]
num_rows = triton.cdiv(max_cols_per_row, block_size_M)
max_cols_per_row = max_cols_per_row
col_count = torch.zeros((batch_size, num_heads, num_rows), dtype=torch.int32, device=seqlens.device)
col_index = torch.zeros((batch_size, num_heads, num_rows, max_cols_per_row), dtype=torch.int32, device=seqlens.device)
num_threads = 64
PYCUDA_BUILD_INDEX_KERNEL(
seqlens, vertical_indexes, slash_indexes,
col_count, col_index,
np.int32(num_heads), np.int32(max_cols_per_row), np.int32(block_size_M), np.int32(num_rows),
np.int32(NNZ_V), np.int32(NNZ_S),
# grid=(triton.cdiv(num_rows, num_threads), N_HEADS, BATCH),
grid=(num_heads, batch_size, triton.cdiv(num_rows, num_threads)),
block=(num_threads, 1, 1),
)
return col_count, col_index
def make_causal_mask(seqlens, device, context_size):
batch_size = seqlens.shape[0]
arange = torch.arange(context_size, dtype=torch.int32, device=device)
causal_mask = arange[None, None, :, None] >= arange[None, None, None, :]
causal_mask = causal_mask.repeat((batch_size, 1, 1, 1))
for b, seqlen in enumerate(seqlens):
causal_mask[b, :, seqlen:, :] = False
causal_mask[b, :, :, seqlen:] = False
return causal_mask
def make_finegrained_mask(vertical_indexes, slash_indexes, causal_mask, device):
batch_size, num_heads, _ = vertical_indexes.shape
context_size = causal_mask.shape[-1]
arange = torch.arange(context_size, dtype=torch.int32, device=device)
sparse_mask = torch.zeros((batch_size, num_heads, context_size, context_size), dtype=torch.bool, device=device)
for b in range(batch_size):
for h in range(num_heads):
for vertical_index in vertical_indexes[b, h]:
sparse_mask[b, h, :, vertical_index] = True
for slash_index in slash_indexes[b, h]:
sparse_mask[b, h].logical_or_(arange[:, None] - arange[None, :] == slash_index)
sparse_mask.logical_and_(causal_mask)
return sparse_mask
def make_block_mask(col_count, col_index, seqlens, causal_mask, device, block_size_M=64):
batch_size, num_heads, _ = col_count.shape
context_size = causal_mask.shape[-1]
block_mask = torch.zeros((batch_size, num_heads, context_size, context_size), dtype=torch.bool, device=device)
for b in range(batch_size):
for h in range(num_heads):
for m, start_m in enumerate(range(0, seqlens[b], block_size_M)):
end_m = start_m + block_size_M
for c in range(col_count[b, h, m]):
block_mask[b, h, start_m:end_m, col_index[b, h, m, c]] = True
block_mask.logical_and_(causal_mask)
return block_mask
def plot_mask(mask, name, batch=0, head=0):
import matplotlib.pyplot as plt
import seaborn as sns
plt.figure(figsize=(16, 12))
plt.clf()
mask = mask[batch, head].cpu().numpy()
sns.heatmap(mask)
plt.savefig(name)
@triton.jit
def triton_dense_fwd_kernel(
Q, K, V, seqlens, sm_scale,
Out,
stride_qz, stride_qh, stride_qm, stride_qk,
stride_kz, stride_kh, stride_kn, stride_kk,
stride_vz, stride_vh, stride_vn, stride_vk,
stride_oz, stride_oh, stride_om, stride_ok,
Z, H, N_CTX,
BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr,
BLOCK_N: tl.constexpr,
dtype: tl.constexpr,
):
start_m = tl.program_id(0)
off_hz = tl.program_id(1)
seqlen = tl.load(seqlens + off_hz // H)
if start_m * BLOCK_M >= seqlen:
return
qo_offset = (off_hz // H) * stride_qz + (off_hz % H) * stride_qh
kv_offset = (off_hz // H) * stride_kz + (off_hz % H) * stride_kh
Q_block_ptr = tl.make_block_ptr(
base=Q + qo_offset,
shape=(N_CTX, BLOCK_DMODEL),
strides=(stride_qm, stride_qk),
offsets=(start_m * BLOCK_M, 0),
block_shape=(BLOCK_M, BLOCK_DMODEL),
order=(1, 0)
)
K_block_ptr = tl.make_block_ptr(
base=K + kv_offset,
shape=(BLOCK_DMODEL, N_CTX),
strides=(stride_kk, stride_kn),
offsets=(0, 0),
block_shape=(BLOCK_DMODEL, BLOCK_N),
order=(0, 1)
)
V_block_ptr = tl.make_block_ptr(
base=V + kv_offset,
shape=(N_CTX, BLOCK_DMODEL),
strides=(stride_vn, stride_vk),
offsets=(0, 0),
block_shape=(BLOCK_N, 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)
acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
# scale sm_scale by log_2(e) and use
# 2^x instead of exp in the loop because CSE and LICM
# don't work as expected with `exp` in the loop
qk_scale = sm_scale * 1.44269504
# load q: it will stay in SRAM throughout
q = tl.load(Q_block_ptr)
q = (q * qk_scale).to(dtype)
# loop over k, v and update accumulator
lo = 0
hi = (start_m + 1) * BLOCK_M
m_mask = offs_m[:, None] < seqlen
for start_n in range(lo, hi, BLOCK_N):
n_mask = (start_n + offs_n[None, :]) <= offs_m[:, None]
# -- load k, v --
k = tl.load(K_block_ptr)
v = tl.load(V_block_ptr)
# -- compute qk --
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
qk = tl.where(m_mask & n_mask, qk, float("-inf"))
qk += tl.dot(q, k)
# -- compute scaling constant --
m_i_new = tl.maximum(m_i, tl.max(qk, 1))
alpha = tl.math.exp2(m_i - m_i_new)
p = tl.math.exp2(qk - m_i_new[:, None])
# -- scale and update acc --
acc_scale = l_i * 0 + alpha # workaround some compiler bug
acc *= acc_scale[:, None]
acc += tl.dot(p.to(dtype), v)
# -- update m_i and l_i --
l_i = l_i * alpha + tl.sum(p, 1)
m_i = m_i_new
# update pointers
K_block_ptr = tl.advance(K_block_ptr, (0, BLOCK_N))
V_block_ptr = tl.advance(V_block_ptr, (BLOCK_N, 0))
# write back O
acc = tl.where(m_mask, acc / l_i[:, None], 0.0)
O_block_ptr = tl.make_block_ptr(
base=Out + qo_offset,
shape=(N_CTX, BLOCK_DMODEL),
strides=(stride_om, stride_ok),
offsets=(start_m * BLOCK_M, 0),
block_shape=(BLOCK_M, BLOCK_DMODEL),
order=(1, 0)
)
tl.store(O_block_ptr, acc.to(dtype), mask=m_mask)
def triton_dense_forward(q, k, v, seqlens, sm_scale, block_size_M=128, block_size_N=64) -> torch.Tensor:
# shape constraints
Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1]
assert Lq == Lk and Lk == Lv
assert Lk in {16, 32, 64, 128}
o = torch.zeros_like(q)
grid = (triton.cdiv(q.shape[2], block_size_M), q.shape[0] * q.shape[1], 1)
num_warps = 4 if Lk <= 64 else 8 # 4
dtype = tl.bfloat16 if q.dtype == torch.bfloat16 else tl.float16
triton_dense_fwd_kernel[grid](
q, k, v, seqlens, sm_scale,
o,
q.stride(0), q.stride(1), q.stride(2), q.stride(3),
k.stride(0), k.stride(1), k.stride(2), k.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),
q.shape[0], q.shape[1], q.shape[2],
BLOCK_M=block_size_M, BLOCK_N=block_size_N,
BLOCK_DMODEL=Lk,
dtype=dtype,
num_warps=num_warps, num_stages=4,
)
return o
def flash_attn_forward(q, k, v, seqlens, sm_scale, context_size) -> torch.Tensor:
return flash_attn_varlen_func(
q,
k,
v,
cu_seqlens_q=seqlens,
cu_seqlens_k=seqlens,
max_seqlen_q=context_size,
max_seqlen_k=context_size,
dropout_p=0.0,
softmax_scale=sm_scale,
causal=True,
)
def torch_forward(
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
mask: torch.Tensor,
sm_scale: float,
) -> torch.Tensor:
p = torch.einsum(f'bhmk, bhnk -> bhmn', query, key) * sm_scale
p = p.where(mask, -torch.inf)
p_max = p.max(-1, keepdim=True).values
p_max = torch.where(p_max < 0, 0.0, p_max)
p_exp = torch.exp(p - p_max)
s = p_exp / (p_exp.sum(-1, keepdim=True) + 1e-6)
out = torch.einsum(f'bhmn, bhnk -> bhmk', s, value)
return out
def profile(fn, total_flops, tag, warmup=25, rep=100):
ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep)
gflops = total_flops / ms * 1e-9
print(f'{tag}: {ms:.3f} ms | {gflops:.3f} GFLOP/s')
def test_flash_attention(
seqlens=None,
vertical_indexes=None,
slash_indexes=None,
dtype=torch.float16,
device="cuda",
torch_test=True,
batch_size=4,
num_heads=32,
context_size=1024,
head_dim=128,
sparsity=0.995,
block_size_M=64,
block_size_N=64,
):
print('========================================')
print(f'BATCH={batch_size}, N_CTX={context_size}, N_HEADS={num_heads}, D_HEAD={head_dim}')
q = torch.randn((batch_size, num_heads, context_size, head_dim), dtype=dtype, device=device)
k = torch.randn((batch_size, num_heads, context_size, head_dim), dtype=dtype, device=device)
v = torch.randn((batch_size, num_heads, context_size, head_dim), dtype=dtype, device=device)
if seqlens is None:
seqlens = torch.randint(context_size // 2, context_size, (batch_size, ), dtype=torch.int32, device=device)
else:
seqlens = torch.tensor(seqlens, dtype=torch.int32, device=device)
dense_mask_nnz = seqlens.to(torch.float32).square().sum().item() * num_heads / 2
sm_scale = head_dim ** -0.5
causal_mask = make_causal_mask(seqlens, device, context_size)
if torch_test:
ref_o_dense = torch_forward(q, k, v, causal_mask, sm_scale)
if vertical_indexes is None or slash_indexes is None:
nnz = int((1 - sparsity) * context_size)
vertical_indexes = torch.stack([
torch.stack([
torch.randperm(seqlen, dtype=torch.int32, device=device)[:nnz].sort(descending=False)[0]
for _ in range(num_heads)
])
for seqlen in seqlens
])
slash_indexes = torch.concatenate([
torch.stack([
torch.stack([
torch.randperm(seqlen - 1, dtype=torch.int32, device=device)[:nnz].sort(descending=True)[0] + 1
for _ in range(num_heads)
])
for seqlen in seqlens
]),
torch.zeros((batch_size, num_heads, 1), dtype=torch.int32, device=device)
], dim=-1)
col_count, col_index = pycuda_build_index(seqlens, vertical_indexes, slash_indexes, block_size_M)
if torch_test:
col_count_ref, col_index_ref = torch_build_index(seqlens, vertical_indexes, slash_indexes, block_size_M)
# import ipdb; ipdb.set_trace()
torch.testing.assert_close(col_count_ref, col_count)
torch.testing.assert_close(col_index_ref, col_index)
sparse_mask_nnz = col_count.to(torch.float32).sum().item() * block_size_M
print(f'block mask sparsity: {1 - sparse_mask_nnz / dense_mask_nnz}')
pycuda_build_index_fn = lambda: pycuda_build_index(seqlens, vertical_indexes, slash_indexes, block_size_M)
profile(pycuda_build_index_fn, 0., 'pycuda-index')
if torch_test:
finegrained_mask = make_finegrained_mask(vertical_indexes, slash_indexes, causal_mask, device)
block_mask = make_block_mask(col_count, col_index, seqlens, causal_mask, device, block_size_M)
# plot_mask(finegrained_mask, 'mask.png', 2, 26)
# plot_mask(block_mask, 'mask-1.png', 2, 26)
ref_o_sparse = torch_forward(q, k, v, block_mask, sm_scale)
triton_dense_fn = lambda: triton_dense_forward(q, k, v, seqlens, sm_scale)
output = triton_dense_fn()
if torch_test:
torch.testing.assert_close(output, ref_o_dense, atol=1e-2, rtol=0)
profile(triton_dense_fn, 2. * head_dim * dense_mask_nnz, 'triton-dense')
triton_sparse_fn = lambda: triton_sparse_forward(q, k, v, seqlens, col_count, col_index, sm_scale, block_size_M, block_size_N)
output = triton_sparse_fn()
if torch_test:
torch.testing.assert_close(output, ref_o_sparse, atol=1e-2, rtol=0)
profile(triton_sparse_fn, 2. * head_dim * sparse_mask_nnz, 'triton-sparse')
q = q.swapaxes(1, 2).contiguous()
k = k.swapaxes(1, 2).contiguous()
v = v.swapaxes(1, 2).contiguous()
q = torch.concatenate([q[i, :seqlen, :, :] for i, seqlen in enumerate(seqlens)])
k = torch.concatenate([k[i, :seqlen, :, :] for i, seqlen in enumerate(seqlens)])
v = torch.concatenate([v[i, :seqlen, :, :] for i, seqlen in enumerate(seqlens)])
seqlens = torch.nn.functional.pad(torch.cumsum(seqlens, dim=0, dtype=torch.int32), (1, 0))
flash_fn = lambda: flash_attn_forward(q, k, v, seqlens, sm_scale, context_size)
output = flash_fn()
output = torch.stack([
torch.nn.functional.pad(
output[seqlens[i]:seqlens[i + 1], :, :],
(0, 0, 0, 0, 0, context_size + seqlens[i] - seqlens[i + 1])
)
for i in range(batch_size)
]).swapaxes(1, 2).contiguous()
if torch_test:
torch.testing.assert_close(output, ref_o_dense, atol=1e-2, rtol=0)
profile(flash_fn, 2. * head_dim * dense_mask_nnz, 'flash-dense')
print('========================================\n')
def pit_sparse_flash_attention_forward(
query: torch.Tensor, # [BATCH, N_HEADS, N_CTX, D_HEAD]
key: torch.Tensor, # [BATCH, N_HEADS, N_CTX, D_HEAD]
value: torch.Tensor, # [BATCH, N_HEADS, N_CTX, D_HEAD]
v_idx: torch.Tensor, # [BATCH, N_HEADS, NNZ_V]
s_idx: torch.Tensor, # [BATCH, N_HEADS, NNZ_S]
block_size_M: int = 64,
block_size_N: int = 64,
):
q_len = query.shape[2]
pad = block_size_M - (query.shape[2] & (block_size_M - 1))
query = torch.nn.functional.pad(query, [0, 0, 0, pad, 0, 0, 0, 0])
key = torch.nn.functional.pad(key, [0, 0, 0, pad, 0, 0, 0, 0])
value = torch.nn.functional.pad(value, [0, 0, 0, pad, 0, 0, 0, 0])
batch_size, num_heads, context_size, head_dim = query.shape
v_idx = v_idx.to(torch.int32).reshape((batch_size, num_heads, -1)).sort(dim=-1, descending=False)[0]
s_idx = s_idx.to(torch.int32).reshape((batch_size, num_heads, -1)).sort(dim=-1, descending=True)[0]
seqlens = torch.tensor([context_size], dtype=torch.int32, device=query.device)
sm_scale = head_dim ** -0.5
col_count, col_index = pycuda_build_index(seqlens, v_idx, s_idx, block_size_M)
out = triton_sparse_forward(query, key, value, seqlens, col_count, col_index, sm_scale, block_size_M, block_size_N)[...,:q_len,:]
return out