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include sparse_sage_API code

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https://github.com/jt-zhang/Sparse_SageAttention_API
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kijai
2025-07-18 17:16:43 +03:00
parent cf18ecc2da
commit 5a305a7f9f
6 changed files with 515 additions and 3 deletions
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nodes.py
+1 -1
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@@ -89,7 +89,7 @@ class WanVideoSetRadialAttention:
"sparse_sage_attention",
], {"default": "sageattn", "tooltip": "The attention mode for dense attention"}),
"dense_blocks": ("INT", {"default": 1, "min": 0, "max": 40, "step": 1, "tooltip": "Number of blocks to apply normal attention to"}),
"dense_vace_blocks": ("INT", {"default": 15, "min": 0, "max": 40, "step": 1, "tooltip": "Number of vace blocks to apply normal attention to"}),
"dense_vace_blocks": ("INT", {"default": 1, "min": 0, "max": 15, "step": 1, "tooltip": "Number of vace blocks to apply normal attention to"}),
"dense_timesteps": ("INT", {"default": 10, "min": 0, "max": 100, "step": 1, "tooltip": "The step to start applying sparse attention"}),
"decay_factor": ("FLOAT", {"default": 0.2, "min": 0, "max": 1, "step": 0.01, "tooltip": "Controls how quickly the attention window shrinks as the distance between frames increases in the sparse attention mask."}),
}
wanvideo/radial_attention/attn_mask.py
+5 -2
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@@ -3,8 +3,11 @@ import torch
try:
from sparse_sageattn import sparse_sageattn
except:
sparse_sageattn = None
raise ImportError("Package is not installed: https://github.com/jt-zhang/Sparse_SageAttention_API")
try:
from .sparse_sage.core import sparse_sageattn
except:
sparse_sageattn = None
raise ImportError("sparse_sageattn is not available. Please install the sparse_sageattn package or check your import path.")
from comfy import model_management as mm
device = mm.get_torch_device()
wanvideo/radial_attention/sparse_sage/LICENSE.txt
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@@ -0,0 +1,201 @@
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Copyright 2025 Jintao Zhang
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software
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wanvideo/radial_attention/sparse_sage/core.py
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@@ -0,0 +1,45 @@
"""
https://github.com/jt-zhang/Sparse_SageAttention_API
Copyright (c) 2024 by SageAttention team.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from .quant_per_block import per_block_int8
from .sparse_int8_attn import forward as sparse_sageattn_fwd
import torch
def sparse_sageattn(q, k, v, mask_id = None, is_causal=False, tensor_layout="HND"):
if mask_id is None:
mask_id = torch.ones((q.shape[0], q.shape[1], (q.shape[2] + 128 - 1)//128, (q.shape[3] + 64 - 1)//64), dtype=torch.int8, device=q.device) # TODO
output_dtype = q.dtype
if output_dtype == torch.bfloat16 or output_dtype == torch.float32:
v = v.to(torch.float16)
seq_dim = 1 if tensor_layout == "NHD" else 2
km = k.mean(dim=seq_dim, keepdim=True)
# km = torch.zeros((k.size(0), k.size(1), 1, k.size(3)), dtype=torch.float16, device=k.device) # Placeholder for mean, not used in quantization
q_int8, q_scale, k_int8, k_scale = per_block_int8(q, k, km=km, tensor_layout=tensor_layout)
o = sparse_sageattn_fwd(
q_int8, k_int8, mask_id, v, q_scale, k_scale,
is_causal=is_causal, tensor_layout=tensor_layout, output_dtype=output_dtype
)
return o
# flops = 4 * q.size(0) * q.size(1) * q.size(2)**2 * q.size(3) / (2 if is_causal else 1)
wanvideo/radial_attention/sparse_sage/quant_per_block.py
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@@ -0,0 +1,101 @@
"""
Copyright (c) 2024 by SageAttention team.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import torch
import triton
import triton.language as tl
@triton.jit
def quant_per_block_int8_kernel(Input, Output, Scale, L,
stride_iz, stride_ih, stride_in,
stride_oz, stride_oh, stride_on,
stride_sz, stride_sh,
sm_scale,
C: tl.constexpr, BLK: tl.constexpr):
off_blk = tl.program_id(0)
off_h = tl.program_id(1)
off_b = tl.program_id(2)
offs_n = off_blk * BLK + tl.arange(0, BLK)
offs_k = tl.arange(0, C)
input_ptrs = Input + off_b * stride_iz + off_h * stride_ih + offs_n[:, None] * stride_in + offs_k[None, :]
output_ptrs = Output + off_b * stride_oz + off_h * stride_oh + offs_n[:, None] * stride_on + offs_k[None, :]
scale_ptrs = Scale + off_b * stride_sz + off_h * stride_sh + off_blk
x = tl.load(input_ptrs, mask=offs_n[:, None] < L)
x = x.to(tl.float32)
x *= sm_scale
scale = tl.max(tl.abs(x)) / 127.
x_int8 = x / scale
x_int8 += 0.5 * tl.where(x_int8 >= 0, 1, -1)
x_int8 = x_int8.to(tl.int8)
tl.store(output_ptrs, x_int8, mask=offs_n[:, None] < L)
tl.store(scale_ptrs, scale)
def per_block_int8(q, k, km=None, BLKQ=128, BLKK=64, sm_scale=None, tensor_layout="HND"):
q_int8 = torch.empty(q.shape, dtype=torch.int8, device=q.device)
k_int8 = torch.empty(k.shape, dtype=torch.int8, device=k.device)
if km is not None:
k = k - km
if tensor_layout == "HND":
b, h_qo, qo_len, head_dim = q.shape
_, h_kv, kv_len, _ = k.shape
stride_bz_q, stride_h_q, stride_seq_q = q.stride(0), q.stride(1), q.stride(2)
stride_bz_qo, stride_h_qo, stride_seq_qo = q_int8.stride(0), q_int8.stride(1), q_int8.stride(2)
stride_bz_k, stride_h_k, stride_seq_k = k.stride(0), k.stride(1), k.stride(2)
stride_bz_ko, stride_h_ko, stride_seq_ko = k_int8.stride(0), k_int8.stride(1), k_int8.stride(2)
elif tensor_layout == "NHD":
b, qo_len, h_qo, head_dim = q.shape
_, kv_len, h_kv, _ = k.shape
stride_bz_q, stride_h_q, stride_seq_q = q.stride(0), q.stride(2), q.stride(1)
stride_bz_qo, stride_h_qo, stride_seq_qo = q_int8.stride(0), q_int8.stride(2), q_int8.stride(1)
stride_bz_k, stride_h_k, stride_seq_k = k.stride(0), k.stride(2), k.stride(1)
stride_bz_ko, stride_h_ko, stride_seq_ko = k_int8.stride(0), k_int8.stride(2), k_int8.stride(1)
else:
raise ValueError(f"Unknown tensor layout: {tensor_layout}")
q_scale = torch.empty((b, h_qo, (qo_len + BLKQ - 1) // BLKQ), device=q.device, dtype=torch.float32)
k_scale = torch.empty((b, h_kv, (kv_len + BLKK - 1) // BLKK), device=q.device, dtype=torch.float32)
if sm_scale is None:
sm_scale = head_dim**-0.5
grid = ((qo_len + BLKQ - 1) // BLKQ, h_qo, b)
quant_per_block_int8_kernel[grid](
q, q_int8, q_scale, qo_len,
stride_bz_q, stride_h_q, stride_seq_q,
stride_bz_qo, stride_h_qo, stride_seq_qo,
q_scale.stride(0), q_scale.stride(1),
sm_scale=(sm_scale * 1.44269504),
C=head_dim, BLK=BLKQ
)
grid = ((kv_len + BLKK - 1) // BLKK, h_kv, b)
quant_per_block_int8_kernel[grid](
k, k_int8, k_scale, kv_len,
stride_bz_k, stride_h_k, stride_seq_k,
stride_bz_ko, stride_h_ko, stride_seq_ko,
k_scale.stride(0), k_scale.stride(1),
sm_scale=1.0,
C=head_dim, BLK=BLKK
)
return q_int8, q_scale, k_int8, k_scale
wanvideo/radial_attention/sparse_sage/sparse_int8_attn.py
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"""
Copyright (c) 2024 by SageAttention team.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import torch, math
import triton
import triton.language as tl
import torch.nn.functional as F
@triton.jit
def _attn_fwd_inner(acc, l_i, old_m, q, q_scale, kv_len,
K_ptrs, K_bid_ptr, K_scale_ptr, V_ptrs, stride_kn, stride_vn, start_m,
BLOCK_M: tl.constexpr, HEAD_DIM: tl.constexpr, BLOCK_N: tl.constexpr,
STAGE: tl.constexpr, offs_m: tl.constexpr, offs_n: tl.constexpr,
):
if STAGE == 1:
lo, hi = 0, start_m * BLOCK_M
elif STAGE == 2:
lo, hi = start_m * BLOCK_M, (start_m + 1) * BLOCK_M
lo = tl.multiple_of(lo, BLOCK_M)
K_scale_ptr += lo // BLOCK_N
K_ptrs += stride_kn * lo
V_ptrs += stride_vn * lo
elif STAGE == 3:
lo, hi = 0, kv_len
for start_n in range(lo, hi, BLOCK_N):
kbid = tl.load(K_bid_ptr + start_n//BLOCK_N)
if kbid:
k_mask = offs_n[None, :] < (kv_len - start_n)
k = tl.load(K_ptrs, mask = k_mask)
k_scale = tl.load(K_scale_ptr)
qk = tl.dot(q, k).to(tl.float32) * q_scale * k_scale
if STAGE == 2:
mask = offs_m[:, None] >= (start_n + offs_n[None, :])
qk = qk + tl.where(mask, 0, -1.0e6)
local_m = tl.max(qk, 1)
new_m = tl.maximum(old_m, local_m)
qk -= new_m[:, None]
else:
local_m = tl.max(qk, 1)
new_m = tl.maximum(old_m, local_m)
qk = qk - new_m[:, None]
p = tl.math.exp2(qk)
l_ij = tl.sum(p, 1)
alpha = tl.math.exp2(old_m - new_m)
l_i = l_i * alpha + l_ij
acc = acc * alpha[:, None]
v = tl.load(V_ptrs, mask = offs_n[:, None] < (kv_len - start_n))
p = p.to(tl.float16)
acc += tl.dot(p, v, out_dtype=tl.float16)
old_m = new_m
K_ptrs += BLOCK_N * stride_kn
K_scale_ptr += 1
V_ptrs += BLOCK_N * stride_vn
return acc, l_i, old_m
@triton.jit
def _attn_fwd(Q, K, K_blkid, V, Q_scale, K_scale, Out,
stride_qz, stride_qh, stride_qn,
stride_kz, stride_kh, stride_kn,
stride_vz, stride_vh, stride_vn,
stride_oz, stride_oh, stride_on,
stride_kbidq, stride_kbidk,
qo_len, kv_len, H:tl.constexpr, num_kv_groups:tl.constexpr,
HEAD_DIM: tl.constexpr,
BLOCK_M: tl.constexpr,
BLOCK_N: tl.constexpr,
STAGE: tl.constexpr
):
start_m = tl.program_id(0)
off_z = tl.program_id(2).to(tl.int64)
off_h = tl.program_id(1).to(tl.int64)
q_scale_offset = (off_z * H + off_h) * tl.cdiv(qo_len, BLOCK_M)
k_scale_offset = (off_z * (H // num_kv_groups) + off_h // num_kv_groups) * tl.cdiv(kv_len, BLOCK_N)
k_bid_offset = (off_z * (H // num_kv_groups) + off_h // num_kv_groups) * stride_kbidq
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
offs_n = tl.arange(0, BLOCK_N)
offs_k = tl.arange(0, HEAD_DIM)
Q_ptrs = Q + (off_z * stride_qz + off_h * stride_qh) + offs_m[:, None] * stride_qn + offs_k[None, :]
Q_scale_ptr = Q_scale + q_scale_offset + start_m
K_ptrs = K + (off_z * stride_kz + (off_h // num_kv_groups) * stride_kh) + offs_n[None, :] * stride_kn + offs_k[:, None]
K_scale_ptr = K_scale + k_scale_offset
K_bid_ptr = K_blkid + k_bid_offset + start_m * stride_kbidk
V_ptrs = V + (off_z * stride_vz + (off_h // num_kv_groups) * stride_vh) + offs_n[:, None] * stride_vn + offs_k[None, :]
O_block_ptr = Out + (off_z * stride_oz + off_h * stride_oh) + offs_m[:, None] * stride_on + offs_k[None, :]
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, HEAD_DIM], dtype=tl.float32)
q = tl.load(Q_ptrs, mask = offs_m[:, None] < qo_len)
q_scale = tl.load(Q_scale_ptr)
acc, l_i, m_i = _attn_fwd_inner(acc, l_i, m_i, q, q_scale, kv_len, K_ptrs, K_bid_ptr, K_scale_ptr, V_ptrs, stride_kn, stride_vn,
start_m,
BLOCK_M, HEAD_DIM, BLOCK_N,
4 - STAGE, offs_m, offs_n
)
if STAGE != 1:
acc, l_i, _ = _attn_fwd_inner(acc, l_i, m_i, q, q_scale, kv_len, K_ptrs, K_bid_ptr, K_scale_ptr, V_ptrs, stride_kn, stride_vn,
start_m,
BLOCK_M, HEAD_DIM, BLOCK_N,
2, offs_m, offs_n
)
acc = acc / l_i[:, None]
tl.store(O_block_ptr, acc.to(Out.type.element_ty), mask = (offs_m[:, None] < qo_len))
def forward(q, k, k_block_id, v, q_scale, k_scale, is_causal=False, tensor_layout="HND", output_dtype=torch.float16):
BLOCK_M = 128
BLOCK_N = 64
stage = 3 if is_causal else 1
o = torch.empty(q.shape, dtype=output_dtype, device=q.device)
if tensor_layout == "HND":
b, h_qo, qo_len, head_dim = q.shape
_, h_kv, kv_len, _ = k.shape
stride_bz_q, stride_h_q, stride_seq_q = q.stride(0), q.stride(1), q.stride(2)
stride_bz_k, stride_h_k, stride_seq_k = k.stride(0), k.stride(1), k.stride(2)
stride_bz_v, stride_h_v, stride_seq_v = v.stride(0), v.stride(1), v.stride(2)
stride_bz_o, stride_h_o, stride_seq_o = o.stride(0), o.stride(1), o.stride(2)
elif tensor_layout == "NHD":
b, qo_len, h_qo, head_dim = q.shape
_, kv_len, h_kv, _ = k.shape
stride_bz_q, stride_h_q, stride_seq_q = q.stride(0), q.stride(2), q.stride(1)
stride_bz_k, stride_h_k, stride_seq_k = k.stride(0), k.stride(2), k.stride(1)
stride_bz_v, stride_h_v, stride_seq_v = v.stride(0), v.stride(2), v.stride(1)
stride_bz_o, stride_h_o, stride_seq_o = o.stride(0), o.stride(2), o.stride(1)
else:
raise ValueError(f"tensor_layout {tensor_layout} not supported")
if is_causal:
assert qo_len == kv_len, "qo_len and kv_len must be equal for causal attention"
HEAD_DIM_K = head_dim
num_kv_groups = h_qo // h_kv
grid = (triton.cdiv(qo_len, BLOCK_M), h_qo, b )
_attn_fwd[grid](
q, k, k_block_id, v, q_scale, k_scale, o,
stride_bz_q, stride_h_q, stride_seq_q,
stride_bz_k, stride_h_k, stride_seq_k,
stride_bz_v, stride_h_v, stride_seq_v,
stride_bz_o, stride_h_o, stride_seq_o,
k_block_id.stride(1), k_block_id.stride(2),
qo_len, kv_len,
h_qo, num_kv_groups,
BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, HEAD_DIM=HEAD_DIM_K,
STAGE=stage,
num_warps=4 if head_dim == 64 else 8,
num_stages=4)
return o