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support speculative decoding kernel in sgl-kernel (#3373)

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Co-authored-by: Ying Sheng <sqy1415@gmail.com>
This commit is contained in:
Yineng Zhang
2025-02-07 20:29:51 +08:00
committed by GitHub
parent 45c87e083f
commit f9905d59a8
13 changed files with 1298 additions and 132 deletions
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584
python/sglang/srt/speculative/build_eagle_tree.py
View File

@@ -1,124 +1,175 @@
import cutex
# NOTE: Please run this file to make sure the test cases are correct.
from typing import List
import torch
# parent_table [bs,topk*depth+)]
# selected_index [bs,draft_token_num-1)]
# verified_seq_len [bs]
# tree_mask [draft_token*(seq_len[0]+draft_token) | draft_token*(seq_len[1]+draft_token) | ..] = [sum(verified_seq_len)*draft_token+bs*draft_token*draft_token]
# positions [bs*draft_token]
# retrive_index [b, draft_token, depth+2]
kernels = cutex.SourceModule(
"""
//cuda
__global__ void build_tree(Tensor<long, 2> parent_list, Tensor<long, 2> selected_index, Tensor<int, 1> verified_seq_len,
Tensor<bool, 1> tree_mask, Tensor<long, 1> positions, Tensor<long, 3> retrive_index, int topk, int depth, int draft_token_num) {
int bid = blockIdx.x;
int tid = threadIdx.x;
if (tid >= draft_token_num){
return;
}
int seq_tree_idx = draft_token_num * draft_token_num * bid;
for(int i=0; i<bid; i++){
seq_tree_idx += verified_seq_len[i] * draft_token_num;
}
int seq_len = verified_seq_len[bid];
int token_tree_idx = seq_tree_idx + (seq_len+draft_token_num)*tid + seq_len + 1;
for(int i=0; i<draft_token_num-1; i++){
tree_mask[token_tree_idx+i] = false;
}
from sglang.srt.utils import is_cuda_available
int position = 0;
if (tid==0){
positions[bid*draft_token_num] = seq_len;
retrive_index[bid][0][0] = bid * draft_token_num;
return;
}
int depends_order[10];
int cur_position = tid-1;
while(true){
depends_order[position] = cur_position+1;
position += 1;
tree_mask[token_tree_idx+cur_position] = true;
int parent_tb_idx = selected_index[bid][cur_position]/topk;
if(parent_tb_idx==0){
break;
}
int token_idx = parent_list[bid][parent_tb_idx];
for(cur_position=0; cur_position<draft_token_num;cur_position++){
if(selected_index[bid][cur_position]==token_idx){
break;
}
}
}
positions[bid*draft_token_num+tid] = position + seq_len;
int is_leaf = 0;
for(int i=1;i<draft_token_num;i++){
if(tree_mask[seq_tree_idx + i * (draft_token_num+seq_len) + seq_len + tid])
{
is_leaf ++;
}
}
if(is_leaf==1){
for(int i=0; i<position; i++){
retrive_index[bid][tid][position-i] = depends_order[i] + bid * draft_token_num;
}
retrive_index[bid][tid][0] = bid*draft_token_num;
}
if is_cuda_available():
from sgl_kernel import build_tree_kernel as sgl_build_tree_kernel
from sgl_kernel import (
build_tree_kernel_efficient as sgl_build_tree_kernel_efficient,
)
}
//!cuda
""",
float_bits=16, # change to 16 to use half precision as `float` type in the above source code.
boundscheck=True, # turning on for debug and off for performance (to use full threads of a block), default is on.
)
def build_tree_kernel(
parent_list, top_score_index, seq_lens, seq_lens_sum, topk, depth, draft_token
def build_tree_kernel_efficient_preprocess(
verified_id: torch.Tensor,
score_list: List[torch.Tensor],
token_list: List[torch.Tensor],
parents_list: List[torch.Tensor],
num_verify_tokens: int,
):
score_list = torch.cat(score_list, dim=1).flatten(
1
) # b, n, topk; n= 1 + (num_steps-1) * self.topk
ss_token_list = torch.cat(
token_list, dim=1
) # b, (self.topk + (num_steps-1) * self.topk)
top_scores = torch.topk(score_list, num_verify_tokens - 1, dim=-1)
top_scores_index = top_scores.indices
top_scores_index = torch.sort(top_scores_index).values
draft_tokens = torch.gather(ss_token_list, index=top_scores_index, dim=1)
draft_tokens = torch.cat((verified_id.unsqueeze(1), draft_tokens), dim=1).flatten()
parent_list = torch.cat(parents_list[:-1], dim=1)
return parent_list, top_scores_index, draft_tokens
def build_tree_kernel_efficient(
verified_id: torch.Tensor,
score_list: List[torch.Tensor],
token_list: List[torch.Tensor],
parents_list: List[torch.Tensor],
seq_lens: torch.Tensor,
seq_lens_sum: int,
topk: int,
spec_steps: int,
num_verify_tokens: int,
):
parent_list, top_scores_index, draft_tokens = (
build_tree_kernel_efficient_preprocess(
verified_id,
score_list,
token_list,
parents_list,
num_verify_tokens,
)
)
# seq_lens_sum == sum(seq_lens); seq_lens: sequence length without draft tokens
bs = seq_lens.numel()
device = parent_list.device
device = seq_lens.device
# e.g. for bs=1, tree_mask: num_draft_token, seq_lens_sum + num_draft_token (flattened)
# where each row indicates the attending pattern of each draft token
# TODO: make them torch.empty and fuse them into `sgl_build_tree_kernel`
tree_mask = torch.full(
(seq_lens_sum * draft_token + draft_token * draft_token * bs,),
(
seq_lens_sum * num_verify_tokens
+ num_verify_tokens * num_verify_tokens * bs,
),
True,
device=device,
)
retrive_index = torch.full(
(bs, draft_token, depth + 2), -1, device=device, dtype=torch.long
(bs, num_verify_tokens), -1, device=device, dtype=torch.long
)
positions = torch.empty((bs * draft_token,), device=device, dtype=torch.long)
retrive_next_token = torch.full(
(bs, num_verify_tokens), -1, device=device, dtype=torch.long
)
retrive_next_sibling = torch.full(
(bs, num_verify_tokens), -1, device=device, dtype=torch.long
)
# position: where each token belongs to
# e.g. if depth of each draft token is [0, 1, 1, 2] and the prompt length is 7
# then, positions = [7, 8, 8, 9]
positions = torch.empty((bs * num_verify_tokens,), device=device, dtype=torch.long)
kernels.build_tree(
sgl_build_tree_kernel_efficient(
parent_list,
top_score_index,
top_scores_index,
seq_lens.to(torch.int32),
tree_mask,
positions,
retrive_index,
retrive_next_token,
retrive_next_sibling,
topk,
spec_steps,
num_verify_tokens,
)
return (
tree_mask,
positions,
retrive_index,
retrive_next_token,
retrive_next_sibling,
draft_tokens,
)
def build_tree_kernel(
verified_id: torch.Tensor,
score_list: List[torch.Tensor],
token_list: List[torch.Tensor],
parents_list: List[torch.Tensor],
seq_lens: torch.Tensor,
seq_lens_sum: int,
topk: int,
spec_steps: int,
num_verify_tokens: int,
):
parent_list, top_scores_index, draft_tokens = (
build_tree_kernel_efficient_preprocess(
verified_id,
score_list,
token_list,
parents_list,
num_verify_tokens,
)
)
bs = seq_lens.numel()
device = seq_lens.device
tree_mask = torch.full(
(
seq_lens_sum * num_verify_tokens
+ num_verify_tokens * num_verify_tokens * bs,
),
True,
device=device,
)
retrive_index = torch.full(
(bs, num_verify_tokens, spec_steps + 2), -1, device=device, dtype=torch.long
)
positions = torch.empty((bs * num_verify_tokens,), device=device, dtype=torch.long)
sgl_build_tree_kernel(
parent_list,
top_scores_index,
seq_lens.to(torch.int32),
tree_mask,
positions,
retrive_index,
topk,
depth,
draft_token,
grid=(bs, 1, 1),
block=(64, 1, 1),
spec_steps,
num_verify_tokens,
)
index = retrive_index.sum(dim=-1) != -depth - 2
index = retrive_index.sum(dim=-1) != -spec_steps - 2
cum_len = torch.cumsum(torch.sum(index, dim=-1), dim=-1)
retrive_cum_len = torch.zeros(
(cum_len.numel() + 1,), dtype=torch.int32, device="cuda"
)
retrive_cum_len[1:] = cum_len
# TODO: this indexing cause a synchronization, optimize this
retrive_index = retrive_index[index]
return tree_mask, positions, retrive_index, retrive_cum_len
return tree_mask, positions, retrive_index, retrive_cum_len, draft_tokens
if __name__ == "__main__":
def test_build_tree_kernel():
def findp(p_i, index, parent_list):
pos = index // 10
index_list = index.tolist()
@@ -311,21 +362,21 @@ if __name__ == "__main__":
bs = verified_seq_len.shape[0]
topk = 10
depth = 5 # depth <= 10
draft_token = 64
num_draft_token = 64
tree_mask = torch.full(
(
torch.sum(verified_seq_len).item() * draft_token
+ draft_token * draft_token * bs,
torch.sum(verified_seq_len).item() * num_draft_token
+ num_draft_token * num_draft_token * bs,
),
True,
).cuda()
retrive_index = torch.full(
(bs, draft_token, depth + 2), -1, device="cuda", dtype=torch.long
(bs, num_draft_token, depth + 2), -1, device="cuda", dtype=torch.long
)
positions = torch.empty((bs * draft_token,), device="cuda", dtype=torch.long)
positions = torch.empty((bs * num_draft_token,), device="cuda", dtype=torch.long)
kernels.build_tree(
sgl_build_tree_kernel(
parent_list.unsqueeze(0),
index.unsqueeze(0),
verified_seq_len,
@@ -334,16 +385,345 @@ if __name__ == "__main__":
retrive_index,
topk,
depth,
draft_token,
grid=(bs, 1, 1),
block=(64, 1, 1),
num_draft_token,
)
retrive_index = retrive_index[retrive_index.sum(dim=-1) != -depth - 2]
c_mask, c_positions, c_retive_index = create_mask(
verified_seq_len, draft_token, index, parent_list, depth
verified_seq_len, num_draft_token, index, parent_list, depth
)
assert torch.allclose(tree_mask, c_mask), "tree mask has error."
assert torch.allclose(positions, c_positions), "positions has error."
assert torch.allclose(retrive_index, c_retive_index), "retrive_index has error."
def test_build_tree_kernel_efficient():
verified_id = torch.tensor([29974, 13], device="cuda", dtype=torch.int32)
score_list = [
torch.tensor(
[
[[7.1127e-01, 2.8292e-01, 2.2995e-03, 1.7357e-03]],
[[9.7476e-01, 2.2219e-02, 6.5031e-04, 1.3212e-04]],
],
dtype=torch.float32,
device="cuda",
),
torch.tensor(
[
[
[6.9142e-01, 1.2863e-02, 1.6873e-03, 1.1871e-03],
[2.4787e-01, 1.8818e-02, 1.4204e-02, 9.2235e-04],
[2.2971e-03, 1.6700e-06, 1.8737e-07, 8.3146e-08],
[1.2771e-03, 2.4374e-04, 1.7832e-04, 1.1947e-05],
],
[
[8.4832e-02, 6.6068e-02, 5.8304e-02, 5.7851e-02],
[2.3616e-03, 1.1243e-03, 5.4368e-04, 2.7768e-04],
[2.5286e-04, 1.5578e-04, 2.8817e-05, 1.2888e-05],
[1.2834e-04, 2.5417e-06, 1.1279e-06, 1.6088e-08],
],
],
dtype=torch.float32,
device="cuda",
),
torch.tensor(
[
[
[6.6438e-01, 2.6997e-02, 2.4236e-05, 4.0821e-06],
[2.4402e-01, 2.8409e-03, 5.0935e-04, 2.9022e-04],
[1.6178e-02, 2.0567e-03, 4.5892e-04, 3.0034e-05],
[1.3023e-02, 5.0497e-04, 3.6371e-04, 8.7750e-05],
],
[
[2.3263e-02, 2.0054e-02, 9.3990e-03, 2.7783e-03],
[6.4156e-02, 5.5506e-04, 1.0429e-04, 9.7211e-05],
[4.9950e-02, 5.0630e-03, 9.0068e-04, 3.3656e-04],
[7.5817e-03, 8.5731e-04, 6.9972e-04, 6.0793e-04],
],
],
dtype=torch.float32,
device="cuda",
),
torch.tensor(
[
[
[6.6420e-01, 1.0525e-04, 6.5864e-05, 1.2253e-06],
[1.3019e-01, 1.0461e-01, 5.2083e-03, 1.6777e-03],
[2.0103e-02, 6.7335e-03, 1.2625e-04, 1.0364e-05],
[1.5142e-02, 7.0819e-04, 9.6595e-05, 8.7951e-05],
],
[
[5.8608e-02, 1.8840e-03, 7.8535e-04, 4.4400e-04],
[1.2185e-02, 2.0684e-03, 1.7418e-03, 1.4327e-03],
[6.2455e-03, 6.1487e-03, 2.6862e-03, 1.8034e-03],
[1.8590e-03, 1.6151e-03, 1.2481e-03, 3.6038e-04],
],
],
dtype=torch.float32,
device="cuda",
),
]
token_list = [
torch.tensor(
[[29896, 29906, 29900, 29945], [13, 2, 29871, 28956]],
dtype=torch.int64,
device="cuda",
),
torch.tensor(
[
[
29889,
29974,
29945,
29900,
29974,
29922,
29930,
29958,
29889,
29974,
29930,
29945,
29974,
29922,
29930,
29958,
],
[
22550,
4136,
16492,
8439,
29871,
2,
3001,
13,
2,
13,
29906,
29946,
2,
13,
29871,
259,
],
],
device="cuda",
),
torch.tensor(
[
[
29946,
29945,
29953,
29906,
29896,
29945,
29900,
29906,
29896,
29945,
29906,
29953,
29896,
29945,
29906,
29946,
],
[
29871,
2,
29901,
29889,
29871,
2,
395,
259,
29901,
29871,
2,
29889,
3001,
1234,
7146,
2186,
],
],
device="cuda",
),
torch.tensor(
[
[
29946,
29974,
29945,
29930,
29889,
29922,
29974,
29930,
29974,
29946,
29930,
29922,
29889,
29974,
29945,
29922,
],
[
29941,
29906,
2,
29946,
29871,
450,
319,
14990,
29946,
29941,
2,
29906,
29871,
2,
3001,
13,
],
],
device="cuda",
),
]
parents_list = [
torch.tensor(
[[-1, 0, 1, 2, 3], [-1, 0, 1, 2, 3]], dtype=torch.int64, device="cuda"
),
torch.tensor([[4, 8, 9, 10], [4, 5, 6, 7]], dtype=torch.int64, device="cuda"),
torch.tensor(
[[20, 24, 21, 28], [24, 28, 20, 21]], dtype=torch.int64, device="cuda"
),
torch.tensor(
[[36, 40, 41, 44], [36, 40, 44, 45]], dtype=torch.int64, device="cuda"
),
]
seq_lens = torch.tensor([5, 10], dtype=torch.int64, device="cuda")
topk = 4
depth = 4
num_draft_token = 8
tree_mask, position, retrive_index, retrive_cum_len, draft_tokens = (
build_tree_kernel(
verified_id=verified_id,
score_list=score_list,
token_list=token_list,
parents_list=parents_list,
seq_lens=seq_lens,
seq_lens_sum=torch.sum(seq_lens).item(),
topk=topk,
spec_steps=depth,
num_verify_tokens=num_draft_token,
)
)
from sglang.srt.utils import first_rank_print
first_rank_print("=========== build tree kernel ==========")
# first_rank_print(f"{tree_mask=}", flush=True)
first_rank_print(f"{position=}", flush=True)
first_rank_print(f"{retrive_index=}", flush=True)
first_rank_print(f"{retrive_cum_len=}", flush=True)
first_rank_print(f"{draft_tokens=}", flush=True)
assert position.tolist() == [5, 6, 6, 7, 7, 8, 8, 9, 10, 11, 12, 12, 12, 12, 13, 14]
assert retrive_index.tolist() == [
[0, -1, -1, -1, -1, -1],
[0, 2, 4, 6, -1, -1],
[0, 1, 3, 5, 7, -1],
[8, -1, -1, -1, -1, -1],
[8, 9, 10, -1, -1, -1],
[8, 9, 12, -1, -1, -1],
[8, 9, 13, -1, -1, -1],
[8, 9, 11, 14, 15, -1],
]
assert retrive_cum_len.tolist() == [0, 3, 8]
assert draft_tokens.tolist() == [
29974,
29896,
29906,
29889,
29974,
29946,
29896,
29946,
13,
13,
22550,
4136,
16492,
8439,
29871,
29941,
]
(
tree_mask,
position,
retrive_index,
retrive_next_token,
retrive_next_sibling,
draft_tokens,
) = build_tree_kernel_efficient(
verified_id=verified_id,
score_list=score_list,
token_list=token_list,
parents_list=parents_list,
seq_lens=seq_lens,
seq_lens_sum=torch.sum(seq_lens).item(),
topk=topk,
spec_steps=depth,
num_verify_tokens=num_draft_token,
)
first_rank_print("=========== build tree kernel efficient ==========")
# first_rank_print(f"{tree_mask=}", flush=True)
first_rank_print(f"{position=}", flush=True)
first_rank_print(f"{retrive_index=}", flush=True)
first_rank_print(f"{retrive_next_token=}", flush=True)
first_rank_print(f"{retrive_next_sibling=}", flush=True)
first_rank_print(f"{draft_tokens=}", flush=True)
assert position.tolist() == [5, 6, 6, 7, 7, 8, 8, 9, 10, 11, 12, 12, 12, 12, 13, 14]
assert retrive_index.tolist() == [
[0, 1, 2, 3, 4, 5, 6, 7],
[8, 9, 10, 11, 12, 13, 14, 15],
]
assert retrive_next_token.tolist() == [
[1, 3, 4, 5, 6, 7, -1, -1],
[1, 2, -1, 6, -1, -1, 7, -1],
]
assert retrive_next_sibling.tolist() == [
[-1, 2, -1, -1, -1, -1, -1, -1],
[-1, -1, 3, 4, 5, -1, -1, -1],
]
assert draft_tokens.tolist() == [
29974,
29896,
29906,
29889,
29974,
29946,
29896,
29946,
13,
13,
22550,
4136,
16492,
8439,
29871,
29941,
]
if __name__ == "__main__":
test_build_tree_kernel_efficient()
test_build_tree_kernel()

94
python/sglang/srt/speculative/eagle_utils.py
View File

@@ -258,39 +258,77 @@ class EagleVerifyInput:
return kv_indices, cum_kv_seq_len, qo_indptr, self.custom_mask
def verify(self, batch: ScheduleBatch, logits_output: torch.Tensor) -> torch.Tensor:
predict = torch.argmax(logits_output.next_token_logits, dim=-1)
predict = torch.cat(
[predict, torch.full([1], -1, dtype=torch.long, device="cuda")], dim=-1
)
draft_token = torch.cat(
[self.draft_token, torch.full([1], -1, dtype=torch.long, device="cuda")],
[self.draft_token, torch.full([1], -1, dtype=torch.int32, device="cuda")],
dim=-1,
)
target_predict = predict[self.retrive_index]
candidates = draft_token[self.retrive_index]
# logits = logits_output.next_token_logits[self.retrive_index]
# target_predict = torch.argmax(logits[:, :-1], dim=-1)
accept_mask = candidates[:, 1:] == target_predict[:, :-1]
accept_mask = (torch.cumprod(accept_mask, dim=1)).sum(dim=1)
bs = self.retrive_cum_len.numel() - 1
if batch.sampling_info.is_all_greedy:
# temp == 0
bs = self.retrive_cum_len.numel() - 1
predict = torch.argmax(logits_output.next_token_logits, dim=-1)
predict = torch.cat(
[predict, torch.full([1], -1, dtype=torch.int32, device="cuda")], dim=-1
)
target_predict = predict[self.retrive_index]
# logits = logits_output.next_token_logits[self.retrive_index]
# target_predict = torch.argmax(logits[:, :-1], dim=-1)
accept_mask = candidates[:, 1:] == target_predict[:, :-1]
max_draft_len = self.retrive_index.shape[-1]
accept_index = torch.full(
(bs, max_draft_len), -1, dtype=torch.long, device="cuda"
)
accept_length = torch.empty((bs,), dtype=torch.int, device="cuda")
extract_index = torch.full((bs * 2,), 0, dtype=torch.int, device="cuda")
eagle_verify_retrive[(bs,)](
self.retrive_index.contiguous(),
accept_mask.contiguous(),
self.retrive_cum_len,
accept_index,
accept_length,
extract_index,
max_draft_len,
self.draft_token_num,
triton.next_power_of_2(max_draft_len),
)
accept_mask = (torch.cumprod(accept_mask, dim=1)).sum(dim=1)
max_draft_len = self.retrive_index.shape[-1]
accept_index = torch.full(
(bs, max_draft_len), -1, dtype=torch.int32, device="cuda"
)
accept_length = torch.empty((bs,), dtype=torch.int, device="cuda")
extract_index = torch.full((bs * 2,), 0, dtype=torch.int, device="cuda")
eagle_verify_retrive[(bs,)](
self.retrive_index.contiguous(),
accept_mask.contiguous(),
self.retrive_cum_len,
accept_index,
accept_length,
extract_index,
max_draft_len,
self.draft_token_num,
triton.next_power_of_2(max_draft_len),
)
else:
# temp > 0
bs = self.retrive_index.shape[0]
predict_shape = list(logits_output.next_token_logits.shape)[:-1]
predict_shape[-1] += 1
target_logits = logits_output.next_token_logits[self.retrive_index]
predict = torch.full(predict_shape, -1, dtype=torch.int32, device="cuda")
accept_index = torch.full(
(bs, self.spec_steps + 1), -1, dtype=torch.int32, device="cuda"
)
accept_length = torch.empty((bs,), dtype=torch.int32, device="cuda")
expanded_temperature = batch.sampling_info.temperatures.unsqueeze(1)
target_probs = F.softmax(target_logits / expanded_temperature, dim=-1)
draft_probs = torch.full_like(
target_probs, 0, dtype=torch.float32, device="cuda"
)
coins = torch.rand_like(candidates, dtype=torch.float32, device="cuda")
tree_speculative_sampling_target_only(
predicts=predict, # mutable
accept_index=accept_index, # mutable
accept_token_num=accept_length, # mutable
candidates=candidates.to(torch.int32),
retrive_index=self.retrive_index.to(torch.int32),
retrive_next_token=self.retrive_next_token.to(torch.int32),
retrive_next_sibling=self.retrive_next_sibling.to(torch.int32),
uniform_samples=coins,
target_probs=target_probs,
draft_probs=draft_probs,
threshold_single=global_server_args_dict[
"speculative_accept_threshold_single"
],
threshold_acc=global_server_args_dict[
"speculative_accept_threshold_acc"
],
deterministic=True,
)
new_accept_index = []
unfinished_index = []

2
sgl-kernel/pyproject.toml
View File

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
[project]
name = "sgl-kernel"
version = "0.0.3.post1"
version = "0.0.3.post2"
description = "Kernel Library for SGLang"
readme = "README.md"
requires-python = ">=3.9"

2
sgl-kernel/setup.py
View File

@@ -99,6 +99,8 @@ sources = [
"src/sgl-kernel/csrc/fp8_gemm_kernel.cu",
"src/sgl-kernel/csrc/lightning_attention_decode_kernel.cu",
"src/sgl-kernel/csrc/fused_add_rms_norm_kernel.cu",
"src/sgl-kernel/csrc/eagle_utils.cu",
"src/sgl-kernel/csrc/speculative_sampling.cu",
"3rdparty/flashinfer/csrc/activation.cu",
"3rdparty/flashinfer/csrc/bmm_fp8.cu",
"3rdparty/flashinfer/csrc/norm.cu",

6
sgl-kernel/src/sgl-kernel/__init__.py
View File

@@ -10,6 +10,8 @@ if os.path.exists("/usr/local/cuda/targets/x86_64-linux/lib/libcudart.so.12"):
from sgl_kernel.ops import (
apply_rope_with_cos_sin_cache_inplace,
bmm_fp8,
build_tree_kernel,
build_tree_kernel_efficient,
custom_dispose,
custom_reduce,
fp8_scaled_mm,
@@ -31,6 +33,7 @@ from sgl_kernel.ops import (
top_k_renorm_prob,
top_k_top_p_sampling_from_probs,
top_p_renorm_prob,
tree_speculative_sampling_target_only,
)
__all__ = [
@@ -57,4 +60,7 @@ __all__ = [
"top_k_renorm_prob",
"top_k_top_p_sampling_from_probs",
"top_p_renorm_prob",
"tree_speculative_sampling_target_only",
"build_tree_kernel_efficient",
"build_tree_kernel",
]

209
sgl-kernel/src/sgl-kernel/csrc/eagle_utils.cu Normal file
View File

@@ -0,0 +1,209 @@
/*
* Copyright (c) 2025 by SGLang 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.
*/
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
// parent_list [bs, topk * (depth - 1) + 1)]
// selected_index [bs, draft_token_num - 1]
// verified_seq_len [bs]
// tree_mask [draft_token*(seq_len[0]+draft_token) | draft_token*(seq_len[1]+draft_token) | ..] =
// [sum(verified_seq_len)*draft_token+bs*draft_token*draft_token] positions [bs * draft_token] retrive_index [b,
// draft_token] retrive_next_token [b, draft_token] retrive_next_sibling [b, draft_token]
__global__ void build_tree_efficient(int64_t* parent_list, int64_t* selected_index, int32_t* verified_seq_len,
bool* tree_mask, int64_t* positions, int64_t* retrive_index,
int64_t* retrive_next_token, int64_t* retrive_next_sibling, int topk, int depth,
int draft_token_num) {
int bid = blockIdx.x;
int tid = threadIdx.x;
if (tid >= draft_token_num) {
return;
}
int seq_tree_idx = draft_token_num * draft_token_num * bid;
for (int i = 0; i < bid; i++) {
seq_tree_idx += verified_seq_len[i] * draft_token_num;
}
int seq_len = verified_seq_len[bid];
int token_tree_idx = seq_tree_idx + (seq_len + draft_token_num) * tid + seq_len + 1;
for (int i = 0; i < draft_token_num - 1; i++) {
tree_mask[token_tree_idx + i] = false;
}
int position = 0;
if (tid == 0) {
positions[bid * draft_token_num] = seq_len;
int retrive_index_offset = bid * draft_token_num;
for (int i = draft_token_num - 1; i > 0; --i) {
int current_token_idx = retrive_index_offset + i;
retrive_index[bid * draft_token_num + i] = current_token_idx;
int parent_tb_idx = selected_index[bid * (draft_token_num - 1) + i - 1] / topk;
int parent_position = 0;
if (parent_tb_idx > 0) {
int parent_token_idx = parent_list[bid * (topk * (depth - 1) + 1) + parent_tb_idx];
for (; parent_position < draft_token_num; ++parent_position) {
if (selected_index[bid * (draft_token_num - 1) + parent_position] == parent_token_idx) {
++parent_position;
break;
}
}
}
if (parent_position == draft_token_num) {
printf(
"ERROR: invalid eagle tree!!! Detected a token with no parent token selected. Check the logprob. The token "
"will be dropped.");
continue;
}
if (retrive_next_token[bid * draft_token_num + parent_position] == -1) {
retrive_next_token[bid * draft_token_num + parent_position] = i;
} else {
int origin_next_token = retrive_next_token[bid * draft_token_num + parent_position];
retrive_next_token[bid * draft_token_num + parent_position] = i;
retrive_next_sibling[bid * draft_token_num + i] = origin_next_token;
}
}
retrive_index[bid * draft_token_num] = bid * draft_token_num;
} else {
int cur_position = tid - 1;
while (true) {
position += 1;
tree_mask[token_tree_idx + cur_position] = true;
int parent_tb_idx = selected_index[bid * (draft_token_num - 1) + cur_position] / topk;
if (parent_tb_idx == 0) {
break;
}
int token_idx = parent_list[bid * (topk * (depth - 1) + 1) + parent_tb_idx];
for (cur_position = 0; cur_position < draft_token_num; ++cur_position) {
if (selected_index[bid * (draft_token_num - 1) + cur_position] == token_idx) {
break;
}
}
}
positions[bid * draft_token_num + tid] = position + seq_len;
}
}
void build_tree_kernel_efficient(at::Tensor parent_list, at::Tensor selected_index, at::Tensor verified_seq_len,
at::Tensor tree_mask, at::Tensor positions, at::Tensor retrive_index,
at::Tensor retrive_next_token, at::Tensor retrive_next_sibling, int64_t topk,
int64_t depth, int64_t draft_token_num) {
// TODO (ying) check shape
// TODO (ying) check type
int bs = parent_list.size(0);
dim3 grid(bs);
dim3 block(draft_token_num);
const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
build_tree_efficient<<<grid, block, 0, stream>>>(
static_cast<int64_t*>(parent_list.data_ptr()), static_cast<int64_t*>(selected_index.data_ptr()),
static_cast<int32_t*>(verified_seq_len.data_ptr()), static_cast<bool*>(tree_mask.data_ptr()),
static_cast<int64_t*>(positions.data_ptr()), static_cast<int64_t*>(retrive_index.data_ptr()),
static_cast<int64_t*>(retrive_next_token.data_ptr()), static_cast<int64_t*>(retrive_next_sibling.data_ptr()),
int32_t(topk), int32_t(depth), int32_t(draft_token_num));
}
// parent_list [bs, topk * (depth - 1) + 1)]
// selected_index [bs, draft_token_num - 1]
// verified_seq_len [bs]
// tree_mask [draft_token*(seq_len[0]+draft_token) | draft_token*(seq_len[1]+draft_token) | ..] =
// [sum(verified_seq_len)*draft_token+bs*draft_token*draft_token] positions [bs * draft_token] retrive_index [b,
// draft_token, depth + 2]
__global__ void build_tree(int64_t* parent_list, int64_t* selected_index, int32_t* verified_seq_len, bool* tree_mask,
int64_t* positions, int64_t* retrive_index, int topk, int depth, int draft_token_num) {
int bid = blockIdx.x;
int tid = threadIdx.x;
if (tid >= draft_token_num) {
return;
}
int seq_tree_idx = draft_token_num * draft_token_num * bid;
for (int i = 0; i < bid; i++) {
seq_tree_idx += verified_seq_len[i] * draft_token_num;
}
int seq_len = verified_seq_len[bid];
int token_tree_idx = seq_tree_idx + (seq_len + draft_token_num) * tid + seq_len + 1;
for (int i = 0; i < draft_token_num - 1; i++) {
tree_mask[token_tree_idx + i] = false;
}
int position = 0;
if (tid == 0) {
positions[bid * draft_token_num] = seq_len;
retrive_index[bid * draft_token_num * (depth + 2)] = bid * draft_token_num;
return;
}
int depends_order[10];
int cur_position = tid - 1;
while (true) {
depends_order[position] = cur_position + 1;
position += 1;
tree_mask[token_tree_idx + cur_position] = true;
int parent_tb_idx = selected_index[bid * (draft_token_num - 1) + cur_position] / topk;
if (parent_tb_idx == 0) {
break;
}
int token_idx = parent_list[bid * (topk * (depth - 1) + 1) + parent_tb_idx];
for (cur_position = 0; cur_position < draft_token_num; cur_position++) {
if (selected_index[bid * (draft_token_num - 1) + cur_position] == token_idx) {
break;
}
}
if (cur_position == draft_token_num) {
printf(
"ERROR: invalid eagle tree!!! Detected a token with no parent token selected. Check the logprob. The token "
"will be dropped.");
break;
}
}
positions[bid * draft_token_num + tid] = position + seq_len;
int is_leaf = 0;
for (int i = 1; i < draft_token_num; i++) {
if (tree_mask[seq_tree_idx + i * (draft_token_num + seq_len) + seq_len + tid]) {
is_leaf++;
}
}
if (is_leaf == 1) {
for (int i = 0; i < position; i++) {
retrive_index[(bid * (draft_token_num) + tid) * (depth + 2) + position - i] =
depends_order[i] + bid * draft_token_num;
}
retrive_index[(bid * (draft_token_num) + tid) * (depth + 2)] = bid * draft_token_num;
}
}
void build_tree_kernel(at::Tensor parent_list, at::Tensor selected_index, at::Tensor verified_seq_len,
at::Tensor tree_mask, at::Tensor positions, at::Tensor retrive_index, int64_t topk,
int64_t depth, int64_t draft_token_num) {
// TODO (ying) check shape
// TODO (ying) check type
int bs = parent_list.size(0);
dim3 grid(bs);
dim3 block(draft_token_num);
const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
build_tree<<<grid, block, 0, stream>>>(
static_cast<int64_t*>(parent_list.data_ptr()), static_cast<int64_t*>(selected_index.data_ptr()),
static_cast<int32_t*>(verified_seq_len.data_ptr()), static_cast<bool*>(tree_mask.data_ptr()),
static_cast<int64_t*>(positions.data_ptr()), static_cast<int64_t*>(retrive_index.data_ptr()), int32_t(topk),
int32_t(depth), int32_t(draft_token_num));
}

120
sgl-kernel/src/sgl-kernel/csrc/speculative_sampling.cu Normal file
View File

@@ -0,0 +1,120 @@
/*
* Copyright (c) 2025 by SGLang team.
* Copyright (c) 2025 by FlashInfer 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.
*/
#include <speculative_sampling.cuh>
#include "pytorch_extension_utils.h"
using namespace flashinfer;
// predicts: [tot_num_draft_tokens]
// accept_index: [bs, num_spec_step]
// accept_token_num: [bs]
// candidates: [bs, num_draft_tokens]
// retrive_index: [bs, num_draft_tokens]
// retrive_next_token: [bs, num_draft_tokens]
// retrive_next_sibling: [bs, num_draft_tokens]
// uniform_samples: [bs, num_draft_tokens]
// target_probs: [bs, num_draft_tokens, vocab_size]
void tree_speculative_sampling_target_only(at::Tensor predicts, at::Tensor accept_index,
at::Tensor accept_token_num, // mutable
at::Tensor candidates, at::Tensor retrive_index,
at::Tensor retrive_next_token, at::Tensor retrive_next_sibling,
at::Tensor uniform_samples, at::Tensor target_probs, at::Tensor draft_probs,
bool deterministic, int64_t cuda_stream = 0) {
CHECK_INPUT(candidates);
CHECK_INPUT(retrive_index);
CHECK_INPUT(retrive_next_token);
CHECK_INPUT(retrive_next_sibling);
CHECK_INPUT(uniform_samples);
CHECK_INPUT(target_probs);
auto device = target_probs.device();
CHECK_EQ(candidates.device(), device);
CHECK_EQ(retrive_index.device(), device);
CHECK_EQ(retrive_next_token.device(), device);
CHECK_EQ(retrive_next_sibling.device(), device);
CHECK_EQ(uniform_samples.device(), device);
CHECK_EQ(target_probs.device(), device);
CHECK_DIM(1, predicts);
CHECK_DIM(2, accept_index);
CHECK_DIM(1, accept_token_num);
CHECK_DIM(2, candidates);
CHECK_DIM(2, retrive_index);
CHECK_DIM(2, retrive_next_token);
CHECK_DIM(2, retrive_next_sibling);
CHECK_DIM(2, uniform_samples);
CHECK_DIM(3, target_probs);
CHECK_DIM(3, draft_probs);
unsigned int batch_size = uniform_samples.size(0);
unsigned int num_spec_step = accept_index.size(1);
unsigned int num_draft_tokens = candidates.size(1);
unsigned int vocab_size = target_probs.size(2);
CHECK_EQ(batch_size, candidates.size(0));
CHECK_EQ(batch_size, retrive_index.size(0));
CHECK_EQ(batch_size, retrive_next_token.size(0));
CHECK_EQ(batch_size, retrive_next_sibling.size(0));
CHECK_EQ(batch_size, target_probs.size(0));
CHECK_EQ(num_draft_tokens, retrive_index.size(1));
CHECK_EQ(num_draft_tokens, retrive_next_token.size(1));
CHECK_EQ(num_draft_tokens, retrive_next_sibling.size(1));
CHECK_EQ(num_draft_tokens, uniform_samples.size(1));
CHECK_EQ(num_draft_tokens, target_probs.size(1));
CHECK_EQ(vocab_size, target_probs.size(2));
CHECK_EQ(batch_size, accept_index.size(0));
CHECK_EQ(batch_size, accept_token_num.size(0));
if (predicts.scalar_type() != at::kInt) {
throw std::runtime_error("Expected 'predicts' to be of type int (torch.int32).");
}
if (accept_index.scalar_type() != at::kInt) {
throw std::runtime_error("Expected 'accept_index' to be of type int (torch.int32).");
}
if (accept_token_num.scalar_type() != at::kInt) {
throw std::runtime_error("Expected 'accept_token_num' to be of type int (torch.int32).");
}
if (candidates.scalar_type() != at::kInt) {
throw std::runtime_error("Expected 'candidates' to be of type int (torch.int32).");
}
if (retrive_index.scalar_type() != at::kInt) {
throw std::runtime_error("Expected 'retrive_index' to be of type int (torch.int32).");
}
if (retrive_next_token.scalar_type() != at::kInt) {
throw std::runtime_error("Expected 'retrive_next_token' to be of type int (torch.int32).");
}
if (retrive_next_sibling.scalar_type() != at::kInt) {
throw std::runtime_error("Expected 'retrive_next_sibling' to be of type int (torch.int32).");
}
if (uniform_samples.scalar_type() != at::kFloat) {
throw std::runtime_error("Expected 'uniform_samples' to be of type float (torch.float32).");
}
if (target_probs.scalar_type() != at::kFloat) {
throw std::runtime_error("Expected 'target_probs' to be of type float (torch.float32).");
}
if (draft_probs.scalar_type() != at::kFloat) {
throw std::runtime_error("Expected 'target_probs' to be of type float (torch.float32).");
}
cudaStream_t stream = reinterpret_cast<cudaStream_t>(cuda_stream);
cudaError_t status = sampling::TreeSpeculativeSamplingTargetOnly<float, int>(
static_cast<int*>(predicts.data_ptr()), static_cast<int*>(accept_index.data_ptr()),
static_cast<int*>(accept_token_num.data_ptr()), static_cast<int*>(candidates.data_ptr()),
static_cast<int*>(retrive_index.data_ptr()), static_cast<int*>(retrive_next_token.data_ptr()),
static_cast<int*>(retrive_next_sibling.data_ptr()), static_cast<float*>(uniform_samples.data_ptr()),
static_cast<float*>(target_probs.data_ptr()), static_cast<float*>(draft_probs.data_ptr()), batch_size,
num_spec_step, num_draft_tokens, vocab_size, deterministic, stream);
TORCH_CHECK(status == cudaSuccess,
"TreeSpeculativeSamplingTargetOnly failed with error code " + std::string(cudaGetErrorString(status)));
}

184
sgl-kernel/src/sgl-kernel/csrc/speculative_sampling.cuh Normal file
View File

@@ -0,0 +1,184 @@
/*
* Copyright (c) 2025 by SGLang team.
* Copyright (c) 2024-2025 by FlashInfer 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.
*/
#ifndef SPECULATIVE_SAMPLING_CUH_
#define SPECULATIVE_SAMPLING_CUH_
#include <assert.h>
#include <flashinfer/sampling.cuh>
namespace flashinfer {
namespace sampling {
using namespace cub;
template <uint32_t BLOCK_THREADS, BlockScanAlgorithm SCAN_ALGORITHM, BlockReduceAlgorithm REDUCE_ALGORITHM,
uint32_t VEC_SIZE, bool DETERMINISTIC, typename DType, typename IdType>
__global__ void TreeSpeculativeSamplingTargetOnly(IdType* predicts, IdType* accept_index,
IdType* accept_token_num, // mutable
IdType* candidates, IdType* retrive_index, IdType* retrive_next_token,
IdType* retrive_next_sibling, DType* uniform_samples,
DType* target_probs, DType* draft_probs, uint32_t batch_size,
uint32_t num_speculative_tokens, uint32_t num_draft_tokens,
uint32_t d) {
const uint32_t bx = blockIdx.x, tx = threadIdx.x;
extern __shared__ __align__(alignof(SamplingTempStorage<DType, BLOCK_THREADS, SCAN_ALGORITHM, REDUCE_ALGORITHM>))
uint8_t smem_sampling[];
auto& temp_storage =
reinterpret_cast<SamplingTempStorage<DType, BLOCK_THREADS, SCAN_ALGORITHM, REDUCE_ALGORITHM>&>(smem_sampling);
DType prob_acc = 0.0;
uint32_t cur_prob_offset = bx * num_draft_tokens * d;
DType coin = uniform_samples[bx * num_draft_tokens];
IdType last_accepted_retrive_idx = retrive_index[bx * num_draft_tokens];
accept_index[bx * num_speculative_tokens] = last_accepted_retrive_idx;
uint32_t num_accepted_tokens = 0;
IdType cur_index = 0;
for (uint32_t j = 1; j < num_speculative_tokens; ++j) {
cur_index = retrive_next_token[bx * num_draft_tokens + cur_index];
while (cur_index != -1) {
IdType draft_index = retrive_index[bx * num_draft_tokens + cur_index];
IdType draft_token_id = candidates[bx * num_draft_tokens + cur_index];
prob_acc += target_probs[cur_prob_offset + draft_token_id];
if (coin < prob_acc) {
// accept token
prob_acc = 0.;
cur_prob_offset = (bx * num_draft_tokens + cur_index) * d;
coin = uniform_samples[bx * num_draft_tokens + cur_index];
predicts[last_accepted_retrive_idx] = draft_token_id;
++num_accepted_tokens;
accept_index[bx * num_speculative_tokens + num_accepted_tokens] = draft_index;
last_accepted_retrive_idx = draft_index;
break;
} else {
// FIXME: leverage draft probs
draft_probs[cur_prob_offset + draft_token_id] = target_probs[cur_prob_offset + draft_token_id];
cur_index = retrive_next_sibling[bx * num_draft_tokens + cur_index];
}
}
if (cur_index == -1) break;
}
accept_token_num[bx] = num_accepted_tokens;
// sample from relu(target_probs - draft_probs)
DType sum_relu_q_minus_p(0);
vec_t<DType, VEC_SIZE> q_vec, p_vec;
DType relu_q_minus_p[VEC_SIZE];
for (uint32_t i = 0; i < ceil_div(d, BLOCK_THREADS * VEC_SIZE); ++i) {
q_vec.fill(DType(0));
p_vec.fill(DType(0));
if ((i * BLOCK_THREADS + tx) * VEC_SIZE < d) {
q_vec.load(target_probs + cur_prob_offset + i * BLOCK_THREADS * VEC_SIZE + tx * VEC_SIZE);
if (num_accepted_tokens != num_speculative_tokens - 1) {
// there is no draft_probs for the bonus token
p_vec.load(draft_probs + cur_prob_offset + i * BLOCK_THREADS * VEC_SIZE + tx * VEC_SIZE);
}
}
#pragma unroll
for (uint32_t j = 0; j < VEC_SIZE; ++j) {
relu_q_minus_p[j] = max(q_vec[j] - p_vec[j], DType(0));
}
sum_relu_q_minus_p += BlockReduce<DType, BLOCK_THREADS, REDUCE_ALGORITHM>(temp_storage.block_prim.reduce)
.Sum<VEC_SIZE>(relu_q_minus_p);
__syncthreads();
}
if (tx == 0) {
temp_storage.block_aggregate.value = sum_relu_q_minus_p;
}
// init the first rejected token to (d - 1)
temp_storage.sampled_id = d - 1;
__syncthreads();
sum_relu_q_minus_p = temp_storage.block_aggregate.value;
DType u = coin * sum_relu_q_minus_p;
DType aggregate_relu_q_minus_p(0);
for (uint32_t i = 0; i < ceil_div(d, BLOCK_THREADS * VEC_SIZE); ++i) {
q_vec.fill(DType(0));
p_vec.fill(DType(0));
if ((i * BLOCK_THREADS + tx) * VEC_SIZE < d) {
q_vec.load(target_probs + cur_prob_offset + i * BLOCK_THREADS * VEC_SIZE + tx * VEC_SIZE);
if (num_accepted_tokens != num_speculative_tokens - 1) {
// there is no draft_probs for the bonus token
p_vec.load(draft_probs + cur_prob_offset + i * BLOCK_THREADS * VEC_SIZE + tx * VEC_SIZE);
}
}
vec_t<DType, VEC_SIZE> relu_q_minus_p_vec;
#pragma unroll
for (uint32_t j = 0; j < VEC_SIZE; ++j) {
relu_q_minus_p_vec[j] = max(q_vec[j] - p_vec[j], DType(0));
}
DeviceSamplingFromProb<VEC_SIZE, BLOCK_THREADS, SCAN_ALGORITHM, REDUCE_ALGORITHM, DETERMINISTIC, DType>(
i, d, [&](DType x) { return x > 0; }, u, relu_q_minus_p_vec, aggregate_relu_q_minus_p, &temp_storage);
if (aggregate_relu_q_minus_p > u) {
break;
}
}
__syncthreads();
// set the first rejected token
predicts[last_accepted_retrive_idx] = temp_storage.sampled_id;
// value at not used indices are undefined
}
template <typename DType, typename IdType>
cudaError_t TreeSpeculativeSamplingTargetOnly(IdType* predicts, IdType* output_token_ids,
IdType* output_accepted_token_num, // mutable
IdType* candidates, IdType* retrive_index, IdType* retrive_next_token,
IdType* retrive_next_sibling, DType* uniform_samples, DType* target_probs,
DType* draft_probs, uint32_t batch_size, uint32_t num_speculative_tokens,
uint32_t num_draft_tokens, uint32_t d, bool deterministic,
cudaStream_t stream = 0) {
constexpr uint32_t BLOCK_THREADS = 1024;
const uint32_t vec_size = std::gcd(16 / sizeof(DType), d);
const uint32_t smem_size = sizeof(SamplingTempStorage<DType, BLOCK_THREADS, SCAN_ALGO, REDUCE_ALGO>);
dim3 nblks(batch_size);
dim3 nthrs(BLOCK_THREADS);
void* args[] = {&predicts,
&output_token_ids,
&output_accepted_token_num,
&candidates,
&retrive_index,
&retrive_next_token,
&retrive_next_sibling,
&uniform_samples,
&target_probs,
&draft_probs,
&batch_size,
&num_speculative_tokens,
&num_draft_tokens,
&d};
DISPATCH_ALIGNED_VEC_SIZE(
vec_size, VEC_SIZE, {DISPATCH_DETERMINISTIC(deterministic, DETERMINISTIC, {
auto kernel = TreeSpeculativeSamplingTargetOnly<BLOCK_THREADS, SCAN_ALGO, REDUCE_ALGO, VEC_SIZE, DETERMINISTIC,
DType, IdType>;
FLASHINFER_CUDA_CALL(cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size));
FLASHINFER_CUDA_CALL(cudaLaunchKernel((void*)kernel, nblks, nthrs, args, smem_size, stream));
})});
return cudaSuccess;
}
} // namespace sampling
} // namespace flashinfer
#endif // SPECULATIVE_SAMPLING_CUH_

16
sgl-kernel/src/sgl-kernel/include/sgl_kernels_ops.h
View File

@@ -127,3 +127,19 @@ void top_p_sampling_from_probs(at::Tensor probs, at::Tensor uniform_samples, at:
void apply_rope_pos_ids_cos_sin_cache(at::Tensor q, at::Tensor k, at::Tensor q_rope, at::Tensor k_rope,
at::Tensor cos_sin_cache, at::Tensor pos_ids, bool interleave,
int64_t cuda_stream);
void tree_speculative_sampling_target_only(at::Tensor predicts, at::Tensor accept_index,
at::Tensor accept_token_num, // mutable
at::Tensor candidates, at::Tensor retrive_index,
at::Tensor retrive_next_token, at::Tensor retrive_next_sibling,
at::Tensor uniform_samples, at::Tensor target_probs, at::Tensor draft_probs,
bool deterministic = true, int64_t cuda_stream = 0);
void build_tree_kernel_efficient(at::Tensor parent_list, at::Tensor selected_index, at::Tensor verified_seq_len,
at::Tensor tree_mask, at::Tensor positions, at::Tensor retrive_index,
at::Tensor retrive_next_token, at::Tensor retrive_next_sibling, int64_t topk,
int64_t depth, int64_t draft_token_num);
void build_tree_kernel(at::Tensor parent_list, at::Tensor selected_index, at::Tensor verified_seq_len,
at::Tensor tree_mask, at::Tensor positions, at::Tensor retrive_index, int64_t topk,
int64_t depth, int64_t draft_token_num);

84
sgl-kernel/src/sgl-kernel/ops/__init__.py
View File

@@ -495,3 +495,87 @@ def min_p_sampling_from_probs(
return _min_p_sampling_from_probs_internal(
probs, uniform_samples, *_to_tensor_scalar_tuple(min_p), deterministic
)
def tree_speculative_sampling_target_only(
predicts: torch.Tensor, # mutable
accept_index: torch.Tensor, # mutable
accept_token_num: torch.Tensor, # mutable
candidates: torch.Tensor,
retrive_index: torch.Tensor,
retrive_next_token: torch.Tensor,
retrive_next_sibling: torch.Tensor,
uniform_samples: torch.Tensor,
target_probs: torch.Tensor,
draft_probs: torch.Tensor,
deterministic: bool = True,
) -> None:
with predicts.device as device:
torch.ops.sgl_kernels.tree_speculative_sampling_target_only(
predicts,
accept_index,
accept_token_num,
candidates,
retrive_index,
retrive_next_token,
retrive_next_sibling,
uniform_samples,
target_probs,
draft_probs,
deterministic,
_get_cuda_stream(device),
)
def build_tree_kernel_efficient(
parent_list: torch.Tensor,
selected_index: torch.Tensor,
verified_seq_len: torch.Tensor,
tree_mask: torch.Tensor,
positions: torch.Tensor,
retrive_index: torch.Tensor,
retrive_next_token: torch.Tensor,
retrive_next_sibling: torch.Tensor,
topk: int,
depth: int,
draft_token_num: int,
) -> None:
with parent_list.device as device:
torch.ops.sgl_kernels.build_tree_kernel_efficient(
parent_list,
selected_index,
verified_seq_len,
tree_mask,
positions,
retrive_index,
retrive_next_token,
retrive_next_sibling,
topk,
depth,
draft_token_num,
)
def build_tree_kernel(
parent_list: torch.Tensor,
selected_index: torch.Tensor,
verified_seq_len: torch.Tensor,
tree_mask: torch.Tensor,
positions: torch.Tensor,
retrive_index: torch.Tensor,
topk: int,
depth: int,
draft_token_num: int,
) -> None:
with parent_list.device as device:
torch.ops.sgl_kernels.build_tree_kernel(
parent_list,
selected_index,
verified_seq_len,
tree_mask,
positions,
retrive_index,
topk,
depth,
draft_token_num,
)

23
sgl-kernel/src/sgl-kernel/torch_extension.cc
View File

@@ -130,6 +130,29 @@ TORCH_LIBRARY_EXPAND(sgl_kernels, m) {
"apply_rope_pos_ids_cos_sin_cache(Tensor q, Tensor k, Tensor! q_rope, Tensor! k_rope, Tensor cos_sin_cache, "
"Tensor pos_ids, bool interleave, int cuda_stream) -> ()");
m.impl("apply_rope_pos_ids_cos_sin_cache", torch::kCUDA, &apply_rope_pos_ids_cos_sin_cache);
// tree spec decode
m.def(
"tree_speculative_sampling_target_only(Tensor! predicts, Tensor! accept_index, Tensor! accept_token_num, "
"Tensor candidates, Tensor retrive_index, Tensor retrive_next_token, Tensor retrive_next_sibling, "
"Tensor uniform_samples, Tensor target_probs, Tensor draft_probs, "
"bool deterministic, int cuda_stream) -> ()");
m.impl("tree_speculative_sampling_target_only", torch::kCUDA, &tree_speculative_sampling_target_only);
// eagle build tree
m.def(
"build_tree_kernel_efficient(Tensor parent_list, Tensor selected_index, Tensor verified_seq_len, "
"Tensor! tree_mask, Tensor! positions, Tensor! retrive_index, Tensor! retrive_next_token, Tensor! "
"retrive_next_sibling, "
"int topk, int depth, int draft_token_num) -> ()");
m.impl("build_tree_kernel_efficient", torch::kCUDA, &build_tree_kernel_efficient);
// eagle build tree
m.def(
"build_tree_kernel(Tensor parent_list, Tensor selected_index, Tensor verified_seq_len, "
"Tensor! tree_mask, Tensor! positions, Tensor! retrive_index, "
"int topk, int depth, int draft_token_num) -> ()");
m.impl("build_tree_kernel", torch::kCUDA, &build_tree_kernel);
}
REGISTER_EXTENSION(_kernels)

104
sgl-kernel/tests/test_speculative_sampling.py Normal file
View File

@@ -0,0 +1,104 @@
import torch
import torch.nn.functional as F
from sgl_kernel import tree_speculative_sampling_target_only
def test_tree_speculative_sampling_target_only():
candidates = torch.tensor(
[
[0, 1, 2, 3, 4, 5],
[7, 8, 9, 10, 11, 12],
],
dtype=torch.int32,
device="cuda",
)
retrive_index = torch.tensor(
[
[0, 1, 2, 3, 4, 5],
[6, 7, 8, 9, 10, 11],
],
dtype=torch.int32,
device="cuda",
)
retrive_next_token = torch.tensor(
[
[1, 2, -1, 4, 5, -1],
[4, 2, 3, -1, 5, -1],
],
dtype=torch.int32,
device="cuda",
)
retrive_next_sibling = torch.tensor(
[
[-1, 3, -1, -1, -1, -1],
[-1, -1, -1, -1, 1, -1],
],
dtype=torch.int32,
device="cuda",
)
target_logits = torch.zeros((2, 6, 20), dtype=torch.float32, device="cuda")
target_logits[0, 0, 3] = 10
target_logits[0, 3, 4] = 10
target_logits[0, 4, 5] = 10
target_logits[1, 0, 11] = 10
target_logits[1, 4, 12] = 10
for i in range(target_logits.shape[0]):
for j in range(target_logits.shape[1]):
if torch.max(target_logits[i][j]) < 10:
target_logits[i][j][18] = 10
temperatures = torch.tensor([0.01, 0.01], dtype=torch.float32, device="cuda")
predict_shape = (12,)
bs = candidates.shape[0]
num_spec_step = 4
num_draft_tokens = candidates.shape[1]
predicts = torch.full(
predict_shape, -1, dtype=torch.int32, device="cuda"
) # mutable
accept_index = torch.full(
(bs, num_spec_step), -1, dtype=torch.int32, device="cuda"
) # mutable
accept_token_num = torch.full((bs,), 0, dtype=torch.int32, device="cuda") # mutable
expanded_temperature = temperatures.unsqueeze(1).unsqueeze(1)
target_probs = F.softmax(target_logits / expanded_temperature, dim=-1)
draft_probs = torch.full_like(target_probs, 0, dtype=torch.float32, device="cuda")
coins = torch.rand(bs, num_draft_tokens, device="cuda").to(torch.float32)
print(f"{candidates=}")
print(f"{retrive_index=}")
print(f"{retrive_next_token=}")
print(f"{retrive_next_sibling=}")
print(f"{coins=}")
tree_speculative_sampling_target_only(
predicts=predicts,
accept_index=accept_index,
accept_token_num=accept_token_num,
candidates=candidates,
retrive_index=retrive_index,
retrive_next_token=retrive_next_token,
retrive_next_sibling=retrive_next_sibling,
uniform_samples=coins,
target_probs=target_probs,
draft_probs=draft_probs,
deterministic=True,
)
print(f"{predicts=}")
print(f"{accept_index=}")
print(f"{accept_token_num=}")
assert predicts.tolist() == [3, -1, -1, 4, 5, 18, 11, -1, -1, -1, 12, 18]
assert accept_index.tolist() == [
[0, 3, 4, 5],
[6, 10, 11, -1],
]
assert accept_token_num.tolist() == [3, 2]
if __name__ == "__main__":
test_tree_speculative_sampling_target_only()

2
sgl-kernel/version.py
View File

@@ -1 +1 @@
__version__ = "0.0.3.post1"
__version__ = "0.0.3.post2"