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chore: vendor sglang v0.5.10 snapshot

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Gahow Wang
2026-04-24 12:29:36 +00:00
parent 78f0d15221
commit bded08301f
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third_party/sglang/sgl-kernel/csrc/moe/moe_align_kernel.cu vendored Normal file
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/* Copyright 2025 SGLang Team. All Rights Reserved.
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>
#include <c10/cuda/CUDAGuard.h>
#include <THC/THCAtomics.cuh>
#include "utils.h"
#define VEC_SIZE 4
using Vec = int4;
template <typename scalar_t>
__global__ void count_and_sort_expert_tokens_kernel(
const scalar_t* __restrict__ topk_ids,
int32_t* __restrict__ sorted_token_ids,
int32_t* __restrict__ cumsum_buffer,
size_t numel) {
const size_t tid = blockIdx.x * blockDim.x + threadIdx.x;
const size_t stride = blockDim.x * gridDim.x;
for (size_t i = tid; i < numel; i += stride) {
int32_t expert_id = topk_ids[i] + 1;
int32_t rank_post_pad = atomicAdd(&cumsum_buffer[expert_id], 1);
sorted_token_ids[rank_post_pad] = i;
}
}
#ifdef __CUDA_ARCH__
__device__ __forceinline__ int warp_exclusive_scan(int v, unsigned mask = 0xffffffffu) {
int original = v;
#pragma unroll
for (int offset = 1; offset < WARP_SIZE; offset <<= 1) {
int n = __shfl_up_sync(mask, v, offset);
if ((threadIdx.x & (WARP_SIZE - 1)) >= offset) v += n;
}
return v - original;
}
#endif
template <typename scalar_t>
__global__ void moe_align_block_size_kernel(
const scalar_t* __restrict__ topk_ids,
int32_t* __restrict__ sorted_token_ids,
int32_t* __restrict__ expert_ids,
int32_t* __restrict__ total_tokens_post_pad,
int32_t num_experts,
int32_t block_size,
size_t numel,
int32_t* __restrict__ cumsum,
bool pad_sorted_token_ids,
const int32_t scan_size,
int32_t max_num_tokens_padded) {
// Use a separate thread block to populate sorted_token_ids
if (blockIdx.x == 1) {
if (pad_sorted_token_ids) {
Vec fill_vec;
fill_vec.x = fill_vec.y = fill_vec.z = fill_vec.w = numel;
int32_t total_vecs = (max_num_tokens_padded + VEC_SIZE - 1) / VEC_SIZE;
Vec* out_ptr = reinterpret_cast<Vec*>(sorted_token_ids);
for (int32_t i = threadIdx.x; i < total_vecs; i += blockDim.x) {
out_ptr[i] = fill_vec;
}
}
return;
}
extern __shared__ int32_t smem[];
int32_t* shared_counts = smem; // [num_experts]
int32_t* prefix = shared_counts + num_experts; // [num_experts + 1]
int32_t* scan_buf = prefix + num_experts + 1; // [scan_size]
__shared__ int32_t s_total_tokens_post_pad;
const size_t tid = threadIdx.x;
const size_t stride = blockDim.x;
if (tid < num_experts) {
shared_counts[tid] = 0;
}
__syncthreads();
for (size_t i = tid; i < numel; i += stride) {
int expert_id = topk_ids[i] + 1;
atomicAdd(&shared_counts[expert_id], 1);
}
__syncthreads();
int32_t padded_count = 0;
if (tid < num_experts) {
int32_t count = shared_counts[tid];
padded_count = (count + block_size - 1) / block_size * block_size;
scan_buf[tid] = padded_count;
}
#ifndef __CUDA_ARCH__ // HIP
if (tid >= num_experts && tid < scan_size) {
scan_buf[tid] = 0;
}
__syncthreads();
// Blelloch scan
int offset = 1;
#pragma unroll
for (int d = scan_size >> 1; d > 0; d >>= 1) {
if (tid < d) {
int ai = offset * (2 * tid + 1) - 1;
int bi = offset * (2 * tid + 2) - 1;
scan_buf[bi] += scan_buf[ai];
}
offset <<= 1;
__syncthreads();
}
// down-sweep
if (tid == 0) {
prefix[num_experts] = scan_buf[scan_size - 1];
scan_buf[scan_size - 1] = 0;
}
__syncthreads();
#pragma unroll
for (int d = 1; d < scan_size; d <<= 1) {
offset >>= 1;
if (tid < d) {
int ai = offset * (2 * tid + 1) - 1;
int bi = offset * (2 * tid + 2) - 1;
if (bi < scan_size) {
int temp = scan_buf[ai];
scan_buf[ai] = scan_buf[bi];
scan_buf[bi] += temp;
}
}
__syncthreads();
}
if (tid < num_experts) {
prefix[tid] = scan_buf[tid];
}
if (tid == 0) {
s_total_tokens_post_pad = prefix[num_experts];
*total_tokens_post_pad = s_total_tokens_post_pad;
}
__syncthreads();
#else // CUDA
// Intra warp prefix sum
int32_t* warp_sums = scan_buf + scan_size; // [<= 32]
const int warp_id = tid / WARP_SIZE;
const int lane_id = tid & (WARP_SIZE - 1);
const int num_warps_for_scan = (scan_size + WARP_SIZE - 1) / WARP_SIZE;
const int warp_sum = warp_exclusive_scan(padded_count) + padded_count;
if (lane_id == WARP_SIZE - 1) warp_sums[warp_id] = warp_sum;
__syncthreads();
// warp0 accumulate all the block's prefix sum
if (tid < WARP_SIZE) {
int val = (tid < num_warps_for_scan) ? warp_sums[tid] : 0;
int incl = warp_exclusive_scan(val) + val;
warp_sums[tid] = incl;
}
__syncthreads();
// Every thread obtains the whole block's sum
if (tid == 0) {
prefix[num_experts] = warp_sums[num_warps_for_scan - 1];
s_total_tokens_post_pad = prefix[num_experts];
*total_tokens_post_pad = s_total_tokens_post_pad;
}
__syncthreads();
// Fill 0 to scan_buf extended area (tid >= num_expert)
if (tid >= num_experts && tid < scan_size) scan_buf[tid] = 0;
__syncthreads();
// Perform 2 level exclusive-prefix-sum to scan_buf
int v = (tid < scan_size) ? scan_buf[tid] : 0;
int pre = warp_exclusive_scan(v);
if (lane_id == WARP_SIZE - 1) warp_sums[warp_id] = pre + v;
__syncthreads();
if (warp_id == 0) {
int val = (lane_id < num_warps_for_scan) ? warp_sums[lane_id] : 0;
warp_sums[lane_id] = warp_exclusive_scan(val);
}
__syncthreads();
int offset = warp_sums[warp_id];
if (tid < scan_size) scan_buf[tid] = pre + offset;
__syncthreads();
// Write prefix[0..num_experts - 1] and cumsum
if (tid < num_experts) prefix[tid] = scan_buf[tid];
#endif
if (tid <= num_experts) {
cumsum[tid] = prefix[tid];
}
// fill expert_ids
const int32_t num_blocks = s_total_tokens_post_pad / block_size;
for (int32_t i = tid; i < num_blocks; i += stride) {
int32_t block_start = i * block_size;
int left = 0, right = num_experts;
while (left < right) {
int mid = (left + right) >> 1;
if (prefix[mid] <= block_start) {
left = mid + 1;
} else {
right = mid;
}
}
expert_ids[i] = left - 2;
}
}
template <typename scalar_t, int32_t fill_threads>
__global__ void moe_align_block_size_small_batch_expert_kernel(
const scalar_t* __restrict__ topk_ids,
int32_t* __restrict__ sorted_token_ids,
int32_t* __restrict__ expert_ids,
int32_t* __restrict__ total_tokens_post_pad,
int32_t num_experts,
int32_t block_size,
size_t numel,
bool pad_sorted_token_ids,
int32_t max_num_tokens_padded) {
// Adapted from
// https://github.com/vllm-project/vllm/pull/29642/files#diff-5647b1413f4ae9aacba904eca8f8a8aee9079321eadff4c10101a2c6962dcc53R226
// Use an additional group of threads to fill sorted_token_ids.
// Since the kernel will use sorted_token_ids afterward,
// we fill sorted_token_ids within the same threadblock to make
// synchronization easier.
if (threadIdx.x < fill_threads) {
// Initialize sorted_token_ids with numel
if (pad_sorted_token_ids) {
for (int32_t it = threadIdx.x; it < max_num_tokens_padded; it += fill_threads) {
sorted_token_ids[it] = numel;
}
}
// Three __syncthreads() corresponding to the other threads
__syncthreads();
__syncthreads();
__syncthreads();
return;
}
const size_t tid = threadIdx.x - fill_threads;
const size_t stride = blockDim.x - fill_threads;
extern __shared__ int32_t shared_mem[];
int32_t* cumsum = shared_mem;
int32_t* tokens_cnts = (int32_t*)(shared_mem + num_experts + 1);
for (int i = 0; i < num_experts; ++i) {
tokens_cnts[(tid + 1) * num_experts + i] = 0;
}
for (size_t i = tid; i < numel; i += stride) {
int32_t expert_id = topk_ids[i] + 1;
++tokens_cnts[(tid + 1) * num_experts + expert_id];
}
__syncthreads();
if (tid < num_experts) {
tokens_cnts[tid] = 0;
for (int i = 1; i <= stride; ++i) {
tokens_cnts[i * num_experts + tid] += tokens_cnts[(i - 1) * num_experts + tid];
}
}
__syncthreads();
if (tid == 0) {
cumsum[0] = 0;
for (int i = 1; i <= num_experts; ++i) {
cumsum[i] = cumsum[i - 1] + CEILDIV(tokens_cnts[stride * num_experts + i - 1], block_size) * block_size;
}
*total_tokens_post_pad = static_cast<int32_t>(cumsum[num_experts]);
}
__syncthreads();
if (tid < num_experts) {
for (int i = cumsum[tid]; i < cumsum[tid + 1]; i += block_size) {
expert_ids[i / block_size] = tid - 1;
}
}
for (size_t i = tid; i < numel; i += stride) {
int32_t expert_id = topk_ids[i] + 1;
int32_t rank_post_pad = tokens_cnts[tid * num_experts + expert_id] + cumsum[expert_id];
sorted_token_ids[rank_post_pad] = i;
++tokens_cnts[tid * num_experts + expert_id];
}
}
void moe_align_block_size(
torch::Tensor topk_ids,
int64_t num_experts,
int64_t block_size,
torch::Tensor sorted_token_ids,
torch::Tensor experts_ids,
torch::Tensor num_tokens_post_pad,
torch::Tensor cumsum_buffer,
bool pad_sorted_token_ids) {
const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
int threads = 1024;
threads = ((threads + WARP_SIZE - 1) / WARP_SIZE) * WARP_SIZE;
int64_t max_num_tokens_padded = sorted_token_ids.size(0);
DISPATCH_INTEGRAL_TYPES(topk_ids.scalar_type(), "moe_align_block_size_kernel", [&] {
bool small_batch_expert_mode = (topk_ids.numel() < 1024) && (num_experts <= 64);
if (small_batch_expert_mode) {
const int32_t threads = max((int32_t)num_experts, WARP_SIZE);
constexpr int32_t fill_threads = 256;
const int32_t shared_mem_size = ((threads + 1) * num_experts + (num_experts + 1)) * sizeof(int32_t);
auto small_batch_expert_kernel = moe_align_block_size_small_batch_expert_kernel<scalar_t, fill_threads>;
small_batch_expert_kernel<<<1, fill_threads + threads, shared_mem_size, stream>>>(
topk_ids.data_ptr<scalar_t>(),
sorted_token_ids.data_ptr<int32_t>(),
experts_ids.data_ptr<int32_t>(),
num_tokens_post_pad.data_ptr<int32_t>(),
num_experts,
block_size,
topk_ids.numel(),
pad_sorted_token_ids,
max_num_tokens_padded);
} else {
auto align_kernel = moe_align_block_size_kernel<scalar_t>;
const size_t scan_size = next_pow2(num_experts);
const size_t shared_mem_size = (num_experts + (num_experts + 1) + scan_size + WARP_SIZE) * sizeof(int32_t);
align_kernel<<<2, threads, shared_mem_size, stream>>>(
topk_ids.data_ptr<scalar_t>(),
sorted_token_ids.data_ptr<int32_t>(),
experts_ids.data_ptr<int32_t>(),
num_tokens_post_pad.data_ptr<int32_t>(),
num_experts,
block_size,
topk_ids.numel(),
cumsum_buffer.data_ptr<int32_t>(),
pad_sorted_token_ids,
scan_size,
max_num_tokens_padded);
const int block_threads = std::min(256, (int)threads);
const int num_blocks = (topk_ids.numel() + block_threads - 1) / block_threads;
const int max_blocks = 65535;
const int actual_blocks = std::min(num_blocks, max_blocks);
auto sort_kernel = count_and_sort_expert_tokens_kernel<scalar_t>;
sort_kernel<<<actual_blocks, block_threads, 0, stream>>>(
topk_ids.data_ptr<scalar_t>(),
sorted_token_ids.data_ptr<int32_t>(),
cumsum_buffer.data_ptr<int32_t>(),
topk_ids.numel());
}
});
}