278 lines
8.7 KiB
Plaintext
278 lines
8.7 KiB
Plaintext
/* 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"
|
|
|
|
template <typename T, int N, int Alignment = sizeof(T) * N>
|
|
class alignas(Alignment) AlignedArray {
|
|
public:
|
|
T data[N];
|
|
};
|
|
|
|
#define WARP_SIZE 32
|
|
|
|
#define VEC_SIZE 4
|
|
using Vec = AlignedArray<int32_t, VEC_SIZE>;
|
|
|
|
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];
|
|
int32_t rank_post_pad = atomicAdd(&cumsum_buffer[expert_id], 1);
|
|
sorted_token_ids[rank_post_pad] = i;
|
|
}
|
|
}
|
|
|
|
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 padded_num_experts,
|
|
int32_t experts_per_warp,
|
|
int32_t block_size,
|
|
size_t numel,
|
|
int32_t* __restrict__ cumsum,
|
|
bool pad_sorted_token_ids) {
|
|
extern __shared__ int32_t shared_counts[];
|
|
|
|
const int warp_id = threadIdx.x / WARP_SIZE;
|
|
const int my_expert_start = warp_id * experts_per_warp;
|
|
|
|
for (int i = 0; i < experts_per_warp; ++i) {
|
|
if (my_expert_start + i < padded_num_experts) {
|
|
shared_counts[warp_id * experts_per_warp + i] = 0;
|
|
}
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
const size_t tid = threadIdx.x;
|
|
const size_t stride = blockDim.x;
|
|
|
|
for (size_t i = tid; i < numel; i += stride) {
|
|
int expert_id = topk_ids[i];
|
|
int warp_idx = expert_id / experts_per_warp;
|
|
int expert_offset = expert_id % experts_per_warp;
|
|
atomicAdd(&shared_counts[warp_idx * experts_per_warp + expert_offset], 1);
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
if (threadIdx.x == 0) {
|
|
cumsum[0] = 0;
|
|
for (int i = 1; i <= num_experts; ++i) {
|
|
int expert_count = 0;
|
|
int warp_idx = (i - 1) / experts_per_warp;
|
|
int expert_offset = (i - 1) % experts_per_warp;
|
|
expert_count = shared_counts[warp_idx * experts_per_warp + expert_offset];
|
|
|
|
cumsum[i] = cumsum[i - 1] + CEILDIV(expert_count, block_size) * block_size;
|
|
}
|
|
*total_tokens_post_pad = cumsum[num_experts];
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
if (threadIdx.x < num_experts) {
|
|
for (int i = cumsum[threadIdx.x]; i < cumsum[threadIdx.x + 1]; i += block_size) {
|
|
expert_ids[i / block_size] = threadIdx.x;
|
|
}
|
|
}
|
|
|
|
if (pad_sorted_token_ids) {
|
|
int32_t fill_val = static_cast<int32_t>(numel);
|
|
int32_t total = *total_tokens_post_pad;
|
|
|
|
Vec fill_vec;
|
|
#pragma unroll
|
|
for (int i = 0; i < VEC_SIZE; ++i) {
|
|
fill_vec.data[i] = fill_val;
|
|
}
|
|
|
|
int32_t total_vec_count = (total + VEC_SIZE - 1) / VEC_SIZE;
|
|
Vec* out_ptr = reinterpret_cast<Vec*>(sorted_token_ids);
|
|
|
|
for (int32_t idx = tid; idx < total_vec_count; idx += stride) {
|
|
out_ptr[idx] = fill_vec;
|
|
}
|
|
}
|
|
}
|
|
|
|
template <typename scalar_t>
|
|
__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) {
|
|
const size_t tid = threadIdx.x;
|
|
const size_t stride = blockDim.x;
|
|
|
|
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[(threadIdx.x + 1) * num_experts + i] = 0;
|
|
}
|
|
|
|
for (size_t i = tid; i < numel; i += stride) {
|
|
++tokens_cnts[(threadIdx.x + 1) * num_experts + topk_ids[i]];
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
if (threadIdx.x < num_experts) {
|
|
tokens_cnts[threadIdx.x] = 0;
|
|
for (int i = 1; i <= blockDim.x; ++i) {
|
|
tokens_cnts[i * num_experts + threadIdx.x] += tokens_cnts[(i - 1) * num_experts + threadIdx.x];
|
|
}
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
if (threadIdx.x == 0) {
|
|
cumsum[0] = 0;
|
|
for (int i = 1; i <= num_experts; ++i) {
|
|
cumsum[i] = cumsum[i - 1] + CEILDIV(tokens_cnts[blockDim.x * num_experts + i - 1], block_size) * block_size;
|
|
}
|
|
*total_tokens_post_pad = static_cast<int32_t>(cumsum[num_experts]);
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
if (threadIdx.x < num_experts) {
|
|
for (int i = cumsum[threadIdx.x]; i < cumsum[threadIdx.x + 1]; i += block_size) {
|
|
expert_ids[i / block_size] = threadIdx.x;
|
|
}
|
|
}
|
|
|
|
if (pad_sorted_token_ids) {
|
|
int32_t fill_val = static_cast<int32_t>(numel);
|
|
int32_t total = *total_tokens_post_pad;
|
|
|
|
Vec fill_vec;
|
|
#pragma unroll
|
|
for (int i = 0; i < VEC_SIZE; ++i) {
|
|
fill_vec.data[i] = fill_val;
|
|
}
|
|
|
|
int32_t total_vec_count = (total + VEC_SIZE - 1) / VEC_SIZE;
|
|
Vec* out_ptr = reinterpret_cast<Vec*>(sorted_token_ids);
|
|
|
|
for (int32_t idx = tid; idx < total_vec_count; idx += stride) {
|
|
out_ptr[idx] = fill_vec;
|
|
}
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
for (size_t i = tid; i < numel; i += stride) {
|
|
int32_t expert_id = topk_ids[i];
|
|
int32_t rank_post_pad = tokens_cnts[threadIdx.x * num_experts + expert_id] + cumsum[expert_id];
|
|
sorted_token_ids[rank_post_pad] = i;
|
|
++tokens_cnts[threadIdx.x * 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 token_cnts_buffer,
|
|
torch::Tensor cumsum_buffer,
|
|
bool pad_sorted_token_ids) {
|
|
const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
|
|
|
|
int64_t padded_num_experts = ((num_experts + WARP_SIZE - 1) / WARP_SIZE) * WARP_SIZE;
|
|
|
|
int experts_per_warp = WARP_SIZE;
|
|
int threads = 1024;
|
|
|
|
threads = ((threads + WARP_SIZE - 1) / WARP_SIZE) * WARP_SIZE;
|
|
|
|
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);
|
|
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>;
|
|
small_batch_expert_kernel<<<1, 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);
|
|
} else {
|
|
auto align_kernel = moe_align_block_size_kernel<scalar_t>;
|
|
|
|
size_t num_warps = CEILDIV(padded_num_experts, experts_per_warp);
|
|
size_t shared_mem_size = num_warps * experts_per_warp * sizeof(int32_t);
|
|
|
|
align_kernel<<<1, 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,
|
|
padded_num_experts,
|
|
experts_per_warp,
|
|
block_size,
|
|
topk_ids.numel(),
|
|
cumsum_buffer.data_ptr<int32_t>(),
|
|
pad_sorted_token_ids);
|
|
|
|
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());
|
|
}
|
|
});
|
|
}
|