vllm/csrc/moe_align_block_size_kernels.cu

#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>

#include <ATen/ATen.h>
#include <THC/THCAtomics.cuh>

#include "cuda_compat.h"
#include "dispatch_utils.h"

#define CEILDIV(x,y) (((x) + (y) - 1) / (y))

namespace vllm {

namespace {
__device__ __forceinline__ int32_t index(int32_t total_col, int32_t row, int32_t col) {
    // don't worry about overflow because num_experts is relatively small
    return row * total_col + col;
}
}

template <typename scalar_t>
__global__ void moe_align_block_size_kernel(scalar_t *__restrict__ topk_ids, 
                                int32_t *sorted_token_ids, 
                                int32_t *expert_ids, 
                                int32_t *total_tokens_post_pad,
                                int32_t num_experts, 
                                int32_t block_size, 
                                size_t numel) {
    const size_t tokens_per_thread = CEILDIV(numel, blockDim.x);
    const size_t start_idx = threadIdx.x * tokens_per_thread;

    extern __shared__ int32_t shared_mem[];

    int32_t* tokens_cnts = shared_mem; // 2d tensor with shape (num_experts + 1, num_experts)
    int32_t* cumsum = shared_mem + (num_experts + 1) * num_experts; // 1d tensor with shape (num_experts + 1)

    for (int i = 0; i < num_experts; ++i) {
        tokens_cnts[index(num_experts, threadIdx.x + 1, i)] = 0;
    }

    /**
    * In the first step we compute token_cnts[thread_index + 1][expert_index],
    * which counts how many tokens in the token shard of thread_index are assigned
    * to expert expert_index.
    */
    for (int i = start_idx; i < numel && i < start_idx + tokens_per_thread; ++i) {
        ++tokens_cnts[index(num_experts, threadIdx.x + 1, topk_ids[i])]; 
    }

    __syncthreads();

    // For each expert we accumulate the token counts from the different threads.
    tokens_cnts[index(num_experts, 0, threadIdx.x)] = 0;
    for (int i = 1; i <= blockDim.x; ++i) {
        tokens_cnts[index(num_experts, i, threadIdx.x)] += tokens_cnts[index(num_experts, i-1, threadIdx.x)];
    }

    __syncthreads();
    
    // We accumulate the token counts of all experts in thread 0.
    if (threadIdx.x == 0) {
        cumsum[0] = 0;
        for (int i = 1; i <= num_experts; ++i) {
            cumsum[i] = cumsum[i-1] + CEILDIV(tokens_cnts[index(num_experts, blockDim.x, i - 1)], block_size) * block_size;
        }
        *total_tokens_post_pad = cumsum[num_experts];
    }

    __syncthreads();

    /**
    * For each expert, each thread processes the tokens of the corresponding blocks
    * and stores the corresponding expert_id for each block.
    */
    for (int i = cumsum[threadIdx.x];i < cumsum[threadIdx.x + 1];i += block_size) {
        expert_ids[i / block_size] = threadIdx.x;
    }
    
    /**
    * Each thread processes a token shard, calculating the index of each token after
    * sorting by expert number. Given the example topk_ids = [0,1,2,1,2,3,0,3,4] and
    * block_size = 4, then the output would be [0, 6, *, *, 1, 3, *, *, 2, 4, *, *, 5, 7, *, *, 8, *, *, *],
    * where * represents a padding value(preset in python).
    */
    for (int i = start_idx; i < numel && i < start_idx + tokens_per_thread; ++i) {
        int32_t expert_id = topk_ids[i];
        /** The cumsum[expert_id] stores the starting index of the tokens that the
        * expert with expert_id needs to process, and tokens_cnts[threadIdx.x][expert_id]
        * stores the indices of the tokens processed by the expert with expert_id within
        * the current thread's token shard.
        */
        int32_t rank_post_pad = tokens_cnts[index(num_experts, threadIdx.x, expert_id)] + cumsum[expert_id];
        sorted_token_ids[rank_post_pad] = i;
        ++tokens_cnts[index(num_experts, threadIdx.x, expert_id)];
    }
}
}

void moe_align_block_size(
    torch::Tensor topk_ids,
    int num_experts,
    int block_size,
    torch::Tensor sorted_token_ids,
    torch::Tensor experts_ids,
    torch::Tensor num_tokens_post_pad) {
    const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
    VLLM_DISPATCH_INTEGRAL_TYPES(
        topk_ids.scalar_type(), "moe_align_block_size_kernel", [&] {
        // calc needed amount of shared mem for `tokens_cnts` and `cumsum` tensors
        const int32_t shared_mem = ((num_experts + 1) * num_experts + (num_experts + 1)) * sizeof(int32_t);

        // set dynamic shared mem
        auto kernel = vllm::moe_align_block_size_kernel<scalar_t>;
        AT_CUDA_CHECK(
            VLLM_DevFuncAttribute_SET_MaxDynamicSharedMemorySize((void *)kernel, shared_mem));
        kernel<<<1, num_experts, shared_mem, 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());
    });
}