DeepEP/csrc/kernels/intranode.cu

#include "configs.cuh"
#include "buffer.cuh"
#include "exception.cuh"
#include "launch.cuh"
#include "utils.cuh"

namespace deep_ep {

namespace intranode {

template<int kNumRanks>
__global__ void
notify_dispatch(const int* num_tokens_per_rank, int* moe_recv_counter_mapped,
                const int* num_tokens_per_expert, int* moe_recv_expert_counter_mapped, int num_experts,
                int num_tokens, int num_channels, const bool* is_token_in_rank, int* channel_prefix_matrix,
                int* rank_prefix_matrix_copy, int num_memset_int, int expert_alignment,
                void** buffer_ptrs, int **task_fifo_ptrs, int head, int rank) {
    auto sm_id = static_cast<int>(blockIdx.x);
    auto thread_id = static_cast<int>(threadIdx.x), num_threads = static_cast<int>(blockDim.x);
    auto lane_id = thread_id % 32, warp_id = thread_id / 32, num_warps = num_threads / 32;

    if (sm_id == 0) {
        // Barrier first
        barrier_device<kNumRanks>(task_fifo_ptrs, head, rank);
        move_fifo_slots<kNumRanks>(head);
        __syncthreads();

        int *per_rank_buffer, *per_expert_buffer;
        if (thread_id < kNumRanks) {
            per_rank_buffer = reinterpret_cast<int*>(buffer_ptrs[thread_id]);
            per_expert_buffer = per_rank_buffer + kNumRanks * kNumRanks;
        }

        // After this loop:
        //  - `per_rank_buffer[rank][i, j]` means the number of tokens from rank i to rank j
        //  - `per_expert_buffer[rank][i, j]` means the number of tokens from rank i to local expert j
        int num_experts_per_rank = num_experts / kNumRanks;
        if (thread_id < kNumRanks) {
            #pragma unroll
            for (int i = 0; i < kNumRanks; ++ i)
                per_rank_buffer[rank * kNumRanks + i] = num_tokens_per_rank[i];
            #pragma unroll
            for (int i = 0; i < num_experts_per_rank; ++ i)
                per_expert_buffer[rank * num_experts_per_rank + i] = num_tokens_per_expert[thread_id * num_experts_per_rank + i];
        }
        __syncthreads();

        // Wait for all ranks to be finished
        barrier_device<kNumRanks>(task_fifo_ptrs, head, rank);
        move_fifo_slots<kNumRanks>(head);
        __syncthreads();

        // Sum per-rank counts and return to CPU
        // Also pre-compute the prefix sum for data sending
        auto local_per_rank_buffer = reinterpret_cast<int*>(buffer_ptrs[rank]);
        if (thread_id < kNumRanks) {
            #pragma unroll
            for (int i = 1; i < kNumRanks; ++ i)
                local_per_rank_buffer[i * kNumRanks + thread_id] += local_per_rank_buffer[(i - 1) * kNumRanks + thread_id];
            if (thread_id == rank)
                *moe_recv_counter_mapped = local_per_rank_buffer[(kNumRanks - 1) * kNumRanks + rank];
        }

        // Sum per-experts counts and return to CPU
        auto local_per_expert_buffer = local_per_rank_buffer + kNumRanks * kNumRanks;
        if (thread_id < num_experts_per_rank) {
            int sum = 0;
            #pragma unroll
            for (int i = 0; i < kNumRanks; ++ i)
                sum += local_per_expert_buffer[i * num_experts_per_rank + thread_id];
            sum = (sum + expert_alignment - 1) / expert_alignment * expert_alignment;
            moe_recv_expert_counter_mapped[thread_id] = sum;
        }
        __syncthreads();

        // Copy rank size prefix matrix to another tensor
        #pragma unroll
        for (int i = thread_id; i < kNumRanks * kNumRanks; i += num_threads)
            rank_prefix_matrix_copy[i] = local_per_rank_buffer[i];

        // Extra memset for later communication queue
        #pragma unroll
        for (int i = thread_id; i < num_memset_int; i += num_threads)
            local_per_expert_buffer[i] = 0;

        // Barrier
        memory_fence();
        __syncthreads();
        barrier_device<kNumRanks>(task_fifo_ptrs, head, rank);
    } else {
        int dst_rank = sm_id - 1;
        for (int channel_id = warp_id; channel_id < num_channels; channel_id += num_warps) {
            int token_start_idx, token_end_idx;
            get_channel_task_range(num_tokens, num_channels, channel_id, token_start_idx, token_end_idx);

            // Iterate over tokens
            int count = 0;
            for (int64_t i = token_start_idx + lane_id; i < token_end_idx; i += 32)
                count += is_token_in_rank[i * kNumRanks + dst_rank];
            count = warp_reduce_sum(count);
            if (lane_id == 0)
                channel_prefix_matrix[dst_rank * num_channels + channel_id] = count;
        }
        __syncthreads();

        // Pre-compute prefix sum for all channels
        if (thread_id == 0) {
            #pragma unroll
            for (int i = 1; i < num_channels; ++ i)
                channel_prefix_matrix[dst_rank * num_channels + i] += channel_prefix_matrix[dst_rank * num_channels + i - 1];
        }
    }
}

void notify_dispatch(const int* num_tokens_per_rank, int* moe_recv_counter_mapped, int num_ranks,
                     const int* num_tokens_per_expert, int* moe_recv_expert_counter_mapped, int num_experts,
                     int num_tokens, const bool* is_token_in_rank, int* channel_prefix_matrix,
                     int* rank_prefix_matrix_copy, int num_memset_int, int expert_alignment,
                     void** buffer_ptrs, int **task_fifo_ptrs, int head, int rank,
                     cudaStream_t stream, int num_channels) {
#define NOTIFY_DISPATCH_LAUNCH_CASE(ranks) \
    LAUNCH_KERNEL(&cfg, notify_dispatch<ranks>, \
        num_tokens_per_rank, moe_recv_counter_mapped, \
        num_tokens_per_expert, moe_recv_expert_counter_mapped, num_experts, \
        num_tokens, num_channels, is_token_in_rank, channel_prefix_matrix, \
        rank_prefix_matrix_copy, num_memset_int, expert_alignment, \
        buffer_ptrs, task_fifo_ptrs, head, rank); \
    break

    constexpr int kNumThreads = 128;
    EP_HOST_ASSERT(num_experts % num_ranks == 0);
    EP_HOST_ASSERT(num_experts / num_ranks <= kNumThreads and num_ranks <= kNumThreads);

    SETUP_LAUNCH_CONFIG(1 + num_ranks, kNumThreads, stream);
    SWITCH_RANKS(NOTIFY_DISPATCH_LAUNCH_CASE);
#undef NOTIFY_DISPATCH_LAUNCH_CASE
}

template<int kNumRanks>
__global__ void
cached_notify_dispatch(const int* rank_prefix_matrix, int num_memset_int,
                       void** buffer_ptrs, int** task_fifo_ptrs, int head, int rank) {
    // A simplified version for cached handles
    barrier_device<kNumRanks>(task_fifo_ptrs, head, rank);
    move_fifo_slots<kNumRanks>(head);
    __syncthreads();

    // Copy and clean
    auto thread_id = static_cast<int>(threadIdx.x), num_threads = static_cast<int>(blockDim.x);
    auto ptr = reinterpret_cast<int*>(buffer_ptrs[rank]);
    #pragma unroll
    for (int i = thread_id; i < kNumRanks * kNumRanks; i += num_threads)
        ptr[i] = rank_prefix_matrix[i];
    #pragma unroll
    for (int i = thread_id; i < num_memset_int; i += num_threads)
        ptr[kNumRanks * kNumRanks + i] = 0;
    memory_fence();
    __syncthreads();

    // Barrier after cleaning
    barrier_device<kNumRanks>(task_fifo_ptrs, head, rank);
}

void cached_notify_dispatch(const int* rank_prefix_matrix, int num_memset_int,
                            void** buffer_ptrs, int** task_fifo_ptrs,
                            int head, int rank, int num_ranks, cudaStream_t stream) {
#define CACHED_NOTIFY_DISPATCH_LAUNCH_CASE(ranks) \
    LAUNCH_KERNEL(&cfg, cached_notify_dispatch<ranks>, \
        rank_prefix_matrix, num_memset_int, buffer_ptrs, task_fifo_ptrs, head, rank); \
    break

    SETUP_LAUNCH_CONFIG(1, 128, stream);
    SWITCH_RANKS(CACHED_NOTIFY_DISPATCH_LAUNCH_CASE);
#undef CACHED_NOTIFY_DISPATCH_LAUNCH_CASE
}

template <int kNumRanks, int kNumThreads>
__global__ void __launch_bounds__(kNumThreads, 1)
dispatch(int4* recv_x, float* recv_x_scales, int* recv_src_idx, int64_t* recv_topk_idx, float* recv_topk_weights, int* recv_channel_offset,
         int* send_head, const int4* x, const float* x_scales, const int64_t* topk_idx, const float* topk_weights,
         const bool* is_token_in_rank, const int* channel_prefix_matrix,
         int num_tokens, int hidden_int4, int num_topk, int num_experts, int num_scales,
         void **buffer_ptrs, int rank,
         int num_max_send_tokens, int num_recv_buffer_tokens) {
    const auto num_sms = static_cast<int>(gridDim.x), sm_id = static_cast<int>(blockIdx.x);
    const auto thread_id = static_cast<int>(threadIdx.x);
    const bool is_sender = sm_id % 2 == 0;
    EP_DEVICE_ASSERT(num_sms % 2 == 0);

    // Several warps are response for a single rank
    const auto num_threads_per_rank = kNumThreads / kNumRanks;
    const auto num_channels = num_sms / 2;
    const auto responsible_rank = (static_cast<int>(thread_id)) / num_threads_per_rank;
    // Even-numbered blocks for sending, odd-numbered blocks for receiving.
    const auto responsible_channel = sm_id / 2;

    int num_experts_per_rank = num_experts / kNumRanks;
    EP_DEVICE_ASSERT(num_experts_per_rank > 0 or num_topk == 0);
    EP_DEVICE_ASSERT(num_topk <= 32);
    EP_DEVICE_ASSERT((topk_idx == nullptr)  == (topk_weights == nullptr));
    EP_DEVICE_ASSERT((recv_topk_idx == nullptr) == (recv_topk_weights == nullptr));

    // Calculate pointers by the specific layout
    // `rank_prefix_matrix`: kNumRanks * kNumRanks * sizeof(int)
    auto ptr = reinterpret_cast<void*>(reinterpret_cast<int8_t*>(buffer_ptrs[is_sender ? responsible_rank : rank]) + kNumRanks * kNumRanks * sizeof(int));
    int target_rank = is_sender ? rank : responsible_rank;
    auto num_channels_total = num_channels * kNumRanks;
    auto channel_rank_offset = responsible_channel * kNumRanks + target_rank;

    // Channel buffer metadata
    // Senders are responsible for tails, and receivers are responsible for heads
    // Stored on the receiver side
    // The retired signals are actually boolean flags, but to align with 16 bytes, we make it `int64_t`
    // `start_offset`: kNumChannels * kNumRanks * sizeof(int)
    // `end_offset`: kNumChannels * kNumRanks * sizeof(int)
    // `head_idx`: kNumChannels * kNumRanks * sizeof(int)
    // `tail_idx`: kNumChannels * kNumRanks * sizeof(int)
    auto channel_start_offset = Buffer<int>(ptr, num_channels_total, channel_rank_offset);
    auto channel_end_offset = Buffer<int>(ptr, num_channels_total, channel_rank_offset);
    auto channel_head_idx = Buffer<int>(ptr, num_channels_total, channel_rank_offset);
    auto channel_tail_idx = Buffer<int>(ptr, num_channels_total, channel_rank_offset);

    // Channel data buffers, stored on the receiver side
    // `x_buffers`: kNumChannels * kNumRanks * num_recv_buffer_tokens * hidden_int4 * sizeof(int4)
    // `src_idx_buffers`: kNumChannels * kNumRanks * num_recv_buffer_tokens * sizeof(int)
    // `topk_idx_buffers`: kNumChannels * kNumRanks * num_recv_buffer_tokens * num_topk * sizeof(int64_t)
    // `topk_weights_buffers`: kNumChannels * kNumRanks * num_recv_buffer_tokens * num_topk * sizeof(float)
    // `x_scales_buffers`: kNumChannels * kNumRanks * num_recv_buffer_tokens * num_scales * sizeof(float)
    auto channel_x_buffers = Buffer<int4>(ptr, num_channels_total * num_recv_buffer_tokens * hidden_int4, channel_rank_offset * num_recv_buffer_tokens * hidden_int4);
    auto channel_src_idx_buffers = Buffer<int>(ptr, num_channels_total * num_recv_buffer_tokens, channel_rank_offset * num_recv_buffer_tokens);
    auto channel_topk_idx_buffers = Buffer<int64_t>(ptr, num_channels_total * num_recv_buffer_tokens * num_topk, channel_rank_offset * num_recv_buffer_tokens * num_topk);
    auto channel_topk_weights_buffers = Buffer<float>(ptr, num_channels_total * num_recv_buffer_tokens * num_topk, channel_rank_offset * num_recv_buffer_tokens * num_topk);
    auto channel_x_scales_buffers = Buffer<float>(ptr, num_channels_total * num_recv_buffer_tokens * num_scales, channel_rank_offset * num_recv_buffer_tokens * num_scales);

    if (is_sender) {
        // Workers for sending
        constexpr int num_send_warps = kNumThreads / 32;
        constexpr int num_send_warps_per_rank = num_send_warps / kNumRanks;
        const auto send_thread_id = thread_id;
        const auto send_lane_id = send_thread_id % 32;
        const auto send_warp_id_in_rank = send_thread_id % num_threads_per_rank / 32;
        EP_DEVICE_ASSERT(kNumRanks <= 32);
        EP_DEVICE_ASSERT(num_send_warps % kNumRanks == 0);

        // Send offset by `-value - 1`, e.g. 0 -> -1, 1 -> -2
        // NOTES: this is for distinguishing zero tokens
        if (send_lane_id == 0 and send_warp_id_in_rank == 0) {
            int value = responsible_channel > 0 ? channel_prefix_matrix[responsible_rank * num_channels + responsible_channel - 1] : 0;
            st_relaxed_sys_global(channel_start_offset.buffer(), -value - 1);
            value = channel_prefix_matrix[responsible_rank * num_channels + responsible_channel];
            st_relaxed_sys_global(channel_end_offset.buffer(), -value - 1);
        }
        __syncwarp();

        // Get tasks
        int token_start_idx, token_end_idx;
        get_channel_task_range(num_tokens, num_channels, responsible_channel, token_start_idx, token_end_idx);

        // Iterate over all tokens and send by chunks
        int cached_channel_tail_idx = 0;
        for (int64_t token_idx = token_start_idx; token_idx < token_end_idx; ) {
            // Check destination queue emptiness, or wait a buffer to be released (rare cases)
            // NOTES: the head index received by different warps may not be the same
            auto start_time = clock64();
            while (send_lane_id == 0) {
                // NOTES: we only consider the worst case, because counting the real numbers are time-consuming
                int num_used_slots = cached_channel_tail_idx - ld_volatile_global(channel_head_idx.buffer());
                if (num_recv_buffer_tokens - num_used_slots >= num_max_send_tokens)
                    break;

                // Rare cases to loop again
                if (clock64() - start_time > NUM_TIMEOUT_CYCLES) {
                    printf("DeepEP timeout for dispatch senders, rank %d, responsible_channel = %d\n", rank, responsible_channel);
                    trap();
                }
            }
            __syncwarp();

            int chunk_token_idx = 0;
            while (chunk_token_idx < num_max_send_tokens and token_idx < token_end_idx) {
                // NOTES: for the same token, the warp assigned to save `send_head` may be different from the warp assigned to send subsequent data
                if (send_lane_id == 0 and token_idx % num_send_warps_per_rank == send_warp_id_in_rank)
                    send_head[token_idx * kNumRanks + responsible_rank] = is_token_in_rank[token_idx * kNumRanks + responsible_rank] ? cached_channel_tail_idx : -1;

                // Skip if not selected
                if (not is_token_in_rank[token_idx * kNumRanks + responsible_rank]) {
                    token_idx ++;
                    continue;
                }

                // Get an empty slot
                int dst_slot_idx = (cached_channel_tail_idx ++) % num_recv_buffer_tokens;
                if (cached_channel_tail_idx % num_send_warps_per_rank == send_warp_id_in_rank) {
                    // Copy data
                    auto shifted_channel_x_buffers = channel_x_buffers.buffer() + dst_slot_idx * hidden_int4;
                    auto shifted_x = x + token_idx * hidden_int4;
                    UNROLLED_WARP_COPY(5, send_lane_id, hidden_int4, shifted_channel_x_buffers, shifted_x,
                                       __ldg, st_na_global);

                    // Copy source index
                    if (send_lane_id == 0)
                        channel_src_idx_buffers[dst_slot_idx] = static_cast<int>(token_idx);

                    // Copy `topk_idx` and `topk_weights` with transformed index
                    if (send_lane_id < num_topk) {
                        // Top-k index
                        int recv_expert_begin = responsible_rank * num_experts_per_rank, recv_expert_end = (responsible_rank + 1) * num_experts_per_rank;
                        auto idx_value = __ldg(topk_idx + token_idx * num_topk + send_lane_id);
                        idx_value = (idx_value >= recv_expert_begin and idx_value < recv_expert_end) ? idx_value - recv_expert_begin : -1;
                        channel_topk_idx_buffers[dst_slot_idx * num_topk + send_lane_id] = idx_value;

                        // Top-k weights
                        auto weight_value = __ldg(topk_weights + token_idx * num_topk + send_lane_id);
                        weight_value = (idx_value >= 0) ? weight_value : 0.0f;
                        channel_topk_weights_buffers[dst_slot_idx * num_topk + send_lane_id] = weight_value;
                    }

                    // Copy `x_scales`
                    #pragma unroll
                    for (int i = send_lane_id; i < num_scales; i += 32)
                        channel_x_scales_buffers[dst_slot_idx * num_scales + i] = __ldg(x_scales + token_idx * num_scales + i);
                }

                // Move token index
                chunk_token_idx ++, token_idx ++;
            }

            // Move tail index
            // NOTES: here all warps should share the same new tail
            asm volatile("bar.sync %0, %1;" :: "r"(responsible_rank), "r"(num_threads_per_rank));
            if (send_warp_id_in_rank == 0 and send_lane_id == 0)
                st_release_sys_global(channel_tail_idx.buffer(), cached_channel_tail_idx);
        }
    } else {
        // Workers for receiving and copying into buffer
        constexpr int num_recv_warps = kNumThreads / 32;
        constexpr int num_recv_warps_per_rank = num_recv_warps / kNumRanks;
        const auto recv_thread_id = thread_id;
        const auto recv_lane_id = recv_thread_id % 32;
        const auto recv_thread_id_in_rank = recv_thread_id % num_threads_per_rank;
        const auto recv_warp_id_in_rank = recv_thread_id_in_rank / 32;
        EP_DEVICE_ASSERT(kNumRanks <= 32);
        EP_DEVICE_ASSERT(recv_thread_id >= 0 and num_recv_warps % kNumRanks == 0);

        // Calculate offset first
        auto rank_prefix_matrix = reinterpret_cast<int*>(buffer_ptrs[rank]);
        int rank_offset = responsible_rank > 0 ? rank_prefix_matrix[(responsible_rank - 1) * kNumRanks + rank] : 0;

        // Receive channel offset
        int total_offset, num_tokens_to_recv;
        while (recv_lane_id == 0 and (total_offset = ld_volatile_global(channel_start_offset.buffer())) == 0);
        while (recv_lane_id == 0 and (num_tokens_to_recv = ld_volatile_global(channel_end_offset.buffer())) == 0);
        if (recv_lane_id == 0) {
            total_offset = -total_offset - 1, num_tokens_to_recv = -num_tokens_to_recv - 1;
            if (recv_warp_id_in_rank == 0)
                recv_channel_offset[responsible_rank * num_channels + responsible_channel] = total_offset;
            num_tokens_to_recv -= total_offset;
        }
        total_offset = __shfl_sync(0xffffffff, total_offset, 0);
        total_offset += rank_offset;
        num_tokens_to_recv = __shfl_sync(0xffffffff, num_tokens_to_recv, 0);

        // Shared tail indices for different warps
        __shared__ volatile int shared_channel_tail_idx[kNumRanks];

        auto start_time = clock64();
        int cached_channel_head_idx = 0, cached_channel_tail_idx = 0;
        while (num_tokens_to_recv > 0) {
            // NOTES: unlike the sender, the receiver must ensure that the tail indices hold by different warps are same
            while (recv_thread_id_in_rank == 0) {
                cached_channel_tail_idx = ld_acquire_sys_global(channel_tail_idx.buffer());;

                // Ready to copy
                if (cached_channel_head_idx != cached_channel_tail_idx) {
                    shared_channel_tail_idx[responsible_rank] = cached_channel_tail_idx;
                    break;
                }

                // Timeout check
                if (clock64() - start_time > NUM_TIMEOUT_CYCLES) {
                    printf("DeepEP timeout for dispatch receivers, rank %d, responsible_channel = %d, tokens remained: %d\n", rank, responsible_channel, num_tokens_to_recv);
                    trap();
                }
            }

            // Synchronize queue tail
            asm volatile("bar.sync %0, %1;" :: "r"(responsible_rank), "r"(num_threads_per_rank));
            cached_channel_tail_idx = shared_channel_tail_idx[responsible_rank];

            // Copy data
            int num_recv_tokens = cached_channel_tail_idx - cached_channel_head_idx;
            for (int chunk_idx = recv_warp_id_in_rank; chunk_idx < num_recv_tokens; chunk_idx += num_recv_warps_per_rank) {
                int token_idx_in_buffer = (cached_channel_head_idx + chunk_idx) % num_recv_buffer_tokens;
                auto shifted_buffer_x_int4 = channel_x_buffers.buffer() + token_idx_in_buffer * hidden_int4;
                auto shifted_recv_x_int4 = recv_x + static_cast<int64_t>(total_offset + chunk_idx) * hidden_int4;
                UNROLLED_WARP_COPY(5, recv_lane_id, hidden_int4, shifted_recv_x_int4, shifted_buffer_x_int4,
                                   ld_nc_global, st_na_global);
            }

            // Copy `src_idx`
            #pragma unroll 4
            for (int chunk_idx = cached_channel_head_idx + recv_thread_id_in_rank; chunk_idx < cached_channel_tail_idx; chunk_idx += 32 * num_recv_warps_per_rank)
                recv_src_idx[total_offset + chunk_idx - cached_channel_head_idx] = ld_nc_global(channel_src_idx_buffers.buffer() + chunk_idx % num_recv_buffer_tokens);

            // Copy `topk_idx` and `topk_weights`
            #pragma unroll 4
            for (int idx = recv_thread_id_in_rank; idx < num_recv_tokens * num_topk; idx += 32 * num_recv_warps_per_rank) {
                int chunk_idx = idx / num_topk, token_topk_idx = idx % num_topk;
                int token_idx_in_buffer = (cached_channel_head_idx + chunk_idx) % num_recv_buffer_tokens;
                auto recv_idx = static_cast<int64_t>(total_offset + chunk_idx) * num_topk + token_topk_idx;
                auto buffer_idx = token_idx_in_buffer * num_topk + token_topk_idx;
                recv_topk_idx[recv_idx] = ld_nc_global(channel_topk_idx_buffers.buffer() + buffer_idx);
                recv_topk_weights[recv_idx] = ld_nc_global(channel_topk_weights_buffers.buffer() + buffer_idx);
            }

            // Copy `x_scales`
            #pragma unroll 4
            for (int i = recv_thread_id_in_rank; i < num_recv_tokens * num_scales; i += 32 * num_recv_warps_per_rank) {
                int chunk_idx = i / num_scales, scales_idx = i % num_scales;
                int token_idx_in_buffer = (cached_channel_head_idx + chunk_idx) % num_recv_buffer_tokens;
                recv_x_scales[static_cast<int64_t>(total_offset + chunk_idx) * num_scales + scales_idx] =
                        ld_nc_global(channel_x_scales_buffers.buffer() + token_idx_in_buffer * num_scales + scales_idx);
            }

            // Move queue
            cached_channel_head_idx += num_recv_tokens;
            total_offset += num_recv_tokens;
            asm volatile("bar.sync %0, %1;" :: "r"(responsible_rank), "r"(num_threads_per_rank));
            if (recv_warp_id_in_rank == num_recv_warps_per_rank - 1 and recv_lane_id == 0)
                st_relaxed_sys_global(channel_head_idx.buffer(), cached_channel_head_idx);

            // Exit
            num_tokens_to_recv -= num_recv_tokens;
        }
    }
}

void dispatch(void* recv_x, float* recv_x_scales, int* recv_src_idx, int64_t* recv_topk_idx, float* recv_topk_weights, int* recv_channel_offset,
              int* send_head, const void* x, const float* x_scales, const int64_t* topk_idx, const float* topk_weights,
              const bool* is_token_in_rank, const int* channel_prefix_matrix,
              int num_tokens, int hidden_int4, int num_topk, int num_experts, int num_scales,
              void** buffer_ptrs, int rank, int num_ranks,
              cudaStream_t stream, int num_sms, int num_max_send_tokens, int num_recv_buffer_tokens) {
    constexpr int kNumThreads = 512;

#define DISPATCH_LAUNCH_CASE(ranks) \
LAUNCH_KERNEL(&cfg, dispatch<ranks, kNumThreads>, \
    reinterpret_cast<int4*>(recv_x), recv_x_scales, recv_src_idx, recv_topk_idx, recv_topk_weights, recv_channel_offset, \
    send_head, reinterpret_cast<const int4*>(x), x_scales, topk_idx, topk_weights, \
    is_token_in_rank, channel_prefix_matrix, \
    num_tokens, hidden_int4, num_topk, num_experts, num_scales, \
    buffer_ptrs, rank, \
    num_max_send_tokens, num_recv_buffer_tokens); \
break

    // Even-numbered blocks for sending, odd-numbered blocks for receiving.
    EP_HOST_ASSERT(num_sms % 2 == 0);
    SETUP_LAUNCH_CONFIG(num_sms, kNumThreads, stream);
    SWITCH_RANKS(DISPATCH_LAUNCH_CASE);
#undef DISPATCH_LAUNCH_CASE
}

template<int kNumRanks>
__global__ void
cached_notify_combine(void** buffer_ptrs, int* send_head, int num_channels, int num_recv_tokens, int num_memset_int,
                      int** task_fifo_ptrs, int head, int rank) {
    const auto sm_id = static_cast<int>(blockIdx.x);
    if (sm_id == 0) {
        // Barrier before cleaning
        barrier_device<kNumRanks>(task_fifo_ptrs, head, rank);
        move_fifo_slots<kNumRanks>(head);
        __syncthreads();

        // Clean
        auto thread_id = static_cast<int>(threadIdx.x), num_threads = static_cast<int>(blockDim.x);
        auto ptr = reinterpret_cast<int*>(buffer_ptrs[rank]);
        #pragma unroll
        for (int i = thread_id; i < num_memset_int; i += num_threads)
            ptr[i] = 0;
        memory_fence();
        __syncthreads();

        // Barrier after cleaning
        barrier_device<kNumRanks>(task_fifo_ptrs, head, rank);
    } else {
        const auto channel_id = sm_id - 1;
        const auto thread_id = static_cast<int>(threadIdx.x);
        const auto rank_id = thread_id / 32;
        const auto lane_id = thread_id % 32;
        if (rank_id >= kNumRanks)
            return;

        int token_start_idx, token_end_idx;
        get_channel_task_range(num_recv_tokens, num_channels, channel_id, token_start_idx, token_end_idx);

        // NOTES: `1 << 25` is a heuristic large number
        int last_head = 1 << 25;
        #pragma unroll
        for (int token_idx_tail = token_end_idx - 1; token_idx_tail >= token_start_idx; token_idx_tail -= 32) {
            int token_idx = token_idx_tail - lane_id, expected_head = 0;
            auto current_head = (token_idx >= token_start_idx) ? __ldg(send_head + token_idx * kNumRanks + rank_id) : -1;
            for (int i = 0; i < min(32, token_idx_tail - token_start_idx + 1); ++ i) {
                head = __shfl_sync(0xffffffff, current_head, i);
                if (head < 0) {
                    if (lane_id == i)
                        expected_head = -last_head - 1;
                } else {
                    last_head = head;
                }
            }
            if (current_head < 0 and token_idx >= token_start_idx)
                send_head[token_idx * kNumRanks + rank_id] = expected_head;
        }
    }
}

void cached_notify_combine(void** buffer_ptrs, int* send_head, int num_channels,
                           int num_recv_tokens, int num_memset_int,
                           int** task_fifo_ptrs, int head, int rank, int num_ranks,
                           cudaStream_t stream) {
#define CACHED_NOTIFY_COMBINE(ranks) \
    LAUNCH_KERNEL(&cfg, cached_notify_combine<ranks>, \
        buffer_ptrs, send_head, num_channels, num_recv_tokens, num_memset_int, task_fifo_ptrs, head, rank); \
    break

    const int num_threads = std::max(128, 32 * num_ranks);
    EP_HOST_ASSERT(num_ranks <= num_threads);
    EP_HOST_ASSERT(num_threads <= 1024);
    EP_HOST_ASSERT(1 + num_channels <= num_channels * 2);
    SETUP_LAUNCH_CONFIG(1 + num_channels, num_threads, stream);
    SWITCH_RANKS(CACHED_NOTIFY_COMBINE);
#undef CACHED_NOTIFY_COMBINE
}

template<typename dtype_t, int kNumRanks, int kNumThreads>
__global__ void __launch_bounds__(kNumThreads, 1)
combine(dtype_t* recv_x, float* recv_topk_weights,
        const dtype_t* x, const float* topk_weights,
        const int* src_idx, const int* rank_prefix_matrix, const int* channel_prefix_matrix,
        int* send_head, int num_tokens, int num_recv_tokens, int hidden, int num_topk,
        void **buffer_ptrs, int rank,
        int num_max_send_tokens, int num_recv_buffer_tokens) {
    const auto num_sms = static_cast<int>(gridDim.x);
    const auto thread_id = static_cast<int>(threadIdx.x);
    const auto sm_id = static_cast<int>(blockIdx.x);
    const auto num_channels = num_sms / 2;
    const bool is_sender = sm_id % 2 == 0;
    const int responsible_channel = sm_id / 2;
    EP_DEVICE_ASSERT(num_topk <= 32);

    constexpr int kDtypePerInt4 = sizeof(int4) / sizeof(dtype_t);
    int hidden_int4 = hidden * sizeof(dtype_t) / sizeof(int4);
    auto x_int4 = reinterpret_cast<const int4*>(x);
    auto recv_int4 = reinterpret_cast<int4*>(recv_x);

    if (is_sender) {
        // Workers for sending
        // Several warps are responsible for a single rank
        constexpr int num_send_warps = kNumThreads / 32;
        constexpr int num_send_warps_per_rank = num_send_warps / kNumRanks;
        const auto num_threads_per_rank = num_send_warps_per_rank * 32;
        const auto send_thread_id = thread_id;
        const auto send_lane_id = send_thread_id % 32;
        const auto send_rank_id = thread_id / num_threads_per_rank;
        const auto send_warp_id_in_rank = send_thread_id % num_threads_per_rank / 32;

        // Calculate pointers by the specific layout
        auto ptr = reinterpret_cast<void*>(reinterpret_cast<int8_t*>(buffer_ptrs[send_rank_id]));
        auto num_channels_total = num_channels * kNumRanks;
        auto channel_rank_offset = responsible_channel * kNumRanks + rank;

        // Channel meta data
        // `head_idx`: kNumChannels * kNumRanks * sizeof(int)
        // `tail_idx`: kNumChannels * kNumRanks * sizeof(int)
        // `x_buffers`: kNumChannels * kNumRanks * num_recv_buffer_tokens * hidden_int4 * sizeof(int4)
        // `src_idx_buffers`: kNumChannels * kNumRanks * num_recv_buffer_tokens * sizeof(int)
        // `topk_weights_buffers`: kNumChannels * kNumRanks * num_recv_buffer_tokens * num_topk * sizeof(float)
        auto channel_head_idx = Buffer<int>(ptr, num_channels_total, channel_rank_offset);
        auto channel_tail_idx = Buffer<int>(ptr, num_channels_total, channel_rank_offset);
        auto channel_x_buffers = Buffer<int4>(ptr, num_channels_total * num_recv_buffer_tokens * hidden_int4, channel_rank_offset * num_recv_buffer_tokens * hidden_int4);
        auto channel_src_idx_buffers = Buffer<int>(ptr, num_channels_total * num_recv_buffer_tokens, channel_rank_offset * num_recv_buffer_tokens);
        auto channel_topk_weights_buffers = Buffer<float>(ptr, num_channels_total * num_recv_buffer_tokens * num_topk, channel_rank_offset * num_recv_buffer_tokens * num_topk);

        // Get tasks
        // NOTES: `channel_offset` is already shifted
        int rank_offset = send_rank_id > 0 ? rank_prefix_matrix[(send_rank_id - 1) * kNumRanks + rank] : 0;
        int num_rank_tokens = rank_prefix_matrix[send_rank_id * kNumRanks + rank] - rank_offset;
        int channel_offset = channel_prefix_matrix[send_rank_id * num_channels + responsible_channel];
        int num_channel_tokens = (responsible_channel == num_channels - 1 ? num_rank_tokens : channel_prefix_matrix[send_rank_id * num_channels + responsible_channel + 1]) - channel_offset;
        int token_start_idx = rank_offset + channel_offset, token_end_idx = rank_offset + channel_offset + num_channel_tokens;

        // Iterate over all tokens and send by chunks
        int current_channel_tail_idx = 0;
        for (int64_t token_idx = token_start_idx; token_idx < token_end_idx; ) {
            // Check destination queue emptiness, or wait a buffer to be released (rare cases)
            auto start_time = clock64();
            int num_round_tokens = min(num_max_send_tokens, token_end_idx - static_cast<int>(token_idx));
            while (send_lane_id == 0) {
                // NOTES: we only consider the worst case, because counting the real numbers are time-consuming
                int num_used_slots = current_channel_tail_idx - ld_volatile_global(channel_head_idx.buffer());
                if (num_recv_buffer_tokens - num_used_slots >= num_round_tokens)
                    break;

                // Rare cases to loop again
                if (clock64() - start_time > NUM_TIMEOUT_CYCLES) {
                    printf("DeepEP timeout for combine senders, rank %d, responsible_channel = %d\n", rank, responsible_channel);
                    trap();
                }
            }
            __syncwarp();

            // Send by chunk
            #pragma unroll
            for (int i = send_warp_id_in_rank; i < num_round_tokens; i += num_send_warps_per_rank) {
                // Get an empty slot
                int dst_slot_idx = (current_channel_tail_idx + i) % num_recv_buffer_tokens;

                // Copy data
                auto shifted_x_buffers = channel_x_buffers.buffer() + dst_slot_idx * hidden_int4;
                auto shifted_x = x_int4 + (token_idx + i) * hidden_int4;
                UNROLLED_WARP_COPY(4, send_lane_id, hidden_int4, shifted_x_buffers, shifted_x, ld_nc_global, st_na_global);

                // Send source index
                if (send_lane_id == 0)
                    channel_src_idx_buffers[dst_slot_idx] = __ldg(src_idx + token_idx + i);

                // Send `topk_weights`
                if (num_topk > 0 and send_lane_id < num_topk)
                    channel_topk_weights_buffers[dst_slot_idx * num_topk + send_lane_id] = __ldg(topk_weights + (token_idx + i) * num_topk + send_lane_id);
            }
            token_idx += num_round_tokens;
            current_channel_tail_idx += num_round_tokens;

            // Move tail index
            asm volatile("bar.sync %0, %1;" :: "r"(send_rank_id), "r"(num_threads_per_rank));
            if (send_lane_id == 0 and send_warp_id_in_rank == 0)
                st_release_sys_global(channel_tail_idx.buffer(), current_channel_tail_idx);
        }
    } else {
        // Workers for receiving
        // One warp for moving the queue head, others for reduction
        constexpr int num_recv_warps = kNumThreads / 32;
        const auto recv_warp_id = thread_id / 32;
        const auto recv_lane_id = thread_id % 32;
        EP_DEVICE_ASSERT(kNumRanks <= 32 and kNumThreads > 32);
        EP_DEVICE_ASSERT(thread_id >= 0 and kNumThreads % 32 == 0);

        // Shared head, tail and retired flags for receiver warps
        __shared__ volatile int warp_channel_head_idx[num_recv_warps][kNumRanks];
        __shared__ volatile int channel_tail_idx[kNumRanks];
        __shared__ volatile bool warp_retired[num_recv_warps];
        if (thread_id < num_recv_warps)
            warp_retired[thread_id] = false;
        if (recv_lane_id < kNumRanks)
            warp_channel_head_idx[recv_warp_id][recv_lane_id] = 0;
        if (thread_id < kNumRanks)
            channel_tail_idx[thread_id] = 0;
        asm volatile("bar.sync 0, %0;" :: "r"(kNumThreads));

        if (thread_id < 32) {
            int* channel_head_idx_ptr = reinterpret_cast<int*>(buffer_ptrs[rank]) + responsible_channel * kNumRanks + recv_lane_id;
            int* channel_tail_idx_ptr = channel_head_idx_ptr + num_channels * kNumRanks;

            // Queue head updater
            int last_head = 0;
            while (recv_lane_id < kNumRanks) {
                // Check retired
                bool retired = true;
                #pragma unroll
                for (int i = 1; i < num_recv_warps; ++ i)
                    retired = retired and warp_retired[i];
                if (retired)
                    break;

                // Update queue tail
                channel_tail_idx[recv_lane_id] = ld_acquire_sys_global(channel_tail_idx_ptr);

                // Update minimum head
                int min_head = std::numeric_limits<int>::max();
                #pragma unroll
                for (int i = 1; i < num_recv_warps; ++ i) if (not warp_retired[i])
                    min_head = min(min_head, warp_channel_head_idx[i][recv_lane_id]);
                if (min_head != std::numeric_limits<int>::max() and min_head > last_head)
                    st_relaxed_sys_global(channel_head_idx_ptr, last_head = min_head);
            }
        } else {
            // Receivers
            // Channel metadata
            // All lanes will use data buffer, but only rank lane will use `head/tail/src_idx`
            Buffer<int4> channel_x_buffers[kNumRanks];
            Buffer<float> channel_topk_weights_buffers[kNumRanks];

            // Calculate pointers by the specific layout
            #pragma unroll
            for (int i = 0; i < kNumRanks; ++ i) {
                auto channel_rank_offset = responsible_channel * kNumRanks + i;
                auto num_channels_total = num_channels * kNumRanks;
                // `head_idx` & `tail_idx`: kNumChannels * kNumRanks * sizeof(int)
                auto ptr = reinterpret_cast<void*>(reinterpret_cast<int8_t*>(buffer_ptrs[rank]) + 2 * num_channels * kNumRanks * sizeof(int));

                // `x_buffers`: kNumChannels * kNumRanks * num_recv_buffer_tokens * hidden_int4 * sizeof(int4)
                channel_x_buffers[i] = Buffer<int4>(ptr, num_channels_total * num_recv_buffer_tokens * hidden_int4, channel_rank_offset * num_recv_buffer_tokens * hidden_int4);

                // `src_idx_buffers`: kNumChannels * kNumRanks * num_recv_buffer_tokens * sizeof(int)
                ptr = reinterpret_cast<void*>(reinterpret_cast<int8_t*>(ptr) + num_channels_total * num_recv_buffer_tokens * sizeof(int));

                // `topk_weights_buffers`: kNumChannels * kNumRanks * num_recv_buffer_tokens * num_topk * sizeof(float)
                channel_topk_weights_buffers[i] = Buffer<float>(ptr, num_channels_total * num_recv_buffer_tokens * num_topk, channel_rank_offset * num_recv_buffer_tokens * num_topk);
            }

            // The same tokens as the dispatch process
            int token_start_idx, token_end_idx;
            get_channel_task_range(num_recv_tokens, num_channels, responsible_channel, token_start_idx, token_end_idx);

            // Iterate over all tokens and combine
            for (int64_t token_idx = token_start_idx + recv_warp_id - 1; token_idx < token_end_idx; token_idx += num_recv_warps - 1) {
                // Read expected head
                int expected_head = -1;
                if (recv_lane_id < kNumRanks)
                    expected_head = ld_nc_global(send_head + token_idx * kNumRanks + recv_lane_id);

                auto start_time = clock64();
                while (channel_tail_idx[recv_lane_id] <= expected_head and expected_head >= 0) {
                    // Timeout check
                    if (clock64() - start_time > NUM_TIMEOUT_CYCLES) {
                        printf("DeepEP timeout for combine receivers, rank %d, responsible_channel = %d, expect = %d\n", rank, responsible_channel, expected_head);
                        trap();
                    }
                }
                __syncwarp();

                // Broadcast current heads
                int num_topk_ranks = 0, topk_ranks[kNumRanks], slot_indices[kNumRanks];
                #pragma unroll
                for (int i = 0; i < kNumRanks; ++ i) {
                    auto expected_head_i = __shfl_sync(0xffffffff, expected_head, i);
                    if (expected_head_i >= 0) {
                        slot_indices[num_topk_ranks] = expected_head_i % num_recv_buffer_tokens;
                        topk_ranks[num_topk_ranks ++] = i;
                    }
                }

                // Reduce data
                #pragma unroll
                for (int i = recv_lane_id; i < hidden_int4; i += 32) {
                    // Read buffers
                    int4 recv_value_int4[kNumRanks];
                    #pragma unroll
                    for (int j = 0; j < num_topk_ranks; ++ j)
                        recv_value_int4[j] = ld_nc_global(channel_x_buffers[topk_ranks[j]].buffer() + slot_indices[j] * hidden_int4 + i);

                    // Reduce all-to-all results
                    float values[kDtypePerInt4] = {0};
                    #pragma unroll
                    for (int j = 0; j < num_topk_ranks; ++ j) {
                        auto recv_value_dtypes = reinterpret_cast<const dtype_t*>(&recv_value_int4[j]);
                        #pragma unroll
                        for (int k = 0; k < kDtypePerInt4; ++ k)
                            values[k] += static_cast<float>(recv_value_dtypes[k]);
                    }

                    // Cast back to `dtype_t` and write
                    int4 out_int4;
                    auto out_dtypes = reinterpret_cast<dtype_t*>(&out_int4);
                    #pragma unroll
                    for (int j = 0; j < kDtypePerInt4; ++ j)
                        out_dtypes[j] = static_cast<dtype_t>(values[j]);
                    recv_int4[token_idx * hidden_int4 + i] = out_int4;
                }

                // Reduce `topk_weights`
                if (recv_lane_id < num_topk) {
                    float value = 0;
                    #pragma unroll
                    for (int i = 0; i < num_topk_ranks; ++ i)
                        value += ld_nc_global(channel_topk_weights_buffers[topk_ranks[i]].buffer() + slot_indices[i] * num_topk + recv_lane_id);
                    recv_topk_weights[token_idx * num_topk + recv_lane_id] = value;
                }

                // Update head
                if (recv_lane_id < kNumRanks)
                    warp_channel_head_idx[recv_warp_id][recv_lane_id] = (expected_head < 0) ? -expected_head - 1 : expected_head + 1;
            }

            // Retired
            __syncwarp();
            if (recv_lane_id == 0)
                warp_retired[recv_warp_id] = true;
        }
    }
}

void combine(cudaDataType_t type,
             void* recv_x, float* recv_topk_weights,
             const void* x, const float* topk_weights,
             const int* src_idx, const int* rank_prefix_matrix, const int* channel_prefix_matrix,
             int* send_head, int num_tokens, int num_recv_tokens, int hidden, int num_topk,
             void** buffer_ptrs, int rank, int num_ranks,
             cudaStream_t stream, int num_sms,
             int num_max_send_tokens, int num_recv_buffer_tokens) {
    constexpr int kNumThreads = 768;

#define COMBINE_LAUNCH_CASE(dtype, ranks) \
    LAUNCH_KERNEL(&cfg, (combine<dtype, ranks, kNumThreads>), \
        reinterpret_cast<dtype*>(recv_x), recv_topk_weights, \
        reinterpret_cast<const dtype*>(x), topk_weights,   \
        src_idx, rank_prefix_matrix, channel_prefix_matrix, \
        send_head, num_tokens, num_recv_tokens, hidden, num_topk, \
        buffer_ptrs, rank, \
        num_max_send_tokens, num_recv_buffer_tokens); \
    break
#define COMBINE_DTYPE_LAUNCH_CASE(dtype) SWITCH_RANKS_WITH_DTYPE(dtype, COMBINE_LAUNCH_CASE); break

    // Even-numbered blocks for sending, odd-numbered blocks for receiving
    EP_HOST_ASSERT(num_sms % 2 == 0);
    EP_HOST_ASSERT(kNumThreads >= num_ranks * 32);
    SETUP_LAUNCH_CONFIG(num_sms, kNumThreads, stream);
    SWITCH_TYPES(COMBINE_DTYPE_LAUNCH_CASE);
#undef COMBINE_DTYPE_LAUNCH_CASE
#undef COMBINE_LAUNCH_CASE
}

} // namespace intranode

} // namespace deep_ep