utils.cuh

#include "hip/hip_runtime.h"
#pragma once
#include "configs.cuh"
#include "exception.cuh"

#define UNROLLED_WARP_COPY(UNROLL_FACTOR, LANE_ID, N, DST, SRC, LD_FUNC, ST_FUNC)                  \
    {                                                                                              \
        constexpr int kLoopStride = kWarpSize * (UNROLL_FACTOR);                                   \
        typename std::remove_reference<decltype(LD_FUNC((SRC) + 0))>::type                         \
             unrolled_values[(UNROLL_FACTOR)];                                                     \
        auto __src = (SRC);                                                                        \
        auto __dst = (DST);                                                                        \
        for (int __i = (LANE_ID); __i < ((N) / kLoopStride) * kLoopStride; __i += kLoopStride) {   \
            _Pragma("unroll") for (int __j = 0; __j < (UNROLL_FACTOR); ++__j)                      \
                unrolled_values[__j] = LD_FUNC(__src + __i + __j * kWarpSize);                     \
            _Pragma("unroll") for (int __j = 0; __j < (UNROLL_FACTOR); ++__j)                      \
                ST_FUNC(__dst + __i + __j * kWarpSize, unrolled_values[__j]);                      \
        }                                                                                          \
        {                                                                                          \
            int __i = ((N) / kLoopStride) * kLoopStride + (LANE_ID);                               \
            _Pragma("unroll") for (int __j = 0; __j < (UNROLL_FACTOR); ++__j) {                    \
                if (__i + __j * kWarpSize < (N)) {                                                 \
                    unrolled_values[__j] = LD_FUNC(__src + __i + __j * kWarpSize);                 \
                }                                                                                  \
            }                                                                                      \
            _Pragma("unroll") for (int __j = 0; __j < (UNROLL_FACTOR); ++__j) {                    \
                if (__i + __j * kWarpSize < (N)) {                                                 \
                    ST_FUNC(__dst + __i + __j * kWarpSize, unrolled_values[__j]);                  \
                }                                                                                  \
            }                                                                                      \
        }                                                                                          \
    }

#define UNROLLED_WARP_COPY_LL(UNROLL_FACTOR, LANE_ID, N, DST, SRC, LD_FUNC, ST_FUNC)                                                        \
    {                                                                                                                                       \
        constexpr int kLoopStride = kWarpSize * (UNROLL_FACTOR);                                                                            \
        typename std::remove_reference<decltype(LD_FUNC((SRC) + 0))>::type unrolled_values[(UNROLL_FACTOR)];                                \
        auto __src = (SRC);                                                                                                                 \
        auto __dst = (DST);                                                                                                                 \
        for(int __i = (LANE_ID); __i < ((N) / kLoopStride) * kLoopStride; __i += kLoopStride) {                                             \
            _Pragma("unroll") for(int __j = 0; __j < (UNROLL_FACTOR); ++__j) unrolled_values[__j] = LD_FUNC(__src + __i + __j * kWarpSize); \
            _Pragma("unroll") for(int __j = 0; __j < (UNROLL_FACTOR); ++__j) ST_FUNC(__dst + __i + __j * kWarpSize, unrolled_values[__j]);  \
        }                                                                                                                                   \
        for(int __i = ((N) / kLoopStride) * kLoopStride + (LANE_ID); __i < (N); __i += kWarpSize)                                           \
            ST_FUNC(__dst + __i, LD_FUNC(__src + __i));                                                                                     \
    }


#define UNROLLED_WARP_COPY_EMULATED(UNROLL_FACTOR, LANE_ID, N, DST, SRC, LD_FUNC, ST_FUNC)         \
    {                                                                                              \
        constexpr int kLoopStride = kEmulatedWarpSize * (UNROLL_FACTOR);                           \
        typename std::remove_reference<decltype(LD_FUNC((SRC) + 0))>::type                         \
             unrolled_values[(UNROLL_FACTOR)];                                                     \
        auto __src = (SRC);                                                                        \
        auto __dst = (DST);                                                                        \
        for (int __i = (LANE_ID); __i < ((N) / kLoopStride) * kLoopStride; __i += kLoopStride) {   \
            _Pragma("unroll") for (int __j = 0; __j < (UNROLL_FACTOR); ++__j)                      \
                unrolled_values[__j] = LD_FUNC(__src + __i + __j * kEmulatedWarpSize);             \
            _Pragma("unroll") for (int __j = 0; __j < (UNROLL_FACTOR); ++__j)                      \
                ST_FUNC(__dst + __i + __j * kEmulatedWarpSize, unrolled_values[__j]);              \
        }                                                                                          \
        for (int __i = ((N) / kLoopStride) * kLoopStride + (LANE_ID); __i < (N);                   \
             __i += kEmulatedWarpSize)                                                             \
            ST_FUNC(__dst + __i, LD_FUNC(__src + __i));                                            \
    }
// HELPER FUNCTIONS
// #####################################################################################

template <typename T>
__device__ __forceinline__ T shfl_xor(const T val, int laneMask, int width = kWarpSize,
                                      uint64_t shfl_sync_mask = kFullWarpMask) {
    return __shfl_xor(val, laneMask, width);
}

__device__ __forceinline__ int
shfl_sync(const int val, int srcLane = 0, int width = kWarpSize,
          uint64_t shfl_sync_mask = kFullWarpMask) { // Let compiler deduce type
    return __shfl(val, srcLane, width);
}

__device__ __forceinline__ int __any_sync(uint64_t mask, int predicate) {
    uint64_t predicate_bit_pattern = __ballot(predicate);
    return (predicate_bit_pattern & mask) > 0;
}

__device__ __forceinline__ int __all_sync(uint64_t mask, int predicate) {
    uint64_t predicate_bit_pattern = __ballot(predicate);
    return (~predicate_bit_pattern & mask) == 0;
}

__device__ __forceinline__ void syncwarp() {
    __builtin_amdgcn_fence(__ATOMIC_RELEASE, "wavefront");
    __builtin_amdgcn_wave_barrier();
    __builtin_amdgcn_fence(__ATOMIC_ACQUIRE, "wavefront");
}
// ######################################################################################################

namespace deep_ep {

template <int kBytes> struct VecInt {};
template <> struct VecInt<1> {
    using vec_t = int8_t;
};
template <> struct VecInt<2> {
    using vec_t = int16_t;
};
template <> struct VecInt<4> {
    using vec_t = int;
};
template <> struct VecInt<8> {
    using vec_t = int64_t;
};
template <> struct VecInt<16> {
    using native_int4 = int __attribute__((ext_vector_type(4)));
    using vec_t       = native_int4;
};

__device__ __forceinline__ void trap() {
    abort();
}

__device__ __forceinline__ void memory_fence() {

    __threadfence_system();
}

__device__ __forceinline__ void memory_fence_gpu() {
    __threadfence();
}

__device__ __forceinline__ void memory_fence_cta() {
    __threadfence_block();
}

__device__ __forceinline__ void st_relaxed_sys_global(int *ptr, int val) {
    __builtin_nontemporal_store(val, ptr);
}

__device__ __forceinline__ void st_release_sys_global(const int *ptr, int val) {
    __hip_atomic_store(const_cast<int *>(ptr), val, __ATOMIC_RELEASE, __HIP_MEMORY_SCOPE_SYSTEM);
}

__device__ __forceinline__ void st_release_sys_global(const int64_t *ptr, int64_t val) {
    __hip_atomic_store(const_cast<int64_t *>(ptr), val, __ATOMIC_RELEASE, __HIP_MEMORY_SCOPE_SYSTEM);
}

__device__ __forceinline__ void st_release_cta(const int *ptr, int val) {
    __hip_atomic_store(const_cast<int *>(ptr), val, __ATOMIC_RELEASE, __HIP_MEMORY_SCOPE_WORKGROUP);
}

__device__ __forceinline__ int ld_relaxed_sys_global(const int *ptr) {
    int res = __builtin_nontemporal_load(ptr);
    return res;
}
__device__ __forceinline__ int ld_relaxed_sys_global(const uint64_t *ptr) {
    uint64_t ret;
    ret = __hip_atomic_load(ptr, __ATOMIC_RELAXED, __HIP_MEMORY_SCOPE_SYSTEM);
    return ret;
}

__device__ __forceinline__ int ld_acquire_sys_global(const int *ptr) {
    int ret;
    ret = __hip_atomic_load(ptr, __ATOMIC_ACQUIRE, __HIP_MEMORY_SCOPE_SYSTEM);
    return ret;
}

__device__ __forceinline__ uint64_t ld_acquire_sys_global(const uint64_t *ptr) {
    uint64_t ret;
    ret = __hip_atomic_load(ptr, __ATOMIC_ACQUIRE, __HIP_MEMORY_SCOPE_SYSTEM);
    return ret;
}

__device__ __forceinline__ int ld_acquire_global(const int *ptr) {
    int ret;
    ret = __hip_atomic_load(ptr, __ATOMIC_ACQUIRE, __HIP_MEMORY_SCOPE_AGENT);
    return ret;
}

__device__ __forceinline__ int64_t ld_acquire_global(const int64_t *ptr) {
    int64_t ret;
    ret = __hip_atomic_load(ptr, __ATOMIC_ACQUIRE, __HIP_MEMORY_SCOPE_AGENT);
    return ret;
}

__device__ __forceinline__ int atomic_add_release_global(const int *ptr, int value) {
    int ret;
    ret = __hip_atomic_fetch_add(const_cast<int *>(ptr), value, __ATOMIC_RELEASE, __HIP_MEMORY_SCOPE_AGENT);
    // ret = atomicAdd((int*)ptr, value);
    return ret;
}

__device__ __forceinline__ int ld_relaxed_global(const int *ptr) {
    int ret;
    ret = __hip_atomic_load(ptr, __ATOMIC_RELAXED, __HIP_MEMORY_SCOPE_AGENT);
    return ret;
}

__device__ __forceinline__ int ld_acquire_cta(const int *ptr) {
    int ret;
    ret = __hip_atomic_load(ptr, __ATOMIC_ACQUIRE, __HIP_MEMORY_SCOPE_WORKGROUP);
    return ret;
}

__device__ __forceinline__ int ld_volatile_global(const volatile int *ptr) {
    int ret;
    ret = __hip_atomic_load(ptr, __ATOMIC_RELAXED, __HIP_MEMORY_SCOPE_SYSTEM);
    return ret;
}

__device__ __forceinline__ float ld_volatile_global(const volatile float *ptr) {
    float ret;
    ret = __hip_atomic_load(ptr, __ATOMIC_RELAXED, __HIP_MEMORY_SCOPE_SYSTEM);
    return ret;
}

__device__ __forceinline__ int64_t ld_volatile_global(const volatile int64_t *ptr) {
    int64_t ret;
    ret = __hip_atomic_load(ptr, __ATOMIC_RELAXED, __HIP_MEMORY_SCOPE_SYSTEM);
    return ret;
}

__device__ __forceinline__ int64_t ld_volatile_global(const volatile uint64_t *ptr) {
    int64_t ret;
    ret = __hip_atomic_load(ptr, __ATOMIC_RELAXED, __HIP_MEMORY_SCOPE_SYSTEM);
    return ret;
}

template <typename dtype_t> __device__ __forceinline__ dtype_t ld_nc_global(const dtype_t *ptr) {
    using T  = typename VecInt<sizeof(dtype_t)>::vec_t;
    auto ret = __builtin_nontemporal_load(reinterpret_cast<const T *>(ptr));
    return *reinterpret_cast<dtype_t *>(&ret);
}

////////////////// used in ibgda
__device__ __forceinline__ void st_na_relaxed(const uint8_t *ptr, uint8_t val) {
    uint8_t *non_const_ptr = const_cast<uint8_t *>(ptr);
    __hip_atomic_store(non_const_ptr, val, __ATOMIC_RELAXED, __HIP_MEMORY_SCOPE_AGENT);
}

__device__ __forceinline__ void st_na_relaxed(const uint16_t *ptr, uint16_t val) {
    uint16_t *non_const_ptr = const_cast<uint16_t *>(ptr);
    __hip_atomic_store(non_const_ptr, val, __ATOMIC_RELAXED, __HIP_MEMORY_SCOPE_AGENT);
}

__device__ __forceinline__ void st_na_relaxed(const uint32_t *ptr, uint32_t val) {
    uint32_t *non_const_ptr = const_cast<uint32_t *>(ptr);
    __hip_atomic_store(non_const_ptr, val, __ATOMIC_RELAXED, __HIP_MEMORY_SCOPE_AGENT);
}

__device__ __forceinline__ void st_na_relaxed(const int *ptr, int val) {
    int *non_const_ptr = const_cast<int *>(ptr);
    __hip_atomic_store(non_const_ptr, val, __ATOMIC_RELAXED, __HIP_MEMORY_SCOPE_AGENT);
}

__device__ __forceinline__ void st_na_relaxed(const int4 *ptr, int4 val) {
    int4 *non_const_ptr = const_cast<int4 *>(ptr);
    non_const_ptr->x    = val.x;
    non_const_ptr->y    = val.y;
    non_const_ptr->z    = val.z;
    non_const_ptr->w    = val.w;
}

__device__ __forceinline__ void st_na_release(const int *ptr, int val) {
    int *non_const_ptr = const_cast<int *>(ptr);
    __hip_atomic_store(non_const_ptr, val, __ATOMIC_RELAXED, __HIP_MEMORY_SCOPE_AGENT);
}

__device__ __forceinline__ void st_na_release(const uint32_t *ptr, uint32_t val) {
    uint32_t *non_const_ptr = const_cast<uint32_t *>(ptr);
    __hip_atomic_store(non_const_ptr, val, __ATOMIC_RELEASE, __HIP_MEMORY_SCOPE_AGENT);
}

__device__ __forceinline__ void st_na_release(const uint64_t *ptr, uint64_t val) {
    uint64_t *non_const_ptr = const_cast<uint64_t *>(ptr);
    __hip_atomic_store(non_const_ptr, val, __ATOMIC_RELEASE, __HIP_MEMORY_SCOPE_AGENT);
}

__device__ __forceinline__ void st_na_release(const int64_t *ptr, int64_t val) {
    int64_t *non_const_ptr = const_cast<int64_t *>(ptr);
    __hip_atomic_store(non_const_ptr, val, __ATOMIC_RELEASE, __HIP_MEMORY_SCOPE_AGENT);
}

// TODO:: apply "st.global.L1::no_allocate" in ROCM
template <typename dtype_t>
__device__ __forceinline__ void st_na_global(const dtype_t *ptr, const dtype_t &value) {
    st_na_global(reinterpret_cast<const typename VecInt<sizeof(dtype_t)>::vec_t *>(ptr),
                 *reinterpret_cast<const typename VecInt<sizeof(dtype_t)>::vec_t *>(&value));
}

template <> __device__ __forceinline__ void st_na_global(const int *ptr, const int &value) {
    int *non_const_ptr = const_cast<int *>(ptr);
    *non_const_ptr     = value;
}

template <> __device__ __forceinline__ void st_na_global(const int64_t *ptr, const int64_t &value) {
    int64_t *non_const_ptr = const_cast<int64_t *>(ptr);
    *non_const_ptr         = value;
}

template <> __device__ __forceinline__ void st_na_global(const float *ptr, const float &value) {
    float *non_const_ptr = const_cast<float *>(ptr);
    *non_const_ptr       = value;
}

template <> __device__ __forceinline__ void st_na_global(const int4 *ptr, const int4 &value) {
    int4 *non_const_ptr = const_cast<int4 *>(ptr);
    *non_const_ptr      = value;
}

__forceinline__ __device__ void get_channel_task_range(int num_tokens, int num_sms, int sm_id,
                                                       int &token_start_idx, int &token_end_idx) {
    int num_tokens_per_sm = DIVUP(num_tokens, num_sms);
    token_start_idx       = min(num_tokens_per_sm * sm_id, num_tokens);
    token_end_idx         = min(token_start_idx + num_tokens_per_sm, num_tokens);
}

template <typename dtype_a_t, typename dtype_b_t>
__device__ __forceinline__ dtype_b_t pack2(const dtype_a_t& x, const dtype_a_t& y) {
    EP_STATIC_ASSERT(sizeof(dtype_a_t) * 2 == sizeof(dtype_b_t), "Invalid dtypes");
    dtype_b_t packed;
    auto unpacked_ptr = reinterpret_cast<dtype_a_t*>(&packed);
    unpacked_ptr[0] = x, unpacked_ptr[1] = y;
    return packed;
}

template <typename dtype_a_t, typename dtype_b_t>
__device__ __forceinline__ void unpack2(const dtype_b_t& packed, dtype_a_t& x, dtype_a_t& y) {
    EP_STATIC_ASSERT(sizeof(dtype_a_t) * 2 == sizeof(dtype_b_t), "Invalid dtypes");
    auto unpacked_ptr = reinterpret_cast<const dtype_a_t*>(&packed);
    x = unpacked_ptr[0], y = unpacked_ptr[1];
}

template <typename dtype_t>
__device__ __forceinline__ dtype_t broadcast(dtype_t &ptr, int src_lane_idx) {
    EP_STATIC_ASSERT(sizeof(dtype_t) % sizeof(int) == 0, "");
    auto send_int_values = reinterpret_cast<int *>(&ptr);
    int  recv_int_values[sizeof(dtype_t) / sizeof(int)];
#pragma unroll
    for (int i = 0; i < sizeof(dtype_t) / sizeof(int); ++i)
        recv_int_values[i] = shfl_sync(send_int_values[i], src_lane_idx);
    return *reinterpret_cast<dtype_t *>(recv_int_values);
}

// 设置不同的量化方式的最大值与相反数
constexpr float kFinfoAmaxE4M3 = 448.0f;
constexpr float kFinfoAmaxInvE4M3 = 1.0f / kFinfoAmaxE4M3;
constexpr float kFinfoAmaxE5M2 = 57344.0f; 
constexpr float kFinfoAmaxInvE5M2 = 1.0f / kFinfoAmaxE5M2;
constexpr float kFinfoAmaxInt8 = 127.0f;
constexpr float kFinfoAmaxInvInt8 = 1.0f / 127.0f;

__forceinline__ __device__ float fast_pow2(int x) {
    // We can ensure `-126 <= x and x <= 127`
    uint32_t bits_x = (x + 127) << 23;
    return *reinterpret_cast<float*>(&bits_x);
}

__forceinline__ __device__ int fast_log2_ceil(float x) {
    auto bits_x = *reinterpret_cast<uint32_t*>(&x);
    auto exp_x = (bits_x >> 23) & 0xff;
    auto man_bits = bits_x & ((1 << 23) - 1);
    return exp_x - 127 + (man_bits != 0);
}

template <int kQuantType>
__forceinline__ __device__ void calculate_quant8bit_scales(float amax, float& scale, float& scale_inv, bool round_scale=0) {
    amax = fmaxf(amax, 1e-6f);
    if constexpr(kQuantType == 1) { // 使用 INT8 对称量化
        scale_inv = kFinfoAmaxInvInt8 * amax;
        scale = kFinfoAmaxInt8 / amax;
    } else if constexpr(kQuantType == 2 || kQuantType == 3) {   // 使用 FP8_E4M3 或 FP8_UE8M0 非对称量化
        if (round_scale) {
            auto exp_scale_inv = fast_log2_ceil(amax * kFinfoAmaxInvE4M3);
            scale = fast_pow2(-exp_scale_inv);
            scale_inv = fast_pow2(exp_scale_inv);
        } else {
            scale_inv = amax * kFinfoAmaxInvE4M3;
            scale = kFinfoAmaxE4M3 / amax;
        }
    } else if constexpr(kQuantType == 4) { // 使用 FP8_E5M2 对称量化
        if (round_scale) {
            auto exp_scale_inv = fast_log2_ceil(amax * kFinfoAmaxInvE5M2);
            scale = fast_pow2(-exp_scale_inv);
            scale_inv = fast_pow2(exp_scale_inv);
        } else {
            scale_inv = amax * kFinfoAmaxInvE5M2;
            scale = kFinfoAmaxE5M2 / amax;
        }
    }
}

template <bool kIsUE8M0, typename out_dtype_t = std::conditional_t<kIsUE8M0, uint8_t, float>>
__forceinline__ __device__ out_dtype_t extract_required_scale_format(float value) {
    if constexpr (kIsUE8M0) {
        return static_cast<uint8_t>((*reinterpret_cast<uint32_t*>(&value)) >> 23);
    } else {
        return value;
    }
}

__forceinline__ __device__ int get_lane_id() {
    int lane_id = threadIdx.x % kWarpSize;
    return lane_id;
}

template <int kNumRanks, bool kSyncOnly = false>
__forceinline__ __device__ void barrier_block(int **barrier_signal_ptrs, int rank) {
    auto thread_id = static_cast<int>(threadIdx.x);

    // For non-sync-only cases, the memory operations by other threads in the block must be visible
    // to the `sys` scope
    if constexpr (not kSyncOnly) {
        memory_fence();
        __syncthreads();
    }

    // Add self-ranks, sub other ranks
    if (thread_id < kNumRanks) {
        atomicAdd_system(barrier_signal_ptrs[rank] + thread_id, FINISHED_SUM_TAG);
        atomicSub_system(barrier_signal_ptrs[thread_id] + rank, FINISHED_SUM_TAG);
    }
    EP_DEVICE_ASSERT(kNumRanks <= blockDim.x);

    // Check timeout
    auto start_time = clock64();
    while (true) {
        auto value =
            thread_id < kNumRanks ? ld_volatile_global(barrier_signal_ptrs[rank] + thread_id) : 0;
        if (__all_sync(kFullWarpMask, value <= 0))
            break;

        if (clock64() - start_time > NUM_TIMEOUT_CYCLES and thread_id < kNumRanks) {
            printf("DeepEP timeout check failed: rank = %d, thread = %d, value = %d)\n", rank,
                   thread_id, value);
            trap();
        }
    }
    __syncthreads();
}

// Operation functors
template <typename T>
struct ReduceSum {
    __device__ T operator()(T a, T b) const { return a + b; }
};
template <typename T>
struct ReduceMax {
    __device__ T operator()(T a, T b) const { return a > b ? a : b; }
};
template <typename T>
struct ReduceMin {
    __device__ T operator()(T a, T b) const { return a < b ? a : b; }
};
template <typename T>
struct ReduceAnd {
    __device__ T operator()(T a, T b) const { return a & b; }
};
template <typename T>
struct ReduceOr {
    __device__ T operator()(T a, T b) const { return a | b; }
};

// Unified reduction function
template <int kNumLanesPerGroup, bool kIntergroupReduce, typename T, typename Op>
__forceinline__ __device__ T warp_reduce(T value, Op op) {
    EP_STATIC_ASSERT(kNumLanesPerGroup == kWarpSize or kNumLanesPerGroup == 32 or
                     kNumLanesPerGroup == 16 or kNumLanesPerGroup == 8 or kNumLanesPerGroup == 4 or
                     kNumLanesPerGroup == 2 or kNumLanesPerGroup == 1,
                     "Invalid number of lanes");
    constexpr uint32_t mask = 0xffffffff;
    if constexpr (kIntergroupReduce) {
        if constexpr (kNumLanesPerGroup <= 1)
        value = op(value, shfl_xor(value, 1));
        if constexpr (kNumLanesPerGroup <= 2)
        value = op(value, shfl_xor(value, 2));
        if constexpr (kNumLanesPerGroup <= 4)
        value = op(value, shfl_xor(value, 4));
        if constexpr (kNumLanesPerGroup <= 8)
        value = op(value, shfl_xor(value, 8));
        if constexpr (kNumLanesPerGroup <= 16)
        value = op(value, shfl_xor(value, 16));
        if constexpr(kWarpSize == 64){
            if constexpr (kNumLanesPerGroup <= 32)
            value = op(value, shfl_xor(value, 32));
        }
    } else {
        if constexpr(kWarpSize == 64){
            if constexpr (kNumLanesPerGroup >= kWarpSize)
            value = op(value, shfl_xor(value, 32));
        }
        if constexpr (kNumLanesPerGroup >= 32)
        value = op(value, shfl_xor(value, 16));
        if constexpr (kNumLanesPerGroup >= 16)
        value = op(value, shfl_xor(value, 8));
        if constexpr (kNumLanesPerGroup >= 8)
        value = op(value, shfl_xor(value, 4));
        if constexpr (kNumLanesPerGroup >= 4)
        value = op(value, shfl_xor(value, 2));
        if constexpr (kNumLanesPerGroup >= 2)
        value = op(value, shfl_xor(value, 1));
    }
    return value;
}

// Convenience aliases
template <int kNumLanesPerGroup = kWarpSize, bool kIntergroupReduce = false, typename T>
__forceinline__ __device__ T warp_reduce_sum(T value) {
    return warp_reduce<kNumLanesPerGroup, kIntergroupReduce, T>(value, ReduceSum<T>{});
}

template <int kNumLanesPerGroup = kWarpSize, bool kIntergroupReduce = false, typename T>
__forceinline__ __device__ T warp_reduce_max(T value) {
    return warp_reduce<kNumLanesPerGroup, kIntergroupReduce, T>(value, ReduceMax<T>{});
}

template <int kNumLanesPerGroup = kWarpSize, bool kIntergroupReduce = false, typename T>
__forceinline__ __device__ T warp_reduce_min(T value) {
    return warp_reduce<kNumLanesPerGroup, kIntergroupReduce, T>(value, ReduceMin<T>{});
}

template <int kNumLanesPerGroup = kWarpSize, bool kIntergroupReduce = false, typename T>
__forceinline__ __device__ T warp_reduce_and(T value) {
    return warp_reduce<kNumLanesPerGroup, kIntergroupReduce, T>(value, ReduceAnd<T>{});
}

template <int kNumLanesPerGroup = kWarpSize, bool kIntergroupReduce = false, typename T>
__forceinline__ __device__ T warp_reduce_or(T value) {
    return warp_reduce<kNumLanesPerGroup, kIntergroupReduce, T>(value, ReduceOr<T>{});
}

} // namespace deep_ep