aphrodite-engine/kernels/moe/marlin_kernels/marlin_moe_kernel.h

#pragma once

#include <torch/all.h>

#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <cuda.h>
#include <cuda_fp16.h>
#include <cuda_runtime.h>

#include <iostream>

#include "core/scalar_type.hpp"

namespace marlin_moe {

constexpr int ceildiv(int a, int b) { return (a + b - 1) / b; }

#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800

// Instances of `Vec` are used to organize groups of >>registers<<, as needed
// for instance as inputs to tensor core operations. Consequently, all
// corresponding index accesses must be compile-time constants, which is why we
// extensively use `#pragma unroll` throughout the kernel code to guarantee
// this.
template <typename T, int n>
struct Vec {
  T elems[n];
  __device__ T& operator[](int i) { return elems[i]; }
};

using I4 = Vec<int, 4>;

// Matrix fragments for tensor core instructions; their precise layout is
// documented here:
// https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#matrix-fragments-for-mma-m16n8k16-with-floating-point-type
using FragA = Vec<half2, 4>;
using FragB = Vec<half2, 2>;
using FragC = Vec<float, 4>;
using FragS = Vec<half2, 1>;  // quantization scales

// Predicated asynchronous global->shared copy; used for inputs A where we apply
// predication to handle batchsizes that are not multiples of 16.
__device__ inline void cp_async4_pred(void* smem_ptr, const void* glob_ptr,
                                      bool pred = true) {
  const int BYTES = 16;
  uint32_t smem = static_cast<uint32_t>(__cvta_generic_to_shared(smem_ptr));
  asm volatile(
      "{\n"
      "   .reg .pred p;\n"
      "   setp.ne.b32 p, %0, 0;\n"
      "   @p cp.async.cg.shared.global [%1], [%2], %3;\n"
      "}\n" ::"r"((int)pred),
      "r"(smem), "l"(glob_ptr), "n"(BYTES));
}

// Asynchronous global->shared copy
__device__ inline void cp_async4(void* smem_ptr, const void* glob_ptr) {
  const int BYTES = 16;
  uint32_t smem = static_cast<uint32_t>(__cvta_generic_to_shared(smem_ptr));
  asm volatile(
      "{\n"
      "   cp.async.cg.shared.global [%0], [%1], %2;\n"
      "}\n" ::"r"(smem),
      "l"(glob_ptr), "n"(BYTES));
}

// Async copy fence.
__device__ inline void cp_async_fence() {
  asm volatile("cp.async.commit_group;\n" ::);
}

// Wait until at most `n` async copy stages are still pending.
template <int n>
__device__ inline void cp_async_wait() {
  asm volatile("cp.async.wait_group %0;\n" ::"n"(n));
}

// m16n8k16 tensor core mma instruction with fp16 inputs and fp32
// output/accumulation.
__device__ inline void mma(const FragA& a_frag, const FragB& frag_b,
                           FragC& frag_c) {
  const uint32_t* a = reinterpret_cast<const uint32_t*>(&a_frag);
  const uint32_t* b = reinterpret_cast<const uint32_t*>(&frag_b);
  float* c = reinterpret_cast<float*>(&frag_c);
  asm volatile(
      "mma.sync.aligned.m16n8k16.row.col.f32.f16.f16.f32 "
      "{%0,%1,%2,%3}, {%4,%5,%6,%7}, {%8,%9}, {%10,%11,%12,%13};\n"
      : "=f"(c[0]), "=f"(c[1]), "=f"(c[2]), "=f"(c[3])
      : "r"(a[0]), "r"(a[1]), "r"(a[2]), "r"(a[3]), "r"(b[0]), "r"(b[1]),
        "f"(c[0]), "f"(c[1]), "f"(c[2]), "f"(c[3]));
}

// Instruction for loading a full 16x16 matrix fragment of operand A from shared
// memory, directly in tensor core layout.
__device__ inline void ldsm4(FragA& frag_a, const void* smem_ptr) {
  uint32_t* a = reinterpret_cast<uint32_t*>(&frag_a);
  uint32_t smem = static_cast<uint32_t>(__cvta_generic_to_shared(smem_ptr));
  asm volatile("ldmatrix.sync.aligned.m8n8.x4.shared.b16 {%0,%1,%2,%3}, [%4];\n"
               : "=r"(a[0]), "=r"(a[1]), "=r"(a[2]), "=r"(a[3])
               : "r"(smem));
}

// Lookup-table based 3-input logical operation; explicitly used for
// dequantization as the compiler does not seem to automatically recognize it in
// all cases.
template <int lut>
__device__ inline int lop3(int a, int b, int c) {
  int res;
  asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
               : "=r"(res)
               : "r"(a), "r"(b), "r"(c), "n"(lut));
  return res;
}

// Constructs destination register by taking bytes from 2 sources (based on
// mask)
template <int start_byte, int mask>
__device__ inline uint32_t prmt(uint32_t a) {
  uint32_t res;
  asm volatile("prmt.b32 %0, %1, %2, %3;\n"
               : "=r"(res)
               : "r"(a), "n"(start_byte), "n"(mask));
  return res;
}

template <aphrodite::ScalarTypeId w_type_id>
__device__ inline FragB dequant(int q);

// Efficiently dequantize 4bit values packed in an int32 value into a full
// B-fragment of 4 fp16 values. We mostly follow the strategy in the link below,
// with some small changes:
// https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h#L215-L287
template <>
__device__ inline FragB dequant<aphrodite::kU4B8.id()>(int q) {
  const int LO = 0x000f000f;
  const int HI = 0x00f000f0;
  const int EX = 0x64006400;
  // Guarantee that the `(a & b) | c` operations are LOP3s.
  int lo = lop3<(0xf0 & 0xcc) | 0xaa>(q, LO, EX);
  int hi = lop3<(0xf0 & 0xcc) | 0xaa>(q, HI, EX);
  // We want signed int4 outputs, hence we fuse the `-8` symmetric zero point
  // directly into `SUB` and `ADD`.
  const int SUB = 0x64086408;
  const int MUL = 0x2c002c00;
  const int ADD = 0xd480d480;
  FragB frag_b;
  frag_b[0] = __hsub2(*reinterpret_cast<half2*>(&lo),
                      *reinterpret_cast<const half2*>(&SUB));
  frag_b[1] = __hfma2(*reinterpret_cast<half2*>(&hi),
                      *reinterpret_cast<const half2*>(&MUL),
                      *reinterpret_cast<const half2*>(&ADD));
  return frag_b;
}

// Fast Int8ToFp16: Efficiently dequantize 8bit int values to fp16
// Reference:
// https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h#L53-L85
template <>
__device__ inline FragB dequant<aphrodite::kU8B128.id()>(int q) {
  static constexpr uint32_t mask_for_elt_01 = 0x5250;
  static constexpr uint32_t mask_for_elt_23 = 0x5351;
  static constexpr uint32_t start_byte_for_fp16 = 0x64646464;

  uint32_t lo = prmt<start_byte_for_fp16, mask_for_elt_01>(q);
  uint32_t hi = prmt<start_byte_for_fp16, mask_for_elt_23>(q);

  static constexpr uint32_t I8s_TO_F16s_MAGIC_NUM = 0x64806480;

  FragB frag_b;
  frag_b[0] = __hsub2(*reinterpret_cast<half2*>(&lo),
                      *reinterpret_cast<const half2*>(&I8s_TO_F16s_MAGIC_NUM));
  frag_b[1] = __hsub2(*reinterpret_cast<half2*>(&hi),
                      *reinterpret_cast<const half2*>(&I8s_TO_F16s_MAGIC_NUM));
  return frag_b;
}

// Multiply dequantized values by the corresponding quantization scale; used
// only for grouped quantization.
__device__ inline void scale(FragB& frag_b, FragS& frag_s, int i) {
  half2 s = __half2half2(reinterpret_cast<__half*>(&frag_s)[i]);
  frag_b[0] = __hmul2(frag_b[0], s);
  frag_b[1] = __hmul2(frag_b[1], s);
}

// Given 2 floats multiply by 2 scales (halves)
__device__ inline void scale_float(float* c, FragS& s) {
  __half* s_ptr = reinterpret_cast<__half*>(&s);
  c[0] = __fmul_rn(c[0], __half2float(s_ptr[0]));
  c[1] = __fmul_rn(c[1], __half2float(s_ptr[1]));
}

// Same as above, but for act_order (each K is multiplied individually)
__device__ inline void scale4(FragB& frag_b, FragS& frag_s_1, FragS& frag_s_2,
                              FragS& frag_s_3, FragS& frag_s_4, int i) {
  __half2 s_val_1_2;
  s_val_1_2.x = reinterpret_cast<__half*>(&frag_s_1)[i];
  s_val_1_2.y = reinterpret_cast<__half*>(&frag_s_2)[i];

  __half2 s_val_3_4;
  s_val_3_4.x = reinterpret_cast<__half*>(&frag_s_3)[i];
  s_val_3_4.y = reinterpret_cast<__half*>(&frag_s_4)[i];

  frag_b[0] = __hmul2(frag_b[0], s_val_1_2);
  frag_b[1] = __hmul2(frag_b[1], s_val_3_4);
}

// Wait until barrier reaches `count`, then lock for current threadblock.
__device__ inline void barrier_acquire(int* lock, int count) {
  if (threadIdx.x == 0) {
    int state = -1;
    do
      // Guarantee that subsequent writes by this threadblock will be visible
      // globally.
      asm volatile("ld.global.acquire.gpu.b32 %0, [%1];\n"
                   : "=r"(state)
                   : "l"(lock));
    while (state != count);
  }
  __syncthreads();
}

// Release barrier and increment visitation count.
__device__ inline void barrier_release(int* lock, bool reset = false) {
  __syncthreads();
  if (threadIdx.x == 0) {
    if (reset) {
      lock[0] = 0;
      return;
    }
    int val = 1;
    // Make sure that all writes since acquiring this barrier are visible
    // globally, while releasing the barrier.
    asm volatile("fence.acq_rel.gpu;\n");
    asm volatile("red.relaxed.gpu.global.add.s32 [%0], %1;\n"
                 :
                 : "l"(lock), "r"(val));
  }
}

template <const aphrodite::ScalarTypeId w_type_id,  // weight ScalarType id
          const int threads,          // number of threads in a threadblock
          const int thread_m_blocks,  // number of 16x16 blocks in the m
                                      // dimension (batchsize) of the
                                      // threadblock
          const int thread_n_blocks,  // same for n dimension (output)
          const int thread_k_blocks,  // same for k dimension (reduction)
          const int stages,  // number of stages for the async global->shared
                             // fetch pipeline
          const bool has_act_order,    // whether act_order is enabled
          const int group_blocks = -1  // number of consecutive 16x16 blocks
                                       // with a separate quantization scale
          >
__device__ inline void MarlinMoESingle(
    const int4* __restrict__ A,  // fp16 input matrix of shape mxk
    const int4* __restrict__ B,  // 4bit quantized weight matrix of shape kxn
    int4* __restrict__ C,        // fp16 output buffer of shape mxn
    const int* __restrict__ sorted_ids,      // int32 sorted ids of experts
    const float* __restrict__ topk_weights,  // float topk weights
    const int4* __restrict__ scales_ptr,  // fp16 quantization scales of shape
                                          // (k/groupsize)xn
    const int* __restrict__ g_idx,        // int32 group indices of shape k
    const int* __restrict__ expert_offsets,
    int num_groups,        // number of scale groups per output channel
    int expert_idx,        // idx of current expert
    int num_experts,       // number of experts
    int topk,              // topk parameter of moe
    int prob_m,            // batch dimension m
    int prob_n,            // output dimension n
    int prob_k,            // reduction dimension k
    int tot_m,             // total number of rows in A and C
    int* locks,            // extra global storage for barrier synchronization
    bool replicate_input,  // do we use the same input for each expert?
    bool apply_weights,    // apply weights to output
    int current_m_block    // current m block to start kernel computation from
) {
  static constexpr auto w_type = aphrodite::ScalarType::from_id(w_type_id);
  constexpr int pack_factor = 32 / w_type.size_bits();

  // For larger GEMMs we run multiple batchsize 64 versions in parallel for a
  // better partitioning with less reductions
  int parallel = 1;
  if (prob_m > 16 * thread_m_blocks) {
    parallel = prob_m / (16 * thread_m_blocks);
    prob_m = 16 * thread_m_blocks;
  }

  int k_tiles = prob_k / 16 / thread_k_blocks;
  int n_tiles = prob_n / 16 / thread_n_blocks;
  int iters = ceildiv(k_tiles * n_tiles * parallel, gridDim.x);

  if constexpr (!has_act_order && group_blocks != -1) {
    if (group_blocks >= thread_k_blocks) {
      // Ensure that the number of tiles in each stripe is a multiple of the
      // groupsize; this avoids an annoying special case where a stripe starts
      // in the middle of group.
      iters = (group_blocks / thread_k_blocks) *
              ceildiv(iters, (group_blocks / thread_k_blocks));
    }
  }

  int slice_row = (iters * blockIdx.x) % k_tiles;
  int slice_col_par = (iters * blockIdx.x) / k_tiles;
  int slice_col = slice_col_par;
  int slice_iters;  // number of threadblock tiles in the current slice
  int slice_count =
      0;          // total number of active threadblocks in the current slice
  int slice_idx;  // index of threadblock in current slice; numbered bottom to
                  // top

  // We can easily implement parallel problem execution by just remapping
  // indices and advancing global pointers
  if (slice_col_par >= n_tiles) {
    locks += (slice_col_par / n_tiles) * n_tiles;
    slice_col = slice_col_par % n_tiles;
    sorted_ids += (slice_col_par / n_tiles) * 16 * thread_m_blocks;
  }

  // Compute all information about the current slice which is required for
  // synchronization.
  auto init_slice = [&]() {
    slice_iters =
        iters * (blockIdx.x + 1) - (k_tiles * slice_col_par + slice_row);
    if (slice_iters < 0 || slice_col_par >= n_tiles * parallel) slice_iters = 0;
    if (slice_iters == 0) return;
    if (slice_row + slice_iters > k_tiles) slice_iters = k_tiles - slice_row;
    slice_count = 1;
    slice_idx = 0;
    int col_first = iters * ceildiv(k_tiles * slice_col_par, iters);
    if (col_first <= k_tiles * (slice_col_par + 1)) {
      int col_off = col_first - k_tiles * slice_col_par;
      slice_count = ceildiv(k_tiles - col_off, iters);
      if (col_off > 0) slice_count++;
      int delta_first = iters * blockIdx.x - col_first;
      if (delta_first < 0 || (col_off == 0 && delta_first == 0))
        slice_idx = slice_count - 1;
      else {
        slice_idx = slice_count - 1 - delta_first / iters;
        if (col_off > 0) slice_idx--;
      }
    }
    if (slice_col == n_tiles) {
      sorted_ids += 16 * thread_m_blocks;
      locks += n_tiles;
      slice_col = 0;
    }
  };
  init_slice();

  // A sizes/strides

  // stride of the A matrix in global memory
  int a_gl_stride = prob_k / 8;
  // stride of an A matrix tile in shared memory
  constexpr int a_sh_stride = 16 * thread_k_blocks / 8;
  // delta between subsequent A tiles in global memory
  constexpr int a_gl_rd_delta_o = 16 * thread_k_blocks / 8;
  // between subsequent accesses within a tile
  int a_gl_rd_delta_i = a_gl_stride * (threads / a_gl_rd_delta_o);
  // between shared memory writes
  constexpr int a_sh_wr_delta = a_sh_stride * (threads / a_gl_rd_delta_o);
  // between shared memory tile reads
  constexpr int a_sh_rd_delta_o = 2 * ((threads / 32) / (thread_n_blocks / 4));
  // within a shared memory tile
  constexpr int a_sh_rd_delta_i = a_sh_stride * 16;
  // overall size of a tile
  constexpr int a_sh_stage = a_sh_stride * (16 * thread_m_blocks);
  // number of shared write iterations for a tile
  constexpr int a_sh_wr_iters = ceildiv(a_sh_stage, a_sh_wr_delta);

  // B sizes/strides
  int b_gl_stride = 16 * prob_n / (pack_factor * 4);
  constexpr int b_sh_stride = ((thread_n_blocks * 16) * 16 / pack_factor) / 4;
  constexpr int b_thread_vecs = w_type.size_bits() == 4 ? 1 : 2;
  constexpr int b_sh_stride_threads = b_sh_stride / b_thread_vecs;

  int b_gl_rd_delta_o = b_gl_stride * thread_k_blocks;
  int b_gl_rd_delta_i = b_gl_stride * (threads / b_sh_stride_threads);
  constexpr int b_sh_wr_delta = threads * b_thread_vecs;
  constexpr int b_sh_rd_delta = threads * b_thread_vecs;
  constexpr int b_sh_stage = b_sh_stride * thread_k_blocks;
  constexpr int b_sh_wr_iters = b_sh_stage / b_sh_wr_delta;

  // Scale sizes/strides without act_order
  int s_gl_stride = prob_n / 8;
  constexpr int s_sh_stride = 16 * thread_n_blocks / 8;
  constexpr int s_tb_groups =
      !has_act_order && group_blocks != -1 && group_blocks < thread_k_blocks
          ? thread_k_blocks / group_blocks
          : 1;
  constexpr int s_sh_stage = s_tb_groups * s_sh_stride;
  int s_gl_rd_delta = s_gl_stride;
  // Scale size/strides with act_order
  constexpr int tb_k = 16 * thread_k_blocks;
  constexpr int g_idx_stage = has_act_order ? (tb_k * sizeof(int)) / 16 : 0;
  // constexpr int act_s_row_stride      = 1;
  // int           act_s_col_stride      = act_s_row_stride * num_groups;
  int act_s_col_stride = 1;
  int act_s_col_warp_stride = act_s_col_stride * 8;
  int tb_n_warps = thread_n_blocks / 4;
  int act_s_col_tb_stride = act_s_col_warp_stride * tb_n_warps;

  constexpr int sorted_sh_stride = threads;
  constexpr int sorted_gl_stride = threads;

  // Global A read index of current thread.
  int a_gl_rd = a_gl_stride * (threadIdx.x / a_gl_rd_delta_o) +
                (threadIdx.x % a_gl_rd_delta_o);
  a_gl_rd += a_gl_rd_delta_o * slice_row;
  // Shared write index of current thread.
  int a_sh_wr = a_sh_stride * (threadIdx.x / a_gl_rd_delta_o) +
                (threadIdx.x % a_gl_rd_delta_o);
  // Shared read index.
  int a_sh_rd =
      a_sh_stride * ((threadIdx.x % 32) % 16) + (threadIdx.x % 32) / 16;
  a_sh_rd += 2 * ((threadIdx.x / 32) / (thread_n_blocks / 4));

  int b_gl_rd = b_gl_stride * (threadIdx.x / b_sh_stride_threads) +
                (threadIdx.x % b_sh_stride_threads) * b_thread_vecs;
  b_gl_rd += b_sh_stride * slice_col;
  b_gl_rd += b_gl_rd_delta_o * slice_row;
  int b_sh_wr = threadIdx.x * b_thread_vecs;
  int b_sh_rd = threadIdx.x * b_thread_vecs;

  // For act_order
  constexpr int k_iter_size = tb_k / b_sh_wr_iters;
  int slice_k_start = tb_k * slice_row;
  int slice_k_finish = slice_k_start + tb_k * slice_iters;
  int slice_k_start_shared_fetch = slice_k_start;
  int slice_n_offset = act_s_col_tb_stride * slice_col;

  // No act_order
  int s_gl_rd;
  if constexpr (!has_act_order) {
    if constexpr (group_blocks == -1) {
      s_gl_rd = s_sh_stride * slice_col + threadIdx.x;
    } else {
      s_gl_rd = s_gl_stride * ((thread_k_blocks * slice_row) / group_blocks) +
                s_sh_stride * slice_col + threadIdx.x;
    }
  }
  int s_sh_wr = threadIdx.x;
  bool s_sh_wr_pred = threadIdx.x < s_sh_stride;

  // We use a different scale layout for grouped and column-wise quantization as
  // we scale a `half2` tile in column-major layout in the former and in
  // row-major in the latter case.
  int s_sh_rd;
  if constexpr (group_blocks != -1)
    s_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) +
              (threadIdx.x % 32) / 4;
  else
    s_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) +
              (threadIdx.x % 32) % 4;

  int sh_first_group_id = -1;
  int sh_num_groups = -1;
  constexpr int sh_max_num_groups = 32;

  int shs_size;
  if constexpr (has_act_order)
    shs_size = sh_max_num_groups * s_sh_stride + threads;
  else
    shs_size = group_blocks > 0 ? stages * s_sh_stage : threads;

  extern __shared__ int4 sh[];
  // Shared memory storage for global fetch pipelines.
  int4* sh_a = sh;
  int4* sh_b = sh_a + (stages * a_sh_stage);
  int4* sh_g_idx = sh_b + (stages * b_sh_stage);
  int4* sh_s = sh_g_idx + (stages * g_idx_stage);
  int* sh_sorted = (int*)(sh_s + shs_size);

  // Precompute which thread should not read memory in which iterations; this is
  // needed if there are more threads than required for a certain tilesize or
  // when the batchsize is not a multiple of 16.
  bool a_sh_wr_pred[a_sh_wr_iters];
  #pragma unroll
  for (int i = 0; i < a_sh_wr_iters; i++) {
    int a_idx = a_sh_wr_delta * i + a_sh_wr;
    int row = a_idx / a_gl_rd_delta_o;
    if (row >= prob_m) {
      a_sh_wr_pred[i] = false;
    } else {
      a_sh_wr_pred[i] = a_sh_wr_delta * i + a_sh_wr < a_sh_stride * prob_m;
    }
  }

  // To ensure that writing and reading A tiles to/from shared memory, the
  // latter in fragment format, is fully bank conflict free, we need to use a
  // rather fancy XOR-based layout. The key here is that neither reads nor
  // writes of the 16-byte `int4` blocks of 8 consecutive threads involve the
  // same shared memory banks. Further, it seems (based on NSight-Compute) that
  // each warp must also write a consecutive memory segment?
  auto transform_a = [&](int i) {
    int row = i / a_gl_rd_delta_o;
    return a_gl_rd_delta_o * row + (i % a_gl_rd_delta_o) ^ row;
  };
  // Since the computation of this remapping is non-trivial and, due to our main
  // loop unrolls, all shared memory accesses are static, we simply precompute
  // both transformed reads and writes.
  int a_sh_wr_trans[a_sh_wr_iters];
  #pragma unroll
  for (int i = 0; i < a_sh_wr_iters; i++)
    a_sh_wr_trans[i] = transform_a(a_sh_wr_delta * i + a_sh_wr);
  int a_sh_rd_trans[b_sh_wr_iters][thread_m_blocks];
  #pragma unroll
  for (int i = 0; i < b_sh_wr_iters; i++) {
  #pragma unroll
    for (int j = 0; j < thread_m_blocks; j++)
      a_sh_rd_trans[i][j] =
          transform_a(a_sh_rd_delta_o * i + a_sh_rd_delta_i * j + a_sh_rd);
  }

  // Since B-accesses have non-constant stride they have to be computed at
  // runtime; we break dependencies between subsequent accesses with a tile by
  // maintining multiple pointers (we have enough registers), a tiny
  // optimization.
  const int4* B_ptr[b_sh_wr_iters];
  #pragma unroll
  for (int i = 0; i < b_sh_wr_iters; i++)
    B_ptr[i] = B + b_gl_rd_delta_i * i + b_gl_rd;

  // Register storage for double buffer of shared memory reads.
  FragA frag_a[2][thread_m_blocks];
  I4 frag_b_quant[2][b_thread_vecs];
  FragC frag_c[thread_m_blocks][4][2];
  FragS frag_s[2][4];         // No act-order
  FragS act_frag_s[2][4][4];  // For act-order

  // Zero accumulators.
  auto zero_accums = [&]() {
  #pragma unroll
    for (int i = 0; i < thread_m_blocks * 4 * 2 * 4; i++)
      reinterpret_cast<float*>(frag_c)[i] = 0;
  };

  auto fetch_scales_to_shared = [&](bool is_async, int first_group_id,
                                    int last_group_id) {
    sh_first_group_id = first_group_id;
    sh_num_groups = last_group_id - first_group_id + 1;

    if (sh_num_groups < sh_max_num_groups) {
      sh_num_groups = sh_max_num_groups;
    }

    if (sh_first_group_id + sh_num_groups > num_groups) {
      sh_num_groups = num_groups - sh_first_group_id;
    }

    int row_offset = first_group_id * s_gl_stride;

    if (is_async) {
      for (int i = 0; i < sh_num_groups; i++) {
        if (threadIdx.x < s_sh_stride) {
          cp_async4_pred(&sh_s[(i * s_sh_stride) + threadIdx.x],
                         &scales_ptr[row_offset + (i * s_gl_stride) +
                                     slice_n_offset + threadIdx.x]);
        }
      }
    } else {
      for (int i = 0; i < sh_num_groups; i++) {
        if (threadIdx.x < s_sh_stride) {
          sh_s[(i * s_sh_stride) + threadIdx.x] =
              scales_ptr[row_offset + (i * s_gl_stride) + slice_n_offset +
                         threadIdx.x];
        }
      }
    }
  };
  // Asynchronously fetch the next A, B and s tile from global to the next
  // shared memory pipeline location.
  auto fetch_to_shared = [&](int pipe, int a_off, bool pred = true) {
    if (pred) {
      int4* sh_a_stage = sh_a + a_sh_stage * pipe;
  #pragma unroll
      for (int i = 0; i < a_sh_wr_iters; i++) {
        int a_idx = a_gl_rd_delta_i * i + a_gl_rd + a_gl_rd_delta_o * a_off;
        int row = a_idx / a_gl_stride;
        int sorted_row =
            replicate_input ? sorted_ids[row] / topk : sorted_ids[row];
        int new_idx = sorted_row * a_gl_stride + a_idx % a_gl_stride;
        if (sorted_row < tot_m * (replicate_input ? 1 : topk) &&
            new_idx < a_gl_stride * tot_m * (replicate_input ? 1 : topk)) {
          cp_async4_pred(&sh_a_stage[a_sh_wr_trans[i]], &A[new_idx],
                         a_sh_wr_pred[i]);
        }
      }
      int4* sh_b_stage = sh_b + b_sh_stage * pipe;
  #pragma unroll
      for (int i = 0; i < b_sh_wr_iters; i++) {
  #pragma unroll
        for (int j = 0; j < b_thread_vecs; j++) {
          cp_async4(&sh_b_stage[b_sh_wr_delta * i + b_sh_wr + j], B_ptr[i] + j);
        }
        B_ptr[i] += b_gl_rd_delta_o;
      }

      if constexpr (has_act_order) {
        // Fetch g_idx thread-block portion
        int full_pipe = a_off;
        int cur_k = slice_k_start_shared_fetch + tb_k * full_pipe;
        if (cur_k < prob_k && cur_k < slice_k_finish) {
          int4* sh_g_idx_stage = sh_g_idx + g_idx_stage * pipe;

          int4 const* cur_g_idx_stage_ptr =
              reinterpret_cast<int4 const*>(&g_idx[cur_k]);

          if (threadIdx.x < g_idx_stage) {
            cp_async4_pred(&sh_g_idx_stage[threadIdx.x],
                           &cur_g_idx_stage_ptr[threadIdx.x]);
          }
        }
      } else {
        if constexpr (group_blocks != -1) {
          int4* sh_s_stage = sh_s + s_sh_stage * pipe;

          if constexpr (group_blocks >= thread_k_blocks) {
            // Only fetch scales if this tile starts a new group
            if (pipe % (group_blocks / thread_k_blocks) == 0) {
              if (s_sh_wr_pred) {
                cp_async4(&sh_s_stage[s_sh_wr], &scales_ptr[s_gl_rd]);
              }
              s_gl_rd += s_gl_rd_delta;
            }
          } else {
            for (int i = 0; i < s_tb_groups; i++) {
              if (s_sh_wr_pred) {
                cp_async4(&sh_s_stage[i * s_sh_stride + s_sh_wr],
                          &scales_ptr[s_gl_rd]);
              }
              s_gl_rd += s_gl_rd_delta;
            }
          }
        }
      }
    }
    // Insert a fence even when we are winding down the pipeline to ensure that
    // waiting is also correct at this point.
    cp_async_fence();
  };

  // TODO we are currently hitting illegal memory accesses when fetching
  // sorted_ids to shared data: fix this
  auto fetch_sorted_ids_to_shared = [&]() {
    const int mpt = ceildiv(prob_m, threads);
    for (int i = 0; i < mpt; i++) {
      if ((i * sorted_gl_stride) + threadIdx.x < prob_m) {
        sh_sorted[(i * sorted_sh_stride) + threadIdx.x] =
            sorted_ids[(i * sorted_gl_stride) + threadIdx.x];
      }
    }
  };

  // Wait until the next thread tile has been loaded to shared memory.
  auto wait_for_stage = [&]() {
    // We only have `stages - 2` active fetches since we are double buffering
    // and can only issue the next fetch when it is guaranteed that the previous
    // shared memory load is fully complete (as it may otherwise be
    // overwritten).
    cp_async_wait<stages - 2>();
    __syncthreads();
  };

  // Load the next sub-tile from the current location in the shared memory pipe
  // into the current register buffer.
  auto fetch_to_registers = [&](int k, int pipe) {
    int4* sh_a_stage = sh_a + a_sh_stage * pipe;
  #pragma unroll
    for (int i = 0; i < thread_m_blocks; i++)
      ldsm4(frag_a[k % 2][i], &sh_a_stage[a_sh_rd_trans[k % b_sh_wr_iters][i]]);
    int4* sh_b_stage = sh_b + b_sh_stage * pipe;

  #pragma unroll
    for (int i = 0; i < b_thread_vecs; i++) {
      frag_b_quant[k % 2][i] = *reinterpret_cast<I4*>(
          &sh_b_stage[b_sh_rd_delta * (k % b_sh_wr_iters) + b_sh_rd + i]);
    }
  };

  bool is_same_group[stages];
  int same_group_id[stages];

  auto init_same_group = [&](int pipe) {
    if constexpr (!has_act_order) {
      is_same_group[pipe] = false;
      same_group_id[pipe] = 0;
      return;
    }

    int4* sh_g_idx_stage = sh_g_idx + g_idx_stage * pipe;
    int* sh_g_idx_int_ptr = reinterpret_cast<int*>(sh_g_idx_stage);

    int group_id_1 = sh_g_idx_int_ptr[0];
    int group_id_2 = sh_g_idx_int_ptr[tb_k - 1];

    is_same_group[pipe] = group_id_1 == group_id_2;
    same_group_id[pipe] = group_id_1;
  };

  auto fetch_scales_to_registers = [&](int k, int full_pipe) {
    int pipe = full_pipe % stages;

    if constexpr (!has_act_order) {
      // No act-order case
      if constexpr (group_blocks != -1) {
        if constexpr (group_blocks >= thread_k_blocks) {
          int4* sh_s_stage =
              sh_s + s_sh_stage * ((group_blocks / thread_k_blocks) *
                                   (pipe / (group_blocks / thread_k_blocks)));
          reinterpret_cast<int4*>(&frag_s[k % 2])[0] = sh_s_stage[s_sh_rd];
        } else {
          int warp_id = threadIdx.x / 32;
          int n_warps = thread_n_blocks / 4;

          int warp_row = warp_id / n_warps;

          int cur_k = warp_row * 16;
          cur_k += k_iter_size * (k % b_sh_wr_iters);

          int k_blocks = cur_k / 16;
          int cur_group_id = k_blocks / group_blocks;

          int4* sh_s_stage = sh_s + s_sh_stage * pipe;

          reinterpret_cast<int4*>(&frag_s[k % 2])[0] =
              sh_s_stage[s_sh_rd + cur_group_id * s_sh_stride];
        }
      }

      return;
    }

    // Act-order case

    // Determine K of the "current" thread-block
    int cur_k = slice_k_start + tb_k * full_pipe;
    if (cur_k >= prob_k || cur_k >= slice_k_finish) {
      return;
    }

    // Reset (to current thread-block) since we read g_idx portion from the
    // shared memory
    cur_k = 0;

    // Progress to current iteration
    cur_k += k_iter_size * (k % b_sh_wr_iters);

    // Determine "position" inside the thread-block (based on warp and
    // thread-id)
    int warp_id = threadIdx.x / 32;
    int n_warps =
        thread_n_blocks / 4;  // Each warp processes 4 16-size tiles over N

    int warp_row = warp_id / n_warps;
    int warp_col = warp_id % n_warps;

    cur_k += warp_row * 16;

    int th_id = threadIdx.x % 32;
    cur_k += (th_id % 4) * 2;  // Due to tensor-core layout for fp16 B matrix

    int s_col_shift =
        /*slice_n_offset +*/ (act_s_col_warp_stride * warp_col) +
        (th_id / 4) * act_s_col_stride;

    if (is_same_group[pipe]) {
      if (k % 2 == 0) {
        *(reinterpret_cast<int4*>(&(act_frag_s[k % 2][0][0]))) =
            sh_s[(same_group_id[pipe] - sh_first_group_id) * s_sh_stride +
                 s_col_shift];
      } else {
        *(reinterpret_cast<int4*>(&(act_frag_s[k % 2][0][0]))) =
            *(reinterpret_cast<int4*>(&(act_frag_s[(k - 1) % 2][0][0])));
      }

      for (int i = 1; i < 4; i++) {
        *(reinterpret_cast<int4*>(&(act_frag_s[k % 2][i][0]))) =
            *(reinterpret_cast<int4*>(&(act_frag_s[k % 2][0][0])));
      }
      return;
    }

    int4* sh_g_idx_stage = sh_g_idx + g_idx_stage * pipe;
    int* sh_g_idx_int_ptr = reinterpret_cast<int*>(sh_g_idx_stage);

    constexpr int k_frag_offsets[4] = {0, 1, 8,
                                       9};  // Tensor core offsets per thread

  #pragma unroll
    for (int i = 0; i < 4; i++) {
      int actual_k = cur_k + k_frag_offsets[i];

      int group_id = sh_g_idx_int_ptr[actual_k];
      int rel_group_id = group_id - sh_first_group_id;

      *(reinterpret_cast<int4*>(&(act_frag_s[k % 2][i][0]))) =
          sh_s[rel_group_id * s_sh_stride + s_col_shift];
    }
  };

  // Execute the actual tensor core matmul of a sub-tile.
  auto matmul = [&](int k) {
  // We have the m dimension as the inner loop in order to encourage overlapping
  // dequantization and matmul operations.
  #pragma unroll
    for (int j = 0; j < 4; j++) {
      int b_quant_0, b_quant_1;
      if constexpr (w_type.size_bits() == 4) {
        b_quant_0 = frag_b_quant[k % 2][0][j];
        b_quant_1 = b_quant_0 >> 8;
      } else {
        static_assert(w_type.size_bits() == 8);
        int* frag_b_quant_ptr = reinterpret_cast<int*>(frag_b_quant[k % 2]);
        b_quant_0 = frag_b_quant_ptr[j * 2 + 0];
        b_quant_1 = frag_b_quant_ptr[j * 2 + 1];
      }

      FragB frag_b0 = dequant<w_type_id>(b_quant_0);
      FragB frag_b1 = dequant<w_type_id>(b_quant_1);

      // Apply scale to frag_b0
      if constexpr (has_act_order) {
        scale4(frag_b0, act_frag_s[k % 2][0][j], act_frag_s[k % 2][1][j],
               act_frag_s[k % 2][2][j], act_frag_s[k % 2][3][j], 0);
      } else {
        if constexpr (group_blocks != -1) {
          scale(frag_b0, frag_s[k % 2][j], 0);
        }
      }

      // Apply scale to frag_b1
      if constexpr (has_act_order) {
        scale4(frag_b1, act_frag_s[k % 2][0][j], act_frag_s[k % 2][1][j],
               act_frag_s[k % 2][2][j], act_frag_s[k % 2][3][j], 1);

      } else {
        if constexpr (group_blocks != -1) {
          scale(frag_b1, frag_s[k % 2][j], 1);
        }
      }

  #pragma unroll
      for (int i = 0; i < thread_m_blocks; i++) {
        mma(frag_a[k % 2][i], frag_b0, frag_c[i][j][0]);
        mma(frag_a[k % 2][i], frag_b1, frag_c[i][j][1]);
      }
    }
  };

  // Since we slice across the k dimension of a tile in order to increase the
  // number of warps while keeping the n dimension of a tile reasonable, we have
  // multiple warps that accumulate their partial sums of the same output
  // location; which we have to reduce over in the end. We do in shared memory.
  auto thread_block_reduce = [&]() {
    constexpr int red_off = threads / b_sh_stride_threads / 2;
    if (red_off >= 1) {
      int red_idx = threadIdx.x / b_sh_stride_threads;
      constexpr int red_sh_stride = b_sh_stride_threads * 4 * 2;
      constexpr int red_sh_delta = b_sh_stride_threads;
      int red_sh_rd = red_sh_stride * (threadIdx.x / b_sh_stride_threads) +
                      (threadIdx.x % b_sh_stride_threads);

      // Parallel logarithmic shared memory reduction. We make sure to avoid any
      // unnecessary read or write iterations, e.g., for two warps we write only
      // once by warp 1 and read only once by warp 0.

  #pragma unroll
      for (int m_block = 0; m_block < thread_m_blocks; m_block++) {
  #pragma unroll
        for (int i = red_off; i > 0; i /= 2) {
          if (i <= red_idx && red_idx < 2 * i) {
  #pragma unroll
            for (int j = 0; j < 4 * 2; j++) {
              int red_sh_wr =
                  red_sh_delta * j + (red_sh_rd - red_sh_stride * i);
              if (i < red_off) {
                float* c_rd =
                    reinterpret_cast<float*>(&sh[red_sh_delta * j + red_sh_rd]);
                float* c_wr = reinterpret_cast<float*>(&sh[red_sh_wr]);
  #pragma unroll
                for (int k = 0; k < 4; k++)
                  reinterpret_cast<FragC*>(frag_c)[4 * 2 * m_block + j][k] +=
                      c_rd[k] + c_wr[k];
              }
              sh[red_sh_wr] =
                  reinterpret_cast<int4*>(&frag_c)[4 * 2 * m_block + j];
            }
          }
          __syncthreads();
        }
        if (red_idx == 0) {
  #pragma unroll
          for (int i = 0; i < 4 * 2; i++) {
            float* c_rd =
                reinterpret_cast<float*>(&sh[red_sh_delta * i + red_sh_rd]);
  #pragma unroll
            for (int j = 0; j < 4; j++)
              reinterpret_cast<FragC*>(frag_c)[4 * 2 * m_block + i][j] +=
                  c_rd[j];
          }
        }
        __syncthreads();
      }
    }
  };

  // Since multiple threadblocks may process parts of the same column slice, we
  // finally have to globally reduce over the results. As the striped
  // partitioning minimizes the number of such reductions and our outputs are
  // usually rather small, we perform this reduction serially in L2 cache.
  auto global_reduce = [&](bool first = false, bool last = false) {
    // We are very careful here to reduce directly in the output buffer to
    // maximize L2 cache utilization in this step. To do this, we write out
    // results in FP16 (but still reduce with FP32 compute).
    constexpr int active_threads = 32 * thread_n_blocks / 4;
    if (threadIdx.x < active_threads) {
      int c_gl_stride = prob_n / 8;
      int c_gl_wr_delta_o = 8 * c_gl_stride;
      int c_gl_wr_delta_i = 4 * (active_threads / 32);
      int c_gl_wr = c_gl_stride * ((threadIdx.x % 32) / 4) +
                    4 * (threadIdx.x / 32) + threadIdx.x % 4;
      c_gl_wr += (2 * thread_n_blocks) * slice_col;
      constexpr int c_sh_wr_delta = active_threads;
      int c_sh_wr = threadIdx.x;

      int row = (threadIdx.x % 32) / 4;

      if (!first) {
  // Interestingly, doing direct global accesses here really seems to mess up
  // the compiler and lead to slowdowns, hence we also use async-copies even
  // though these fetches are not actually asynchronous.
  #pragma unroll
        for (int i = 0; i < thread_m_blocks * 4; i++) {
          int c_idx =
              c_gl_wr + c_gl_wr_delta_o * (i / 2) + c_gl_wr_delta_i * (i % 2);
          int sorted_row = sorted_ids[c_idx / c_gl_stride];
          int new_idx = sorted_row * c_gl_stride + c_idx % c_gl_stride;
          cp_async4_pred(&sh[c_sh_wr + c_sh_wr_delta * i], &C[new_idx],
                         sorted_row < tot_m * topk &&
                             (8 * (i / 2) + row < prob_m &&
                              (i < (thread_m_blocks - 1) * 4 ||
                               sorted_ids[8 * (i / 2) + row] < tot_m * topk)));
        }
        cp_async_fence();
        cp_async_wait<0>();
      }

  #pragma unroll
      for (int i = 0; i < thread_m_blocks * 4; i++) {
        if (8 * (i / 2) + row < prob_m &&
            (i < (thread_m_blocks - 1) * 4 ||
             sorted_ids[8 * (i / 2) + row] < tot_m * topk)) {
          if (!first) {
            int4 c_red = sh[c_sh_wr + i * c_sh_wr_delta];
  #pragma unroll
            for (int j = 0; j < 2 * 4; j++) {
              reinterpret_cast<float*>(
                  &frag_c)[4 * 2 * 4 * (i / 4) + 4 * j + (i % 4)] +=
                  __half2float(reinterpret_cast<__half*>(&c_red)[j]);
            }
          }
          if (!last) {
            int4 c;
  #pragma unroll
            for (int j = 0; j < 2 * 4; j++) {
              reinterpret_cast<__half*>(&c)[j] =
                  __float2half(reinterpret_cast<float*>(
                      &frag_c)[4 * 2 * 4 * (i / 4) + 4 * j + (i % 4)]);
            }
            int c_idx =
                c_gl_wr + c_gl_wr_delta_o * (i / 2) + c_gl_wr_delta_i * (i % 2);
            int row = sorted_ids[c_idx / c_gl_stride];
            if (row < tot_m * topk) {
              int new_idx = row * c_gl_stride + c_idx % c_gl_stride;
              C[new_idx] = c;
            }
          }
        }
      }
    }
  };

  // Write out the reduce final result in the correct layout. We only actually
  // reshuffle matrix fragments in this step, the reduction above is performed
  // in fragment layout.
  auto write_result = [&]() {
    int c_gl_stride = prob_n / 8;
    constexpr int c_sh_stride = 2 * thread_n_blocks + 1;
    int c_gl_wr_delta = c_gl_stride * (threads / (2 * thread_n_blocks));
    constexpr int c_sh_rd_delta =
        c_sh_stride * (threads / (2 * thread_n_blocks));

    int c_gl_wr = c_gl_stride * (threadIdx.x / (2 * thread_n_blocks)) +
                  (threadIdx.x % (2 * thread_n_blocks));
    c_gl_wr += (2 * thread_n_blocks) * slice_col;
    int c_sh_wr =
        (4 * c_sh_stride) * ((threadIdx.x % 32) / 4) + (threadIdx.x % 32) % 4;
    c_sh_wr += 32 * (threadIdx.x / 32);
    int c_sh_rd = c_sh_stride * (threadIdx.x / (2 * thread_n_blocks)) +
                  (threadIdx.x % (2 * thread_n_blocks));

    int c_gl_wr_end = c_gl_stride * prob_m;

    // We first reorder in shared memory to guarantee the most efficient final
    // global write patterns
    auto write = [&](int idx, float c0, float c1, FragS& s) {
      half2 res = __halves2half2(__float2half(c0), __float2half(c1));

      // For per-column quantization we finally apply the scale here (only for
      // 4-bit)
      if constexpr (!has_act_order && group_blocks == -1 &&
                    w_type.size_bits() == 4) {
        res = __hmul2(res, s[0]);
      }

      ((half2*)sh)[idx] = res;
    };
    if (threadIdx.x / 32 < thread_n_blocks / 4) {
  #pragma unroll
      for (int i = 0; i < thread_m_blocks; i++) {
  #pragma unroll
        for (int j = 0; j < 4; j++) {
          int wr = c_sh_wr + 8 * j;
          write(wr + (4 * c_sh_stride) * 0 + 0, frag_c[i][j][0][0],
                frag_c[i][j][0][1], frag_s[j / 2][2 * (j % 2) + 0]);
          write(wr + (4 * c_sh_stride) * 8 + 0, frag_c[i][j][0][2],
                frag_c[i][j][0][3], frag_s[j / 2][2 * (j % 2) + 0]);
          write(wr + (4 * c_sh_stride) * 0 + 4, frag_c[i][j][1][0],
                frag_c[i][j][1][1], frag_s[j / 2][2 * (j % 2) + 1]);
          write(wr + (4 * c_sh_stride) * 8 + 4, frag_c[i][j][1][2],
                frag_c[i][j][1][3], frag_s[j / 2][2 * (j % 2) + 1]);
        }
        c_sh_wr += 16 * (4 * c_sh_stride);
      }
    }
    __syncthreads();

  #pragma unroll
    for (int i = 0;
         i < ceildiv(16 * thread_m_blocks, threads / (2 * thread_n_blocks));
         i++) {
      if (c_gl_wr < c_gl_wr_end) {
        int row = sorted_ids[c_gl_wr / c_gl_stride];
        if (row < tot_m * topk) {
          int off = row * c_gl_stride + c_gl_wr % c_gl_stride;
          if (!apply_weights) {
            C[off] = sh[c_sh_rd];
          } else {
            __half* ctrg = reinterpret_cast<__half*>(&C[off]);
            __half* csrc = reinterpret_cast<__half*>(&sh[c_sh_rd]);
            for (int j = 0; j < 8; ++j) {
              ctrg[j] = __float2half(topk_weights[row] * __half2float(csrc[j]));
            }
          }
          c_gl_wr += c_gl_wr_delta;
          c_sh_rd += c_sh_rd_delta;
        }
      }
    }
  };

  // Start global fetch and register load pipelines.
  auto start_pipes = [&]() {
  // TODO re-enable after fixing this function
  // fetch_sorted_ids_to_shared();
  // __syncthreads();

  #pragma unroll
    for (int i = 0; i < stages - 1; i++) {
      if (has_act_order && i == 0) {
        int last_g_idx = slice_k_start + stages * tb_k * 2;
        if (last_g_idx >= prob_k) {
          last_g_idx = prob_k - 1;
        }
        fetch_scales_to_shared(true, g_idx[slice_k_start], g_idx[last_g_idx]);
      }
      fetch_to_shared(i, i, i < slice_iters);
    }

    zero_accums();
    wait_for_stage();
    init_same_group(0);
    fetch_to_registers(0, 0);
    fetch_scales_to_registers(0, 0);
    a_gl_rd += a_gl_rd_delta_o * (stages - 1);
    slice_k_start_shared_fetch += tb_k * (stages - 1);
  };
  if (slice_iters) {
    start_pipes();
  }

  // Main loop.
  while (slice_iters) {
    // We unroll over both the global fetch and the register load pipeline to
    // ensure all shared memory accesses are static. Note that both pipelines
    // have even length meaning that the next iteration will always start at
    // index 0.
  #pragma unroll
    for (int pipe = 0; pipe < stages;) {
  #pragma unroll
      for (int k = 0; k < b_sh_wr_iters; k++) {
        fetch_to_registers(k + 1, pipe % stages);
        fetch_scales_to_registers(k + 1, pipe);
        if (k == b_sh_wr_iters - 2) {
          fetch_to_shared((pipe + stages - 1) % stages, pipe,
                          slice_iters >= stages);
          pipe++;
          wait_for_stage();
          init_same_group(pipe % stages);
        }
        matmul(k);
      }
      slice_iters--;
      if (slice_iters == 0) {
        break;
      }
    }

    a_gl_rd += a_gl_rd_delta_o * stages;
    slice_k_start += tb_k * stages;
    slice_k_start_shared_fetch += tb_k * stages;

    if constexpr (has_act_order) {
      int first_group_id = g_idx[slice_k_start];
      int last_g_idx = slice_k_start + stages * tb_k * 2;
      if (last_g_idx >= prob_k) {
        last_g_idx = prob_k - 1;
      }
      int last_group_id = g_idx[last_g_idx];
      if (last_group_id >= sh_first_group_id + sh_num_groups) {
        fetch_scales_to_shared(false, first_group_id, last_group_id);
        __syncthreads();
      }
    }

    // Process results and, if necessary, proceed to the next column slice.
    // While this pattern may not be the most readable, other ways of writing
    // the loop seemed to noticeably worse performance after compilation.
    if (slice_iters == 0) {
      cp_async_wait<0>();
      bool last = slice_idx == slice_count - 1;
      if constexpr (!has_act_order && group_blocks == -1) {
        if constexpr (w_type.size_bits() == 8) {
          if (s_sh_wr_pred) {
            cp_async4(&sh_s[s_sh_wr], &scales_ptr[s_gl_rd]);
          }
          cp_async_fence();
        } else {
          // For 4-bit per-column scales, we only fetch them here in the
          // final step before write-out
          if (last) {
            if (s_sh_wr_pred) {
              cp_async4(&sh_s[s_sh_wr], &scales_ptr[s_gl_rd]);
            }
            cp_async_fence();
          }
        }
      }

      thread_block_reduce();
      if constexpr (!has_act_order && group_blocks == -1) {
        if constexpr (w_type.size_bits() == 8) {
          cp_async_wait<0>();
          __syncthreads();
          if (threadIdx.x / 32 < thread_n_blocks / 4) {
            reinterpret_cast<int4*>(&frag_s)[0] = sh_s[s_sh_rd + 0];
            reinterpret_cast<int4*>(&frag_s)[1] = sh_s[s_sh_rd + 4];
          }

        } else {
          if (last) {
            cp_async_wait<0>();
            __syncthreads();
            if (threadIdx.x / 32 < thread_n_blocks / 4) {
              reinterpret_cast<int4*>(&frag_s)[0] = sh_s[s_sh_rd + 0];
              reinterpret_cast<int4*>(&frag_s)[1] = sh_s[s_sh_rd + 4];
            }
          }
        }
      }

      // For 8-bit channelwise, we apply the scale before the global reduction
      // that converts the fp32 results to fp16 (so that we avoid possible
      // overflow in fp16)
      if constexpr (!has_act_order && group_blocks == -1 &&
                    w_type.size_bits() == 8) {
        if (threadIdx.x / 32 < thread_n_blocks / 4) {
  #pragma unroll
          for (int i = 0; i < thread_m_blocks; i++) {
  #pragma unroll
            for (int j = 0; j < 4; j++) {
              scale_float(reinterpret_cast<float*>(&frag_c[i][j][0][0]),
                          frag_s[j / 2][2 * (j % 2) + 0]);
              scale_float(reinterpret_cast<float*>(&frag_c[i][j][0][2]),
                          frag_s[j / 2][2 * (j % 2) + 0]);

              scale_float(reinterpret_cast<float*>(&frag_c[i][j][1][0]),
                          frag_s[j / 2][2 * (j % 2) + 1]);
              scale_float(reinterpret_cast<float*>(&frag_c[i][j][1][2]),
                          frag_s[j / 2][2 * (j % 2) + 1]);
            }
          }
        }
      }

      if (slice_count > 1) {  // only globally reduce if there is more than one
                              // block in a slice
        barrier_acquire(&locks[slice_col], slice_idx);
        global_reduce(slice_idx == 0, last);
        barrier_release(&locks[slice_col], last);
      }
      if (last)  // only the last block in a slice actually writes the result
        write_result();
      slice_row = 0;
      slice_col_par++;
      slice_col++;
      init_slice();
      if (slice_iters) {
        a_gl_rd = a_gl_stride * (threadIdx.x / a_gl_rd_delta_o) +
                  (threadIdx.x % a_gl_rd_delta_o);
  #pragma unroll
        for (int i = 0; i < b_sh_wr_iters; i++)
          B_ptr[i] += b_sh_stride - b_gl_rd_delta_o * k_tiles;
        if (slice_col == 0) {
  #pragma unroll
          for (int i = 0; i < b_sh_wr_iters; i++) B_ptr[i] -= b_gl_stride;
        }

        // Update slice k/n for scales loading
        if constexpr (has_act_order) {
          slice_k_start = tb_k * slice_row;
          slice_k_finish = slice_k_start + tb_k * slice_iters;
          slice_k_start_shared_fetch = slice_k_start;
          slice_n_offset = act_s_col_tb_stride * slice_col;

        } else {
          s_gl_rd = s_sh_stride * slice_col + threadIdx.x;
        }
        start_pipes();
      }
    }
  }
}

template <const aphrodite::ScalarTypeId w_type_id,  // weight ScalarType id
          const int threads,          // number of threads in a threadblock
          const int thread_n_blocks,  // same for n dimension (output)
          const int thread_k_blocks,  // same for k dimension (reduction)
          const int stages,  // number of stages for the async global->shared
                             // fetch pipeline
          const bool has_act_order,    // whether act_order is enabled
          const int group_blocks = -1  // number of consecutive 16x16 blocks
                                       // with a separate quantization scale
          >
__global__ void MarlinMoE(
    const int4* __restrict__ A,  // fp16 input matrix of shape mxk
    const int4* __restrict__ B,  // 4bit quantized weight matrix of shape kxn
    int4* __restrict__ C,        // fp16 output buffer of shape mxn
    const int* __restrict__ sorted_ids_base,  // int32 sorted ids of experts
    const float* __restrict__ topk_weights,   // float topk weights
    const int4* __restrict__ scales_ptr,  // fp16 quantization scales of shape
                                          // (k/groupsize)xn
    const int* __restrict__ g_idx,        // int32 group indices of shape k
    const int* __restrict__ expert_offsets,
    int num_groups,        // number of scale groups per output channel
    int expert_idx,        // idx of current expert
    int num_experts,       // number of experts
    int topk,              // topk parameter of moe
    int prob_m,            // batch dimension m
    int prob_n,            // output dimension n
    int prob_k,            // reduction dimension k
    int tot_m,             // total number of rows in A and C
    int* locks,            // extra global storage for barrier synchronization
    bool replicate_input,  // do we use the same input for each expert?
    bool apply_weights,    // apply weights to output
    int current_m_block,   // current m block to start kernel computation from
    int max_par,           // maximum parallelism
    int cfg_max_m_blocks   // upper bound on m blocks
) {
  int m_block_ctr = current_m_block;

  const int* sorted_ids_expert =
      sorted_ids_base + expert_offsets[expert_idx] + m_block_ctr * 4 * max_par;
  int tot_its = expert_offsets[expert_idx + 1] - expert_offsets[expert_idx];
  if (tot_its == 0) {
    return;
  }
  int tot_m_blocks = ceildiv(tot_its, 16);
  int pad = 16 * tot_m_blocks - tot_its;

  if (m_block_ctr >= tot_m_blocks) {
    return;
  }

  int max_block = tot_m_blocks - m_block_ctr;
  prob_m = tot_its - 16 * m_block_ctr;

  int par = 1;
  if (max_block > cfg_max_m_blocks) {
    // Note that parallel > 1 currently only works for inputs without any
    // padding
    par = (16 * max_block - pad) / (16 * cfg_max_m_blocks);
    if (par > max_par) par = max_par;
    prob_m = (16 * cfg_max_m_blocks) * par;
    m_block_ctr += cfg_max_m_blocks * (par - 1);
    max_block = cfg_max_m_blocks;
  }

  if (max_block == 1) {
    MarlinMoESingle<w_type_id, threads, 1, thread_n_blocks, thread_k_blocks,
                    stages, has_act_order, group_blocks>(
        A, B, C, sorted_ids_expert, topk_weights, scales_ptr, g_idx,
        expert_offsets, num_groups, expert_idx, num_experts, topk, prob_m,
        prob_n, prob_k, tot_m, locks, replicate_input, apply_weights,
        current_m_block);
  } else if (max_block == 2) {
    MarlinMoESingle<w_type_id, threads, 2, thread_n_blocks, thread_k_blocks,
                    stages, has_act_order, group_blocks>(
        A, B, C, sorted_ids_expert, topk_weights, scales_ptr, g_idx,
        expert_offsets, num_groups, expert_idx, num_experts, topk, prob_m,
        prob_n, prob_k, tot_m, locks, replicate_input, apply_weights,
        current_m_block);
  } else if (max_block == 3) {
    MarlinMoESingle<w_type_id, threads, 3, thread_n_blocks, thread_k_blocks,
                    stages, has_act_order, group_blocks>(
        A, B, C, sorted_ids_expert, topk_weights, scales_ptr, g_idx,
        expert_offsets, num_groups, expert_idx, num_experts, topk, prob_m,
        prob_n, prob_k, tot_m, locks, replicate_input, apply_weights,
        current_m_block);
  } else {
    MarlinMoESingle<w_type_id, threads, 4, thread_n_blocks, thread_k_blocks,
                    stages, has_act_order, group_blocks>(
        A, B, C, sorted_ids_expert, topk_weights, scales_ptr, g_idx,
        expert_offsets, num_groups, expert_idx, num_experts, topk, prob_m,
        prob_n, prob_k, tot_m, locks, replicate_input, apply_weights,
        current_m_block);
  }
}

#else

template <const aphrodite::ScalarTypeId w_type_id,  // weight ScalarType id
          const int threads,          // number of threads in a threadblock
          const int thread_n_blocks,  // same for n dimension (output)
          const int thread_k_blocks,  // same for k dimension (reduction)
          const int stages,  // number of stages for the async global->shared
                             // fetch pipeline
          const bool has_act_order,    // whether act_order is enabled
          const int group_blocks = -1  // number of consecutive 16x16 blocks
                                       // with a separate quantization scale
          >
__global__ void MarlinMoE(
    const int4* __restrict__ A,  // fp16 input matrix of shape mxk
    const int4* __restrict__ B,  // 4bit quantized weight matrix of shape kxn
    int4* __restrict__ C,        // fp16 output buffer of shape mxn
    const int* __restrict__ sorted_ids,      // int32 sorted ids of experts
    const float* __restrict__ topk_weights,  // float topk weights
    const int4* __restrict__ scales_ptr,  // fp16 quantization scales of shape
                                          // (k/groupsize)xn
    const int* __restrict__ g_idx,        // int32 group indices of shape k
    const int* __restrict__ expert_offsets,
    int num_groups,        // number of scale groups per output channel
    int expert_idx,        // idx of current expert
    int num_experts,       // number of experts
    int topk,              // topk parameter of moe
    int prob_m,            // batch dimension m
    int prob_n,            // output dimension n
    int prob_k,            // reduction dimension k
    int tot_m,             // total number of rows in A and C
    int* locks,            // extra global storage for barrier synchronization
    bool replicate_input,  // do we use the same input for each expert?
    bool apply_weights,    // apply weights to output
    int current_m_block,   // current m block to start kernel computation from
    int max_par,           // maximum parallelism
    int cfg_max_m_blocks   // upper bound on m blocks

) {
  // Marlin is not implemented yet for SM < 8.0
  assert(false);
  return;
}

#endif

// 8 warps are a good choice since every SM has 4 schedulers and having more
// than 1 warp per schedule allows some more latency hiding. At the same time,
// we want relatively few warps to have many registers per warp and small tiles.
const int USER_THREADS =
    256;               // Note: This is only used with user-provided thread_k/n
const int STAGES = 4;  // 4 pipeline stages fit into shared memory
// const int SHARED_MEM =
//     96 * 1024; // max shared memory on compute capability 8.6 (< 8.0)

static constexpr int min_thread_n = 64;
static constexpr int min_thread_k = 64;

#define __CALL_IF_MOE(W_TYPE, THREAD_N_BLOCKS, THREAD_K_BLOCKS, HAS_ACT_ORDER, \
                      GROUP_BLOCKS, NUM_THREADS)                               \
  else if (q_type == W_TYPE && thread_n_blocks == THREAD_N_BLOCKS &&           \
           thread_k_blocks == THREAD_K_BLOCKS &&                               \
           has_act_order == HAS_ACT_ORDER && group_blocks == GROUP_BLOCKS &&   \
           num_threads == NUM_THREADS) {                                       \
    cudaFuncSetAttribute(                                                      \
        MarlinMoE<W_TYPE.id(), NUM_THREADS, THREAD_N_BLOCKS, THREAD_K_BLOCKS,  \
                  STAGES, HAS_ACT_ORDER, GROUP_BLOCKS>,                        \
        cudaFuncAttributeMaxDynamicSharedMemorySize, max_shared_mem);          \
    MarlinMoE<W_TYPE.id(), NUM_THREADS, THREAD_N_BLOCKS, THREAD_K_BLOCKS,      \
              STAGES, HAS_ACT_ORDER, GROUP_BLOCKS>                             \
        <<<blocks, NUM_THREADS, max_shared_mem, stream>>>(                     \
            A_ptr, B_ptr, C_ptr, sorted_ids_ptr, topk_weights_ptr, s_ptr,      \
            g_idx_ptr, expert_offsets_ptr, num_groups, expert_idx,             \
            num_experts, topk, prob_m, prob_n, prob_k, tot_m, locks,           \
            replicate_input, apply_weights, m_block, max_par,                  \
            cfg_max_m_blocks);                                                 \
  }

#define GPTQ_CALL_IF_MOE(W_TYPE, N_BLOCKS, K_BLOCKS, NUM_THREADS)   \
  __CALL_IF_MOE(W_TYPE, N_BLOCKS, K_BLOCKS, true, 0, NUM_THREADS)   \
  __CALL_IF_MOE(W_TYPE, N_BLOCKS, K_BLOCKS, false, -1, NUM_THREADS) \
  __CALL_IF_MOE(W_TYPE, N_BLOCKS, K_BLOCKS, false, 2, NUM_THREADS)  \
  __CALL_IF_MOE(W_TYPE, N_BLOCKS, K_BLOCKS, false, 4, NUM_THREADS)  \
  __CALL_IF_MOE(W_TYPE, N_BLOCKS, K_BLOCKS, false, 8, NUM_THREADS)

}  // namespace marlin_moe