/*
 * Copyright (C) Marlin.2024 Elias Frantar (elias.frantar@ist.ac.at)
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *         http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */


#include <torch/extension.h>
#include <c10/cuda/CUDAStream.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_fp16.h>
#include <cuda_runtime.h>


#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 750

#else

namespace aphrodite {
namespace marlin {

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

// 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 with a cache hint indicating that the values may be evicted immediately; used for
// quantized weights B, which are only accessed precisely once and should thus not pollute the L2 cache which we need
// for inputs A and outputs C.
__device__ inline void cp_async4_stream(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"
    "   .reg .b64 p;\n"
    "   createpolicy.fractional.L2::evict_first.b64 p, 1.0;"
    "   cp.async.cg.shared.global.L2::cache_hint [%0], [%1], %2, p;\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;
}

// Efficiently dequantize 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/main/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h
__device__ inline FragB dequant(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 = 0x64006400;
  const int MUL = 0x2c002c00;
  const int ADD = 0xd400d400;
  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;
}

// Multiply dequantized values by the corresponding quantization scale; used only for grouped quantization.
__device__ inline void scale(FragB& frag_b, FragS& frag_s, FragS& frag_z, int i) {
  half2 s = __half2half2(reinterpret_cast<__half*>(&frag_s)[i]);
  half2 z = __half2half2(reinterpret_cast<__half*>(&frag_z)[i]);
  frag_b[0] = __hfma2(frag_b[0], s, z);
  frag_b[1] = __hfma2(frag_b[1], s, z);
}

// 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 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 int group_blocks = -1 // number of consecutive 16x16 blocks with a separate quantization scale
>
__global__ void Marlin(
  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 int4* __restrict__ s, // fp16 quantization scales of shape (k/groupsize)xn
  const int4* __restrict__ z, // fp16 quantization zeros of shape (k/groupsize)xn
  int  prob_m, // batch dimension m
  int  prob_n, // output dimension n
  int  prob_k, // reduction dimension k
  int* locks // extra global storage for barrier synchronization
) {
  // Each threadblock processes one "stripe" of the B matrix with (roughly) the same size, which might involve multiple
  // column "slices" (of width 16 * `thread_n_blocks`). Stripes are defined as shown in the 3x3 matrix 5 SM example:
  //   0 1 3
  //   0 2 3
  //   1 2 4
  // While this kind of partitioning makes things somewhat more complicated, it ensures good utilization of all SMs
  // for many kinds of shape and GPU configurations, while requiring as few slow global cross-threadblock reductions as
  // possible.

  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, gridDim.x);
  // 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.
  if (group_blocks != -1)
    iters = (group_blocks / thread_k_blocks) * ceildiv(iters, (group_blocks / thread_k_blocks));

  int slice_row = (iters * blockIdx.x) % k_tiles;
  int slice_col = (iters * blockIdx.x) / k_tiles;
  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

  // 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 + slice_row);
    if (slice_iters < 0 || slice_col >= n_tiles)
      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, iters);
    if (col_first <= k_tiles * (slice_col + 1)) {
      int col_off = col_first - k_tiles * slice_col;
      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--;
      }
    }
  };
  init_slice();

  int a_gl_stride = prob_k / 8; // stride of the A matrix in global memory
  // We typically use `constexpr` to indicate that this value is a compile-time constant
  constexpr int a_sh_stride = 16 * thread_k_blocks / 8; // stride of an A matrix tile in shared memory
  constexpr int a_gl_rd_delta_o = 16 * thread_k_blocks / 8; // delta between subsequent A tiles in global memory
  int a_gl_rd_delta_i = a_gl_stride * (threads / a_gl_rd_delta_o); // between subsequent accesses within a tile
  constexpr int a_sh_wr_delta = a_sh_stride * (threads / a_gl_rd_delta_o); // between shared memory writes
  constexpr int a_sh_rd_delta_o = 2 * ((threads / 32) / (thread_n_blocks / 4)); // between shared memory tile reads
  constexpr int a_sh_rd_delta_i = a_sh_stride * 16; // within a shared memory tile
  constexpr int a_sh_stage = a_sh_stride * (16 * thread_m_blocks); // overall size of a tile
  constexpr int a_sh_wr_iters = ceildiv(a_sh_stage, a_sh_wr_delta); // number of shared write iterations for a tile

  int b_gl_stride = 16 * prob_n / 32;
  constexpr int b_sh_stride = 32 * thread_n_blocks / 4;
  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);
  constexpr int b_sh_wr_delta = threads;
  constexpr int b_sh_rd_delta = threads;
  constexpr int b_sh_stage = b_sh_stride * thread_k_blocks;
  constexpr int b_sh_wr_iters = b_sh_stage / b_sh_wr_delta;

  int s_gl_stride = prob_n / 8;
  constexpr int s_sh_stride = 16 * thread_n_blocks / 8;
  constexpr int s_sh_stage = s_sh_stride;
  int s_gl_rd_delta = s_gl_stride;

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

  int 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;
  int s_sh_rd;
  // 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.
  if (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;

  // 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++)
    a_sh_wr_pred[i] = a_sh_wr_delta * i + a_sh_wr < a_sh_stride * prob_m;
  bool s_sh_wr_pred = threadIdx.x < s_sh_stride;

  // 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;

  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_s = sh_b + (stages * b_sh_stage);
  int4* sh_z = sh_s + (stages * s_sh_stage);
  // Register storage for double buffer of shared memory reads.
  FragA frag_a[2][thread_m_blocks];
  I4 frag_b_quant[2];
  FragC frag_c[thread_m_blocks][4][2];
  FragS frag_s[2][4];
  FragS frag_z[2][4];

  // 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;
  };

  // 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++) {
        cp_async4_pred(
          &sh_a_stage[a_sh_wr_trans[i]],
          &A[a_gl_rd_delta_i * i + a_gl_rd + a_gl_rd_delta_o * a_off],
          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++) {
        cp_async4_stream(&sh_b_stage[b_sh_wr_delta * i + b_sh_wr], B_ptr[i]);
        B_ptr[i] += b_gl_rd_delta_o;
      }
      // Only fetch scales if this tile starts a new group
      if (group_blocks != -1 && pipe % (group_blocks / thread_k_blocks) == 0) {
        int4* sh_s_stage = sh_s + s_sh_stage * pipe;
        int4* sh_z_stage = sh_z + s_sh_stage * pipe;
        if (s_sh_wr_pred) {
          cp_async4_stream(&sh_s_stage[s_sh_wr], &s[s_gl_rd]);
          cp_async4_stream(&sh_z_stage[s_sh_wr], &z[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();
  };

  // 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) {
    // It may seem inefficient that we reload the groups for every sub-tile; however, this does not seem to be a
    // significant bottleneck, while some theoretically better attempts have lead to bad instruction ordering by the
    // compiler and correspondingly a noticeable drop in performance.
    if (group_blocks != -1) {
      int4* sh_s_stage = sh_s + s_sh_stage * ((group_blocks / thread_k_blocks) * (pipe / (group_blocks / thread_k_blocks)));
      int4* sh_z_stage = sh_z + 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];
      reinterpret_cast<int4*>(&frag_z[k % 2])[0] = sh_z_stage[s_sh_rd];
    }
    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;
    frag_b_quant[k % 2] = *reinterpret_cast<I4*>(&sh_b_stage[b_sh_rd_delta * (k % b_sh_wr_iters) + b_sh_rd]);
  };

  // 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 = frag_b_quant[k % 2][j];
      int b_quant_shift = b_quant >> 8;
      FragB frag_b0 = dequant(b_quant);
      // If there are no groups, we can just scale the final output once and can avoid doing so for each weight.
      if (group_blocks != -1)
        scale(frag_b0, frag_s[k % 2][j], frag_z[k % 2][j], 0);
      FragB frag_b1 = dequant(b_quant_shift);
      if (group_blocks != -1)
        scale(frag_b1, frag_s[k % 2][j], frag_z[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 / 2;
    if (red_off >= 1) {
      int red_idx = threadIdx.x / b_sh_stride;
      constexpr int red_sh_stride = b_sh_stride * 4 * 2;
      constexpr int red_sh_delta = b_sh_stride;
      int red_sh_rd = red_sh_stride * (threadIdx.x / b_sh_stride) + (threadIdx.x % b_sh_stride);

      // 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++) {
          cp_async4_pred(
            &sh[c_sh_wr + c_sh_wr_delta * i],
            &C[c_gl_wr + c_gl_wr_delta_o * (i / 2) + c_gl_wr_delta_i * (i % 2)],
            i < (thread_m_blocks - 1) * 4 || 8 * (i / 2) + row < prob_m
          );
        }
        cp_async_fence();
        cp_async_wait<0>();
      }

      #pragma unroll
      for (int i = 0; i < thread_m_blocks * 4; i++) {
        if (i < (thread_m_blocks - 1) * 4 || 8 * (i / 2) + row < prob_m) {
          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)]
              );
            }
            C[c_gl_wr + c_gl_wr_delta_o * (i / 2) + c_gl_wr_delta_i * (i % 2)] = 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, FragS& z) {
      half2 res = __halves2half2(__float2half(c0), __float2half(c1));
      if (group_blocks == -1) // for per-column quantization we finally apply the scale here
        res = __hfma2(res, s[0], z[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], frag_z[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], frag_z[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], frag_z[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], frag_z[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) {
        C[c_gl_wr] = sh[c_sh_rd];
        c_gl_wr += c_gl_wr_delta;
        c_sh_rd += c_sh_rd_delta;
      }
    }
  };

  // Start global fetch and register load pipelines.
  auto start_pipes = [&] () {
    #pragma unroll
    for (int i = 0; i < stages - 1; i++)
      fetch_to_shared(i, i, i < slice_iters);
    zero_accums();
    wait_for_stage();
    fetch_to_registers(0, 0);
    a_gl_rd += a_gl_rd_delta_o * (stages - 1);
  };
  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);
        if (k == b_sh_wr_iters - 2) {
          fetch_to_shared((pipe + stages - 1) % stages, pipe, slice_iters >= stages);
          pipe++;
          wait_for_stage();
        }
        matmul(k);
      }
      slice_iters--;
      if (slice_iters == 0)
        break;
    }
    a_gl_rd += a_gl_rd_delta_o * stages;

    // 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;
      // For per-column scales, we only fetch them here in the final step before write-out
      if (group_blocks == -1 && last) {
        if (s_sh_wr_pred) {
          cp_async4_stream(&sh_s[s_sh_wr], &s[s_gl_rd]);
          cp_async4_stream(&sh_z[s_sh_wr], &z[s_gl_rd]);
        }
        cp_async_fence();
      }
      thread_block_reduce();
      if (group_blocks == -1 && 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];
          reinterpret_cast<int4*>(&frag_z)[0] = sh_z[s_sh_rd + 0];
          reinterpret_cast<int4*>(&frag_z)[1] = sh_z[s_sh_rd + 4];
        }
      }
      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++;
      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;
        s_gl_rd = s_sh_stride * slice_col + threadIdx.x;
        start_pipes();
      }
    }
  }
}


// 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 THREADS = 256;
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)

#define CALL_IF(THREAD_M_BLOCKS, THREAD_N_BLOCKS, THREAD_K_BLOCKS, GROUP_BLOCKS) \
  else if ( \
    thread_m_blocks == THREAD_M_BLOCKS && thread_n_blocks == THREAD_N_BLOCKS && thread_k_blocks == THREAD_K_BLOCKS && \
    group_blocks == GROUP_BLOCKS \
  ) { \
    cudaFuncSetAttribute( \
      Marlin<THREADS, THREAD_M_BLOCKS, THREAD_N_BLOCKS, THREAD_K_BLOCKS, STAGES, GROUP_BLOCKS>, \
      cudaFuncAttributeMaxDynamicSharedMemorySize, \
      SHARED_MEM \
    ); \
    Marlin<THREADS, THREAD_M_BLOCKS, THREAD_N_BLOCKS, THREAD_K_BLOCKS, STAGES, GROUP_BLOCKS><<<blocks, THREADS, SHARED_MEM, stream>>>( \
      A_ptr, B_ptr, C_ptr, s_ptr, z_ptr,\
      prob_m, prob_n, prob_k, \
      locks \
    ); \
  }

const int ERR_PROB_SHAPE = 1;
const int ERR_KERN_SHAPE = 2;

int marlin_cuda(
  const void* A,
  const void* B,
        void* C,
        void* s,
        void* z,
  int prob_m,
  int prob_n,
  int prob_k,
  void* workspace,
  int groupsize = -1,
  int dev = 0,
  cudaStream_t stream = 0,
  int thread_k = -1,
  int thread_n = -1,
  int sms = -1
) {
  int tot_m = prob_m;
  int tot_m_blocks = ceildiv(tot_m, 16);

  if (sms == -1)
    cudaDeviceGetAttribute(&sms, cudaDevAttrMultiProcessorCount, dev);
  if (thread_k == -1 || thread_n == -1) {
    if (prob_m <= 16) {
      // For small batchizes, better partitioning is slightly more important than better compute utilization
      thread_k = 128;
      thread_n = 128;
    } else {
      thread_k = 64;
      thread_n = 256;
    }
  }

  int thread_k_blocks = thread_k / 16;
  int thread_n_blocks = thread_n / 16;
  int group_blocks = (groupsize == -1) ? -1 : groupsize / 16;
  int blocks = sms;

  if (prob_n % thread_n != 0 || prob_k % thread_k != 0 || (group_blocks != -1 && prob_k % group_blocks != 0))
    return ERR_PROB_SHAPE;
  if (prob_m == 0 || prob_n == 0 || prob_k == 0)
    return 0;

  const int4* A_ptr = (const int4*) A;
  const int4* B_ptr = (const int4*) B;
  int4* C_ptr = (int4*) C;
  const int4* s_ptr = (const int4*) s;
  const int4* z_ptr = (const int4*) z;

  int cols = prob_n / thread_n;
  int* locks = (int*) workspace;

  int ret = 0;
  for (int i = 0; i < tot_m_blocks; i += 4) {
    int thread_m_blocks = tot_m_blocks - i;
    prob_m = tot_m - 16 * i;
    if (thread_m_blocks > 4) {
      thread_m_blocks = 4;
      prob_m = 64;
    }

    // For compilation speed, we only define the kernel configurations that have seemed useful (in terms of performance)
    // in our testing, however many more are, in principle, possible.
    if (false) {}
    CALL_IF(1,  8,  8, -1)
    CALL_IF(1,  8,  8,  8)
    CALL_IF(1, 16,  4, -1)
    CALL_IF(1, 16,  4,  8)
    CALL_IF(2, 16,  4, -1)
    CALL_IF(2, 16,  4,  8)
    CALL_IF(3, 16,  4, -1)
    CALL_IF(3, 16,  4,  8)
    CALL_IF(4, 16,  4, -1)
    CALL_IF(4, 16,  4,  8)
    else
      ret = ERR_KERN_SHAPE;

    A_ptr += 16 * thread_m_blocks * (prob_k / 8);
    C_ptr += 16 * thread_m_blocks * (prob_n / 8);
  }

  return ret;
}

__global__ void dequant_marlin(
  const uint32_t* qweight,
  const half* scales,
  const half* zeros,
  half* out,
  int m,
  int n,
  int groupsize
) {
  int t = threadIdx.x;
  int group = blockIdx.x * 16 / groupsize;
  int4 pack = *reinterpret_cast<const int4*>(qweight + blockIdx.x * n * 2 + blockIdx.y * 128 + t * 4);
  const FragS* sscale = reinterpret_cast<const FragS*>(scales + group * n + blockIdx.y * 64 + t / 4 * 8);
  const FragS* szero = reinterpret_cast<const FragS*>(zeros + group * n + blockIdx.y * 64 + t / 4 * 8);
  out = out + (blockIdx.x * 16 + (t % 4) * 2) * n + blockIdx.y * 64 + t / 4;
  uint32_t* unpack = reinterpret_cast<uint32_t*>(&pack);
  for (int i = 0; i < 4; i += 1) {
    FragB frag_b0 = dequant(unpack[i]);
    FragB frag_b1 = dequant(unpack[i] >> 8);
    FragS frag_s = sscale[i];
    FragS frag_z = szero[i];
    scale(frag_b0, frag_s, frag_z, 0);
    scale(frag_b1, frag_s, frag_z, 1);
    *out = frag_b0[0].x;
    *(out + n) = frag_b0[0].y;
    *(out + 8 * n) = frag_b0[1].x;
    *(out + 9 * n) = frag_b0[1].y;
    *(out + 8) = frag_b1[0].x;
    *(out + n + 8) = frag_b1[0].y;
    *(out + 8 * n + 8) = frag_b1[1].x;
    *(out + 9 * n + 8) = frag_b1[1].y;
    out = out + 16;
  }
}

__global__ void awq_to_marlin(
  uint32_t* in,
  uint32_t* out,
  int m,
  int n
) {
  uint32_t row = blockIdx.x * 16;
  uint32_t col = blockIdx.y * 8;
  uint32_t t = threadIdx.x;

  // marlin packs 4 16x16 blocks one time;
  const int pad_len = 18;
  __shared__ uint8_t block[4][16][pad_len];

  // unpack
  int row_offset = t / 8;
  int col_offset = t % 8;
  int order_map[8] = {0, 2, 4, 6, 1, 3, 5, 7};
  for (int offset = row_offset; offset < 16; offset += 4) {
    uint32_t v = in[(row + offset) * n + col + col_offset];
#pragma unroll
    for (int i = 0; i < 8; i += 1) {
      block[col_offset / 2][offset][8 * (col_offset % 2) + order_map[i]] = v & 0xf;
      v >>= 4;
    }
  }

  // repack
  // ref: _get_perms @ https://github.com/IST-DASLab/marlin/blob/master/marlin/__init__.py
  uint32_t srow = (t % 4) * 2;
  uint32_t scol = t / 4;

  uint32_t idx[8][2];
  idx[0][0] = srow;     idx[0][1] = scol;
  idx[1][0] = srow + 8; idx[1][1] = scol;
  idx[2][0] = srow;     idx[2][1] = scol + 8;
  idx[3][0] = srow + 8; idx[3][1] = scol + 8;

  idx[4][0] = srow + 1; idx[4][1] = scol;
  idx[5][0] = srow + 9; idx[5][1] = scol;
  idx[6][0] = srow + 1; idx[6][1] = scol + 8;
  idx[7][0] = srow + 9; idx[7][1] = scol + 8;

#pragma unroll
  for (int i = 0; i < 4; i += 1) {
    uint32_t v[8];
#pragma unroll
    for (int j = 0; j < 8; ++j) {
      v[j] = block[i][idx[j][0]][idx[j][1]];
    }

    uint32_t pack = (v[7] << 28) | (v[6] << 24) | (v[5] << 20) | (v[4] << 16) |
        (v[3] << 12) | (v[2] << 8) | (v[1] << 4) | v[0];

    out[blockIdx.x * n * 16 + blockIdx.y * 128 + t * 4 + i] = pack;
  }
}

__global__ void gptq_to_marlin(
  uint32_t* in,
  uint32_t* out,
  int* g_idx,
  int m,
  int n
) {
  uint32_t col = blockIdx.y * 64;
  uint32_t t = threadIdx.x;

  // marlin packs 4 16x16 blocks one time;
  const int pad_len = 18;
  __shared__ uint8_t block[4][16][pad_len];

  // unpack
  #pragma unroll
  for (int i = 0; i < 16; i += 1) {
    uint32_t source_row = g_idx? g_idx[blockIdx.x * 16 + i] : (blockIdx.x * 16 + i);
    int in_row = source_row >> 3;
    int in_subrow = source_row & 0x07;
    int in_row_shift = in_subrow << 2;
    for (int offset = t; offset < 64; offset += 32) {
      //printf("in_row: %d, n: %d, col: %d, offset: %d\n", in_row, n, col, offset);
      uint32_t v = in[in_row * n + col + offset];
      block[offset / 16][i][offset % 16] = (v >> in_row_shift) & 0xf;
    }
  }

  // repack
  // ref: _get_perms @ https://github.com/IST-DASLab/marlin/blob/master/marlin/__init__.py
  uint32_t srow = (t % 4) * 2;
  uint32_t scol = t / 4;

  uint32_t idx[8][2];
  idx[0][0] = srow;     idx[0][1] = scol;
  idx[1][0] = srow + 8; idx[1][1] = scol;
  idx[2][0] = srow;     idx[2][1] = scol + 8;
  idx[3][0] = srow + 8; idx[3][1] = scol + 8;

  idx[4][0] = srow + 1; idx[4][1] = scol;
  idx[5][0] = srow + 9; idx[5][1] = scol;
  idx[6][0] = srow + 1; idx[6][1] = scol + 8;
  idx[7][0] = srow + 9; idx[7][1] = scol + 8;

#pragma unroll
  for (int i = 0; i < 4; i += 1) {
    uint32_t v[8];
#pragma unroll
    for (int j = 0; j < 8; ++j) {
      v[j] = block[i][idx[j][0]][idx[j][1]];
    }

    uint32_t pack = (v[7] << 28) | (v[6] << 24) | (v[5] << 20) | (v[4] << 16) |
        (v[3] << 12) | (v[2] << 8) | (v[1] << 4) | v[0];

    out[blockIdx.x * n * 2 + blockIdx.y * 128 + t * 4 + i] = pack;
  }
}

} // namespace marlin
} // namespace aphrodite

const int ERR_PROB_SHAPE = 1;
const int ERR_KERN_SHAPE = 2;

// input:     `torch.half` input matrix of shape `(m, k)` in standard row-major layout
// weights:   `torch.int` weight matrix of original shape `(k, n)` in Marlin format; see `Layer.pack()`
// output:    `torch.half` out matrix of shape `(m, n)` in standard row-major layout
// scales:    `torch.half` scales of shape `(m / groupsize, n)`
// workspace: `torch.int` tensor with at least `n / 128` entries that are all zero

void marlin_gemm_zero(
  const torch::Tensor& input,
  const torch::Tensor& weights,
        torch::Tensor& output,
  const torch::Tensor& scales,
  const torch::Tensor& zeros,
        torch::Tensor& workspace
) {
  // thread_k: `k` size of a thread_tile in `weights` (can usually be left as auto -1)
  int thread_k = -1;
  // thread_n: `n` size of a thread_tile in `weights` (can usually be left as auto -1)
  int thread_n = -1;
  // sms: number of SMs to use for the kernel (can usually be left as auto -1)
  int sms = -1;

  int prob_m = input.size(0);
  int prob_n = output.size(1);
  int prob_k = input.size(1);
  int groupsize = (scales.size(0) == 1) ? -1 : prob_k / scales.size(0);
  if (groupsize != -1 && groupsize * scales.size(0) != prob_k)
    AT_ERROR("k=", prob_k, " not compatible with ", scales.size(0), " groups.");
  int dev = input.get_device();
  if (prob_m >= 256) {
    auto options = torch::TensorOptions().dtype(input.dtype()).device(input.device());
    at::Tensor temp_dq = torch::empty({prob_k, prob_n}, options);
    dim3 blockDim, gridDim;
    gridDim.x = weights.size(0);
    gridDim.y = weights.size(1) / 128;
    blockDim.x = 32;
    const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
    aphrodite::marlin::dequant_marlin<<<gridDim, blockDim, 0, stream>>>
    (
        (const uint32_t*) weights.data_ptr(),
        (const half*)scales.data_ptr(),
        (const half*)zeros.data_ptr(),
        (half*)temp_dq.data_ptr(),
        prob_k,
        prob_n,
        groupsize
    );

    const half alpha = __float2half(1.0f);
    const half beta = __float2half(0.0f);
    cublasHgemm(at::cuda::getCurrentCUDABlasHandle(),
                CUBLAS_OP_N,
                CUBLAS_OP_N,
                prob_n, prob_m, prob_k,
                &alpha, (half*)temp_dq.data_ptr(), prob_n,
                (const half*)input.data_ptr(), prob_k,
                &beta, (half*)output.data_ptr(), prob_n);
  } else {
    int err = aphrodite::marlin::marlin_cuda(
      input.data_ptr(),
      weights.data_ptr(),
      output.data_ptr(),
      scales.data_ptr(),
      zeros.data_ptr(),
      prob_m, prob_n, prob_k,
      workspace.data_ptr(),
      groupsize,
      dev,
      at::cuda::getCurrentCUDAStream(dev),
      thread_k,
      thread_n,
      sms
    );
    if (err == ERR_PROB_SHAPE) {
      AT_ERROR(
        "Problem (m=", prob_m, ", n=", prob_n, ", k=", prob_k, ")",
        " not compatible with thread_k=", thread_k, ", thread_n=", thread_n, "."
      );
    } else if (err == ERR_KERN_SHAPE) {
      AT_ERROR(
        "No kernel implementation for thread_k=", thread_k, ", thread_n=", thread_n, ", groupsize=", groupsize, "."
      );
    }
  }
}


torch::Tensor gptq_to_marlin(
    torch::Tensor W,
    torch::Tensor g_idx
){
  int m = W.sizes()[0];
  int n = W.sizes()[1];

  assert(W.is_contiguous());
  assert(W.dtype() == at::kInt);
  assert(m % 2 == 0);
  assert(n % 64 == 0);
  auto result = at::empty(
      {m / 2, n * 2}, at::TensorOptions().dtype(at::kInt).device(W.device()));

  const dim3 threads(32);
  // marlin packs 16 x 64 block and gptq packs 8 x 1
  const dim3 blocks(m / 2, n / 64);
  cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
  aphrodite::marlin::gptq_to_marlin<<<blocks, threads, 0, stream>>>(
    (uint32_t*)W.data_ptr(),
    (uint32_t*)result.data_ptr(),
    g_idx.device().is_meta() ? NULL : (int*)g_idx.data_ptr(),
    m,
    n
  );
  return result;
}


torch::Tensor awq_to_marlin(
    torch::Tensor W
){
  int m = W.sizes()[0];
  int n = W.sizes()[1];

  assert(W.dtype() == at::kInt);
  assert(m % 16 == 0);
  auto result = at::empty(
      {m / 16, n * 16}, at::TensorOptions().dtype(at::kInt).device(W.device()));

  const dim3 threads(32);
  // marlin packs 16 x 64 block and awq packs 1 x 8
  const dim3 blocks(m / 16, n / 8);
  cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
  aphrodite::marlin::awq_to_marlin<<<blocks, threads, 0, stream>>>(
    (uint32_t*)W.data_ptr(),
    (uint32_t*)result.data_ptr(),
    m,
    n
  );
  return result;
}
#endif