aphrodite-engine/kernels/quantization/awq/gemm_kernels.cu

/*
Adapted from https://github.com/mit-han-lab/llm-awq
@article{lin2023awq,
  title={AWQ: Activation-aware Weight Quantization for LLM Compression and
Acceleration}, author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang,
Shang and Dang, Xingyu and Han, Song}, journal={arXiv}, year={2023}
}
 */

#include <torch/all.h>
#include <c10/cuda/CUDAGuard.h>

#include "dequantize.cuh"

#include <cuda_fp16.h>

namespace aphrodite {
namespace awq {

template <int N>
__global__ void __launch_bounds__(64)
    gemm_forward_4bit_cuda_m16nXk32(int G, int split_k_iters,
                                    half* __restrict__ A, int* __restrict__ B,
                                    half* __restrict__ scaling_factors,
                                    int* __restrict__ zeros, int M, int IC,
                                    int OC, half* __restrict__ C) {
  // Only support matrix n = 64 or 128
  assert(N == 64 || N == 128);
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 750
  assert(false);
#else
  static constexpr uint32_t ZERO = 0x0;
  float C_warp[32];
  __shared__ half A_shared[16 * (32 + 8)];
  __shared__ half B_shared[32 * (N + 8)];

  int j_factors1 = ((OC + N - 1) / N);
  int blockIdx_y = blockIdx.x % ((M + 16 - 1) / 16 * j_factors1);
  int blockIdx_z = blockIdx.x / ((M + 16 - 1) / 16 * j_factors1);

  half A_shared_warp[8];
  half B_shared_warp[N / 4];
  for (int j_0_4_init = 0; j_0_4_init < N / 32; ++j_0_4_init) {
    for (int i = 0; i < 8; ++i) {
      C_warp[(j_0_4_init * 8) + i] = 0.0;
    }
  }

  static constexpr int row_stride_warp = 32 * 8 / 32;
  static constexpr int row_stride = 2 * 32 * 8 / N;
  // TODO: Haotian: blockIdx_y / j_factors1 in A loading to support bsz > 16
  bool ld_A_flag =
      (blockIdx_y / j_factors1 * 16 + threadIdx.y * row_stride_warp +
       threadIdx.x * 8 / 32) < M;  // threadIdx.y is warp_id
  // bool wb_C_flag = (threadIdx.x / 4) < M;

  half* A_ptr =
      A +
      (((int)blockIdx_y) / j_factors1 * 16 +
       (((int)threadIdx.y) * row_stride_warp) + ((int)threadIdx.x) / (32 / 8)) *
          IC +
      (((int)threadIdx.x) % (32 / 8)) * 8;

  int* B_ptr = B + ((int)threadIdx.y) * (OC / 8) * (256 / N) +
               (((int)threadIdx.x) / (N / 8)) * (OC / 8) +
               (((int)blockIdx_y) % j_factors1) * (N / 8) +
               (((int)threadIdx.x) % (N / 8)) * 1;
  // Why * 1 in the above line?

  half* A_shared_ptr = A_shared +
                       ((int)threadIdx.y) * row_stride_warp * (32 + 8) +
                       (((int)threadIdx.x) / (32 / 8)) * (32 + 8) +
                       (((int)threadIdx.x) % (32 / 8)) * 8;

  half* B_shared_ptr = B_shared +
                       ((int)threadIdx.y) * (row_stride / 2) * (N + 8) +
                       (((int)threadIdx.x) / (N / 8)) * (N + 8) +
                       (((int)threadIdx.x) % (N / 8)) * 8;

  int* zeros_ptr = zeros + (((int)blockIdx_y) % j_factors1) * (N / 8) +
                   ((int)threadIdx.x) % (N / 8);

  half* scaling_factors_ptr = scaling_factors +
                              (((int)blockIdx_y) % j_factors1) * N +
                              (((int)threadIdx.x) % (N / 8)) * 8;

  half* C_ptr =
      C +
      static_cast<long long>(blockIdx_z) * M * OC  // blockIdz.x -> split_k dim
      + (((int)blockIdx_y) % j_factors1) * N + ((int)threadIdx.y) * (N / 2) +
      (((int)threadIdx.x) % 4) * 2;

  // preload s.f. and zeros
  int k_bound = (IC / 32 + split_k_iters - 1) / split_k_iters;
  if ((k_bound - 1) * split_k_iters * 32 + blockIdx_z * 32 >= IC) k_bound -= 1;
  for (int _k_0_0 = 0; _k_0_0 < k_bound; ++_k_0_0) {
    int k_0_0 = _k_0_0 * split_k_iters + blockIdx_z;
    __syncthreads();
    // TODO: Haotian: blockIdx_y / j_factors1 in A loading to support bsz > 16
    if (ld_A_flag) {
      *(uint4*)(A_shared_ptr) = *(uint4*)(A_ptr + (k_0_0 * 32));
    } else {
      *(uint4*)(A_shared_ptr) = make_uint4(0, 0, 0, 0);
    }

    // for (int ax0_ax1_fused_0 = 0; ax0_ax1_fused_0 < 2; ++ax0_ax1_fused_0) {
    uint32_t zeros_loaded = *(uint32_t*)(zeros_ptr + k_0_0 * 32 / G * (OC / 8));
    uint4 B_loaded_zero = dequantize_s4_to_fp16x2(zeros_loaded);
    uint4 B_loaded_scale =
        *(uint4*)(scaling_factors_ptr + k_0_0 * 32 / G * (OC));
    /*
    if (blockIdx_z == 0 && blockIdx_y == 0 && k_0_0 == 0 && threadIdx.x == 0 &&
    threadIdx.y == 0){ printf("%x %x %x %x %x %x %x %x\n", B_loaded_scale.x,
    B_loaded_scale.y, B_loaded_scale.z, B_loaded_scale.w, B_loaded_zero.x,
    B_loaded_zero.y, B_loaded_zero.z, B_loaded_zero.w);
    }
    */
    // uint4 B_loaded_scale = make_uint4(0, 0, 0, 0);
    int* B_ptr_local = B_ptr + k_0_0 * 32 * (OC / 8);

    for (int ax0_ax1_fused_0 = 0; ax0_ax1_fused_0 < N / 16; ++ax0_ax1_fused_0) {
      // B: 32 x 136 (128+8) float16
      // each warp: 32 x 4
      // each thr: read 32 bit -> convert to 8xFP16 (a UINT4) -> scale and minus
      // zero -> WB UINT4
      // *(uint4*)(B_shared + ((((ax0_ax1_fused_0 * 544) + (((int)threadIdx.y) *
      // 272)) + ((((int)threadIdx.x) >> 4) * 136)) + ((((int)threadIdx.x) & 15)
      // * 8))) = *(uint4*)(B + ((((((k_0_0 * 163840) + (ax0_ax1_fused_0 *
      // 20480)) + (((int)threadIdx.y) * 10240)) + ((((int)threadIdx.x) >> 4) *
      // 5120)) + (((int)blockIdx_y) * 128)) + ((((int)threadIdx.x) & 15) *
      // 8))); row stride in shared memory: (NWARPS * 32 * 8 / cta_N)
      uint32_t B_loaded =
          *(uint32_t*)(B_ptr_local + ax0_ax1_fused_0 * row_stride * (OC / 8));
      uint4 B_loaded_fp16 = dequantize_s4_to_fp16x2(B_loaded);

      // - zero and * scale
      // TODO (Haotian): can save 4 assembly instructions if sormulate as deq =
      // q * scale - zero * scale.
      asm volatile("sub.f16x2 %0, %1, %2;\n"
                   : "=r"(B_loaded_fp16.x)
                   : "r"(B_loaded_fp16.x), "r"(B_loaded_zero.x));
      asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n"
                   : "=r"(B_loaded_fp16.x)
                   : "r"(B_loaded_fp16.x), "r"(B_loaded_scale.x), "r"(ZERO));
      asm volatile("sub.f16x2 %0, %1, %2;\n"
                   : "=r"(B_loaded_fp16.y)
                   : "r"(B_loaded_fp16.y), "r"(B_loaded_zero.y));
      asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n"
                   : "=r"(B_loaded_fp16.y)
                   : "r"(B_loaded_fp16.y), "r"(B_loaded_scale.y), "r"(ZERO));
      asm volatile("sub.f16x2 %0, %1, %2;\n"
                   : "=r"(B_loaded_fp16.z)
                   : "r"(B_loaded_fp16.z), "r"(B_loaded_zero.z));
      asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n"
                   : "=r"(B_loaded_fp16.z)
                   : "r"(B_loaded_fp16.z), "r"(B_loaded_scale.z), "r"(ZERO));
      asm volatile("sub.f16x2 %0, %1, %2;\n"
                   : "=r"(B_loaded_fp16.w)
                   : "r"(B_loaded_fp16.w), "r"(B_loaded_zero.w));
      asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n"
                   : "=r"(B_loaded_fp16.w)
                   : "r"(B_loaded_fp16.w), "r"(B_loaded_scale.w), "r"(ZERO));
      /*
      if (ax0_ax1_fused_0 == 0 && blockIdx_z == 0 && blockIdx_y == 0 && k_0_0 ==
      0 && threadIdx.x == 17 && threadIdx.y == 0){ printf("[x] %X %X %X %X\n",
      B_loaded_fp16.x, B_loaded_fp16.y, B_loaded_fp16.z, B_loaded_fp16.w);
      }
      */

      // write back
      *(uint4*)(B_shared_ptr + ax0_ax1_fused_0 * row_stride * (N + 8)) =
          B_loaded_fp16;
    }
    __syncthreads();

    for (int k_0_1 = 0; k_0_1 < 2; ++k_0_1) {
      {
        unsigned int addr;
        __asm__ __volatile__(
            "{ .reg .u64 addr; cvta.to.shared.u64 addr, %1; cvt.u32.u64 %0, "
            "addr; }\n"
            : "=r"(addr)
            : "l"((void*)((&(A_shared[(k_0_1 * 16)])) +
                          (((((int)threadIdx.x) & 15) * 40) +
                           ((((int)threadIdx.x) >> 4) * 8)))));

        __asm__ __volatile__(
            "ldmatrix.sync.aligned.m8n8.x4.shared.b16"
            "{%0, %1, %2, %3}, [%4];\n"
            : "=r"(((unsigned*)(A_shared_warp + 0))[0]),
              "=r"(((unsigned*)(A_shared_warp + 0))[1]),
              "=r"(((unsigned*)(A_shared_warp + 0))[2]),
              "=r"(((unsigned*)(A_shared_warp + 0))[3])
            : "r"(addr));
      }

      for (int ax1_0 = 0; ax1_0 < N / 32; ++ax1_0) {
        {
          unsigned int addr;
          __asm__ __volatile__(
              "{ .reg .u64 addr; cvta.to.shared.u64 addr, %1; cvt.u32.u64 %0, "
              "addr; }\n"
              : "=r"(addr)
              : "l"((void*)((&(B_shared[(((k_0_1 * (N * 16 + 128)) +
                                          (((int)threadIdx.y) * (N / 2))) +
                                         (ax1_0 * 16))])) +
                            (((((int)threadIdx.x) & 15) * (N + 8)) +
                             ((((int)threadIdx.x) >> 4) * 8)))));
          __asm__ __volatile__(
              "ldmatrix.sync.aligned.m8n8.x4.trans.shared.b16"
              "{%0, %1, %2, %3}, [%4];\n"
              : "=r"(((unsigned*)(B_shared_warp + (ax1_0 * 8)))[0]),
                "=r"(((unsigned*)(B_shared_warp + (ax1_0 * 8)))[1]),
                "=r"(((unsigned*)(B_shared_warp + (ax1_0 * 8)))[2]),
                "=r"(((unsigned*)(B_shared_warp + (ax1_0 * 8)))[3])
              : "r"(addr));
        }
      }
      for (int j_0_4 = 0; j_0_4 < N / 32; ++j_0_4) {
  #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ == 750
        {
          __asm__ __volatile__(
              "mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32"
              "{%0, %1, %2, %3}, {%4, %5}, {%6}, {%7, %8, %9, %10};\n"
              : "=f"(((float*)(C_warp + (j_0_4 * 8)))[0]),
                "=f"(((float*)(C_warp + (j_0_4 * 8)))[1]),
                "=f"(((float*)(C_warp + (j_0_4 * 8)))[2]),
                "=f"(((float*)(C_warp + (j_0_4 * 8)))[3])
              : "r"(((unsigned*)(A_shared_warp + 0))[0]),
                "r"(((unsigned*)(A_shared_warp + 0))[1]),
                "r"(((unsigned*)(B_shared_warp + (j_0_4 * 8)))[0]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[0]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[1]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[2]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[3]));
        }

        {
          __asm__ __volatile__(
              "mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32"
              "{%0, %1, %2, %3}, {%4, %5}, {%6}, {%7, %8, %9, %10};\n"
              : "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[0]),
                "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[1]),
                "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[2]),
                "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[3])
              : "r"(((unsigned*)(A_shared_warp + 0))[0]),
                "r"(((unsigned*)(A_shared_warp + 0))[1]),
                "r"(((unsigned*)(B_shared_warp + ((j_0_4 * 8) + 4)))[0]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[0]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[1]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[2]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[3]));
        }

        {
          __asm__ __volatile__(
              "mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32"
              "{%0, %1, %2, %3}, {%4, %5}, {%6}, {%7, %8, %9, %10};\n"
              : "=f"(((float*)(C_warp + (j_0_4 * 8)))[0]),
                "=f"(((float*)(C_warp + (j_0_4 * 8)))[1]),
                "=f"(((float*)(C_warp + (j_0_4 * 8)))[2]),
                "=f"(((float*)(C_warp + (j_0_4 * 8)))[3])
              : "r"(((unsigned*)(A_shared_warp + 0))[2]),
                "r"(((unsigned*)(A_shared_warp + 0))[3]),
                "r"(((unsigned*)(B_shared_warp + (j_0_4 * 8)))[1]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[0]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[1]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[2]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[3]));
        }

        {
          __asm__ __volatile__(
              "mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32"
              "{%0, %1, %2, %3}, {%4, %5}, {%6}, {%7, %8, %9, %10};\n"
              : "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[0]),
                "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[1]),
                "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[2]),
                "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[3])
              : "r"(((unsigned*)(A_shared_warp + 0))[2]),
                "r"(((unsigned*)(A_shared_warp + 0))[3]),
                "r"(((unsigned*)(B_shared_warp + ((j_0_4 * 8) + 4)))[1]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[0]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[1]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[2]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[3]));
        }
  #else
        {
          __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"(((float*)(C_warp + (j_0_4 * 8)))[0]),
                "=f"(((float*)(C_warp + (j_0_4 * 8)))[1]),
                "=f"(((float*)(C_warp + (j_0_4 * 8)))[2]),
                "=f"(((float*)(C_warp + (j_0_4 * 8)))[3])
              : "r"(((unsigned*)(A_shared_warp + 0))[0]),
                "r"(((unsigned*)(A_shared_warp + 0))[1]),
                "r"(((unsigned*)(A_shared_warp + 0))[2]),
                "r"(((unsigned*)(A_shared_warp + 0))[3]),
                "r"(((unsigned*)(B_shared_warp + (j_0_4 * 8)))[0]),
                "r"(((unsigned*)(B_shared_warp + (j_0_4 * 8)))[1]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[0]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[1]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[2]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[3]));
        }

        {
          __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"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[0]),
                "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[1]),
                "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[2]),
                "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[3])
              : "r"(((unsigned*)(A_shared_warp + 0))[0]),
                "r"(((unsigned*)(A_shared_warp + 0))[1]),
                "r"(((unsigned*)(A_shared_warp + 0))[2]),
                "r"(((unsigned*)(A_shared_warp + 0))[3]),
                "r"(((unsigned*)(B_shared_warp + ((j_0_4 * 8) + 4)))[0]),
                "r"(((unsigned*)(B_shared_warp + ((j_0_4 * 8) + 4)))[1]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[0]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[1]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[2]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[3]));
        }

  #endif
      }
    }
  }

  // TODO: Shang: Hoist loop invariance.
  for (int ax1_0_1 = 0; ax1_0_1 < 4; ++ax1_0_1) {
    for (int local_id = 0; local_id < 8; ++local_id) {
      int row_offset = (((int)blockIdx_y) / j_factors1) * 16 +
                       ((int)threadIdx.x) / 4 + (local_id % 4) / 2 * 8;
      if (row_offset < M) {
        *(C_ptr + ax1_0_1 * 16 + row_offset * OC + (local_id / 4) * 8 +
          local_id % 2) = __float2half(C_warp[(ax1_0_1 * 8) + local_id]);
      }
    }
  }
#endif
}

__global__ void __launch_bounds__(64)
    dequantize_weights(int* __restrict__ B, half* __restrict__ scaling_factors,
                       int* __restrict__ zeros, half* __restrict__ C, int G,
                       int in_c, int out_c) {
  if (blockIdx.z > 0) {
    B = B + blockIdx.z * in_c * out_c / 8;
    scaling_factors = scaling_factors + blockIdx.z * in_c * out_c / G;
    zeros = zeros + blockIdx.z * in_c * out_c / G / 8;
    C = C + blockIdx.z * in_c * out_c;
  }
  static constexpr uint32_t ZERO = 0x0;
  half B_shared[32 * (128 + 8)];

  half* B_shared_ptr2 = B_shared;

  int N = blockDim.x * gridDim.x;  // 2
  int col = (blockIdx.x * blockDim.x + threadIdx.x);
  int row = blockIdx.y * blockDim.y + threadIdx.y;
  int index1 = 8 * col + 8 * row * N;
  half* C_ptr2 = C + index1;

  int index2 = col + row * N;
  int* B_ptr2 = B + index2;

  int index3 = col + (int)(row / G) * N;
  int* zeros_ptr2 = zeros + index3;
  int index4 = 8 * col + (int)(row / G) * N * 8;
  half* scaling_factors_ptr2 = scaling_factors + index4;

  uint32_t zeros_loaded = *(uint32_t*)(zeros_ptr2);
  uint4 B_loaded_zero = dequantize_s4_to_fp16x2(zeros_loaded);
  uint4 B_loaded_scale = *(uint4*)(scaling_factors_ptr2);

  uint32_t B_loaded = *(uint32_t*)B_ptr2;
  uint4 B_loaded_fp16 = dequantize_s4_to_fp16x2(B_loaded);
  asm volatile("sub.f16x2 %0, %1, %2;\n"
               : "=r"(B_loaded_fp16.x)
               : "r"(B_loaded_fp16.x), "r"(B_loaded_zero.x));
  asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n"
               : "=r"(B_loaded_fp16.x)
               : "r"(B_loaded_fp16.x), "r"(B_loaded_scale.x), "r"(ZERO));
  asm volatile("sub.f16x2 %0, %1, %2;\n"
               : "=r"(B_loaded_fp16.y)
               : "r"(B_loaded_fp16.y), "r"(B_loaded_zero.y));
  asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n"
               : "=r"(B_loaded_fp16.y)
               : "r"(B_loaded_fp16.y), "r"(B_loaded_scale.y), "r"(ZERO));
  asm volatile("sub.f16x2 %0, %1, %2;\n"
               : "=r"(B_loaded_fp16.z)
               : "r"(B_loaded_fp16.z), "r"(B_loaded_zero.z));
  asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n"
               : "=r"(B_loaded_fp16.z)
               : "r"(B_loaded_fp16.z), "r"(B_loaded_scale.z), "r"(ZERO));
  asm volatile("sub.f16x2 %0, %1, %2;\n"
               : "=r"(B_loaded_fp16.w)
               : "r"(B_loaded_fp16.w), "r"(B_loaded_zero.w));
  asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n"
               : "=r"(B_loaded_fp16.w)
               : "r"(B_loaded_fp16.w), "r"(B_loaded_scale.w), "r"(ZERO));

  *(uint4*)B_shared_ptr2 = B_loaded_fp16;

  for (int i = 0; i < 8; ++i) {
    *(C_ptr2 + i) = B_shared[i];
  }
}

template <int N>
__global__ void __launch_bounds__(64) group_gemm_forward_4bit_cuda_m16nXk32(
    int G, int split_k_iters, half* __restrict__ A, int* __restrict__ B,
    half* __restrict__ scaling_factors, int* __restrict__ zeros,
    const float* __restrict__ topk_weights,
    const int* __restrict__ sorted_token_ids_ptr,
    const int* __restrict__ expert_ids_ptr,
    const int* __restrict__ num_tokens_post_padded, const int num_valid_tokens,
    const int top_k, const int expert_num, int pad_M, int M, int IC, int OC,
    half* __restrict__ C) {
  // Only support matrix n = 64 or 128
  assert(N == 64 || N == 128);
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 750
  assert(false);
#else
  int num_tokens = *num_tokens_post_padded;
  int j_factors1 = ((OC + N - 1) / N);
  [[maybe_unused]] int blockIdx_x = 0;
  int blockIdx_y = blockIdx.x % ((pad_M + 16 - 1) / 16 * j_factors1);
  int blockIdx_z = blockIdx.x / ((pad_M + 16 - 1) / 16 * j_factors1);
  int block = blockIdx_y / j_factors1;
  if (block * 16 >= num_tokens) return;

  static constexpr uint32_t ZERO = 0x0;
  float C_warp[32];
  __shared__ half A_shared[16 * (32 + 8)];
  __shared__ half B_shared[32 * (N + 8)];

  [[maybe_unused]] __shared__ half scaling_factors_shared[N];
  [[maybe_unused]] __shared__ half zeros_shared[N];

  half A_shared_warp[8];
  half B_shared_warp[N / 4];
  for (int j_0_4_init = 0; j_0_4_init < N / 32; ++j_0_4_init) {
    for (int i = 0; i < 8; ++i) {
      C_warp[(j_0_4_init * 8) + i] = 0.0;
    }
  }

  static constexpr int row_stride_warp = 32 * 8 / 32;
  static constexpr int row_stride = 2 * 32 * 8 / N;
  [[maybe_unused]] bool ld_zero_flag = (threadIdx.y * 32 + threadIdx.x) * 8 < N;
  // TODO: Haotian: blockIdx_y / j_factors1 in A loading to support bsz > 16

  int row = (block * 16 + threadIdx.y * row_stride_warp + threadIdx.x * 8 / 32);
  int token_id = sorted_token_ids_ptr[row];
  bool ld_A_flag = (token_id < num_valid_tokens);
  half* A_ptr = A + token_id / top_k * IC + (((int)threadIdx.x) % (32 / 8)) * 8;

  int expert_id = expert_ids_ptr[block];
  B = B + OC * IC / 8 * expert_id;
  scaling_factors = scaling_factors + OC * IC / G * expert_id;
  zeros = zeros + OC * IC / G / 8 * expert_id;

  int* B_ptr = B + ((int)threadIdx.y) * (OC / 8) * (256 / N) +
               (((int)threadIdx.x) / (N / 8)) * (OC / 8) +
               (((int)blockIdx_y) % j_factors1) * (N / 8) +
               (((int)threadIdx.x) % (N / 8)) * 1;
  // Why * 1 in the above line?

  half* A_shared_ptr = A_shared +
                       ((int)threadIdx.y) * row_stride_warp * (32 + 8) +
                       (((int)threadIdx.x) / (32 / 8)) * (32 + 8) +
                       (((int)threadIdx.x) % (32 / 8)) * 8;

  half* B_shared_ptr = B_shared +
                       ((int)threadIdx.y) * (row_stride / 2) * (N + 8) +
                       (((int)threadIdx.x) / (N / 8)) * (N + 8) +
                       (((int)threadIdx.x) % (N / 8)) * 8;

  int* zeros_ptr = zeros + (((int)blockIdx_y) % j_factors1) * (N / 8) +
                   ((int)threadIdx.x) % (N / 8);

  half* scaling_factors_ptr = scaling_factors +
                              (((int)blockIdx_y) % j_factors1) * N +
                              (((int)threadIdx.x) % (N / 8)) * 8;

  half* C_ptr = C +
                static_cast<long long>(blockIdx_z) * M * OC *
                    expert_num  // blockIdz.x -> split_k dim
                + (((int)blockIdx_y) % j_factors1) * N +
                ((int)threadIdx.y) * (N / 2) + (((int)threadIdx.x) % 4) * 2;

  // preload s.f. and zeros
  int k_bound = (IC / 32 + split_k_iters - 1) / split_k_iters;
  if ((k_bound - 1) * split_k_iters * 32 + blockIdx_z * 32 >= IC) k_bound -= 1;
  for (int _k_0_0 = 0; _k_0_0 < k_bound; ++_k_0_0) {
    int k_0_0 = _k_0_0 * split_k_iters + blockIdx_z;
    __syncthreads();
    // TODO: Haotian: blockIdx_y / j_factors1 in A loading to support bsz > 16
    if (ld_A_flag) {
      *(uint4*)(A_shared_ptr) = *(uint4*)(A_ptr + (k_0_0 * 32));
    } else {
      *(uint4*)(A_shared_ptr) = make_uint4(0, 0, 0, 0);
    }

    uint32_t zeros_loaded = *(uint32_t*)(zeros_ptr + k_0_0 * 32 / G * (OC / 8));
    uint4 B_loaded_zero = dequantize_s4_to_fp16x2(zeros_loaded);
    uint4 B_loaded_scale =
        *(uint4*)(scaling_factors_ptr + k_0_0 * 32 / G * (OC));

    int* B_ptr_local = B_ptr + k_0_0 * 32 * (OC / 8);

    for (int ax0_ax1_fused_0 = 0; ax0_ax1_fused_0 < N / 16; ++ax0_ax1_fused_0) {
      uint32_t B_loaded =
          *(uint32_t*)(B_ptr_local + ax0_ax1_fused_0 * row_stride * (OC / 8));
      uint4 B_loaded_fp16 = dequantize_s4_to_fp16x2(B_loaded);

      // TODO (Haotian): can save 4 assembly instructions if sormulate as deq =
      // q * scale - zero * scale.
      asm volatile("sub.f16x2 %0, %1, %2;\n"
                   : "=r"(B_loaded_fp16.x)
                   : "r"(B_loaded_fp16.x), "r"(B_loaded_zero.x));
      asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n"
                   : "=r"(B_loaded_fp16.x)
                   : "r"(B_loaded_fp16.x), "r"(B_loaded_scale.x), "r"(ZERO));
      asm volatile("sub.f16x2 %0, %1, %2;\n"
                   : "=r"(B_loaded_fp16.y)
                   : "r"(B_loaded_fp16.y), "r"(B_loaded_zero.y));
      asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n"
                   : "=r"(B_loaded_fp16.y)
                   : "r"(B_loaded_fp16.y), "r"(B_loaded_scale.y), "r"(ZERO));
      asm volatile("sub.f16x2 %0, %1, %2;\n"
                   : "=r"(B_loaded_fp16.z)
                   : "r"(B_loaded_fp16.z), "r"(B_loaded_zero.z));
      asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n"
                   : "=r"(B_loaded_fp16.z)
                   : "r"(B_loaded_fp16.z), "r"(B_loaded_scale.z), "r"(ZERO));
      asm volatile("sub.f16x2 %0, %1, %2;\n"
                   : "=r"(B_loaded_fp16.w)
                   : "r"(B_loaded_fp16.w), "r"(B_loaded_zero.w));
      asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n"
                   : "=r"(B_loaded_fp16.w)
                   : "r"(B_loaded_fp16.w), "r"(B_loaded_scale.w), "r"(ZERO));

      // write back
      *(uint4*)(B_shared_ptr + ax0_ax1_fused_0 * row_stride * (N + 8)) =
          B_loaded_fp16;
    }
    __syncthreads();

    for (int k_0_1 = 0; k_0_1 < 2; ++k_0_1) {
      {
        unsigned int addr;
        __asm__ __volatile__(
            "{ .reg .u64 addr; cvta.to.shared.u64 addr, %1; cvt.u32.u64 %0, "
            "addr; }\n"
            : "=r"(addr)
            : "l"((void*)((&(A_shared[(k_0_1 * 16)])) +
                          (((((int)threadIdx.x) & 15) * 40) +
                           ((((int)threadIdx.x) >> 4) * 8)))));

        __asm__ __volatile__(
            "ldmatrix.sync.aligned.m8n8.x4.shared.b16"
            "{%0, %1, %2, %3}, [%4];\n"
            : "=r"(((unsigned*)(A_shared_warp + 0))[0]),
              "=r"(((unsigned*)(A_shared_warp + 0))[1]),
              "=r"(((unsigned*)(A_shared_warp + 0))[2]),
              "=r"(((unsigned*)(A_shared_warp + 0))[3])
            : "r"(addr));
      }

      for (int ax1_0 = 0; ax1_0 < N / 32; ++ax1_0) {
        {
          unsigned int addr;
          __asm__ __volatile__(
              "{ .reg .u64 addr; cvta.to.shared.u64 addr, %1; cvt.u32.u64 %0, "
              "addr; }\n"
              : "=r"(addr)
              : "l"((void*)((&(B_shared[(((k_0_1 * (N * 16 + 128)) +
                                          (((int)threadIdx.y) * (N / 2))) +
                                         (ax1_0 * 16))])) +
                            (((((int)threadIdx.x) & 15) * (N + 8)) +
                             ((((int)threadIdx.x) >> 4) * 8)))));
          __asm__ __volatile__(
              "ldmatrix.sync.aligned.m8n8.x4.trans.shared.b16"
              "{%0, %1, %2, %3}, [%4];\n"
              : "=r"(((unsigned*)(B_shared_warp + (ax1_0 * 8)))[0]),
                "=r"(((unsigned*)(B_shared_warp + (ax1_0 * 8)))[1]),
                "=r"(((unsigned*)(B_shared_warp + (ax1_0 * 8)))[2]),
                "=r"(((unsigned*)(B_shared_warp + (ax1_0 * 8)))[3])
              : "r"(addr));
        }
      }
      for (int j_0_4 = 0; j_0_4 < N / 32; ++j_0_4) {
  #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ == 750
        {
          __asm__ __volatile__(
              "mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32"
              "{%0, %1, %2, %3}, {%4, %5}, {%6}, {%7, %8, %9, %10};\n"
              : "=f"(((float*)(C_warp + (j_0_4 * 8)))[0]),
                "=f"(((float*)(C_warp + (j_0_4 * 8)))[1]),
                "=f"(((float*)(C_warp + (j_0_4 * 8)))[2]),
                "=f"(((float*)(C_warp + (j_0_4 * 8)))[3])
              : "r"(((unsigned*)(A_shared_warp + 0))[0]),
                "r"(((unsigned*)(A_shared_warp + 0))[1]),
                "r"(((unsigned*)(B_shared_warp + (j_0_4 * 8)))[0]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[0]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[1]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[2]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[3]));
        }

        {
          __asm__ __volatile__(
              "mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32"
              "{%0, %1, %2, %3}, {%4, %5}, {%6}, {%7, %8, %9, %10};\n"
              : "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[0]),
                "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[1]),
                "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[2]),
                "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[3])
              : "r"(((unsigned*)(A_shared_warp + 0))[0]),
                "r"(((unsigned*)(A_shared_warp + 0))[1]),
                "r"(((unsigned*)(B_shared_warp + ((j_0_4 * 8) + 4)))[0]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[0]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[1]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[2]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[3]));
        }

        {
          __asm__ __volatile__(
              "mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32"
              "{%0, %1, %2, %3}, {%4, %5}, {%6}, {%7, %8, %9, %10};\n"
              : "=f"(((float*)(C_warp + (j_0_4 * 8)))[0]),
                "=f"(((float*)(C_warp + (j_0_4 * 8)))[1]),
                "=f"(((float*)(C_warp + (j_0_4 * 8)))[2]),
                "=f"(((float*)(C_warp + (j_0_4 * 8)))[3])
              : "r"(((unsigned*)(A_shared_warp + 0))[2]),
                "r"(((unsigned*)(A_shared_warp + 0))[3]),
                "r"(((unsigned*)(B_shared_warp + (j_0_4 * 8)))[1]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[0]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[1]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[2]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[3]));
        }

        {
          __asm__ __volatile__(
              "mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32"
              "{%0, %1, %2, %3}, {%4, %5}, {%6}, {%7, %8, %9, %10};\n"
              : "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[0]),
                "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[1]),
                "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[2]),
                "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[3])
              : "r"(((unsigned*)(A_shared_warp + 0))[2]),
                "r"(((unsigned*)(A_shared_warp + 0))[3]),
                "r"(((unsigned*)(B_shared_warp + ((j_0_4 * 8) + 4)))[1]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[0]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[1]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[2]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[3]));
        }
  #else
        {
          __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"(((float*)(C_warp + (j_0_4 * 8)))[0]),
                "=f"(((float*)(C_warp + (j_0_4 * 8)))[1]),
                "=f"(((float*)(C_warp + (j_0_4 * 8)))[2]),
                "=f"(((float*)(C_warp + (j_0_4 * 8)))[3])
              : "r"(((unsigned*)(A_shared_warp + 0))[0]),
                "r"(((unsigned*)(A_shared_warp + 0))[1]),
                "r"(((unsigned*)(A_shared_warp + 0))[2]),
                "r"(((unsigned*)(A_shared_warp + 0))[3]),
                "r"(((unsigned*)(B_shared_warp + (j_0_4 * 8)))[0]),
                "r"(((unsigned*)(B_shared_warp + (j_0_4 * 8)))[1]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[0]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[1]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[2]),
                "f"(((float*)(C_warp + (j_0_4 * 8)))[3]));
        }

        {
          __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"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[0]),
                "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[1]),
                "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[2]),
                "=f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[3])
              : "r"(((unsigned*)(A_shared_warp + 0))[0]),
                "r"(((unsigned*)(A_shared_warp + 0))[1]),
                "r"(((unsigned*)(A_shared_warp + 0))[2]),
                "r"(((unsigned*)(A_shared_warp + 0))[3]),
                "r"(((unsigned*)(B_shared_warp + ((j_0_4 * 8) + 4)))[0]),
                "r"(((unsigned*)(B_shared_warp + ((j_0_4 * 8) + 4)))[1]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[0]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[1]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[2]),
                "f"(((float*)(C_warp + ((j_0_4 * 8) + 4)))[3]));
        }

  #endif
      }
    }
  }

  // TODO: Shang: Hoist loop invariance.
  for (int ax1_0_1 = 0; ax1_0_1 < N / 32; ++ax1_0_1) {
    for (int local_id = 0; local_id < 8; ++local_id) {
      int row_offset =
          block * 16 + ((int)threadIdx.x) / 4 + (local_id % 4) / 2 * 8;
      int token_id = sorted_token_ids_ptr[row_offset];
      if (token_id < num_valid_tokens) {
        float value = C_warp[(ax1_0_1 * 8) + local_id];
        if (topk_weights) {
          value = value * topk_weights[token_id];
        }
        *(C_ptr + ax1_0_1 * 16 + token_id * OC + (local_id / 4) * 8 +
          local_id % 2) = __float2half(value);
      }
    }
  }
#endif
}

}  // namespace awq
}  // namespace aphrodite

torch::Tensor awq_dequantize(torch::Tensor _kernel,
                             torch::Tensor _scaling_factors,
                             torch::Tensor _zeros, int64_t split_k_iters,
                             int64_t thx, int64_t thy) {
  int in_c = _kernel.dim() == 2 ? _kernel.size(0) : _kernel.size(1);
  int qout_c = _kernel.dim() == 2 ? _kernel.size(1) : _kernel.size(2);
  int num_experts = _kernel.dim() == 2 ? 1 : _kernel.size(0);
  int out_c = qout_c * 8;
  int G = in_c / (_kernel.dim() == 2 ? _scaling_factors.size(0)
                                     : _scaling_factors.size(1));

  int x_thread = thx;
  int y_thread = thy;

  int x_blocks = 1;
  int y_blocks = 1;
  if (thx == 0) {
    x_thread = qout_c;
  }
  if (thy == 0) {
    y_thread = in_c;
  }
  if (thx == 0 && thy == 0) {
    x_thread = 8;
    y_thread = 8;
    x_blocks = (int)(qout_c / 8);
    y_blocks = (int)(in_c / 8);
  }

  const at::cuda::OptionalCUDAGuard device_guard(device_of(_scaling_factors));

  auto options = torch::TensorOptions()
                     .dtype(_scaling_factors.dtype())
                     .device(_scaling_factors.device());
  at::Tensor _de_kernel;
  if (num_experts == 1) {
    _de_kernel = torch::empty({in_c, out_c}, options);
  } else {
    _de_kernel = torch::empty({num_experts, in_c, out_c}, options);
  }

  auto kernel = reinterpret_cast<int*>(_kernel.data_ptr<int>());
  auto de_kernel = reinterpret_cast<half*>(_de_kernel.data_ptr<at::Half>());
  auto scaling_factors =
      reinterpret_cast<half*>(_scaling_factors.data_ptr<at::Half>());
  auto zeros = reinterpret_cast<int*>(_zeros.data_ptr<int>());

  dim3 num_blocks(x_blocks, y_blocks, num_experts);
  dim3 threads_per_block(x_thread, y_thread);

  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
  aphrodite::awq::
      dequantize_weights<<<num_blocks, threads_per_block, 0, stream>>>(
          kernel, scaling_factors, zeros, de_kernel, G, in_c, out_c);

  return _de_kernel;
}

// in_feats: M, IC [float16]
// kernel: IC, OC // 8 [int32] -> cast to IC, OC [uint4b]
// scaling_factors: IC // G, OC [float16]
// zeros: IC // G, OC // 8 [int32] -> cast to IC // G, OC [uint4b]
// assume that batch_size < 16 for now

torch::Tensor awq_gemm(torch::Tensor _in_feats, torch::Tensor _kernel,
                       torch::Tensor _scaling_factors, torch::Tensor _zeros,
                       int64_t split_k_iters) {
  int num_in_feats = _in_feats.size(0);
  int num_in_channels = _in_feats.size(1);
  const at::cuda::OptionalCUDAGuard device_guard(device_of(_in_feats));

  auto options = torch::TensorOptions()
                     .dtype(_in_feats.dtype())
                     .device(_in_feats.device());
  at::Tensor _out_feats =
      torch::empty({split_k_iters, num_in_feats, _kernel.size(1) * 8}, options);
  int num_out_feats = _out_feats.size(-2);
  int num_out_channels = _out_feats.size(-1);

  auto in_feats = reinterpret_cast<half*>(_in_feats.data_ptr<at::Half>());
  auto kernel = reinterpret_cast<int*>(_kernel.data_ptr<int>());
  auto out_feats = reinterpret_cast<half*>(_out_feats.data_ptr<at::Half>());
  auto scaling_factors =
      reinterpret_cast<half*>(_scaling_factors.data_ptr<at::Half>());
  auto zeros = reinterpret_cast<int*>(_zeros.data_ptr<int>());
  int group_size = num_in_channels / _scaling_factors.size(0);

  if (num_out_channels % 64 != 0)
    throw std::invalid_argument("OC is not multiple of cta_N = 64");
  if (num_out_channels % 8 != 0)
    throw std::invalid_argument("OC is not multiple of pack_num = 8");
  if (group_size % 32 != 0)
    throw std::invalid_argument("Group size should be a multiple of 32");
  if (num_out_channels % group_size != 0)
    throw std::invalid_argument("OC is not multiple of Group size");

  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
  if (num_out_channels % 128 == 0) {
    int j_factors1 = num_out_channels / 128 / 1;
    dim3 num_blocks((num_out_feats + 16 - 1) / 16 * j_factors1 * split_k_iters);
    // threadIdx.x: 32
    // threadIdx.y: i_factors[2] * j_factors[2]
    dim3 threads_per_block(32, 2);
    aphrodite::awq::gemm_forward_4bit_cuda_m16nXk32<128>
        <<<num_blocks, threads_per_block, 0, stream>>>(
            group_size, split_k_iters, in_feats, kernel, scaling_factors, zeros,
            num_in_feats, num_in_channels, num_out_channels, out_feats);
  } else if (num_out_channels % 64 == 0) {
    int j_factors1 = num_out_channels / 64 / 1;
    dim3 num_blocks(1 * (num_out_feats + 16 - 1) / 16 * j_factors1 *
                    split_k_iters);

    // threadIdx.x: 32
    // threadIdx.y: i_factors[2] * j_factors[2]
    dim3 threads_per_block(32, 2);
    aphrodite::awq::gemm_forward_4bit_cuda_m16nXk32<64>
        <<<num_blocks, threads_per_block, 0, stream>>>(
            group_size, split_k_iters, in_feats, kernel, scaling_factors, zeros,
            num_in_feats, num_in_channels, num_out_channels, out_feats);
  }
  return _out_feats.sum(0);
}

torch::Tensor awq_group_gemm(torch::Tensor _in_feats, torch::Tensor _kernel,
                             torch::Tensor _scaling_factors,
                             torch::Tensor _zeros, torch::Tensor _topk_weights,
                             torch::Tensor _sorted_token_ids_ptr,
                             torch::Tensor _expert_ids_ptr,
                             torch::Tensor _num_tokens_post_padded,
                             bool mul_weights, int split_k_iters) {
  int num_in_feats = _in_feats.size(0);
  int pad_num_in_feats = _sorted_token_ids_ptr.size(0);
  int num_in_channels = _in_feats.size(2);
  const at::cuda::OptionalCUDAGuard device_guard(device_of(_in_feats));

  auto options = torch::TensorOptions()
                     .dtype(_in_feats.dtype())
                     .device(_in_feats.device());
  int num_experts = _topk_weights.size(1);
  int top_k = num_experts / _in_feats.size(1);
  int group_size = num_in_channels / _scaling_factors.size(1);

  at::Tensor _out_feats = torch::empty(
      {split_k_iters, num_in_feats, _topk_weights.size(1), _kernel.size(2) * 8},
      options);
  int num_out_channels = _out_feats.size(-1);

  auto in_feats = reinterpret_cast<half*>(_in_feats.data_ptr<at::Half>());
  auto kernel = reinterpret_cast<int*>(_kernel.data_ptr<int>());
  auto out_feats = reinterpret_cast<half*>(_out_feats.data_ptr<at::Half>());
  auto scaling_factors =
      reinterpret_cast<half*>(_scaling_factors.data_ptr<at::Half>());
  auto zeros = reinterpret_cast<int*>(_zeros.data_ptr<int>());
  auto topk_weights = mul_weights
                          ? reinterpret_cast<float*>(_topk_weights.data_ptr())
                          : nullptr;
  auto sorted_token_ids_ptr =
      reinterpret_cast<int*>(_sorted_token_ids_ptr.data_ptr());
  auto expert_ids_ptr = reinterpret_cast<int*>(_expert_ids_ptr.data_ptr());
  auto num_tokens_post_padded =
      reinterpret_cast<int*>(_num_tokens_post_padded.data_ptr());

  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
  if (num_out_channels % 128 == 0) {
    int j_factors1 = num_out_channels / 128 / 1;
    dim3 num_blocks((pad_num_in_feats + 16 - 1) / 16 * j_factors1 *
                    split_k_iters);
    // threadIdx.x: 32
    // threadIdx.y: i_factors[2] * j_factors[2]
    dim3 threads_per_block(32, 2);
    aphrodite::awq::group_gemm_forward_4bit_cuda_m16nXk32<128>
        <<<num_blocks, threads_per_block, 0, stream>>>(
            group_size, split_k_iters, in_feats, kernel, scaling_factors, zeros,
            topk_weights, sorted_token_ids_ptr, expert_ids_ptr,
            num_tokens_post_padded, _topk_weights.numel(), top_k, num_experts,
            pad_num_in_feats, num_in_feats, num_in_channels, num_out_channels,
            out_feats);
  } else if (num_out_channels % 64 == 0) {
    int j_factors1 = num_out_channels / 64 / 1;
    dim3 num_blocks((pad_num_in_feats + 16 - 1) / 16 * j_factors1 *
                    split_k_iters);

    // threadIdx.x: 32
    // threadIdx.y: i_factors[2] * j_factors[2]
    dim3 threads_per_block(32, 2);
    aphrodite::awq::group_gemm_forward_4bit_cuda_m16nXk32<64>
        <<<num_blocks, threads_per_block, 0, stream>>>(
            group_size, split_k_iters, in_feats, kernel, scaling_factors, zeros,
            topk_weights, sorted_token_ids_ptr, expert_ids_ptr,
            num_tokens_post_padded, _topk_weights.numel(), top_k, num_experts,
            pad_num_in_feats, num_in_feats, num_in_channels, num_out_channels,
            out_feats);
  }
  return _out_feats.sum(0);
}