/*
 * Modified by Neural Magic
 * Copyright (C) Marlin.2024 Elias Frantar
 *
 * 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.
 */

 /*
  * Adapted from https://github.com/IST-DASLab/marlin
  */
  /*
   * Adapted from
   * https://github.com/vllm-project/vllm/tree/main/csrc/quantization/gptq_marlin
   */
#include "gptq_marlin.cuh"
#include "gptq_marlin_dtypes.cuh"
#include <c10/cuda/CUDAGuard.h>
#define STATIC_ASSERT_SCALAR_TYPE_VALID(scalar_t)                              \
    static_assert(std::is_same<scalar_t, half>::value ||                       \
                      std::is_same<scalar_t, nv_bfloat16>::value,              \
                  "only float16 and bfloat16 is supported");

template <typename T> inline std::string str(T x) { return std::to_string(x); }

namespace gptq_marlin {

#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 800

    __global__ void permute_cols_kernel(int4 const* __restrict__ a_int4_ptr,
        int const* __restrict__ perm_int_ptr,
        int4* __restrict__ out_int4_ptr, int size_m,
        int size_k, int block_rows) {}

    template <typename scalar_t,         // compute dtype, half or nv_float16
        const int num_bits,        // number of bits used for weights
        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
    >
    __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__ scales_ptr, // fp16 quantization scales of shape
            // (k/groupsize)xn
            const int* __restrict__ g_idx, // int32 group indices of shape k
            int num_groups, // number of scale groups per output channel
            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
        ) {}

} // namespace gptq_marlin

torch::Tensor gptq_marlin_gemm(torch::Tensor& a, torch::Tensor& b_q_weight,
    torch::Tensor& b_scales, torch::Tensor& g_idx,
    torch::Tensor& perm, torch::Tensor& workspace,
    int64_t num_bits, int64_t size_m, int64_t size_n,
    int64_t size_k, bool is_k_full) {
    TORCH_CHECK_NOT_IMPLEMENTED(false,
        "marlin_gemm(..) requires CUDA_ARCH >= 8.0");
    return torch::empty({ 1, 1 });
}

#else

    // m16n8k16 tensor core mma instruction with fp16 inputs and fp32
    // output/accumulation.
    template <typename scalar_t>
    __device__ inline void mma(const typename ScalarType<scalar_t>::FragA& a_frag,
        const typename ScalarType<scalar_t>::FragB& frag_b,
        typename ScalarType<scalar_t>::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);
        if constexpr (std::is_same<scalar_t, half>::value) {
            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]));
        }
        else if constexpr (std::is_same<scalar_t, nv_bfloat16>::value) {
            asm volatile(
                "mma.sync.aligned.m16n8k16.row.col.f32.bf16.bf16.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]));
        }
        else {
            STATIC_ASSERT_SCALAR_TYPE_VALID(scalar_t);
        }
    }

    // Instruction for loading a full 16x16 matrix fragment of operand A from shared
    // memory, directly in tensor core layout.
    template <typename scalar_t>
    __device__ inline void ldsm4(typename ScalarType<scalar_t>::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;
    }

    // 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:
    // - FP16:
    // https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h#L215-L287
    // - BF16:
    // https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h#L327-L385
    template <typename scalar_t>
    __device__ inline typename ScalarType<scalar_t>::FragB dequant_4bit(int q) {
        STATIC_ASSERT_SCALAR_TYPE_VALID(scalar_t);
    }

    template <>
    __device__ inline typename ScalarType<half>::FragB dequant_4bit<half>(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;
        typename ScalarType<half>::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;
    }

    template <>
    __device__ inline typename ScalarType<nv_bfloat16>::FragB
        dequant_4bit<nv_bfloat16>(int q) {
        static constexpr uint32_t MASK = 0x000f000f;
        static constexpr uint32_t EX = 0x43004300;

        // Guarantee that the `(a & b) | c` operations are LOP3s.

        int lo = lop3<(0xf0 & 0xcc) | 0xaa>(q, MASK, EX);
        q >>= 4;
        int hi = lop3<(0xf0 & 0xcc) | 0xaa>(q, MASK, EX);

        typename ScalarType<nv_bfloat16>::FragB frag_b;
        static constexpr uint32_t MUL = 0x3F803F80;
        static constexpr uint32_t ADD = 0xC308C308;

        frag_b[0] = __hfma2(*reinterpret_cast<nv_bfloat162*>(&lo),
            *reinterpret_cast<const nv_bfloat162*>(&MUL),
            *reinterpret_cast<const nv_bfloat162*>(&ADD));
        frag_b[1] = __hfma2(*reinterpret_cast<nv_bfloat162*>(&hi),
            *reinterpret_cast<const nv_bfloat162*>(&MUL),
            *reinterpret_cast<const nv_bfloat162*>(&ADD));
        return frag_b;
    }

    // Fast Int8ToFp16/Int8ToBf16: Efficiently dequantize 8bit int values to fp16 or
    // bf16 Reference:
    // - FP16:
    // https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h#L53-L85
    // - BF16:
    // https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h#L125-L175
    template <typename scalar_t>
    __device__ inline typename ScalarType<scalar_t>::FragB dequant_8bit(int q) {
        STATIC_ASSERT_SCALAR_TYPE_VALID(scalar_t);
    }

    template <>
    __device__ inline typename ScalarType<half>::FragB dequant_8bit<half>(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;

        typename ScalarType<half>::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;
    }

    template <>
    __device__ inline typename ScalarType<nv_bfloat16>::FragB
        dequant_8bit<nv_bfloat16>(int q) {
        typename ScalarType<nv_bfloat16>::FragB frag_b;

        float fp32_intermediates[4];
        uint32_t* fp32_intermediates_casted =
            reinterpret_cast<uint32_t*>(fp32_intermediates);

        static constexpr uint32_t fp32_base = 0x4B000000;
        fp32_intermediates_casted[0] = __byte_perm(q, fp32_base, 0x7650);
        fp32_intermediates_casted[1] = __byte_perm(q, fp32_base, 0x7652);
        fp32_intermediates_casted[2] = __byte_perm(q, fp32_base, 0x7651);
        fp32_intermediates_casted[3] = __byte_perm(q, fp32_base, 0x7653);

        fp32_intermediates[0] -= 8388736.f;
        fp32_intermediates[1] -= 8388736.f;
        fp32_intermediates[2] -= 8388736.f;
        fp32_intermediates[3] -= 8388736.f;

        uint32_t* bf16_result_ptr = reinterpret_cast<uint32_t*>(&frag_b);
        bf16_result_ptr[0] = __byte_perm(fp32_intermediates_casted[0],
            fp32_intermediates_casted[1], 0x7632);
        bf16_result_ptr[1] = __byte_perm(fp32_intermediates_casted[2],
            fp32_intermediates_casted[3], 0x7632);

        return frag_b;
    }

    // Multiply dequantized values by the corresponding quantization scale; used
    // only for grouped quantization.
    template <typename scalar_t>
    __device__ inline void scale(typename ScalarType<scalar_t>::FragB& frag_b,
        typename ScalarType<scalar_t>::FragS& frag_s,
        int i) {
        using scalar_t2 = typename ScalarType<scalar_t>::scalar_t2;
        scalar_t2 s = ScalarType<scalar_t>::num2num2(
            reinterpret_cast<scalar_t*>(&frag_s)[i]);
        frag_b[0] = __hmul2(frag_b[0], s);
        frag_b[1] = __hmul2(frag_b[1], s);
    }

    // Same as above, but for act_order (each K is multiplied individually)
    template <typename scalar_t>
    __device__ inline void scale4(typename ScalarType<scalar_t>::FragB& frag_b,
        typename ScalarType<scalar_t>::FragS& frag_s_1,
        typename ScalarType<scalar_t>::FragS& frag_s_2,
        typename ScalarType<scalar_t>::FragS& frag_s_3,
        typename ScalarType<scalar_t>::FragS& frag_s_4,
        int i) {
        using scalar_t2 = typename ScalarType<scalar_t>::scalar_t2;
        scalar_t2 s_val_1_2;
        s_val_1_2.x = reinterpret_cast<scalar_t*>(&frag_s_1)[i];
        s_val_1_2.y = reinterpret_cast<scalar_t*>(&frag_s_2)[i];

        scalar_t2 s_val_3_4;
        s_val_3_4.x = reinterpret_cast<scalar_t*>(&frag_s_3)[i];
        s_val_3_4.y = reinterpret_cast<scalar_t*>(&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);
    }

    // Given 2 floats multiply by 2 scales (halves)
    template <typename scalar_t>
    __device__ inline void scale_float(float* c,
        typename ScalarType<scalar_t>::FragS& s) {
        scalar_t* s_ptr = reinterpret_cast<scalar_t*>(&s);
        c[0] = __fmul_rn(c[0], ScalarType<scalar_t>::num2float(s_ptr[0]));
        c[1] = __fmul_rn(c[1], ScalarType<scalar_t>::num2float(s_ptr[1]));
    }

    // 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));
        }
    }

    // For a given "a" of size [M,K] performs a permutation of the K columns based
    // on the given "perm" indices.
    __global__ void permute_cols_kernel(int4 const* __restrict__ a_int4_ptr,
        int const* __restrict__ perm_int_ptr,
        int4* __restrict__ out_int4_ptr, int size_m,
        int size_k, int block_rows) {
        int start_row = block_rows * blockIdx.x;
        int finish_row = start_row + block_rows;
        if (finish_row > size_m) {
            finish_row = size_m;
        }
        int cur_block_rows = finish_row - start_row;

        int row_stride = size_k * sizeof(half) / 16;

        auto permute_row = [&](int row) {
            int iters = size_k / default_threads;
            int rest = size_k % default_threads;

            int offset = row * row_stride;

            half const* a_row_half =
                reinterpret_cast<half const*>(a_int4_ptr + offset);
            half* out_half = reinterpret_cast<half*>(out_int4_ptr + offset);

            int base_k = 0;

            for (int i = 0; i < iters; i++) {
                int cur_k = base_k + threadIdx.x;
                int src_pos = perm_int_ptr[cur_k];

                out_half[cur_k] = a_row_half[src_pos];

                base_k += default_threads;
            }

            if (rest) {
                if (threadIdx.x < rest) {
                    int cur_k = base_k + threadIdx.x;
                    int src_pos = perm_int_ptr[cur_k];

                    out_half[cur_k] = a_row_half[src_pos];
                }
            }
            };

        for (int i = 0; i < cur_block_rows; i++) {
            int cur_row = start_row + i;
            if (cur_row < size_m) {
                permute_row(cur_row);
            }
        }
    }

    template <typename scalar_t,         // compute dtype, half or nv_float16
        const int num_bits,        // number of bits used for weights
        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__ 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__ scales_ptr, // fp16 quantization scales of shape
            // (k/groupsize)xn
            const int* __restrict__ g_idx, // int32 group indices of shape k
            int num_groups, // number of scale groups per output channel
            int prob_m,     // batch dimension m, should be divisible by (16 * thread_m_blocks) if bigger than that
            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.
        using Dtype = ScalarType<scalar_t>;
        using scalar_t2 = typename ScalarType<scalar_t>::scalar_t2;
        using FragA = typename ScalarType<scalar_t>::FragA;
        using FragB = typename ScalarType<scalar_t>::FragB;
        using FragC = typename ScalarType<scalar_t>::FragC;
        using FragS = typename ScalarType<scalar_t>::FragS;

        constexpr int pack_factor = 32 / num_bits;

        // int prob_m = *prob_m_ptr;
        // const int thread_m_blocks = min(div_ceil(prob_m, 16), template_thread_m_blocks);
        // constexpr int thread_m_blocks = template_thread_m_blocks;

        // 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 = div_ceil(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) *
                    div_ceil(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) {
            A += (slice_col_par / n_tiles) * 16 * thread_m_blocks * prob_k / 8;
            C += (slice_col_par / n_tiles) * 16 * thread_m_blocks * prob_n / 8;
            locks += (slice_col_par / n_tiles) * n_tiles;
            slice_col = slice_col_par % n_tiles;
        }

        // 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 * div_ceil(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 = div_ceil(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) {
                A += 16 * thread_m_blocks * prob_k / 8;
                C += 16 * thread_m_blocks * prob_n / 8;
                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 = div_ceil(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 = num_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;

        // 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;

        // 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;
        }

        // 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_g_idx = sh_b + (stages * b_sh_stage);
        int4* sh_s = sh_g_idx + (stages * g_idx_stage);

        // 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;
            }
            };

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

        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++) {
                    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++) {
#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();
            };

        // 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<scalar_t>(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++) {
                FragB frag_b0;
                FragB frag_b1;
                if constexpr (num_bits == 4) {
                    int b_quant = frag_b_quant[k % 2][0][j];
                    int b_quant_shift = b_quant >> 8;

                    frag_b0 = dequant_4bit<scalar_t>(b_quant);
                    frag_b1 = dequant_4bit<scalar_t>(b_quant_shift);

                }
                else {
                    int* frag_b_quant_ptr = reinterpret_cast<int*>(frag_b_quant[k % 2]);
                    int b_quant_0 = frag_b_quant_ptr[j * 2 + 0];
                    int b_quant_1 = frag_b_quant_ptr[j * 2 + 1];

                    frag_b0 = dequant_8bit<scalar_t>(b_quant_0);
                    frag_b1 = dequant_8bit<scalar_t>(b_quant_1);
                }

                // Apply scale to frag_b0
                if constexpr (has_act_order) {
                    scale4<scalar_t>(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<scalar_t>(frag_b0, frag_s[k % 2][j], 0);
                    }
                }

                // Apply scale to frag_b1
                if constexpr (has_act_order) {
                    scale4<scalar_t>(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<scalar_t>(frag_b1, frag_s[k % 2][j], 1);
                    }
                }

#pragma unroll
                for (int i = 0; i < thread_m_blocks; i++) {
                    mma<scalar_t>(frag_a[k % 2][i], frag_b0, frag_c[i][j][0]);
                    mma<scalar_t>(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++) {
                        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)] +=
                                    Dtype::num2float(reinterpret_cast<scalar_t*>(&c_red)[j]);
                            }
                        }
                        if (!last) {
                            int4 c;
#pragma unroll
                            for (int j = 0; j < 2 * 4; j++) {
                                reinterpret_cast<scalar_t*>(&c)[j] =
                                    Dtype::float2num(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) {
                scalar_t2 res =
                    Dtype::nums2num2(Dtype::float2num(c0), Dtype::float2num(c1));

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

                ((scalar_t2*)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 < div_ceil(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++) {
                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;
                // For per-column scales, we only fetch them here in the final step before
                // write-out
                if constexpr (!has_act_order && group_blocks == -1) {
                    if constexpr (num_bits == 8) {
                        if (s_sh_wr_pred) {
                            cp_async4(&sh_s[s_sh_wr], &scales_ptr[s_gl_rd]);
                        }
                        cp_async_fence();
                    }
                    else {
                        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 (num_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 && num_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<scalar_t>(
                                    reinterpret_cast<float*>(&frag_c[i][j][0][0]),
                                    frag_s[j / 2][2 * (j % 2) + 0]);
                                scale_float<scalar_t>(
                                    reinterpret_cast<float*>(&frag_c[i][j][0][2]),
                                    frag_s[j / 2][2 * (j % 2) + 0]);

                                scale_float<scalar_t>(
                                    reinterpret_cast<float*>(&frag_c[i][j][1][0]),
                                    frag_s[j / 2][2 * (j % 2) + 1]);
                                scale_float<scalar_t>(
                                    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 <typename scalar_t,         // compute dtype, half or nv_float16
        const int num_bits,        // number of bits used for weights
        const int threads,         // number of threads in a threadblock
        const int template_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
    >
    __global__ void
        Marlin_wrapper(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__ scales_ptr, // fp16 quantization scales of shape
            // (k/groupsize)xn
            const int* __restrict__ g_idx, // int32 group indices of shape k
            int num_groups, // number of scale groups per output channel
            const int* __restrict__ prob_m_ptr,     // batch dimension m
            int prob_n,     // output dimension n
            int prob_k,     // reduction dimension k
            int* locks      // extra global storage for barrier synchronization
        ) {
        int prob_m = *prob_m_ptr;
        prob_m = min(prob_m, 1024);
        const int thread_m_blocks = min(div_ceil(prob_m, 16), template_thread_m_blocks);
        if(prob_m > 16 * thread_m_blocks)
            prob_m = (16 * thread_m_blocks) * div_ceil(prob_m, (16 * thread_m_blocks));
        /*if (blockIdx.x == 0 && threadIdx.x == 0)
            printf("marlin prob_m %d\n", prob_m);*/
        if (thread_m_blocks == 1) {
            Marlin<scalar_t, num_bits, threads, 1,
                thread_n_blocks, thread_k_blocks, stages, has_act_order,
                group_blocks>(
                    A, B, C, scales_ptr, g_idx, num_groups, prob_m, prob_n,
                    prob_k, locks);
        }
        else if (thread_m_blocks == 2) {
            Marlin<scalar_t, num_bits, threads, 2,
                thread_n_blocks, thread_k_blocks, stages, has_act_order,
                group_blocks>(
                    A, B, C, scales_ptr, g_idx, num_groups, prob_m, prob_n,
                    prob_k, locks);
        }
        else if (thread_m_blocks == 3) {
            Marlin<scalar_t, num_bits, threads, 3,
                thread_n_blocks, thread_k_blocks, stages, has_act_order,
                group_blocks>(
                    A, B, C, scales_ptr, g_idx, num_groups, prob_m, prob_n,
                    prob_k, locks);
        }
        else if (thread_m_blocks == 4) {
            Marlin<scalar_t, num_bits, threads, 4,
                thread_n_blocks, thread_k_blocks, stages, has_act_order,
                group_blocks>(
                    A, B, C, scales_ptr, g_idx, num_groups, prob_m, prob_n,
                    prob_k, locks);
        }
    }

#define __CALL_IF(NUM_BITS, THREAD_M_BLOCKS, THREAD_N_BLOCKS, THREAD_K_BLOCKS, \
                  HAS_ACT_ORDER, GROUP_BLOCKS, NUM_THREADS)                    \
    else if (num_bits == NUM_BITS && thread_m_blocks == THREAD_M_BLOCKS &&     \
             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(                                                  \
            Marlin_wrapper<scalar_t, NUM_BITS, NUM_THREADS, THREAD_M_BLOCKS,           \
                   THREAD_N_BLOCKS, THREAD_K_BLOCKS, pipe_stages,              \
                   HAS_ACT_ORDER, GROUP_BLOCKS>,                               \
            cudaFuncAttributeMaxDynamicSharedMemorySize, max_shared_mem);      \
        Marlin_wrapper<scalar_t, NUM_BITS, NUM_THREADS, THREAD_M_BLOCKS,               \
               THREAD_N_BLOCKS, THREAD_K_BLOCKS, pipe_stages, HAS_ACT_ORDER,   \
               GROUP_BLOCKS><<<blocks, NUM_THREADS, max_shared_mem, stream>>>( \
            A_ptr, B_ptr, C_ptr, s_ptr, g_idx_ptr, num_groups, prob_m_ptr, prob_n, \
            prob_k, locks);                                                    \
    }

    typedef struct {
        int thread_k;
        int thread_n;
        int num_threads;
    } thread_config_t;

    typedef struct {
        int max_m_blocks;
        thread_config_t tb_cfg;
    } exec_config_t;

    thread_config_t small_batch_thread_configs[] = {
        // Ordered by priority

        // thread_k, thread_n, num_threads
        {128, 128, 256},
        {64, 128, 128},
        {128, 64, 128},
    };

    thread_config_t large_batch_thread_configs[] = {
        // Ordered by priority

        // thread_k, thread_n, num_threads
        {64, 256, 256},
        // {128, 128, 256},
        {64, 128, 128},
        {128, 64, 128},

    };

    int get_scales_cache_size(thread_config_t const& th_config, int prob_m,
        int prob_n, int prob_k, int num_bits, int group_size,
        bool has_act_order, bool is_k_full) {
        bool cache_scales_chunk = has_act_order && !is_k_full;

        int tb_n = th_config.thread_n;
        int tb_k = th_config.thread_k;

        // Get max scale groups per thread-block
        int tb_groups;
        if (group_size == -1) {
            tb_groups = 1;
        }
        else if (group_size == 0) {
            tb_groups = div_ceil(tb_k, 32); // Worst case is 32 group size
        }
        else {
            tb_groups = div_ceil(tb_k, group_size);
        }

        if (cache_scales_chunk) {
            int load_groups =
                tb_groups * pipe_stages * 2; // Chunk size is 2x pipeline over dim K
            load_groups = max(load_groups, 32); // We load at least 32 scale groups
            return load_groups * tb_n * 2;

        }
        else {
            int tb_scales = tb_groups * tb_n * 2;

            return tb_scales * pipe_stages;
        }
    }

    bool is_valid_cache_size(thread_config_t const& th_config, int max_m_blocks,
        int prob_m, int prob_n, int prob_k, int num_bits,
        int scales_cache_size, int max_shared_mem) {
        int pack_factor = 32 / num_bits;

        // Get B size
        int tb_k = th_config.thread_k;
        int tb_n = th_config.thread_n;

        int b_size = (tb_k * tb_n / pack_factor) * 4;

        // Get A size
        int m_blocks = div_ceil(prob_m, 16);
        int tb_max_m = 16;

        // zbx: too ugly
        // origin
        /*while (true) {
          if (m_blocks >= max_m_blocks) {
            tb_max_m *= max_m_blocks;
            break;
          }

          max_m_blocks--;
          if (max_m_blocks == 0) {
            TORCH_CHECK(false, "Unexpected m_blocks = ", m_blocks);
          }
        }*/
        // refactor
        tb_max_m *= std::min(m_blocks, max_m_blocks);

        int a_size = (tb_max_m * tb_k) * 2;

        float pipe_size = (a_size + b_size) * pipe_stages;

        TORCH_CHECK(max_shared_mem / 2 > scales_cache_size); // Sanity
        return pipe_size < 0.95f * (max_shared_mem - scales_cache_size);
    }

    bool is_valid_config(thread_config_t const& th_config, int max_m_blocks,
        int prob_m, int prob_n, int prob_k, int num_bits,
        int group_size, bool has_act_order, bool is_k_full,
        int max_shared_mem) {
        // Sanity
        if (th_config.thread_k == -1 || th_config.thread_n == -1 ||
            th_config.num_threads == -1) {
            return false;
        }

        // Verify K/N are divisible by thread K/N
        if (prob_k % th_config.thread_k != 0 || prob_n % th_config.thread_n != 0) {
            return false;
        }

        // Verify min for thread K/N
        if (th_config.thread_n < min_thread_n ||
            th_config.thread_k < min_thread_k) {
            return false;
        }

        // num_threads must be at least 128 (= 4 warps)
        if (th_config.num_threads < 128) {
            return false;
        }

        //  Determine cache for scales
        int scales_cache_size =
            get_scales_cache_size(th_config, prob_m, prob_n, prob_k, num_bits,
                group_size, has_act_order, is_k_full);

        // Check that pipeline fits into cache
        if (!is_valid_cache_size(th_config, max_m_blocks, prob_m, prob_n, prob_k,
            num_bits, scales_cache_size, max_shared_mem)) {
            return false;
        }

        return true;
    }

    exec_config_t determine_thread_config(int prob_m, int prob_n, int prob_k,
        int num_bits, int group_size,
        bool has_act_order, bool is_k_full,
        int max_shared_mem) {
        int max_m_blocks = 4;
        while (max_m_blocks > 0) {
            if (prob_m <= 16) {
                for (auto th_config : small_batch_thread_configs) {
                    if (is_valid_config(th_config, max_m_blocks, prob_m, prob_n,
                        prob_k, num_bits, group_size, has_act_order,
                        is_k_full, max_shared_mem)) {
                        return exec_config_t{ max_m_blocks, th_config };
                    }
                }
            }
            else {
                for (auto th_config : large_batch_thread_configs) {
                    if (is_valid_config(th_config, max_m_blocks, prob_m, prob_n,
                        prob_k, num_bits, group_size, has_act_order,
                        is_k_full, max_shared_mem)) {
                        return exec_config_t{ max_m_blocks, th_config };
                    }
                }
            }

            max_m_blocks--; // Process less M blocks per invocation to reduce cache
            // usage
        }

        return exec_config_t{ 0, {-1, -1, -1} };
    }

#define CALL_IF(NUM_BITS, N_BLOCKS, K_BLOCKS, NUM_THREADS)                     \
    __CALL_IF(NUM_BITS, 1, N_BLOCKS, K_BLOCKS, false, 4, NUM_THREADS)          \
    __CALL_IF(NUM_BITS, 2, N_BLOCKS, K_BLOCKS, false, 4, NUM_THREADS)          \
    __CALL_IF(NUM_BITS, 3, N_BLOCKS, K_BLOCKS, false, 4, NUM_THREADS)          \
    __CALL_IF(NUM_BITS, 4, N_BLOCKS, K_BLOCKS, false, 4, NUM_THREADS)          \
    __CALL_IF(NUM_BITS, 1, N_BLOCKS, K_BLOCKS, false, 8, NUM_THREADS)          \
    __CALL_IF(NUM_BITS, 2, N_BLOCKS, K_BLOCKS, false, 8, NUM_THREADS)          \
    __CALL_IF(NUM_BITS, 3, N_BLOCKS, K_BLOCKS, false, 8, NUM_THREADS)          \
    __CALL_IF(NUM_BITS, 4, N_BLOCKS, K_BLOCKS, false, 8, NUM_THREADS)        

    template <typename scalar_t>
    void marlin_mm_f16i4(const void* A, const void* B, void* C, void* s,
        void* g_idx, void* perm, void* a_tmp, int* prob_m_ptr, int prob_m,
        int prob_n, int prob_k, void* workspace, int num_bits,
        bool has_act_order, bool is_k_full, int num_groups,
        int group_size, int dev, cudaStream_t stream, int thread_k,
        int thread_n, int sms, int max_par) {
        TORCH_CHECK(num_bits == 4 || num_bits == 8,
            "num_bits must be 4 or 8. Got = ", num_bits);
        TORCH_CHECK(prob_m > 0 && prob_n > 0 && prob_k > 0, "Invalid MNK = [",
            prob_m, ", ", prob_n, ", ", prob_k, "]");

        int tot_m = prob_m;
        int tot_m_blocks = div_ceil(tot_m, 16);
        int pad = 16 * tot_m_blocks - tot_m;

        if (sms == -1) {
            cudaDeviceGetAttribute(&sms, cudaDevAttrMultiProcessorCount, dev);
        }

        int max_shared_mem = 0;
        cudaDeviceGetAttribute(&max_shared_mem,
            cudaDevAttrMaxSharedMemoryPerBlockOptin, dev);
        TORCH_CHECK(max_shared_mem > 0);

        // Set thread config
        exec_config_t exec_cfg;
        if (thread_k != -1 && thread_n != -1) {
            // User-defined config
            exec_cfg = exec_config_t{
                4, thread_config_t{thread_k, thread_n, default_threads} };
        }
        else {
            // Auto config
            exec_cfg = determine_thread_config(prob_m, prob_n, prob_k, num_bits,
                group_size, has_act_order, is_k_full,
                max_shared_mem);
        }

        TORCH_CHECK(
            exec_cfg.max_m_blocks > 0 &&
            is_valid_config(exec_cfg.tb_cfg, exec_cfg.max_m_blocks, prob_m,
                prob_n, prob_k, num_bits, group_size, has_act_order,
                is_k_full, max_shared_mem),
            "Invalid thread config: max_m_blocks = ", exec_cfg.max_m_blocks,
            ", thread_k = ", exec_cfg.tb_cfg.thread_k,
            ", thread_n = ", exec_cfg.tb_cfg.thread_n,
            ", num_threads = ", exec_cfg.tb_cfg.num_threads, " for MKN = [", prob_m,
            ", ", prob_k, ", ", prob_n, "] and num_bits = ", num_bits,
            ", group_size = ", group_size, ", has_act_order = ", has_act_order,
            ", is_k_full = ", is_k_full, ", max_shared_mem = ", max_shared_mem);

        int num_threads = exec_cfg.tb_cfg.num_threads;
        thread_k = exec_cfg.tb_cfg.thread_k;
        thread_n = exec_cfg.tb_cfg.thread_n;

        int thread_k_blocks = thread_k / 16;
        int thread_n_blocks = thread_n / 16;

        int blocks = sms;

        TORCH_CHECK(prob_n % thread_n == 0, "prob_n = ", prob_n,
            " is not divisible by thread_n = ", thread_n);
        TORCH_CHECK(prob_k % thread_k == 0, "prob_k = ", prob_k,
            " is not divisible by thread_k = ", thread_k);

        int group_blocks = 0;
        if (has_act_order) {
            if (is_k_full) {
                TORCH_CHECK(group_size != -1);
                group_blocks = group_size / 16;
                TORCH_CHECK(prob_k % group_blocks == 0, "prob_k = ", prob_k,
                    " is not divisible by group_blocks = ", group_blocks);
            }
            else {
                TORCH_CHECK(group_size == 0);
                group_blocks = 0;
            }

        }
        else {
            if (group_size == -1) {
                group_blocks = -1;
            }
            else {
                group_blocks = group_size / 16;
                TORCH_CHECK(prob_k % group_blocks == 0, "prob_k = ", prob_k,
                    " is not divisible by group_blocks = ", group_blocks);
            }
        }

        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 int* g_idx_ptr = (const int*)g_idx;
        const int* perm_ptr = (const int*)perm;
        int4* a_tmp_ptr = (int4*)a_tmp;

        int* locks = (int*)workspace;

        if (has_act_order) {
            // Permute A columns
            int block_rows = div_ceil(prob_m, blocks);
            permute_cols_kernel <<<blocks, default_threads, 0, stream >>> (
                A_ptr, perm_ptr, a_tmp_ptr, prob_m, prob_k, block_rows);
            A_ptr = a_tmp_ptr;
        }

        // If we have a full K, then we can run the non-act-order version of Marlin
        // (since the weight rows are reordered by increasing group ids, and by
        // having a full K, we have full original groups)
        if (is_k_full) {
            has_act_order = false;
        }

        // Main loop
        for (int i = 0; i < tot_m_blocks; i += exec_cfg.max_m_blocks) {
            int thread_m_blocks = tot_m_blocks - i;
            prob_m = tot_m - 16 * i;
            int par = 1;
            if (thread_m_blocks > exec_cfg.max_m_blocks) {
                // Note that parallel > 1 currently only works for inputs without
                // any padding
                par = (16 * thread_m_blocks - pad) / (16 * exec_cfg.max_m_blocks);
                if (par > max_par)
                    par = max_par;
                prob_m = (16 * exec_cfg.max_m_blocks) * par;
                i += exec_cfg.max_m_blocks * (par - 1);
                thread_m_blocks = exec_cfg.max_m_blocks;
            }

            // Define kernel configurations
#define undefined_error                                                        \
    TORCH_CHECK(false, "Unsupported shapes: MNK = [" + str(prob_m) + ", " +    \
                           str(prob_n) + ", " + str(prob_k) + "]" +            \
                           ", has_act_order = " + str(has_act_order) +         \
                           ", num_groups = " + str(num_groups) +               \
                           ", group_size = " + str(group_size) +               \
                           ", thread_m_blocks = " + str(thread_m_blocks) +     \
                           ", thread_n_blocks = " + str(thread_n_blocks) +     \
                           ", thread_k_blocks = " + str(thread_k_blocks));

        /* std::cout << "MNK = [" + str(prob_m) + ", " + \
             str(prob_n) + ", " + str(prob_k) + "]" + \
             ", has_act_order = " + str(has_act_order) + \
             ", num_groups = " + str(num_groups) + \
             ", group_size = " + str(group_size) + \
             ", thread_m_blocks = " + str(thread_m_blocks) + \
             ", thread_n_blocks = " + str(thread_n_blocks) + \
             ", thread_k_blocks = " + str(thread_k_blocks) << std::endl;*/

             /*if (false) {
             }
             // CALL_IF(4, 32, 2, 256)
             // CALL_IF(4, 16, 4, 256)
             __CALL_IF(4, 1, 16, 4, false, 4, 256)
             __CALL_IF(4, 2, 16, 4, false, 4, 256)
             // CALL_IF(4, 8, 8, 256)
             __CALL_IF(4, 1, 8, 8, false, 4, 256)
             __CALL_IF(4, 2, 8, 8, false, 4, 256)
             // CALL_IF(4, 16, 4, 128)
             __CALL_IF(4, 1, 16, 4, false, 4, 128)
             __CALL_IF(4, 2, 16, 4, false, 4, 128)
             // CALL_IF(4, 8, 8, 128)
             __CALL_IF(4, 1, 8, 8, false, 4, 128)
             __CALL_IF(4, 2, 8, 8, false, 4, 128)
             else {undefined_error}*/

            if (num_bits == 4 && num_threads == 256)
            {
                if (false) {
                }
                CALL_IF(4, 32, 2, 256)
                    CALL_IF(4, 16, 4, 256)
                    CALL_IF(4, 8, 8, 256)
                else {
                    undefined_error
                }
            }
            else if (num_bits == 4 && num_threads == 128)
            {
                if (false) {
                }
                CALL_IF(4, 8, 4, 128)
                    CALL_IF(4, 16, 4, 128)
                    CALL_IF(4, 4, 8, 128)
                else {
                    undefined_error
                }
            }
            // else if (num_bits == 8 && num_threads == 256)
            // {
            //     if (false) {
            //     }
            //     CALL_IF(8, 32, 2, 256)
            //     CALL_IF(8, 16, 4, 256)
            //     CALL_IF(8, 8, 8, 256)
            //     else {
            //         undefined_error
            //     }
            // }
            // else if (num_bits == 8 && num_threads == 128)
            // {
            //     if (false) {
            //     }
            //     CALL_IF(8, 8, 4, 128)
            //     CALL_IF(8, 16, 4, 128)
            //     CALL_IF(8, 4, 8, 128)
            //     else {
            //         undefined_error
            //     }
            // }
            else {
                undefined_error
            }

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

} // namespace gptq_marlin

torch::Tensor gptq_marlin_gemm(torch::Tensor& a, torch::Tensor& b_q_weight,
    torch::Tensor& b_scales, torch::Tensor& g_idx,
    torch::Tensor& perm, torch::Tensor& workspace,
    int64_t num_bits, torch::Tensor size_m_tensor, int64_t size_m, int64_t size_n,
    int64_t size_k, int sms, bool is_k_full) {
    const at::cuda::OptionalCUDAGuard device_guard(device_of(a));
    // Verify num_bits
    TORCH_CHECK(num_bits == 4 || num_bits == 8,
        "num_bits must be 4 or 8. Got = ", num_bits);
    int pack_factor = 32 / num_bits;

    // Verify A
    TORCH_CHECK(a.size(0) == size_m, "Shape mismatch: a.size(0) = ", a.size(0),
        ", size_m = ", size_m);
    TORCH_CHECK(a.size(1) == size_k, "Shape mismatch: a.size(1) = ", a.size(1),
        ", size_k = ", size_k);

    // Verify B
    TORCH_CHECK(size_k % gptq_marlin::tile_size == 0, "size_k = ", size_k,
        " is not divisible by tile_size = ", gptq_marlin::tile_size);
    TORCH_CHECK((size_k / gptq_marlin::tile_size) == b_q_weight.size(0),
        "Shape mismatch: b_q_weight.size(0) = ", b_q_weight.size(0),
        ", size_k = ", size_k,
        ", tile_size = ", gptq_marlin::tile_size);
    TORCH_CHECK(b_q_weight.size(1) % gptq_marlin::tile_size == 0,
        "b_q_weight.size(1) = ", b_q_weight.size(1),
        " is not divisible by tile_size = ", gptq_marlin::tile_size);
    int actual_size_n =
        (b_q_weight.size(1) / gptq_marlin::tile_size) * pack_factor;
    TORCH_CHECK(size_n == actual_size_n, "size_n = ", size_n,
        ", actual_size_n = ", actual_size_n);

    // Verify device and strides
    TORCH_CHECK(a.device().is_cuda(), "A is not on GPU");
    TORCH_CHECK(a.is_contiguous(), "A is not contiguous");

    TORCH_CHECK(b_q_weight.device().is_cuda(), "b_q_weight is not on GPU");
    TORCH_CHECK(b_q_weight.is_contiguous(), "b_q_weight is not contiguous");

    TORCH_CHECK(b_scales.device().is_cuda(), "b_scales is not on GPU");
    TORCH_CHECK(b_scales.is_contiguous(), "b_scales is not contiguous");

    TORCH_CHECK(g_idx.device().is_cuda(), "g_idx is not on GPU");
    TORCH_CHECK(g_idx.is_contiguous(), "g_idx is not contiguous");

    TORCH_CHECK(perm.device().is_cuda(), "perm is not on GPU");
    TORCH_CHECK(perm.is_contiguous(), "perm is not contiguous");

    // Alloc buffers
    auto options = torch::TensorOptions().dtype(a.dtype()).device(a.device());
    torch::Tensor c = torch::empty({ size_m, size_n }, options);
    torch::Tensor a_tmp = torch::empty({ size_m, size_k }, options);

    // 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; //zbx

    // Verify g_idx and perm
    TORCH_CHECK((g_idx.size(0) == 0 && perm.size(0) == 0) ||
        (g_idx.size(0) == size_k && perm.size(0) == size_k),
        "Unexpected g_idx.size(0) = ", g_idx.size(0),
        " and perm.size(0) = ", perm.size(0),
        ", where size_k = ", size_k);

    // Detect groupsize and act_order
    int num_groups = -1;
    int group_size = -1;
    bool has_act_order = g_idx.size(0) != 0;

    int b_rank = b_scales.sizes().size();
    TORCH_CHECK(b_rank == 2, "b_scales rank = ", b_rank, " is not 2");
    TORCH_CHECK(b_scales.size(1) == size_n,
        "b_scales dim 1 = ", b_scales.size(1),
        " is not size_n = ", size_n);
    num_groups = b_scales.size(0);

    if (has_act_order) {
        if (is_k_full) {
            TORCH_CHECK(num_groups > 1,
                "For act_order, num_groups must be > 1");
            TORCH_CHECK(size_k % num_groups == 0, "size_k = ", size_k,
                ", is not divisible by num_groups = ", num_groups);
            group_size = size_k / num_groups;
        }
        else {
            group_size = 0;
        }

    }
    else {
        if (num_groups > 1) {
            TORCH_CHECK(
                size_k % num_groups == 0, "size_k = ", size_k,
                ", is not divisible by b_scales.size(0) = ", b_scales.size(0));
            group_size = size_k / num_groups;
        }
        else {
            group_size = -1;
        }
    }

    // Verify workspace size
    TORCH_CHECK(
        size_n % gptq_marlin::min_thread_n == 0, "size_n = ", size_n,
        ", is not divisible by min_thread_n = ", gptq_marlin::min_thread_n);
    int min_workspace_size =
        (size_n / gptq_marlin::min_thread_n) * gptq_marlin::max_par;
    TORCH_CHECK(workspace.numel() >= min_workspace_size,
        "workspace.numel = ", workspace.numel(),
        " is below min_workspace_size = ", min_workspace_size);

    int dev = a.get_device();
    if (a.scalar_type() == at::ScalarType::Half) {
        gptq_marlin::marlin_mm_f16i4<half>(
            a.data_ptr<at::Half>(), b_q_weight.data_ptr(),
            c.data_ptr<at::Half>(), b_scales.data_ptr<at::Half>(),
            g_idx.data_ptr(), perm.data_ptr(), a_tmp.data_ptr<at::Half>(),
            size_m_tensor.data_ptr<int>(),
            size_m, size_n, size_k, workspace.data_ptr(), num_bits,
            has_act_order, is_k_full, num_groups, group_size, dev,
            at::cuda::getCurrentCUDAStream(dev), thread_k, thread_n, sms,
            gptq_marlin::max_par);
    }
    else if (a.scalar_type() == at::ScalarType::BFloat16) {
        gptq_marlin::marlin_mm_f16i4<nv_bfloat16>(
            a.data_ptr<at::BFloat16>(), b_q_weight.data_ptr(),
            c.data_ptr<at::BFloat16>(), b_scales.data_ptr<at::BFloat16>(),
            g_idx.data_ptr(), perm.data_ptr(), a_tmp.data_ptr<at::BFloat16>(),
            size_m_tensor.data_ptr<int>(),
            size_m, size_n, size_k, workspace.data_ptr(), num_bits,
            has_act_order, is_k_full, num_groups, group_size, dev,
            at::cuda::getCurrentCUDAStream(dev), thread_k, thread_n, sms,
            gptq_marlin::max_par);
    }
    else {
        TORCH_CHECK(false,
            "gpt_marlin_gemm only supports bfloat16 and float16");
    }

    return c;
}

#endif