llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp

#version 450

#extension GL_EXT_control_flow_attributes : enable
#extension GL_EXT_shader_16bit_storage : require

#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require
#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require
#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require

#extension GL_KHR_memory_scope_semantics : enable
#extension GL_KHR_cooperative_matrix : enable
#extension GL_NV_cooperative_matrix2 : enable
#extension GL_EXT_buffer_reference : enable
#extension GL_KHR_shader_subgroup_ballot : enable
#extension GL_KHR_shader_subgroup_vote : enable
#ifdef DATA_A_BF16
#extension GL_EXT_bfloat16 : enable
#endif

#include "types.comp"
#include "utils.comp"

layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;

#define IS_MUL_MM2 1

layout (constant_id = 0) const uint BLOCK_SIZE = 256;
layout (constant_id = 1) const uint BM = 64;
layout (constant_id = 2) const uint BN = 64;
layout (constant_id = 3) const uint BK = 16;  // Assumed to be 32 if working with a quant

layout (constant_id = 4) const bool enable_smaller_matrices = false;
const uint BNover2 = enable_smaller_matrices ? (BN / 2) : BN;
const uint BNover4 = enable_smaller_matrices ? (BN / 4) : BN;

layout (push_constant) uniform parameter
{
    uint M;
    uint N;
    uint K;
    uint stride_a;
    uint stride_b;
    uint stride_d;

    uint batch_stride_a;
    uint batch_stride_b;
    uint batch_stride_d;

#ifdef MUL_MAT_ID
    uint nei0;
    uint nei1;
    uint nbi1;
    uint ne11;
#else
    uint k_split;
    uint ne02;
    uint ne12;
    uint broadcast2;
    uint broadcast3;
#endif
    // N dimension for the B matrix can be >= p.N
    uint padded_N;
} p;


layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};

#if QUANT_K > 1
#define DECODEFUNCA , dequantFuncA

#include "dequant_funcs_cm2.comp"

#else
#define DECODEFUNCA
#endif

#if !defined(fetch_scales)
#define fetch_scales(a, b, c, d, e, f)
#endif
#if !defined(store_scales)
#define store_scales(a)
#endif

#if defined(DATA_A_BF16)
#define MAT_TYPE bfloat16_t
#else
#define MAT_TYPE FLOAT_TYPE
#endif

#ifdef MUL_MAT_ID
layout (binding = 3) readonly buffer IDS {int data_ids[];};

shared u16vec4 row_ids[BN];

layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufB {
   B_TYPE b[];
};

uint _ne1;
layout (constant_id = 5) const uint subgroup_size = 32;
shared uvec4 ballots_sh[BLOCK_SIZE / subgroup_size];

B_TYPE decodeFuncB(const in decodeBufB bl, const in uint blockCoords[2], const in uint coordInBlock[2])
{
    const uint row_i = blockCoords[0];

    if (row_i >= _ne1) {
        return B_TYPE(0.0);
    }

    const u16vec4 row_idx = row_ids[row_i & (BN - 1)];
    B_TYPE ret = data_b[row_idx.y * p.batch_stride_b + row_idx.x * p.stride_b + blockCoords[1]];

    return ret;
}

D_TYPE perElemOpD(const in uint32_t r, const in uint32_t c, const in D_TYPE elem, const in uint32_t ir, const in uint32_t ic)
{
    uint dr = ir * BM + r;
    uint dc = ic * BN + c;

    if (dr < p.M && dc < _ne1) {
        uint row_i = c;
        const u16vec4 row_idx = row_ids[row_i];
        data_d[row_idx.y * p.batch_stride_d + row_idx.z * p.stride_d + dr] = elem;
    }
    return elem;
}

void load_row_ids(uint expert_idx, bool nei0_is_pow2, uint ic) {
    _ne1 = 0;
    uint num_elements = p.nei1 * p.nei0;
    uint nei0shift = findLSB(p.nei0);

    uint ids[16];
    uint iter = 0;

    for (uint j = 0; j < num_elements; j += BLOCK_SIZE) {
        // prefetch up to 16 elements
        if (iter == 0) {
            [[unroll]] for (uint k = 0; k < 16; ++k) {
                uint i = j + gl_LocalInvocationIndex + k*BLOCK_SIZE;
                bool in_range = i < num_elements;
                uint ii1;
                if (nei0_is_pow2) {
                    ii1 = i >> nei0shift;
                } else {
                    ii1 = i / p.nei0;
                }
                uint ii0 = i - ii1 * p.nei0;
                ids[k] = in_range ? data_ids[ii1*p.nbi1 + ii0] : 0;
            }
        }
        uint i = j + gl_LocalInvocationIndex;
        bool in_range = i < num_elements;
        uint ii1;
        if (nei0_is_pow2) {
            ii1 = i >> nei0shift;
        } else {
            ii1 = i / p.nei0;
        }
        uint ii0 = i - ii1 * p.nei0;
        uint id = ids[iter++];
        uvec4 ballot = subgroupBallot(in_range && id == expert_idx);

        ballots_sh[gl_SubgroupID] = ballot;
        barrier();

        uint subgroup_base = 0;
        uint total = 0;
        for (uint k = 0; k < gl_NumSubgroups; ++k) {
            if (k == gl_SubgroupID) {
                subgroup_base = total;
            }
            total += subgroupBallotBitCount(ballots_sh[k]);
        }
        barrier();

        uint idx = subgroup_base + subgroupBallotExclusiveBitCount(ballot);
        if (in_range && id == expert_idx && _ne1 + idx >= ic * BN && _ne1 + idx < (ic + 1) * BN) {
            row_ids[_ne1 + idx - ic * BN] = u16vec4(fastmod(ii0, p.ne11), ii1, ii0, 0);
        }
        _ne1 += total;
        iter &= 15;
        if (_ne1 >= (ic + 1) * BN) {
            break;
        }
    }
    barrier();
}
#endif

void main() {
#ifdef NEEDS_INIT_IQ_SHMEM
    init_iq_shmem(gl_WorkGroupSize);
#endif

    const uint tid = gl_LocalInvocationIndex;

#ifdef MUL_MAT_ID
    const uint expert_idx = gl_GlobalInvocationID.z;
#else
    const uint batch_idx = gl_GlobalInvocationID.z;

    const uint i13 = batch_idx / p.ne12;
    const uint i12 = batch_idx % p.ne12;

    const uint i03 = i13 / p.broadcast3;
    const uint i02 = i12 / p.broadcast2;

    const uint batch_idx_a = i03 * p.ne02 + i02;
#endif

    const uint blocks_m = (p.M + BM - 1) / BM;
    const uint ir = gl_WorkGroupID.x % blocks_m;
    const uint ik = gl_WorkGroupID.x / blocks_m;
    const uint ic = gl_WorkGroupID.y;

#ifdef MUL_MAT_ID
    if (bitCount(p.nei0) == 1) {
        load_row_ids(expert_idx, true, ic);
    } else {
        load_row_ids(expert_idx, false, ic);
    }

    // Workgroup has no work
    if (ic * BN >= _ne1) return;
#endif

#ifdef MUL_MAT_ID
    uint start_k = 0;
    const uint end_k = p.K;
#else
    uint start_k = ik * p.k_split;
    const uint end_k = min(p.K, (ik + 1) * p.k_split);
#endif

#ifdef MUL_MAT_ID
    uint pos_a = (expert_idx * p.batch_stride_a) / QUANT_K;
    uint pos_b = 0;
#else
    uint pos_a = (batch_idx_a * p.batch_stride_a) / QUANT_K;
    uint pos_b = batch_idx * p.batch_stride_b;
    uint pos_d = batch_idx * p.batch_stride_d + ik * p.batch_stride_d * gl_NumWorkGroups.z;
#endif

    uint stride_a = p.stride_a / QUANT_K;
    uint stride_b = p.stride_b;

    // Hint to the compiler that values are aligned (want 16B alignment).
    // Quants are always block-aligned, no alignment needed.
#if ALIGNED
#if QUANT_K == 1
    stride_a &= ~7;
#endif
    stride_b &= ~7;
#endif

    // Create layouts for both clamped and unclamped accesses
    tensorLayoutNV<2> tensorLayoutA = createTensorLayoutNV(2);
    tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutAClamp = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV);
    tensorLayoutNV<2> tensorLayoutB = createTensorLayoutNV(2);
    tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutBClamp = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV);
    tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutD = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV);
    tensorLayoutD = setTensorLayoutStrideNV(tensorLayoutD, p.stride_d, 1);

#if QUANT_K > 1
    tensorLayoutA = setTensorLayoutBlockSizeNV(tensorLayoutA, 1, QUANT_K);
    tensorLayoutAClamp = setTensorLayoutBlockSizeNV(tensorLayoutAClamp, 1, QUANT_K);
#endif

    // Use end_k rather than p.K as the dimension because that's what
    // we need to bound check against when using split_k.
    // Bounds check B against padded_N, but bounds check D against N.
    tensorLayoutA = setTensorLayoutDimensionNV(tensorLayoutA, p.M, end_k);
    tensorLayoutB = setTensorLayoutDimensionNV(tensorLayoutB, p.padded_N, end_k);
    tensorLayoutD = setTensorLayoutDimensionNV(tensorLayoutD, p.N, p.M);
    tensorLayoutAClamp = setTensorLayoutDimensionNV(tensorLayoutAClamp, p.M, end_k);
    tensorLayoutBClamp = setTensorLayoutDimensionNV(tensorLayoutBClamp, p.padded_N, end_k);

    tensorViewNV<2, false, 1, 0> tensorViewTranspose = createTensorViewNV(2, false, 1, 0);

#if !defined(MUL_MAT_ID)

    const uint START_ALIGN_K = 256;
    // For Qi_K (block size 256), unroll whole 256 element tiles.
    // For legacy quants (block size 32), unroll 8x.
    const uint UNROLL_K = (QUANT_K == 256) ? 256 : (BK * 8);
    const uint unroll_count = UNROLL_K / BK;

    // Detect a fast path where all loads are entirely in bounds and no clamping is required
    if ((ir + 1) * BM <= p.M && (ic + 1) * BN <= p.padded_N && (start_k % START_ALIGN_K) == 0 && (end_k % BK) == 0 &&
#if QUANT_K == 1
        (stride_a % 8) == 0 &&
#endif
        (stride_b % 8) == 0) {
        // Hint to the compiler that values are aligned (want 16B alignment)
        start_k &= ~(START_ALIGN_K-1);
        stride_b &= ~7;
#if QUANT_K == 1
        stride_a &= ~7;
#endif

        tensorLayoutA = setTensorLayoutStrideNV(tensorLayoutA, stride_a, 1);
        tensorLayoutB = setTensorLayoutStrideNV(tensorLayoutB, stride_b, 1);

        uint k_iters = (end_k - start_k) / UNROLL_K;
        uint block_k = start_k;

        // fetch scale values for a tile of quants. These will be copied into shared memory.
        // The fetches and stores are pipelined to hide the latency.
        fetch_scales(ir * BM, pos_a, stride_a, start_k, tid, true);

        if (enable_smaller_matrices && ic * BN + BNover4 >= p.N) {
            coopmat<ACC_TYPE, gl_ScopeWorkgroup, BM, BNover4, gl_MatrixUseAccumulator> sum = coopmat<ACC_TYPE, gl_ScopeWorkgroup, BM, BNover4, gl_MatrixUseAccumulator>(0.0);
            for (uint i = 0; i < k_iters; ++i) {

                store_scales(tid);
                if (block_k + UNROLL_K < end_k) {
                    fetch_scales(ir * BM, pos_a, stride_a, block_k + UNROLL_K, tid, true);
                }

                // Manually partial unroll
                [[unroll]] for (uint j = 0; j < unroll_count; ++j) {
                    coopmat<MAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a;
                    coopmat<MAT_TYPE, gl_ScopeWorkgroup, BK, BNover4, gl_MatrixUseB> mat_b;

                    coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA);
                    coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BNover4, block_k, BK), tensorViewTranspose);

                    sum = coopMatMulAdd(mat_a, mat_b, sum);
                    block_k += BK;
                }
            }
            // Do any remaining iterations that were not unrolled
            if (block_k < end_k) {
                store_scales(tid);
            }
            while (block_k < end_k) {
                coopmat<MAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a;
                coopmat<MAT_TYPE, gl_ScopeWorkgroup, BK, BNover4, gl_MatrixUseB> mat_b;

                coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA);
                coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BNover4, block_k, BK), tensorViewTranspose);

                sum = coopMatMulAdd(mat_a, mat_b, sum);
                block_k += BK;
            }
#if defined(ACC_TYPE_MAX)
            [[unroll]] for (uint i = 0; i < sum.length(); ++i) { sum[i] = clamp(sum[i], -ACC_TYPE_MAX, ACC_TYPE_MAX); }
#endif

            coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BNover4, gl_MatrixUseAccumulator> mat_d = coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BNover4, gl_MatrixUseAccumulator>(sum);

            coopMatStoreTensorNV(mat_d, data_d, pos_d, sliceTensorLayoutNV(tensorLayoutD, ic * BN, BNover4, ir * BM, BM), tensorViewTranspose);
            return;
        } else if (enable_smaller_matrices && ic * BN + BNover2 >= p.N) {
            coopmat<ACC_TYPE, gl_ScopeWorkgroup, BM, BNover2, gl_MatrixUseAccumulator> sum = coopmat<ACC_TYPE, gl_ScopeWorkgroup, BM, BNover2, gl_MatrixUseAccumulator>(0.0);
            for (uint i = 0; i < k_iters; ++i) {

                store_scales(tid);
                if (block_k + UNROLL_K < end_k) {
                    fetch_scales(ir * BM, pos_a, stride_a, block_k + UNROLL_K, tid, true);
                }

                // Manually partial unroll
                [[unroll]] for (uint j = 0; j < unroll_count; ++j) {
                    coopmat<MAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a;
                    coopmat<MAT_TYPE, gl_ScopeWorkgroup, BK, BNover2, gl_MatrixUseB> mat_b;

                    coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA);
                    coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BNover2, block_k, BK), tensorViewTranspose);

                    sum = coopMatMulAdd(mat_a, mat_b, sum);
                    block_k += BK;
                }
            }
            // Do any remaining iterations that were not unrolled
            if (block_k < end_k) {
                store_scales(tid);
            }
            while (block_k < end_k) {
                coopmat<MAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a;
                coopmat<MAT_TYPE, gl_ScopeWorkgroup, BK, BNover2, gl_MatrixUseB> mat_b;

                coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA);
                coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BNover2, block_k, BK), tensorViewTranspose);

                sum = coopMatMulAdd(mat_a, mat_b, sum);
                block_k += BK;
            }
#if defined(ACC_TYPE_MAX)
            [[unroll]] for (uint i = 0; i < sum.length(); ++i) { sum[i] = clamp(sum[i], -ACC_TYPE_MAX, ACC_TYPE_MAX); }
#endif

            coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BNover2, gl_MatrixUseAccumulator> mat_d = coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BNover2, gl_MatrixUseAccumulator>(sum);

            coopMatStoreTensorNV(mat_d, data_d, pos_d, sliceTensorLayoutNV(tensorLayoutD, ic * BN, BNover2, ir * BM, BM), tensorViewTranspose);
            return;
        } else {
            coopmat<ACC_TYPE, gl_ScopeWorkgroup, BM, BN, gl_MatrixUseAccumulator> sum = coopmat<ACC_TYPE, gl_ScopeWorkgroup, BM, BN, gl_MatrixUseAccumulator>(0.0);

            for (uint i = 0; i < k_iters; ++i) {

                store_scales(tid);
                if (block_k + UNROLL_K < end_k) {
                    fetch_scales(ir * BM, pos_a, stride_a, block_k + UNROLL_K, tid, true);
                }

                // Manually partial unroll
                [[unroll]] for (uint j = 0; j < unroll_count; ++j) {
                    coopmat<MAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a;
                    coopmat<MAT_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB> mat_b;

                    coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA);
                    coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, block_k, BK), tensorViewTranspose);

                    sum = coopMatMulAdd(mat_a, mat_b, sum);
                    block_k += BK;
                }
            }
            // Do any remaining iterations that were not unrolled
            if (block_k < end_k) {
                store_scales(tid);
            }
            while (block_k < end_k) {
                coopmat<MAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a;
                coopmat<MAT_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB> mat_b;

                coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA);
                coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, block_k, BK), tensorViewTranspose);

                sum = coopMatMulAdd(mat_a, mat_b, sum);
                block_k += BK;
            }
#if defined(ACC_TYPE_MAX)
            [[unroll]] for (uint i = 0; i < sum.length(); ++i) { sum[i] = clamp(sum[i], -ACC_TYPE_MAX, ACC_TYPE_MAX); }
#endif

            coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BN, gl_MatrixUseAccumulator> mat_d = coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BN, gl_MatrixUseAccumulator>(sum);

            coopMatStoreTensorNV(mat_d, data_d, pos_d, sliceTensorLayoutNV(tensorLayoutD, ic * BN, BN, ir * BM, BM), tensorViewTranspose);
            return;
        }
    } else
#endif // !defined(MUL_MAT_ID)
    {
        tensorLayoutA = setTensorLayoutStrideNV(tensorLayoutA, stride_a, 1);

        tensorLayoutAClamp = setTensorLayoutStrideNV(tensorLayoutAClamp, stride_a, 1);

        tensorLayoutB = setTensorLayoutStrideNV(tensorLayoutB, stride_b, 1);

        tensorLayoutBClamp = setTensorLayoutStrideNV(tensorLayoutBClamp, stride_b, 1);

        coopmat<ACC_TYPE, gl_ScopeWorkgroup, BM, BN, gl_MatrixUseAccumulator> sum;
        sum = coopmat<ACC_TYPE, gl_ScopeWorkgroup, BM, BN, gl_MatrixUseAccumulator>(0.0);

        uint k_iters = (end_k - start_k + BK - 1) / BK;

        fetch_scales(ir * BM, pos_a, stride_a, start_k, tid, false);

        [[dont_unroll]]
        for (uint block_k = start_k, i = 0; i < k_iters; block_k += BK, ++i) {

            store_scales(tid);
            if (block_k + BK < end_k) {
                fetch_scales(ir * BM, pos_a, stride_a, block_k + BK, tid, false);
            }

            if ((ir + 1) * BM <= p.M && block_k + BK <= end_k) {
                coopmat<MAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a;
                coopmat<MAT_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB> mat_b;

                coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA);
#ifdef MUL_MAT_ID
                coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, block_k, BK), tensorViewTranspose, decodeFuncB);
#else
                coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutBClamp, ic * BN, BN, block_k, BK), tensorViewTranspose);
#endif

                sum = coopMatMulAdd(mat_a, mat_b, sum);
            } else {
                coopmat<MAT_TYPE, gl_ScopeWorkgroup, BM, BK, gl_MatrixUseA> mat_a;
                coopmat<MAT_TYPE, gl_ScopeWorkgroup, BK, BN, gl_MatrixUseB> mat_b;

                coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutAClamp, ir * BM, BM, block_k, BK) DECODEFUNCA);
#ifdef MUL_MAT_ID
                coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, block_k, BK), tensorViewTranspose, decodeFuncB);
#else
                coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutBClamp, ic * BN, BN, block_k, BK), tensorViewTranspose);
#endif

                sum = coopMatMulAdd(mat_a, mat_b, sum);
            }
        }
#if defined(ACC_TYPE_MAX)
        [[unroll]] for (uint i = 0; i < sum.length(); ++i) { sum[i] = clamp(sum[i], -ACC_TYPE_MAX, ACC_TYPE_MAX); }
#endif

        // Convert from ACC_TYPE to D_TYPE
        coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BN, gl_MatrixUseAccumulator> mat_d;
        mat_d = coopmat<D_TYPE, gl_ScopeWorkgroup, BM, BN, gl_MatrixUseAccumulator>(sum);

#ifdef MUL_MAT_ID
        // Call callback to store each element, remapping row through shared memory
        coopMatPerElementNV(mat_d, mat_d, perElemOpD, ir, ic);
#else
        coopMatStoreTensorNV(mat_d, data_d, pos_d, sliceTensorLayoutNV(tensorLayoutD, ic * BN, BN, ir * BM, BM), tensorViewTranspose);
#endif
    }
}