#include "models.h"

#include "llama-kv-cache-dsa.h"

// https://huggingface.co/zai-org/GLM-5.2/blob/main/config.json#L26

const std::array<uint32_t, LLAMA_MAX_LAYERS> GLM_5_2_DEFAULT_INDEXER_TYPES = {

    1, 1,

    1, 0, 0, 0,

    1, 0, 0, 0,

    1, 0, 0, 0,

    1, 0, 0, 0,

    1, 0, 0, 0,

    1, 0, 0, 0,

    1, 0, 0, 0,

    1, 0, 0, 0,

    1, 0, 0, 0,

    1, 0, 0, 0,

    1, 0, 0, 0,

    1, 0, 0, 0,

    1, 0, 0, 0,

    1, 0, 0, 0,

    1, 0, 0, 0,

    1, 0, 0, 0,

    1, 0, 0, 0,

    1, 0, 0, 0,

    1, 0, 0, 0,

};

void llama_model_glm_dsa::load_arch_hparams(llama_model_loader & ml) {

    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,     hparams.n_ff_exp);

    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,    hparams.f_norm_rms_eps);

    ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);

    // MoE parameters

    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);

    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);

    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);

    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);

    // deepseek MLA parameters

    ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK,      hparams.n_lora_q);

    ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK,     hparams.n_lora_kv);

    ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA,   hparams.n_embd_head_k_mla_impl, false);

    ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl, false);

    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);

    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,        hparams.n_expert_shared);

    // DSA parameters

    ml.get_key(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head);

    ml.get_key(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size);

    ml.get_key(LLM_KV_ATTENTION_INDEXER_TOP_K,      hparams.indexer_top_k);

    // Expert gating function (GLM-4.5 uses sigmoid)

    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);

    if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {

        hparams.expert_gating_func =  LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;

    }

    // NextN/MTP parameters

    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);

    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_all");

    // BC for GLM 5, 5.1 (full indexers) without indexer_types metadata

    const bool is_pre_5_2 = hparams.n_ctx_train < 1048576;

    if (is_pre_5_2) {

        std::fill(hparams.is_indexer_full_impl.begin(), hparams.is_indexer_full_impl.end(), 1);

    } else {

        hparams.is_indexer_full_impl = GLM_5_2_DEFAULT_INDEXER_TYPES;

    }

    ml.get_key_or_arr(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl, hparams.n_layer(), false);

    switch (hparams.n_layer()) {

        case 78: // GGUF with NextN/MTP metadata: n_layer() excludes the nextn layer

        case 79:

            type = LLM_TYPE_744B_A40B; break;

        default: type = LLM_TYPE_UNKNOWN;

    }

}

void llama_model_glm_dsa::load_arch_tensors(llama_model_loader & ml) {

    LLAMA_LOAD_LOCALS;

    const int64_t n_expert_shared = hparams.n_expert_shared;

    // MTP-only: the GGUF carries only the NextN/MTP block(s) (user split target/draft).

    const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);

    // Trunk-only: the GGUF declares MTP layers in metadata but the actual MTP

    // tensors live in a separate file (or were stripped at conversion). Mark

    // MTP tensors NOT_REQUIRED so the trunk loads cleanly.

    const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";

    const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);

    const int trunk_flags = mtp_only   ? TENSOR_NOT_REQUIRED : 0;

    int mtp_flags         = trunk_only ? TENSOR_NOT_REQUIRED : 0;

    if (!ml.load_mtp) {

        mtp_flags |= TENSOR_SKIP;

    }

    const bool is_mla = hparams.is_mla();

    if (!is_mla) {

        throw std::runtime_error("GLM_DSA architecture requires MLA");

    }

    // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA

    const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();

    const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla();

    const int64_t n_embd_head_qk_rope = hparams.n_rot();

    const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;

    const int64_t q_lora_rank  = hparams.n_lora_q;

    const int64_t kv_lora_rank = hparams.n_lora_kv;

    const int64_t n_ff_exp        = hparams.n_ff_exp;

    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

    // output

    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);

    // try to load output.weight, if not found, use token_embd (tied embeddings)

    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);

    if (!output) {

        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);

    }

    for (int i = 0; i < n_layer_all; ++i) {

        // NextN/MTP layers (i >= n_layer) are full decoder blocks used by the

        // LLM_GRAPH_TYPE_DECODER_MTP draft head; load them like qwen35moe/step35/hy_v3.

        const int flags = (i >= n_layer) ? mtp_flags : trunk_flags;

        auto & layer = layers[i];

        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);

        layer.attn_q_a_norm  = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, flags);

        layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, flags);

        layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, flags);

        layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, flags);

        layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}, flags);

        // note: only old legacy GGUF files will have the unsplit wkv_b tensor in

        layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head}, flags);

        layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, flags);

        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, flags);

        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);

        // DSA indexer

        layer.indexer_k_norm   = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "weight", i), {hparams.indexer_head_size}, flags | TENSOR_NOT_REQUIRED);

        layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "bias",   i), {hparams.indexer_head_size}, flags | TENSOR_NOT_REQUIRED);

        layer.indexer_proj     = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ,     "weight", i), {n_embd, hparams.indexer_n_head}, flags | TENSOR_NOT_REQUIRED);

        layer.indexer_attn_k   = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K,   "weight", i), {n_embd, hparams.indexer_head_size}, flags | TENSOR_NOT_REQUIRED);

        layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * hparams.indexer_head_size}, flags | TENSOR_NOT_REQUIRED);

        if (i < (int) hparams.n_layer_dense_lead) {

            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, flags);

            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, flags);

            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, flags);

        } else {

            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags);

            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);

            if (n_expert == 0) {

                throw std::runtime_error("n_expert must be > 0");

            }

            if (n_expert_used == 0) {

                throw std::runtime_error("n_expert_used must be > 0");

            }

            // MoE branch

            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, flags);

            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, flags);

            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, flags);

            // Shared expert branch

            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);

            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, flags);

            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);

        }

        // NextN/MTP tensors - the NextN-specific wiring around the extra decoder block

        if (i >= n_layer) {

            layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);

            layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);

            layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);

            // Optional tensors

            layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);

            layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);

            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags | TENSOR_NOT_REQUIRED);

        }

    }

}

std::unique_ptr<llm_graph_context> llama_model_glm_dsa::build_arch_graph(const llm_graph_params & params) const {

    if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {

        return std::make_unique<graph_mtp>(*this, params);

    }

    return std::make_unique<graph>(*this, params);

}

llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_params & params) :

    llm_graph_context(params) {

    const bool is_mla = hparams.is_mla();

    GGML_ASSERT(is_mla);

    // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA

    const int64_t n_embd_head_k = hparams.n_embd_head_k_mla();

    const int64_t n_embd_head_v = hparams.n_embd_head_v_mla();

    GGML_UNUSED(n_embd_head_v);

    const int64_t n_embd_head_qk_rope = hparams.n_rot();

    const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope;

    const int64_t n_indexer_head = hparams.indexer_n_head;

    const int64_t n_embd_indexer_head = hparams.indexer_head_size;

    const uint32_t n_indexer_top_k = hparams.indexer_top_k;

    // the indexer head layout is [rope | nope]

    GGML_ASSERT(hparams.n_rot() <= n_embd_indexer_head);

    const uint32_t kv_lora_rank = hparams.n_lora_kv;

    // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.

    // See https://github.com/ggml-org/llama.cpp/discussions/7416 for detailed explanation.

    // And also: https://github.com/ggml-org/llama.cpp/pull/17945 [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]

    // first cancel the adjustment from llama_hparams::yarn_attn_factor_adjust to get the original attn_factor

    GGML_ASSERT(ext_factor >= 0.0f);

    const float attn_factor_org = attn_factor * (1.0f + 0.1f * logf(1.0f / freq_scale));

    // use the original attn_factor to pre-scale the kq_scale

    const float mscale   = attn_factor_org * (1.0f + 0.1f * hparams.rope_yarn_log_mul * logf(1.0f / freq_scale));

    const float kq_scale = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k));

    ggml_tensor * cur;

    ggml_tensor * inpL;

    // {n_embd, n_tokens}

    inpL = build_inp_embd(model.tok_embd);

    // inp_pos - contains the positions

    ggml_tensor * inp_pos = build_inp_pos();

    llm_graph_input_attn_k_dsa * inp_attn_dsa = build_attn_inp_k_dsa();

    ggml_tensor * inp_out_ids = build_inp_out_ids();

    // Difference vs Deepseek 3.2: shared indexer layers reuse the top_k from the previous full indexer layers

    // See https://huggingface.co/zai-org/GLM-5.2/blob/main/config.json#L30

    ggml_tensor * prev_top_k = nullptr;

    for (int il = 0; il < n_layer; ++il) {

        ggml_tensor * inpSA = inpL;

        // norm

        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);

        cb(cur, "attn_norm", il);

        // self_attention

        {

            ggml_tensor * qr = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);

            cb(qr, "qr", il);

            qr = build_norm(qr, model.layers[il].attn_q_a_norm, nullptr, LLM_NORM_RMS, il);

            cb(qr, "qr", il);

            ggml_tensor * top_k = nullptr;

            // lightning indexer

            if (hparams.is_indexer_full(il)) {

                // "full" layer

                ggml_tensor * indexer_q = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_q_b, qr);

                cb(indexer_q, "indexer_q", il);

                // {n_embd_indexer_head, n_indexer_head, n_tokens}

                indexer_q = ggml_reshape_3d(ctx0, indexer_q, n_embd_indexer_head, n_indexer_head, n_tokens);

                indexer_q = ggml_rope_ext(ctx0, indexer_q, inp_pos, nullptr, n_rot,

                                     LLAMA_ROPE_TYPE_NORM, n_ctx_orig, freq_base, freq_scale,

                                     ext_factor, attn_factor, beta_fast, beta_slow);

                cb(indexer_q, "indexer_q", il);

                ggml_tensor * indexer_k = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_k, cur);

                cb(indexer_k, "indexer_k", il);

                indexer_k = build_norm(indexer_k, model.layers[il].indexer_k_norm, model.layers[il].indexer_k_norm_b, LLM_NORM, il);

                cb(indexer_k, "indexer_k", il);

                // {n_embd_indexer_head, 1, n_tokens}

                indexer_k = ggml_reshape_3d(ctx0, indexer_k, n_embd_indexer_head, 1, n_tokens);

                indexer_k = ggml_rope_ext(ctx0, indexer_k, inp_pos, nullptr, n_rot,

                                     LLAMA_ROPE_TYPE_NORM, n_ctx_orig, freq_base, freq_scale,

                                     ext_factor, attn_factor, beta_fast, beta_slow);

                cb(indexer_k, "indexer_k", il);

                // perform Hadamard transform on indexer q and k

                indexer_q = ggml_mul_mat(ctx0, inp_attn_dsa->self_k_rot_lid, indexer_q);

                cb(indexer_q, "indexer_q", il);

                indexer_k = ggml_mul_mat(ctx0, inp_attn_dsa->self_k_rot_lid, indexer_k);

                cb(indexer_k, "indexer_k", il);

                // store indexer keys to KV cache

                const auto * mctx_lid = inp_attn_dsa->mctx->get_lid();

                const auto & k_idxs_lid = inp_attn_dsa->get_k_idxs_lid();

                ggml_build_forward_expand(gf, mctx_lid->cpy_k(ctx0, indexer_k, k_idxs_lid, il));

                // prepare indexer weights

                ggml_tensor * indexer_weights = ggml_mul_mat(ctx0, model.layers[il].indexer_proj, cur);

                cb(indexer_weights, "indexer_weights", il);

                // get cached indexer keys

                indexer_k = mctx_lid->get_k(ctx0, il);

                // split the batch into streams if needed

                const auto n_stream = indexer_k->ne[3];

                indexer_q = ggml_view_4d(ctx0, indexer_q, indexer_q->ne[0], indexer_q->ne[1], indexer_q->ne[2]/n_stream, n_stream, indexer_q->nb[1], indexer_q->nb[2], indexer_q->nb[3]/n_stream, 0);

                indexer_weights = ggml_view_4d(ctx0, indexer_weights, indexer_weights->ne[0], indexer_weights->ne[1]/n_stream, indexer_weights->ne[2], n_stream, indexer_weights->nb[1], indexer_weights->nb[2]/n_stream, indexer_weights->nb[3]/n_stream, 0);

                // pre-scale weights to avoid scaling operations on huge indexer_score tensor

                indexer_weights = ggml_scale(ctx0, indexer_weights, 1.0f / sqrtf(float(n_embd_indexer_head * n_indexer_head)));

                cb(indexer_weights, "indexer_weights", il);

                ggml_tensor * indexer_score = nullptr;

                if (cparams.fused_lid) {

                    indexer_score = ggml_lightning_indexer(ctx0, indexer_q, indexer_k, indexer_weights, inp_attn_dsa->get_kq_mask_lid());

                    cb(indexer_score, "indexer_score", il);

                    res->add_fused_node({LLM_FUSED_OP_LIGHTNING_INDEXER, indexer_score, il});

                } else {

                    // calculate indexer kq

                    indexer_q = ggml_permute(ctx0, indexer_q, 0, 2, 1, 3);

                    cb(indexer_q, "indexer_q", il);

                    indexer_k = ggml_permute(ctx0, indexer_k, 0, 2, 1, 3);

                    cb(indexer_k, "indexer_k", il);

                    ggml_tensor * indexer_kq = ggml_mul_mat(ctx0, indexer_k, indexer_q);

                    cb(indexer_kq, "indexer_kq", il);

                    // ReLU requires contiguous tensors

                    indexer_kq = ggml_cont(ctx0, ggml_permute(ctx0, indexer_kq, 2, 1, 0, 3));

                    cb(indexer_kq, "indexer_kq", il);

                    // apply ReLU

                    indexer_score = ggml_relu(ctx0, indexer_kq);

                    cb(indexer_score, "indexer_score", il);

                    // multiply scores by indexer weights

                    indexer_score = ggml_mul(ctx0, indexer_score, indexer_weights);

                    cb(indexer_score, "indexer_score", il);

                    // sum by q n_indexer_head dimension

                    indexer_score = ggml_sum_rows(ctx0, indexer_score);

                    cb(indexer_score, "indexer_score", il);

                    // permute result to match KQ mask

                    indexer_score = ggml_cont(ctx0, ggml_permute(ctx0, indexer_score, 2, 1, 0, 3));

                    cb(indexer_score, "indexer_score", il);

                    // mask indexer scores

                    ggml_tensor * indexer_kq_mask = inp_attn_dsa->get_kq_mask_lid();

                    indexer_score = ggml_add(ctx0, indexer_score, indexer_kq_mask);

                    cb(indexer_score, "indexer_score", il);

                }

                // get indices of top k indexer scores

                uint32_t n_top_k = indexer_score->ne[0] < n_indexer_top_k ? indexer_score->ne[0] : n_indexer_top_k;

                top_k = ggml_cont(ctx0, ggml_top_k(ctx0, indexer_score, n_top_k));

                prev_top_k = top_k;

                cb(top_k, "top_k", il);

            } else {

                // "shared" indexer layer - reuse top-k from a previous full layer

                GGML_ASSERT(prev_top_k != nullptr && "shared indexer layer must follow a previous full indexer layer");

                top_k = prev_top_k;

                cb(top_k, "top_k", il);

            }

            ggml_tensor * q = ggml_mul_mat(ctx0, model.layers[il].wq_b, qr);

            cb(q, "q", il);

            // split into {n_embd_head_qk_nope, n_head, n_tokens}

            ggml_tensor * q_nope =

                ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),

                             ggml_row_size(q->type, n_embd_head_k) * n_head, 0);

            cb(q_nope, "q_nope", il);

            // and {n_embd_head_qk_rope, n_head, n_tokens}

            ggml_tensor * q_pe = ggml_view_3d(

                ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),

                ggml_row_size(q->type, n_embd_head_k) * n_head, ggml_row_size(q->type, n_embd_head_qk_nope));

            cb(q_pe, "q_pe", il);

            ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);

            cb(kv_cmpr_pe, "kv_cmpr_pe", il);

            // split into {kv_lora_rank, n_tokens}

            ggml_tensor * kv_cmpr =

                ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,

                             ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);

            cb(kv_cmpr, "kv_cmpr", il);

            // and {n_embd_head_qk_rope, 1, n_tokens}

            ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,

                                              ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),

                                              ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),

                                              ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));

            cb(k_pe, "k_pe", il);

            q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,

                                 ext_factor, attn_factor, beta_fast, beta_slow);

            cb(q_pe, "q_pe", il);

            k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,

                                 ext_factor, attn_factor, beta_fast, beta_slow);

            cb(k_pe, "k_pe", il);

            kv_cmpr = build_norm(kv_cmpr, model.layers[il].attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);

            cb(kv_cmpr, "kv_cmpr", il);

            // MLA attention

            {

                // {n_embd_head_qk_nope, n_tokens, n_head}

                q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);

                cb(q_nope, "q_nope_perm", il);

                // {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head}

                ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, model.layers[il].wk_b, q_nope);

                cb(q_nope_absorbed, "q_nope_absorbed", il);

                // {kv_lora_rank, n_head, n_tokens}

                q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);

                cb(q_nope_absorbed, "q_nope_absorbed_perm", il);

                // {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens}

                // note: rope must go first for in-place context shifting in build_rope_shift()

                ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);

                cb(Qcur, "Qcur", il);

                kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);

                cb(kv_cmpr, "kv_cmpr_reshape", il);

                // {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens}

                ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);

                cb(Kcur, "Kcur", il);

                // {kv_lora_rank, 1, n_tokens}

                ggml_tensor * Vcur = kv_cmpr;

                cb(Vcur, "Vcur", il);

                // note: MLA with the absorption optimization converts into MQA (ie: GQA with 1 group)

                cur = build_attn(inp_attn_dsa,

                        model.layers[il].wo, NULL, model.layers[il].wo_s,

                        Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, top_k, kq_scale, il);

            }

        }

        // when unmasked nextn embeddings are requested, t_h_nextn must keep all rows,

        // so the early output masking has to be skipped (it is applied after the final norm instead)

        if (il == n_layer - 1 && inp_out_ids && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked)) {

            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);

            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);

        }

        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);

        cb(ffn_inp, "ffn_inp", il);

        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);

        cb(cur, "ffn_norm", il);

        if ((uint32_t) il < hparams.n_layer_dense_lead) {

            cur = build_ffn(cur,

                model.layers[il].ffn_up, NULL, model.layers[il].ffn_up_s,

                model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_s,

                model.layers[il].ffn_down, NULL, model.layers[il].ffn_down_s,

                NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);

            cb(cur, "ffn_out", il);

        } else {

            // MoE branch

            ggml_tensor * moe_out = build_moe_ffn(cur,

                model.layers[il].ffn_gate_inp,

                model.layers[il].ffn_up_exps,

                model.layers[il].ffn_gate_exps,

                model.layers[il].ffn_down_exps,

                model.layers[il].ffn_exp_probs_b,

                n_expert, n_expert_used,

                LLM_FFN_SILU, hparams.expert_weights_norm,

                hparams.expert_weights_scale,

                (llama_expert_gating_func_type) hparams.expert_gating_func,

                il,

                nullptr,

                model.layers[il].ffn_gate_up_exps,

                model.layers[il].ffn_up_exps_s,

                model.layers[il].ffn_gate_exps_s,

                model.layers[il].ffn_down_exps_s);

            cb(moe_out, "ffn_moe_out", il);

            // FFN shared expert

            {

                ggml_tensor * ffn_shexp =

                    build_ffn(cur,

                        model.layers[il].ffn_up_shexp, NULL, model.layers[il].ffn_up_shexp_s,

                        model.layers[il].ffn_gate_shexp, NULL, model.layers[il].ffn_gate_shexp_s,

                        model.layers[il].ffn_down_shexp, NULL, model.layers[il].ffn_down_shexp_s,

                        NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);

                cb(ffn_shexp, "ffn_shexp", il);

                cur = ggml_add(ctx0, moe_out, ffn_shexp);

                cb(cur, "ffn_out", il);

            }

        }

        cur = ggml_add(ctx0, cur, ffn_inp);

        cur = build_cvec(cur, il);

        cb(cur, "l_out", il);

        // input for next layer

        inpL = cur;

    }

    cur = inpL;

    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);

    // post-norm hidden state feeds the NextN/MTP draft head

    cb(cur, "h_nextn", -1);

    res->t_h_nextn = cur;

    if (cparams.embeddings_nextn && !cparams.embeddings_nextn_masked && inp_out_ids) {

        cur = ggml_get_rows(ctx0, cur, inp_out_ids);

    }

    cb(cur, "result_norm", -1);

    res->t_embd = cur;

    // lm_head

    cur = ggml_mul_mat(ctx0, model.output, cur);

    cb(cur, "result_output", -1);

    res->t_logits = cur;

    ggml_build_forward_expand(gf, cur);

}

// LLM_GRAPH_TYPE_DECODER_MTP draft head for GLM-5.2 (GLM_DSA).

// Semantics mirror the deepseek-family NextN/MTP layer:

//   enorm(embed) + hnorm(prev_hidden) -> concat(e, h) -> eh_proj ->

//   full glm_dsa decoder block (dense MLA attention + sigmoid-gated MoE FFN

//   with shared expert, exactly as the trunk deepseek2 graph builds it) ->

//   shared_head_norm (fallback output_norm) -> shared LM head.

// The DSA indexer is not used at runtime (same as the trunk graph).

llama_model_glm_dsa::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)

    : llm_graph_context(params) {

    GGML_ASSERT(hparams.n_layer_nextn > 0 && "GLM_DSA MTP requires n_layer_nextn > 0");

    GGML_ASSERT(hparams.n_layer_nextn == 1 && "GLM_DSA MTP currently only supports a single MTP block");

    GGML_ASSERT(hparams.is_mla() && "GLM_DSA MTP requires MLA");

    const int il = hparams.n_layer() + cparams.nextn_layer_offset;

    GGML_ASSERT(cparams.nextn_layer_offset >= 0 &&

                cparams.nextn_layer_offset < (int) hparams.n_layer_nextn &&

                "nextn_layer_offset out of range [0, n_layer_nextn)");

    const auto & layer = model.layers[il];

    GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");

    GGML_ASSERT(layer.nextn.enorm   && "MTP block missing nextn.enorm");

    GGML_ASSERT(layer.nextn.hnorm   && "MTP block missing nextn.hnorm");

    GGML_ASSERT(layer.ffn_gate_inp  && "MTP block missing ffn_gate_inp");

    // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA

    const int64_t n_embd_head_k = hparams.n_embd_head_k_mla();

    const int64_t n_embd_head_qk_rope = hparams.n_rot();

    const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope;

    const uint32_t kv_lora_rank = hparams.n_lora_kv;

    // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.

    // See the deepseek2 trunk graph for the detailed explanation - this must match it EXACTLY.

    GGML_ASSERT(ext_factor >= 0.0f);

    const float attn_factor_org = attn_factor * (1.0f + 0.1f * logf(1.0f / freq_scale));

    const float mscale   = attn_factor_org * (1.0f + 0.1f * hparams.rope_yarn_log_mul * logf(1.0f / freq_scale));

    const float kq_scale = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k));

    // TODO: extract in a common llm_graph_context::build_inp_embd_h()

    auto inp = std::make_unique<llm_graph_input_embd_h>(hparams.n_embd);

    inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);

    ggml_set_input(inp->tokens);

    inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens);

    ggml_set_input(inp->embd);

    ggml_tensor * tok_embd;

    if (ubatch.token) {

        ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;

        tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);

    } else {

        tok_embd = inp->embd;

    }

    cb(tok_embd, "mtp_tok_embd", il);

    inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);

    ggml_set_input(inp->h);

    ggml_set_name(inp->h, "mtp_h_input");

    ggml_tensor * h_embd = inp->h;

    res->add_input(std::move(inp));

    ggml_tensor * inp_pos     = build_inp_pos();

    ggml_tensor * inp_out_ids = build_inp_out_ids();

    // MLA with the absorption optimization uses a K-only cache (V is a view of K)

    auto * inp_attn = build_attn_inp_k();

    ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);

    cb(h_norm, "mtp_hnorm", il);

    ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il);

    cb(e_norm, "mtp_enorm", il);

    ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0);

    cb(concat, "mtp_concat", il);

    ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s);

    cb(cur, "mtp_eh_proj", il);

    ggml_tensor * inpSA = cur;

    cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);

    cb(cur, "mtp_attn_norm", il);

    // self-attention: dense MLA, same construction as the deepseek2 trunk graph

    {

        ggml_tensor * q = ggml_mul_mat(ctx0, layer.wq_a, cur);

        cb(q, "mtp_q", il);

        q = build_norm(q, layer.attn_q_a_norm, nullptr, LLM_NORM_RMS, il);

        cb(q, "mtp_q", il);

        q = ggml_mul_mat(ctx0, layer.wq_b, q);

        cb(q, "mtp_q", il);

        // split into {n_embd_head_qk_nope, n_head, n_tokens}

        ggml_tensor * q_nope =

            ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),

                         ggml_row_size(q->type, n_embd_head_k) * n_head, 0);

        cb(q_nope, "mtp_q_nope", il);

        // and {n_embd_head_qk_rope, n_head, n_tokens}

        ggml_tensor * q_pe = ggml_view_3d(

            ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),

            ggml_row_size(q->type, n_embd_head_k) * n_head, ggml_row_size(q->type, n_embd_head_qk_nope));

        cb(q_pe, "mtp_q_pe", il);

        ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur);

        cb(kv_cmpr_pe, "mtp_kv_cmpr_pe", il);

        // split into {kv_lora_rank, n_tokens}

        ggml_tensor * kv_cmpr =

            ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,

                         ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);

        cb(kv_cmpr, "mtp_kv_cmpr", il);

        // and {n_embd_head_qk_rope, 1, n_tokens}

        ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,

                                          ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),

                                          ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),

                                          ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));

        cb(k_pe, "mtp_k_pe", il);

        q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,

                             ext_factor, attn_factor, beta_fast, beta_slow);

        cb(q_pe, "mtp_q_pe", il);

        k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,

                             ext_factor, attn_factor, beta_fast, beta_slow);

        cb(k_pe, "mtp_k_pe", il);

        kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);

        cb(kv_cmpr, "mtp_kv_cmpr", il);

        // {n_embd_head_qk_nope, n_tokens, n_head}

        q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);

        cb(q_nope, "mtp_q_nope_perm", il);

        // {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head}

        ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope);

        cb(q_nope_absorbed, "mtp_q_nope_absorbed", il);

        // {kv_lora_rank, n_head, n_tokens}

        q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);

        cb(q_nope_absorbed, "mtp_q_nope_absorbed_perm", il);

        // {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens}

        // note: rope must go first for in-place context shifting in build_rope_shift()

        ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);

        cb(Qcur, "mtp_Qcur", il);

        kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);

        cb(kv_cmpr, "mtp_kv_cmpr_reshape", il);

        // {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens}

        ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);

        cb(Kcur, "mtp_Kcur", il);

        // {kv_lora_rank, 1, n_tokens}

        ggml_tensor * Vcur = kv_cmpr;

        cb(Vcur, "mtp_Vcur", il);

        // note: MLA with the absorption optimization converts into MQA (ie: GQA with 1 group)

        cur = build_attn(inp_attn,

                layer.wo, NULL, layer.wo_s,

                Qcur, Kcur, Vcur, nullptr, nullptr, layer.wv_b, kq_scale, il);

        cb(cur, "mtp_attn_out", il);

    }

    ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);

    cb(ffn_inp, "mtp_ffn_inp", il);

    cur = build_norm(ffn_inp, layer.ffn_norm, NULL, LLM_NORM_RMS, il);

    cb(cur, "mtp_ffn_norm", il);

    // MoE FFN with shared expert - same construction as the deepseek2 trunk graph

    ggml_tensor * moe_out = build_moe_ffn(cur,

        layer.ffn_gate_inp,

        layer.ffn_up_exps,

        layer.ffn_gate_exps,

        layer.ffn_down_exps,

        layer.ffn_exp_probs_b,

        n_expert, n_expert_used,

        LLM_FFN_SILU, hparams.expert_weights_norm,

        hparams.expert_weights_scale,

        (llama_expert_gating_func_type) hparams.expert_gating_func,

        il,

        nullptr,

        layer.ffn_gate_up_exps,

        layer.ffn_up_exps_s,

        layer.ffn_gate_exps_s,

        layer.ffn_down_exps_s);

    cb(moe_out, "mtp_ffn_moe_out", il);

    // FFN shared expert

    ggml_tensor * ffn_shexp =

        build_ffn(cur,

            layer.ffn_up_shexp, NULL, layer.ffn_up_shexp_s,

            layer.ffn_gate_shexp, NULL, layer.ffn_gate_shexp_s,

            layer.ffn_down_shexp, NULL, layer.ffn_down_shexp_s,

            NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);

    cb(ffn_shexp, "mtp_ffn_shexp", il);

    cur = ggml_add(ctx0, moe_out, ffn_shexp);

    cb(cur, "mtp_ffn_out", il);

    cur = ggml_add(ctx0, cur, ffn_inp);

    cb(cur, "mtp_post_ffn", il);

    // shared_head_norm applied after the decoder block, before the shared LM head.

    // The post-norm hidden state seeds the next MTP step.

    ggml_tensor * head_norm_w = layer.nextn.shared_head_norm

            ? layer.nextn.shared_head_norm

            : model.output_norm;

    GGML_ASSERT(head_norm_w && "GLM_DSA MTP: missing both nextn.shared_head_norm and output_norm");

    cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1);

    cb(cur, "h_nextn", -1);

    res->t_h_nextn = cur;

    cur = ggml_get_rows(ctx0, cur, inp_out_ids);

    cb(cur, "mtp_shared_head_norm", -1);

    ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output;

    ggml_tensor * head_s = layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : model.output_s;

    GGML_ASSERT(head_w && "GLM_DSA MTP: missing LM head (nextn.shared_head_head or model.output)");

    cur = build_lora_mm(head_w, cur, head_s);

    cb(cur, "result_output", -1);

    res->t_logits = cur;

    ggml_build_forward_expand(gf, cur);

}