#include "models.h"

#include "llama-memory-recurrent.h"

void llama_model_qwen35moe::load_arch_hparams(llama_model_loader & ml) {

    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp, false);

    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);

    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, true);

    // Load linear attention (gated delta net) parameters

    ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);

    ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);

    ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);

    ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);

    ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);

    // NextN/MTP (Qwen3.5/3.6): extra decoder block appended beyond the main stack

    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_impl");

    // Mark recurrent layers (linear attention layers). MTP layers are dense

    // attention-only and must be flagged non-recurrent.

    if (!ml.get_key_or_arr(LLM_KV_ATTENTION_RECURRENT_LAYERS, hparams.is_recr_impl, hparams.n_layer_all, false)) {

        uint32_t full_attn_interval = 4;

        ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);

        for (uint32_t i = 0; i < hparams.n_layer_all; ++i) {

            hparams.is_recr_impl[i] = (i < hparams.n_layer()) && ((i + 1) % full_attn_interval != 0);

        }

    }

    switch (hparams.n_layer()) {

        case 40: type = LLM_TYPE_35B_A3B; break;

        case 48: type = LLM_TYPE_122B_A10B; break;

        case 60: type = LLM_TYPE_397B_A17B; break;

        default: type = LLM_TYPE_UNKNOWN;

    }

}

void llama_model_qwen35moe::load_arch_tensors(llama_model_loader & ml) {

    LLAMA_LOAD_LOCALS;

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

    const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;

    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);

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

    // if output is NULL, init from the input tok embed

    if (output == NULL) {

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

    }

    auto load_block_trunk = [&](int il, int flags) {

        auto & layer = layers[il];

        const int64_t n_ff_exp   = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;

        const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;

        // Calculate dimensions from hyperparameters

        const int64_t head_k_dim = hparams.ssm_d_state;

        const int64_t head_v_dim = hparams.ssm_d_state;

        const int64_t n_k_heads  = hparams.ssm_n_group;

        const int64_t n_v_heads  = hparams.ssm_dt_rank;

        const int64_t key_dim    = head_k_dim * n_k_heads;

        const int64_t value_dim  = head_v_dim * n_v_heads;

        const int64_t conv_dim   = key_dim * 2 + value_dim;

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

        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", il), { n_embd }, flags);

        if (!hparams.is_recr(il)) {

            // Attention layers

            create_tensor_qkv(layer, il, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, flags);

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

            // Q/K normalization for attention layers

            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", il), { n_embd_head_k }, flags);

            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", il), { n_embd_head_k }, flags);

        } else {

            // Linear attention (gated delta net) specific tensors

            // Create tensors with calculated dimensions

            layer.wqkv           = create_tensor(tn(LLM_TENSOR_ATTN_QKV,       "weight", il), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED);

            layer.wqkv_gate      = create_tensor(tn(LLM_TENSOR_ATTN_GATE,      "weight", il), { n_embd, value_dim }, TENSOR_NOT_REQUIRED);

            layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", il), { hparams.ssm_d_conv, conv_dim }, flags);

            layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   il), { hparams.ssm_dt_rank }, flags);

            layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN,             il), { hparams.ssm_dt_rank }, flags);

            layer.ssm_beta       = create_tensor(tn(LLM_TENSOR_SSM_BETA,       "weight", il), { n_embd, n_v_heads }, flags);

            layer.ssm_alpha      = create_tensor(tn(LLM_TENSOR_SSM_ALPHA,      "weight", il), { n_embd, n_v_heads }, flags);

            layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", il), { head_v_dim }, flags);

            layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", il), { value_dim, n_embd }, flags);

        }

        // Routed experts

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

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

        create_tensor_gate_up_exps(layer, il, n_embd, n_ff_exp, n_expert, flags);

        // Shared experts

        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", il), { n_embd }, flags);

        layer.ffn_gate_shexp     = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP,     "weight", il), { n_embd, n_ff_shexp }, flags);

        layer.ffn_up_shexp       = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,       "weight", il), { n_embd, n_ff_shexp }, flags);

        layer.ffn_down_shexp     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP,     "weight", il), { n_ff_shexp, n_embd }, flags);

    };

    auto load_block_mtp = [&](int il) {

        auto & layer = layers[il];

        const int64_t n_ff_exp   = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;

        const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;

        // MTP block looks like a full-attention Qwen3.5 decoder block with MoE FFN.

        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", il), { n_embd }, 0);

        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", il), { n_embd }, 0);

        create_tensor_qkv(layer, il, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, 0);

        layer.wo          = create_tensor(tn(LLM_TENSOR_ATTN_OUT,    "weight", il), { n_embd_head_k * n_head, n_embd }, 0);

        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", il), { n_embd_head_k }, 0);

        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", il), { n_embd_head_k }, 0);

        // Routed experts

        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", il), { n_embd, n_expert }, 0);

        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", il), { n_ff_exp, n_embd, n_expert }, 0);

        create_tensor_gate_up_exps(layer, il, n_embd, n_ff_exp, n_expert, 0);

        // Shared experts

        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", il), { n_embd }, 0);

        layer.ffn_gate_shexp     = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP,     "weight", il), { n_embd, n_ff_shexp }, 0);

        layer.ffn_up_shexp       = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,       "weight", il), { n_embd, n_ff_shexp }, 0);

        layer.ffn_down_shexp     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP,     "weight", il), { n_ff_shexp, n_embd }, 0);

        // NextN-specific tensors that define the MTP block.

        layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ,          "weight", il), { 2 * n_embd, n_embd }, 0);

        layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM,            "weight", il), { n_embd },              0);

        layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM,            "weight", il), { n_embd },              0);

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

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

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

    };

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

        load_block_trunk(i, trunk_flags);

    }

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

        load_block_mtp(i);

    }

}

std::unique_ptr<llm_graph_context> llama_model_qwen35moe::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_qwen35moe::graph::graph(const llama_model & model, const llm_graph_params & params) :

    llm_build_delta_net_base(params), model(model) {

    const int64_t n_embd_head = hparams.n_embd_head_v();

    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());

    int sections[4];

    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);

    ggml_tensor * cur;

    ggml_tensor * inpL;

    inpL = build_inp_embd(model.tok_embd);

    cb(inpL, "model.input_embed", -1);

    auto * inp = build_inp_mem_hybrid();

    ggml_tensor * inp_pos     = build_inp_pos();

    ggml_tensor * inp_out_ids = build_inp_out_ids();

    // MTP/NextN layers are loaded as extra decoder blocks but not executed in the main pass.

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

        ggml_tensor * inpSA = inpL;

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

        cb(cur, "attn_norm", il);

        ggml_build_forward_expand(gf, cur);

        // Determine layer type and build appropriate attention mechanism

        if (hparams.is_recr(il)) {

            // Linear attention layer (gated delta net)

            cur = build_layer_attn_linear(inp->get_recr(), cur, il);

        } else {

            // Full attention layer

            cur = build_layer_attn(inp->get_attn(), cur, inp_pos, sections, il);

        }

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

            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);

            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);

        }

        // Residual connection

        cur = ggml_add(ctx0, cur, inpSA);

        cb(cur, "attn_residual", il);

        // Save the tensor before post-attention norm for residual connection

        ggml_tensor * ffn_residual = cur;

        // Post-attention norm

        ggml_tensor * attn_post_norm = build_norm(cur, model.layers[il].attn_post_norm, nullptr, LLM_NORM_RMS, il);

        cb(attn_post_norm, "attn_post_norm", il);

        // MOE FFN layer

        cur = build_layer_ffn(attn_post_norm, il);

        cb(cur, "ffn_out", il);

        // Residual connection for FFN - add to the tensor from before post_attention_layernorm

        cur = ggml_add(ctx0, cur, ffn_residual);

        cb(cur, "post_moe", il);

        cur = build_cvec(cur, il);

        cb(cur, "l_out", il);

        // Input for next layer

        inpL = cur;

    }

    cur = inpL;

    // post-norm hidden state feeds both the LM head and the MTP seed below

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

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

    res->t_h_nextn = cur;

    if (!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 = build_lora_mm(model.output, cur, model.output_s);

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

    res->t_logits = cur;

    ggml_build_forward_expand(gf, cur);

}

std::pair<ggml_tensor *, ggml_tensor *> llama_model_qwen35moe::graph::build_qkvz(

                ggml_tensor * input,

                        int   il) {

    const int64_t n_seqs       = ubatch.n_seqs;

    const int64_t n_seq_tokens = ubatch.n_seq_tokens;

    ggml_tensor * qkv_mixed = build_lora_mm(model.layers[il].wqkv, input, model.layers[il].wqkv_s);

    qkv_mixed = ggml_reshape_3d(ctx0, qkv_mixed, qkv_mixed->ne[0], n_seq_tokens, n_seqs);

    cb(qkv_mixed, "linear_attn_qkv_mixed", il);

    ggml_tensor * z = build_lora_mm(model.layers[il].wqkv_gate, input, model.layers[il].wqkv_gate_s);

    cb(z, "z", il);

    return { qkv_mixed, z };

}

ggml_tensor * llama_model_qwen35moe::graph::build_norm_gated(

        ggml_tensor * input,

        ggml_tensor * weights,

        ggml_tensor * gate,

        int           layer) {

    ggml_tensor * normalized = build_norm(input, weights, nullptr, LLM_NORM_RMS, layer);

    ggml_tensor * gated_silu = ggml_silu(ctx0, gate);

    return ggml_mul(ctx0, normalized, gated_silu);

}

ggml_tensor * llama_model_qwen35moe::graph::build_layer_attn(

        llm_graph_input_attn_kv * inp,

        ggml_tensor *             cur,

        ggml_tensor *             inp_pos,

        int *                     sections,

        int                       il) {

    const int64_t n_embd_head = hparams.n_embd_head_v();

    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());

    // Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention

    // Qwen3Next uses a single Q projection that outputs query + gate

    ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s); // [ (n_embd_head * 2) * n_head, n_tokens ]

    cb(Qcur_full, "Qcur_full", il);

    ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,

        ggml_element_size(Qcur_full) * n_embd_head * 2,

        ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head, 0);

    cb(Qcur, "Qcur_reshaped", il);

    // Apply Q normalization

    Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);

    cb(Qcur, "Qcur_normed", il);

    ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s);

    cb(Kcur, "Kcur", il);

    ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s);

    cb(Vcur, "Vcur", il);

    // Apply K normalization

    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);

    Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);

    cb(Kcur, "Kcur_normed", il);

    ggml_tensor * gate = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,

        ggml_element_size(Qcur_full) * n_embd_head * 2,

        ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head,

        ggml_element_size(Qcur_full) * n_embd_head);

    gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);

    cb(gate, "gate_reshaped", il);

    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);

    // Apply IMRoPE

    Qcur = ggml_rope_multi(

            ctx0, Qcur, inp_pos, nullptr,

            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,

            ext_factor, attn_factor, beta_fast, beta_slow

            );

    Kcur = ggml_rope_multi(

            ctx0, Kcur, inp_pos, nullptr,

            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,

            ext_factor, attn_factor, beta_fast, beta_slow

            );

    cb(Qcur, "Qcur", il);

    cb(Kcur, "Kcur", il);

    cb(Vcur, "Vcur", il);

    // Attention computation

    const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;

    cur = build_attn(inp,

                nullptr, nullptr, nullptr,

                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);

    cb(cur, "attn_pregate", il);

    ggml_tensor * gate_sigmoid = ggml_sigmoid(ctx0, gate);

    cb(gate_sigmoid, "gate_sigmoid", il);

    cur = ggml_mul(ctx0, cur, gate_sigmoid);

    cb(cur, "attn_gated", il);

    cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s);

    cb(cur, "attn_output", il);

    return cur;

}

ggml_tensor * llama_model_qwen35moe::graph::build_layer_attn_linear(

        llm_graph_input_rs * inp,

        ggml_tensor *        cur,

        int                  il) {

    const auto * mctx_cur = inp->mctx;

    const int64_t d_inner      = hparams.ssm_d_inner;

    const int64_t n_seqs       = ubatch.n_seqs;

    const int64_t head_k_dim   = hparams.ssm_d_state;

    const int64_t num_k_heads  = hparams.ssm_n_group;

    const int64_t num_v_heads  = hparams.ssm_dt_rank;

    const int64_t head_v_dim   = d_inner / num_v_heads;

    const int64_t n_seq_tokens = ubatch.n_seq_tokens;

    GGML_ASSERT(n_seqs != 0);

    GGML_ASSERT(ubatch.equal_seqs());

    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);

    // Input projections

    auto qkvz = build_qkvz(cur, il);

    ggml_tensor * qkv_mixed = qkvz.first;

    ggml_tensor * z         = qkvz.second;

    ggml_tensor * beta = build_lora_mm(model.layers[il].ssm_beta, cur, model.layers[il].ssm_beta_s);

    beta = ggml_reshape_4d(ctx0, beta, 1, num_v_heads, n_seq_tokens, n_seqs);

    cb(beta, "beta", il);

    beta = ggml_sigmoid(ctx0, beta);

    cb(beta, "beta_sigmoid", il);

    ggml_tensor * alpha = build_lora_mm(model.layers[il].ssm_alpha, cur, model.layers[il].ssm_alpha_s);

    alpha = ggml_reshape_3d(ctx0, alpha, num_v_heads, n_seq_tokens, n_seqs);

    cb(alpha, "alpha", il);

    ggml_tensor * alpha_biased   = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);

    ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);

    cb(alpha_softplus, "a_softplus", il);

    ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a);  // -A_log.exp() * softplus

    cb(gate, "gate", il);

    gate = ggml_reshape_4d(ctx0, gate, 1, num_v_heads, n_seq_tokens, n_seqs);

    ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);

    ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);

    ggml_tensor * conv_kernel      = model.layers[il].ssm_conv1d;

    const int64_t conv_kernel_size = conv_kernel->ne[0];

    const int64_t conv_channels    = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;

    ggml_tensor * conv_input = build_conv_state(inp, conv_states_all, qkv_mixed, conv_kernel_size, conv_channels, il);

    ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);

    state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim, num_v_heads, n_seqs);

    cb(state, "state_predelta", il);

    ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel);

    cb(conv_output_proper, "conv_output_raw", il);

    ggml_tensor * conv_output_silu = ggml_silu(ctx0, conv_output_proper);

    cb(conv_output_silu, "conv_output_silu", il);

    ggml_tensor * conv_qkv_mix = conv_output_silu;

    // Calculate the total conv dimension

    int64_t qkv_dim = head_k_dim * num_k_heads * 2 + head_v_dim * num_v_heads;

    int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim);

    // Extract the convolved Q, K, V from conv_output

    ggml_tensor * q_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_k_dim, num_k_heads, n_seq_tokens, n_seqs,

            ggml_row_size(conv_qkv_mix->type, head_k_dim),

            nb1_qkv,

            nb1_qkv * n_seq_tokens,

            0);

    ggml_tensor * k_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_k_dim, num_k_heads, n_seq_tokens, n_seqs,

            ggml_row_size(conv_qkv_mix->type, head_k_dim),

            nb1_qkv,

            nb1_qkv * n_seq_tokens,

            head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));

    ggml_tensor * v_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_v_dim, num_v_heads, n_seq_tokens, n_seqs,

            ggml_row_size(conv_qkv_mix->type, head_v_dim),

            nb1_qkv,

            nb1_qkv * n_seq_tokens,

            ggml_row_size(conv_qkv_mix->type, 2 * head_k_dim * num_k_heads));

    cb(q_conv, "q_conv", il);

    cb(k_conv, "k_conv", il);

    cb(v_conv, "v_conv", il);

    const float eps_norm = hparams.f_norm_rms_eps;

    q_conv = ggml_l2_norm(ctx0, q_conv, eps_norm);

    k_conv = ggml_l2_norm(ctx0, k_conv, eps_norm);

    //q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);

    //k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);

    //v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);

    // if head keys and value keys are different, repeat to force tensors into matching shapes

    // note: need explicit repeat only if we are not using the fused GDN.

    if (num_k_heads != num_v_heads && (!cparams.fused_gdn_ar || !cparams.fused_gdn_ch)) {

        GGML_ASSERT(num_v_heads % num_k_heads == 0);

        q_conv = ggml_repeat_4d(ctx0, q_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);

        k_conv = ggml_repeat_4d(ctx0, k_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);

    }

    cb(q_conv, "q_conv_predelta", il);

    cb(k_conv, "k_conv_predelta", il);

    cb(v_conv, "v_conv_predelta", il);

    ggml_tensor * output = build_recurrent_attn(inp, ssm_states_all, q_conv, k_conv, v_conv, gate, beta, state, il);

    // z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]

    ggml_tensor * z_2d = ggml_reshape_4d(ctx0, z, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);

    // Apply gated normalization: self.norm(core_attn_out, z)

    ggml_tensor * attn_out_norm = build_norm_gated(output, model.layers[il].ssm_norm, z_2d, il);

    // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim]

    ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);

    cb(final_output, "final_output", il);

    // Output projection

    cur = build_lora_mm(model.layers[il].ssm_out, final_output, model.layers[il].ssm_out_s);

    cb(cur, "linear_attn_out", il);

    // Reshape back to original dimensions

    cur = ggml_reshape_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);

    return cur;

}

ggml_tensor * llama_model_qwen35moe::graph::build_layer_ffn(ggml_tensor * cur, const int il) {

    // Check if this is an MoE layer

    GGML_ASSERT(model.layers[il].ffn_gate_inp != nullptr);

    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,

            nullptr,

            n_expert, n_expert_used,

            LLM_FFN_SILU, true,

            hparams.expert_weights_scale,

            LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, 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);

    // Add shared experts if present - following Qwen3Next reference implementation

    if (model.layers[il].ffn_up_shexp != nullptr) {

        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);

        // Apply shared expert gating as in the reference implementation

        // The shared expert has its own gate that is sigmoided

        // Note: ffn_gate_inp_shexp is the shared expert gate (outputs 1 value per token)

        ggml_tensor * shared_gate = build_lora_mm(model.layers[il].ffn_gate_inp_shexp, cur);

        cb(shared_gate, "shared_expert_gate", il);

        // Apply sigmoid to the gate

        shared_gate = ggml_sigmoid(ctx0, shared_gate);

        cb(shared_gate, "shared_expert_gate_sigmoid", il);

        // Apply the gate to the shared expert output

        ffn_shexp = ggml_mul(ctx0, ffn_shexp, shared_gate);

        cb(ffn_shexp, "ffn_shexp_gated", il);

        cur = ggml_add(ctx0, moe_out, ffn_shexp);

        cb(cur, "ffn_out", il);

    } else {

        cur = moe_out;

    }

    return cur;

}

// LLM_GRAPH_TYPE_DECODER_MTP draft head for Qwen3.5/3.6 MoE

llama_model_qwen35moe::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)

    : llm_graph_context(params) {

    GGML_ASSERT(hparams.n_layer_nextn > 0 && "QWEN35MOE MTP requires n_layer_nextn > 0");

    GGML_ASSERT(hparams.n_layer_nextn == 1 && "QWEN35MOE MTP currently only supports a single MTP block");

    const int64_t n_embd_head = hparams.n_embd_head_v();

    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());

    const int il = hparams.n_layer();

    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");

    int sections[4];

    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);

    // 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);

    // TODO: make static using `ggml_build_forward_select()`

    //       see llm_graph_context::build_inp_embd() for reference

    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();

    auto * inp_attn = build_attn_inp_kv();

    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);

    ggml_tensor * Qcur_full = build_lora_mm(layer.wq, cur, layer.wq_s);

    cb(Qcur_full, "mtp_Qcur_full", il);

    ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full,

            n_embd_head, n_head, n_tokens,

            ggml_element_size(Qcur_full) * n_embd_head * 2,

            ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head,

            0);

    Qcur = build_norm(Qcur, layer.attn_q_norm, nullptr, LLM_NORM_RMS, il);

    cb(Qcur, "mtp_Qcur_normed", il);

    ggml_tensor * gate = ggml_view_3d(ctx0, Qcur_full,

            n_embd_head, n_head, n_tokens,

            ggml_element_size(Qcur_full) * n_embd_head * 2,

            ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head,

            ggml_element_size(Qcur_full) * n_embd_head);

    gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);

    cb(gate, "mtp_gate", il);

    ggml_tensor * Kcur = build_lora_mm(layer.wk, cur, layer.wk_s);

    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);

    Kcur = build_norm(Kcur, layer.attn_k_norm, nullptr, LLM_NORM_RMS, il);

    cb(Kcur, "mtp_Kcur_normed", il);

    ggml_tensor * Vcur = build_lora_mm(layer.wv, cur, layer.wv_s);

    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);

    cb(Vcur, "mtp_Vcur", il);

    Qcur = ggml_rope_multi(ctx0, Qcur, inp_pos, nullptr,

            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,

            ext_factor, attn_factor, beta_fast, beta_slow);

    Kcur = ggml_rope_multi(ctx0, Kcur, inp_pos, nullptr,

            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,

            ext_factor, attn_factor, beta_fast, beta_slow);

    const float kq_scale = hparams.f_attention_scale == 0.0f

            ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;

    cur = build_attn(inp_attn,

            nullptr, nullptr, nullptr,

            Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);

    cb(cur, "mtp_attn_pregate", il);

    cur = ggml_mul(ctx0, cur, ggml_sigmoid(ctx0, gate));

    cur = build_lora_mm(layer.wo, cur, layer.wo_s);

    cb(cur, "mtp_attn_out", il);

    cur = ggml_add(ctx0, cur, inpSA);

    cb(cur, "mtp_attn_residual", il);

    ggml_tensor * ffn_residual = cur;

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

    cb(cur, "mtp_attn_post_norm", il);

    // MoE FFN — routed experts plus gated shared expert (mirrors qwen35moe).

    ggml_tensor * moe_out =

        build_moe_ffn(cur,

            layer.ffn_gate_inp,

            layer.ffn_up_exps,

            layer.ffn_gate_exps,

            layer.ffn_down_exps,

            nullptr,

            n_expert, n_expert_used,

            LLM_FFN_SILU, true,

            hparams.expert_weights_scale,

            LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, 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);

    if (layer.ffn_up_shexp != nullptr) {

        ggml_tensor * ffn_shexp =

            build_ffn(cur,

                layer.ffn_up_shexp,   nullptr, layer.ffn_up_shexp_s,

                layer.ffn_gate_shexp, nullptr, layer.ffn_gate_shexp_s,

                layer.ffn_down_shexp, nullptr, layer.ffn_down_shexp_s,

                nullptr,

                LLM_FFN_SILU, LLM_FFN_PAR, il);

        cb(ffn_shexp, "mtp_ffn_shexp", il);

        ggml_tensor * shared_gate = build_lora_mm(layer.ffn_gate_inp_shexp, cur);

        shared_gate = ggml_sigmoid(ctx0, shared_gate);

        cb(shared_gate, "mtp_shared_expert_gate_sigmoid", il);

        ffn_shexp = ggml_mul(ctx0, ffn_shexp, shared_gate);

        cb(ffn_shexp, "mtp_ffn_shexp_gated", il);

        cur = ggml_add(ctx0, moe_out, ffn_shexp);

    } else {

        cur = moe_out;

    }

    cb(cur, "mtp_ffn_out", il);

    cur = ggml_add(ctx0, cur, ffn_residual);

    cb(cur, "mtp_post_ffn", il);

    ggml_tensor * head_norm_w = layer.nextn.shared_head_norm

            ? layer.nextn.shared_head_norm

            : model.output_norm;

    GGML_ASSERT(head_norm_w && "QWEN35MOE 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 && "QWEN35MOE 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);

}