#include "models.h"

void llama_model_glm4_moe::load_arch_hparams(llama_model_loader & ml) {
    ml.get_key_or_arr(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_arr, hparams.n_layer_all);
    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);

    // 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;
    }

    switch (hparams.n_layer()) {
        case 46: type = LLM_TYPE_106B_A12B; break; // GLM-4.5-Air
        case 48: type = LLM_TYPE_102B_A12B; break; // Solar Open
        case 92: type = LLM_TYPE_355B_A32B; break; // GLM-4.5
        default: type = LLM_TYPE_UNKNOWN;
    }
}

void llama_model_glm4_moe::load_arch_tensors(llama_model_loader & ml) {
    LLAMA_LOAD_LOCALS;
    const int64_t n_expert_shared = hparams.n_expert_shared;

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

    GGML_ASSERT(hparams.n_expert > 0 && "n_expert must be > 0 for GLM4_MOE MoE layers");
    GGML_ASSERT(hparams.n_expert_used() > 0 && "n_expert_used must be > 0 for GLM4_MOE MoE layers");

    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);
    }

    for (int i = 0; i < n_layer_all; ++i) {
        auto & layer = layers[i];
        const int flags = i < n_layer ? trunk_flags : mtp_flags;

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

        // GLM-style attention with bias terms
        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, flags);

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

        // K/Q norm tensors (optional for GLM-4.5 355B variant)
        layer.attn_q_norm = create_tensor(
            tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, TENSOR_NOT_REQUIRED | flags);
        layer.attn_k_norm = create_tensor(
            tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, TENSOR_NOT_REQUIRED | flags);

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

        // Check if this layer uses MoE or dense FFN based on n_layer_dense_lead
        // GLM 4.5 uses hybrid architecture: layer 0 is dense, layers 1+ are MoE
        const bool use_moe = (static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead);

        if (use_moe) {
            // MoE layers
            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 }, flags);

            // MoE branch
            const int64_t n_ff_exp = hparams.n_ff_exp() ? hparams.n_ff_exp() : n_ff / n_expert_used;

            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
            if (n_expert_shared > 0) {
                const int64_t n_ff_shexp = n_ff_exp * n_expert_shared;
                layer.ffn_gate_shexp = create_tensor(
                    tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
                layer.ffn_down_shexp = create_tensor(
                    tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shexp, n_embd }, flags);
                layer.ffn_up_shexp = create_tensor(
                    tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
            }
        } else {
            // Dense layers (first k layers) - GLM uses separate gate/up projections
            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);
        }

        // NextN/MTP tensors
        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 }, TENSOR_NOT_REQUIRED | flags);
            layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags);
            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, TENSOR_NOT_REQUIRED | flags);
        }
    }
}

std::unique_ptr<llm_graph_context> llama_model_glm4_moe::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_glm4_moe::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)
    : llm_graph_context(params) {
    GGML_ASSERT(hparams.n_layer_nextn > 0 && "GLM4_MOE MTP requires n_layer_nextn > 0");
    GGML_ASSERT(hparams.n_layer_nextn == 1 && "GLM4_MOE 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() + 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");

    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();

    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, 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);

    auto [Qcur, Kcur, Vcur] = build_qkv(layer, cur,
            n_embd_head, n_head, n_head_kv, il);

    if (layer.attn_q_norm) {
        Qcur = build_norm(Qcur, layer.attn_q_norm, nullptr, LLM_NORM_RMS, il);
        cb(Qcur, "mtp_Qcur_normed", il);
    }
    if (layer.attn_k_norm) {
        Kcur = build_norm(Kcur, layer.attn_k_norm, nullptr, LLM_NORM_RMS, il);
        cb(Kcur, "mtp_Kcur_normed", il);
    }

    Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot,
            rope_type, n_ctx_orig, freq_base, freq_scale,
            ext_factor, attn_factor, beta_fast, beta_slow);

    Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot,
            rope_type, n_ctx_orig, freq_base, freq_scale,
            ext_factor, attn_factor, beta_fast, beta_slow);

    cb(Qcur, "mtp_Qcur", il);
    cb(Kcur, "mtp_Kcur", il);
    cb(Vcur, "mtp_Vcur", il);

    cur = build_attn(inp_attn,
            layer.wo, nullptr, layer.wo_s,
            Qcur, Kcur, Vcur, nullptr, nullptr, nullptr,
            1.0f / sqrtf(float(n_embd_head)), 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.attn_post_norm, nullptr, LLM_NORM_RMS, il);
    cb(cur, "mtp_post_attn_norm", il);

    ggml_tensor * routed_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);
    cb(routed_out, "mtp_ffn_moe_out", il);

    ggml_tensor * shared_out = build_ffn(cur,
            layer.ffn_up_shexp,   nullptr, nullptr,
            layer.ffn_gate_shexp, nullptr, nullptr,
            layer.ffn_down_shexp, nullptr, nullptr,
            nullptr,
            LLM_FFN_SILU, LLM_FFN_PAR, il);
    cb(shared_out, "mtp_ffn_shexp_out", il);

    cur = ggml_add(ctx0, routed_out, shared_out);
    cb(cur, "mtp_ffn_out", il);

    cur = ggml_add(ctx0, cur, ffn_inp);
    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 && "GLM4_MOE 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;

    if (inp_out_ids) {
        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 && "GLM4_MOE 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);
}

llama_model_glm4_moe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
    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);

    bool use_mrope = hparams.use_mrope();
    if (ubatch.embd && !use_mrope) {
        // unfortunately, we need to forcefully stop here, to avoid users complaining about wrong results
        GGML_ABORT("This GGUF does not support multimodal. Please reconvert it.");
    }

    // inp_pos - contains the positions
    ggml_tensor * inp_pos = build_inp_pos();

    auto * inp_attn = build_attn_inp_kv();

    ggml_tensor * inp_out_ids = build_inp_out_ids();

    // NextN layers are processed by graph_mtp.
    for (int il = 0; il < n_layer; ++il) {
        ggml_tensor * inpSA = inpL;

        // Pre-attention norm
        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
        cb(cur, "attn_norm", il);

        // self-attention
        {
            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
                    n_embd_head, n_head, n_head_kv, il);

            // Apply Q/K norm if available (GLM-4.5 355B variant)
            if (model.layers[il].attn_q_norm) {
                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
                cb(Qcur, "Qcur_normed", il);
            }
            if (model.layers[il].attn_k_norm) {
                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
                cb(Kcur, "Kcur_normed", il);
            }

            if (use_mrope) {
                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);
            } else {
                // Normal RoPE
                Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot,
                                    rope_type, n_ctx_orig, freq_base, freq_scale,
                                    ext_factor, attn_factor, beta_fast, beta_slow);

                Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot,
                                    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);

            cur = build_attn(inp_attn,
                    model.layers[il].wo, NULL, model.layers[il].wo_s,
                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
        }
        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);

        // Post-attention norm
        cur = build_norm(ffn_inp, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il);
        cb(cur, "post_attn_norm", il);

        // Check if this is a dense layer (n_layer_dense_lead=1, so layer 0 is dense)
        if (static_cast<uint32_t>(il) < hparams.n_layer_dense_lead) {
            // Dense FFN layer
            cur = build_ffn(cur,
                    model.layers[il].ffn_up,   NULL, NULL,
                    model.layers[il].ffn_gate, NULL, NULL,
                    model.layers[il].ffn_down, NULL, NULL,
                    NULL,
                    LLM_FFN_SILU, LLM_FFN_PAR, il);
            cb(cur, "ffn_out", il);
        } else {
            // Process routed experts using existing MoE infrastructure
            ggml_tensor * routed_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);
            cb(routed_out, "ffn_moe_out", il);

            // Process shared expert on original input
            ggml_tensor * shared_out = build_ffn(cur,
                    model.layers[il].ffn_up_shexp,   NULL, NULL,
                    model.layers[il].ffn_gate_shexp, NULL, NULL,
                    model.layers[il].ffn_down_shexp, NULL, NULL,
                    NULL,
                    LLM_FFN_SILU, LLM_FFN_PAR, il);
            cb(shared_out, "ffn_shexp_out", il);

            // Final output: routed_output + shared_output
            cur = ggml_add(ctx0, routed_out, shared_out);
            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);

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

    cb(cur, "result_output", -1);
    res->t_logits = cur;

    ggml_build_forward_expand(gf, cur);
}