#include "models.h"

llm_build_glm4_moe::llm_build_glm4_moe(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);

    ggml_tensor * cur;

    ggml_tensor * inpL;

    inpL = build_inp_embd(model.tok_embd);

    // 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();

    // Only process up to last layer (skip final NextN layer)

    // Final layer tensors are loaded but not processed in forward pass

    const int n_transformer_layers = n_layer - hparams.nextn_predict_layers;

    for (int il = 0; il < n_transformer_layers; ++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

        {

            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);

            if (model.layers[il].bq) {

                Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);

            }

            cb(Qcur, "Qcur", il);

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

            if (model.layers[il].bk) {

                Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);

            }

            cb(Kcur, "Kcur", il);

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

            if (model.layers[il].bv) {

                Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);

            }

            cb(Vcur, "Vcur", il);

            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);

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

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

            // 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);

            }

            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,

                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);

        }

        if (il == n_transformer_layers - 1 && inp_out_ids) {

            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,

                    true, 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, "result_norm", -1);

    res->t_embd = cur;

    // lm_head

    cur = build_lora_mm(model.output, cur);

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

    res->t_logits = cur;

    ggml_build_forward_expand(gf, cur);

}