#include "models.h"

llm_build_glm4::llm_build_glm4(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {

    const int64_t n_embd_head = hparams.n_embd_head_v;

    const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();

    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();

    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

        {

            ggml_tensor * Qcur = nullptr;

            ggml_tensor * Kcur = nullptr;

            ggml_tensor * Vcur = nullptr;

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

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

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

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

                }

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

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

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

                }

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

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

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

                }

                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);

            } else {

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

                cb(cur, "wqkv", il);

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

                    cur = ggml_add(ctx0, cur, model.layers[il].bqkv);

                    cb(cur, "bqkv", il);

                }

                Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, n_embd_head * sizeof(float), cur->nb[1],

                                    0 * sizeof(float) * (n_embd));

                Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float),

                                    cur->nb[1], 1 * sizeof(float) * (n_embd));

                Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float),

                                    cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa));

            }

            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_layer - 1 && inp_out_ids) {

            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);

            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);

        }

        // Post-attention norm (new!)

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

        cb(cur, "post_attn_norm", il);

        // Add the input (residual connection after post-attention norm)

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

        cb(ffn_inp, "ffn_inp", il);

        // FF

        {

            // Pre-MLP norm

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

            cb(cur, "ffn_norm", il);

            // MLP

            cur = build_ffn(cur,

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

                    NULL, NULL, NULL,

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

                    NULL, LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);

            cb(cur, "ffn_out", il);

            // Post-MLP norm

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

            cb(cur, "post_mlp_norm", il);

        }

        // Add residual connection after post-MLP norm

        inpL = ggml_add(ctx0, cur, ffn_inp);

        cb(inpL, "l_out", il);

    }

    // Final norm

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

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

    res->t_embd = cur;

    // Output projection

    cur = build_lora_mm(model.output, cur);

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

    res->t_logits = cur;

    ggml_build_forward_expand(gf, cur);

}