#include "models.h"

// JAIS-2 model graph builder

// Uses: LayerNorm (not RMSNorm), relu2 activation, separate Q/K/V, RoPE embeddings

llm_build_jais2::llm_build_jais2(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_ASSERT(n_embd_head == hparams.n_rot);

    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();

    // KV input for attention

    auto * inp_attn = build_attn_inp_kv();

    ggml_tensor * inp_out_ids = build_inp_out_ids();

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

        // Pre-attention LayerNorm

        cur = build_norm(inpL,

                model.layers[il].attn_norm,

                model.layers[il].attn_norm_b,

                LLM_NORM, il);

        cb(cur, "attn_norm", il);

        // Self-attention with separate Q, K, V projections

        {

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

            cb(Qcur, "Qcur", il);

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

            cb(Qcur, "Qcur_bias", il);

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

            cb(Kcur, "Kcur", il);

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

            cb(Kcur, "Kcur_bias", il);

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

            cb(Vcur, "Vcur", il);

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

            cb(Vcur, "Vcur_bias", il);

            // Reshape for attention

            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 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_rope", il);

            cb(Kcur, "Kcur_rope", il);

            cur = build_attn(inp_attn,

                    model.layers[il].wo, model.layers[il].bo,

                    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);

            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);

        }

        // Residual connection

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

        cb(ffn_inp, "ffn_inp", il);

        // Pre-FFN LayerNorm

        cur = build_norm(ffn_inp,

                model.layers[il].ffn_norm,

                model.layers[il].ffn_norm_b,

                LLM_NORM, il);

        cb(cur, "ffn_norm", il);

        // FFN with relu2 activation (ReLU squared) - no gate projection

        // up -> relu2 -> down

        cur = build_ffn(cur,

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

                NULL, NULL, NULL,  // no gate

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

                NULL,

                LLM_FFN_RELU_SQR, LLM_FFN_SEQ, il);

        cb(cur, "ffn_out", il);

        // Residual connection

        inpL = ggml_add(ctx0, cur, ffn_inp);

        inpL = build_cvec(inpL, il);

        cb(inpL, "l_out", il);

    }

    // Final LayerNorm

    cur = build_norm(inpL,

            model.output_norm,

            model.output_norm_b,

            LLM_NORM, -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);

}