#include "models.h"

llm_build_t5_dec::llm_build_t5_dec(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);

    ggml_tensor * embd_enc       = build_inp_cross_embd();

    ggml_tensor * pos_bucket_dec = build_inp_pos_bucket_dec();

    const int64_t n_outputs_enc = embd_enc->ne[1];

    auto * inp_attn_self  = build_attn_inp_kv();

    auto * inp_attn_cross = build_attn_inp_cross();

    ggml_tensor * inp_out_ids = build_inp_out_ids();

    const int64_t dec_n_layer = hparams.dec_n_layer;

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

        ggml_tensor * inpSA = inpL;

        // 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);

            cb(Qcur, "Qcur", il);

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

            cb(Kcur, "Kcur", il);

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

            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);

            ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b ? model.layers[il].attn_rel_b : model.layers[0].attn_rel_b;

            ggml_tensor * kq_b = build_pos_bias(pos_bucket_dec, attn_rel_b);

            cur = build_attn(inp_attn_self,

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

                    Qcur, Kcur, Vcur, kq_b, nullptr, nullptr, 1.0f, il);

            cb(cur, "kqv_out", il);

        }

        cur = ggml_add(ctx0, cur, inpSA);

        cb(cur, "cross_inp", il);

        ggml_tensor * inpCA = cur;

        // norm

        cur = build_norm(cur,

                model.layers[il].attn_norm_cross, NULL,

                LLM_NORM_RMS, il);

        cb(cur, "attn_norm_cross", il);

        // cross-attention

        {

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

            cb(Qcur, "Qcur", il);

            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk_cross, embd_enc);

            cb(Kcur, "Kcur", il);

            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv_cross, embd_enc);

            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_outputs_enc);

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

            cur = build_attn(inp_attn_cross,

                    model.layers[il].wo_cross, nullptr,

                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);

            cb(cur, "kqv_out", il);

            //ggml_tensor * q =                 ggml_permute(ctx0, Qcur, 0, 2, 1, 3);

            //ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));

            //ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);

            //cb(kq, "kq", il);

            //kq = ggml_soft_max_ext(ctx0, kq, KQ_mask_cross, 1.0f, hparams.f_max_alibi_bias);

            //cb(kq, "kq_soft_max_ext", il);

            //ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_outputs_enc)));

            //cb(v, "v", il);

            //ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_outputs_enc, n_embd_head, n_head_kv), kq);

            //cb(kqv, "kqv", il);

            //ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);

            //cb(kqv_merged, "kqv_merged", il);

            //cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);

            //cb(cur, "kqv_merged_cont", il);

            //ggml_build_forward_expand(gf, cur);

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

            //cb(cur, "kqv_out", il);

        }

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

            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);

            inpCA = ggml_get_rows(ctx0, inpCA, inp_out_ids);

        }

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

        cb(ffn_inp, "ffn_inp", il);

        // feed-forward network

        {

            cur = build_norm(ffn_inp,

                    model.layers[il].ffn_norm, NULL,

                    LLM_NORM_RMS, il);

            cb(cur, "ffn_norm", il);

            // T5 uses relu, flan-T5 uses gelu-gated

            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,

                    model.layers[il].ffn_gate ? LLM_FFN_GELU : LLM_FFN_RELU,

                    model.layers[il].ffn_gate ? LLM_FFN_PAR : LLM_FFN_SEQ,

                    il);

            cb(cur, "ffn_out", il);

        }

        cur = ggml_add(ctx0, cur, ffn_inp);

        cb(cur, "ffn_out", il);

        cur = build_cvec(cur, il);

        cb(cur, "l_out", il);

        // input for next layer

        inpL = cur;

    }

    cur = inpL;

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

    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);

}