#include "../llama-build-context.h"
#include "../llama-model.h"
#include "../llama-context.h"

ggml_cgraph * llm_build_context::build_ernie4_5() {
    struct ggml_cgraph* gf = ggml_new_graph_custom(ctx0, model.max_nodes(n_tokens), false);
    const int64_t n_embd_head = hparams.n_embd_head_v(0);

    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k(0));
    GGML_ASSERT(n_embd_head == hparams.n_rot);

    ggml_tensor * cur;
    ggml_tensor * inpL;

    inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

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

    // output token IDs (for last layer cropping)
    ggml_tensor * inp_out_ids = build_inp_out_ids();

    for (int il = 0; il < n_layer; ++il) {
        ggml_tensor * inpSA = inpL;
        // norm
        // Pre-attention norm
        cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, cb, il);
        cb(cur, "attn_norm", il);

        // self-attention
        // self-attention
        {
            // Q, K, V projections
            ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
            cb(Qcur, "Qcur", il);
            if (model.layers[il].bq) {
                Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                cb(Qcur, "Qcur", il);
            }

            ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
            cb(Kcur, "Kcur", il);
            if (model.layers[il].bk) {
                Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                cb(Kcur, "Kcur", il);
            }
            cb(Kcur, "Kcur", il);
            ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
            cb(Vcur, "Vcur", il);
            if (model.layers[il].bv) {
                Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                cb(Vcur, "Vcur", il);
            }

            // reshape for multi-head
            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", il);
            cb(Kcur, "Kcur", il);
            cb(Vcur, "Vcur", il);

            cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                    model.layers[il].wo, NULL,
                    Kcur, Vcur, Qcur, KQ_mask,
                    n_tokens, kv_head, n_kv,
                    1.0f / sqrtf(float(n_embd_head)), cb, 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);
        }

        // residual connection for attention output
        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
        cb(ffn_inp, "ffn_inp", il);

        // feed-forward network
        {
            cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, ffn_inp,
                    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, cb, il);
            cb(cur, "ffn_out", il);
        }
        cur = ggml_add(ctx0, cur, ffn_inp);
        cb(cur, "ffn_out", il);

        cur = lctx.cvec.apply_to(ctx0, cur, il);
        cb(cur, "l_out", il);

        // input for next layer
        inpL = cur;
    }

    cur = inpL;

    cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM_RMS, cb, -1);

    cb(cur, "result_norm", -1);
    // lm_head
    cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);

    cb(cur, "result_output", -1);
    ggml_build_forward_expand(gf, cur);
    return gf;
}

ggml_cgraph * llm_build_context::build_ernie4_5_moe() {
    struct ggml_cgraph* gf = ggml_new_graph_custom(ctx0, model.max_nodes(n_tokens), false);
    const int64_t n_embd_head = hparams.n_embd_head_v(0);

    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k(0));
    GGML_ASSERT(n_embd_head == hparams.n_rot);

    ggml_tensor * cur;
    ggml_tensor * inpL;

    inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

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

    struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

    // output token IDs (for last layer cropping)
    struct ggml_tensor * inp_out_ids = n_tokens > 1 ? build_inp_out_ids() : nullptr;

    GGML_ASSERT(hparams.n_moe_layer_step > 0 && "Ernie 4.5 MoE requires n_moe_layer_step > 0");
    for (int il = 0; il < n_layer; ++il) {

        cur = build_std_attention(gf, model.layers[il].attn_norm, inpL,
                inp_pos, il == n_layer - 1 ? inp_out_ids : nullptr, nullptr, KQ_mask, nullptr, nullptr,
                1.0f/sqrtf(float(n_embd_head)), 0.0f, 0, il, true, false, true);

        // feed-forward network
        bool is_moe_layer = static_cast<uint32_t>(il) >= hparams.n_layer_dense_lead && (il + 1) % hparams.n_moe_layer_step == 0;

        if (!is_moe_layer) {
            // dense FFN
            cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, 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, cb, il, gf, true);
            cb(cur, "ffn_out", il);
        } else {
            cur = llm_build_std_moe_ffn(ctx0, lctx, model.layers[il].ffn_norm, cur,
                    model.layers[il].ffn_gate_inp,  model.layers[il].ffn_gate_inp_b,
                    model.layers[il].ffn_up_exps,   model.layers[il].ffn_up_exps_b,
                    model.layers[il].ffn_gate_exps, model.layers[il].ffn_gate_exps_b,
                    model.layers[il].ffn_down_exps, model.layers[il].ffn_down_exps_b,
                    model.layers[il].ffn_exp_probs_b,
                    model.layers[il].ffn_up_shexp,    nullptr, // we don't have shared expert biases?
                    model.layers[il].ffn_gate_shexp,  nullptr,
                    model.layers[il].ffn_down_shexp,  nullptr,
                    n_expert, n_expert_used,
                    LLM_FFN_SILU, true, false, 0.0f,
                    LLM_EXPERT_GATING_FUNC_SOFTMAX,
                    LLM_FFN_SILU, cb, il, gf, true);
        }

        cur = lctx.cvec.apply_to(ctx0, cur, il);
        cb(cur, "l_out", il);

        // input for next layer
        inpL = cur;
    }

    cur = build_output(lctx, ctx0, inpL, model.output, model.output_norm, cb);
    cb(cur, "result_output", -1);

    ggml_build_forward_expand(gf, cur);
    return gf;
}

