#include "models.h"

llm_build_step35_iswa::llm_build_step35_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {

    ggml_tensor * cur;

    ggml_tensor * inpL;

    inpL = build_inp_embd(model.tok_embd);

    ggml_tensor * inp_pos     = build_inp_pos();

    auto        * inp_attn    = build_attn_inp_kv_iswa();

    ggml_tensor * inp_out_ids = build_inp_out_ids();

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

        ggml_tensor * inpSA = inpL;

        const uint32_t n_head_l    = hparams.n_head(il);

        const uint32_t n_head_kv_l = hparams.n_head_kv(il);

        const float freq_base_l  = model.get_rope_freq_base(cparams, il);

        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);

        cur = inpL;

        // dump pre-attn RMSNorm input to pinpoint layer boundary issues

        cb(cur, "attn_norm_in", il);

        // self-attention

        {

            cur = build_norm(cur, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);

            cb(cur, "attn_norm", il);

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

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

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

            cb(Qcur, "Qcur", il);

            cb(Kcur, "Kcur", il);

            cb(Vcur, "Vcur", il);

            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head_l,    n_tokens);

            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv_l, n_tokens);

            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head_v, n_head_kv_l, n_tokens);

            // Q/K per-head RMSNorm (Step35 q_norm / k_norm)

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

                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, 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, nullptr, LLM_NORM_RMS, il);

                cb(Kcur, "Kcur_normed", il);

            }

            // RoPE (partial rotary factors per layer)

            const bool is_swa = hparams.is_swa(il);

            ggml_tensor * rope_factors = is_swa ? nullptr : model.get_rope_factors(cparams, il);

            const int64_t n_rot_l = is_swa ? hparams.n_rot : (hparams.n_rot / 2);

            Qcur = ggml_rope_ext(

                ctx0, Qcur, inp_pos, rope_factors,

                n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,

                ext_factor, attn_factor, beta_fast, beta_slow

            );

            Kcur = ggml_rope_ext(

                ctx0, Kcur, inp_pos, rope_factors,

                n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,

                ext_factor, attn_factor, beta_fast, beta_slow

            );

            cb(Qcur, "Qcur_pos", il);

            cb(Kcur, "Kcur_pos", il);

            const float kq_scale = 1.0f / sqrtf(float(n_embd_head_k));

            ggml_tensor * attn_out = build_attn(inp_attn,

                    nullptr, nullptr,

                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);

            cb(attn_out, "attn_out", il);

            // head-wise attention gate: sigmoid(g_proj(x)) in torch

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

                ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, cur); // [n_head_l, n_tokens]

                cb(gate, "attn_gate", il);

                gate = ggml_sigmoid(ctx0, gate);

                cb(gate, "attn_gate_sigmoid", il);

                // reshape + broadcast to [n_embd_head_v, n_head_l, n_tokens]

                ggml_tensor * attn_3d = ggml_reshape_3d(ctx0, attn_out, n_embd_head_v, n_head_l, n_tokens);

                ggml_tensor * gate_3d = ggml_reshape_3d(ctx0, gate,       1,          n_head_l, n_tokens);

                cb(gate_3d, "attn_gate_3d", il);

                attn_3d = ggml_mul(ctx0, attn_3d, gate_3d);

                cb(attn_3d, "attn_gated_3d", il);

                attn_out = ggml_reshape_2d(ctx0, attn_3d, n_embd_head_v * n_head_l, n_tokens);

                cb(attn_out, "attn_gated", il);

            }

            // output projection

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

            cb(cur, "attn_proj", 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);

        }

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

        cb(ffn_inp, "ffn_inp", il);

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

        cb(cur, "ffn_norm", il);

        // feed-forward

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

            // dense MLP

            cur = build_ffn(cur,

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

                    model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, nullptr,

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

                    nullptr,

                    LLM_FFN_SILU, LLM_FFN_PAR, il);

            cb(cur, "ffn_out", il);

        } else {

            // MoE routed experts

            const bool  norm_w  = hparams.expert_weights_norm;

            const float w_scale = hparams.expert_weights_scale;

            const bool  scale_w = w_scale != 0.0f;

            ggml_tensor * moe_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,

                    norm_w, scale_w, w_scale,

                    (llama_expert_gating_func_type) hparams.expert_gating_func,

                    il);

            cb(moe_out, "ffn_moe_out", il);

            // shared expert MLP (always added on MoE layers in Step35)

            ggml_tensor * sh_out = build_ffn(cur,

                    model.layers[il].ffn_up_shexp,   nullptr, nullptr,

                    model.layers[il].ffn_gate_shexp, nullptr, nullptr,

                    model.layers[il].ffn_down_shexp, nullptr, nullptr,

                    nullptr,

                    LLM_FFN_SILU, LLM_FFN_PAR, il);

            cb(sh_out, "ffn_shared_out", il);

            cur = ggml_add(ctx0, moe_out, sh_out);

            cb(cur, "ffn_out", il);

        }

        cur = ggml_add(ctx0, cur, ffn_inp);

        cur = build_cvec(cur, il);

        cb(cur, "l_out", il);

        inpL = cur;

    }

    cur = inpL;

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

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

    res->t_embd = cur;

    cur = build_lora_mm(model.output, cur);

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

    res->t_logits = cur;

    ggml_build_forward_expand(gf, cur);

}