#include "models.h"

void llama_model_afmoe::load_arch_hparams(llama_model_loader & ml) {

    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);

    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);

    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);

    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);

    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);

    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);

    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);

    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa, false);

    // Set up interleaved sliding window attention (ISWA)

    // Pattern: 3 sliding - 1 full (global_attn_every_n_layers = 4)

    if (hparams.n_swa > 0) {

        hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;

        uint32_t swa_period = 4;

        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);

        hparams.set_swa_pattern(swa_period);

        hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;

        hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;

        ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);

    } else {

        hparams.swa_type = LLAMA_SWA_TYPE_NONE;

    }

    // Default to sigmoid if not set

    if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {

        hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;

    }

    switch (hparams.n_layer()) {

        case 56: type = LLM_TYPE_6B; break;

        case 32: type = LLM_TYPE_26B; break;

        default: type = LLM_TYPE_UNKNOWN;

    }

}

void llama_model_afmoe::load_arch_tensors(llama_model_loader &) {

    LLAMA_LOAD_LOCALS;

    const int64_t n_expert_shared = hparams.n_expert_shared;

    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

    // output

    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);

    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);

    // if output is NULL, init from the input tok embed

    if (output == NULL) {

        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);

    }

    const int64_t n_ff_exp = hparams.n_ff_exp;

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

        auto & layer = layers[i];

        // dual attention normalization

        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), {n_embd}, 0);

        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);

        // attention projections

        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);

        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);

        // Q/K normalization

        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);

        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);

        // attention gating

        layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);

        // dual ffn normalization

        layer.ffn_norm      = create_tensor(tn(LLM_TENSOR_FFN_NORM,      "weight", i), {n_embd}, 0);

        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);

        if (static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead) {

            // MoE layers

            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);

            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);

            // grouped expert weights

            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);

            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);

            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff_exp, n_expert}, 0);

            // shared expert

            if (n_expert_shared > 0) {

                const int64_t n_ff_shexp = n_ff_exp * n_expert_shared;

                layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0);

                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);

                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp}, 0);

            }

        } else {

            // Dense layers

            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);

            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);

            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);

        }

    }

}

std::unique_ptr<llm_graph_context> llama_model_afmoe::build_arch_graph(const llm_graph_params & params) const {

    return std::make_unique<graph>(*this, params);

}

llama_model_afmoe::graph::graph(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_tensor * cur;

    ggml_tensor * inpL;

    inpL = build_inp_embd(model.tok_embd);

    // MuP scaling: embeddings * sqrt(hidden_size)

    // mup_enabled = true, hidden_size = 1024, scale = 32.0

    inpL = ggml_scale(ctx0, inpL, sqrtf(float(n_embd)));

    cb(inpL, "inp_embd_scaled", -1);

    // inp_pos - contains the positions

    ggml_tensor * inp_pos = build_inp_pos();

    auto * inp_attn = build_attn_inp_kv_iswa();

    ggml_tensor * inp_out_ids = build_inp_out_ids();

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

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

        ggml_tensor * inpSA = inpL;

        // This overlaps with SWA layers in current models, so get_rope_freq_base/scale may be superfluous

        const bool use_rope = hparams.n_no_rope_layer_step > 0 &&

                              (il + 1) % hparams.n_no_rope_layer_step != 0;

        // dual attention normalization (pre)

        cur = build_norm(inpL,

                model.layers[il].attn_norm, NULL,

                LLM_NORM_RMS, il);

        cb(cur, "attn_norm", il);

        // self-attention

        {

            ggml_tensor * attn_inp = cur;  // save input for gate computation

            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,

                    n_embd_head, n_head, n_head_kv, il);

            // compute gate from input

            ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, attn_inp);

            cb(gate, "attn_gate_proj", il);

            // Q/K normalization

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

            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);

            cb(Qcur, "Qcur_normed", il);

            cb(Kcur, "Kcur_normed", il);

            if (use_rope) {

                Qcur = ggml_rope_ext(

                        ctx0, Qcur, inp_pos, nullptr,

                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,

                        ext_factor, attn_factor, beta_fast, beta_slow);

                cb(Qcur, "Qcur_rope", il);

                Kcur = ggml_rope_ext(

                        ctx0, Kcur, inp_pos, nullptr,

                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,

                        ext_factor, attn_factor, beta_fast, beta_slow);

                cb(Kcur, "Kcur_rope", il);

            }

            cur = build_attn(inp_attn,

                    NULL, NULL, NULL,  // wo will be applied after gating

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

            cb(cur, "attn_out", il);

            // attention gating: attn_out * sigmoid(gate) BEFORE o_proj

            gate = ggml_sigmoid(ctx0, gate);

            cb(gate, "attn_gate_sig", il);

            cur = ggml_mul(ctx0, cur, gate);

            cb(cur, "attn_gated", il);

            // now apply output projection

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

            cb(cur, "attn_o_proj", il);

        }

        // dual attention normalization (post)

        cur = build_norm(cur,

                model.layers[il].attn_post_norm, NULL,

                LLM_NORM_RMS, il);

        cb(cur, "attn_post_norm", 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);

        // dual ffn normalization (pre)

        cur = build_norm(ffn_inp,

                model.layers[il].ffn_norm, NULL,

                LLM_NORM_RMS, il);

        cb(cur, "ffn_norm", il);

        // MoE or dense FFN

        if ((uint32_t)il >= hparams.n_layer_dense_lead) {

            // MoE layer with sigmoid routing, normalization, and scaling

            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,

                    hparams.expert_weights_norm,           // norm_w (route_norm=True)

                    hparams.expert_weights_scale,          // w_scale (route_scale=2.826)

                    (llama_expert_gating_func_type) hparams.expert_gating_func,

                    il);

            cb(moe_out, "ffn_moe_out", il);

            // shared expert

            if (hparams.n_expert_shared > 0) {

                ggml_tensor * ffn_shexp = build_ffn(cur,

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

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

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

                        NULL,

                        LLM_FFN_SILU, LLM_FFN_PAR, il);

                cb(ffn_shexp, "ffn_shexp", il);

                cur = ggml_add(ctx0, moe_out, ffn_shexp);

                cb(cur, "ffn_out", il);

            } else {

                cur = moe_out;

            }

        } else {

            // dense layer

            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,

                    LLM_FFN_SILU, LLM_FFN_PAR, il);

            cb(cur, "ffn_out", il);

        }

        // dual ffn normalization (post)

        cur = build_norm(cur,

                model.layers[il].ffn_post_norm, NULL,

                LLM_NORM_RMS, il);

        cb(cur, "ffn_post_norm", il);

        cur = ggml_add(ctx0, cur, ffn_inp);

        cur = build_cvec(cur, il);

        cb(cur, "l_out", il);

        // input for next layer

        inpL = cur;

    }

    cur = inpL;

    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, model.output_s);

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

    res->t_logits = cur;

    ggml_build_forward_expand(gf, cur);

}