#include "models.h"

void llama_model_arwkv7::load_arch_hparams(llama_model_loader & ml) {

    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,                hparams.f_norm_eps, false);

    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,            hparams.f_norm_rms_eps, false);

    ml.get_key(LLM_KV_WKV_HEAD_SIZE,                          hparams.wkv_head_size);

    ml.get_key(LLM_KV_ATTENTION_DECAY_LORA_RANK,              hparams.n_lora_decay);

    ml.get_key(LLM_KV_ATTENTION_ICLR_LORA_RANK,               hparams.n_lora_iclr);

    ml.get_key(LLM_KV_ATTENTION_VALUE_RESIDUAL_MIX_LORA_RANK, hparams.n_lora_value_res_mix);

    ml.get_key(LLM_KV_ATTENTION_GATE_LORA_RANK,               hparams.n_lora_gate, false);

    ml.get_key(LLM_KV_TOKEN_SHIFT_COUNT,                      hparams.token_shift_count, false);

    switch (hparams.n_layer()) {

        case 12:

            switch (hparams.n_embd) {

                case 768: type = LLM_TYPE_190M; break;

                default: type = LLM_TYPE_UNKNOWN;

            } break;

        case 24:

            switch (hparams.n_embd) {

                case 1024: type = LLM_TYPE_450M; break;

                case 2048: type = LLM_TYPE_1_5B; break;

                default: type = LLM_TYPE_UNKNOWN;

            } break;

        case 28:

            switch (hparams.n_embd) {

                case 1536: type = LLM_TYPE_1_5B; break;

                case 3584: type = LLM_TYPE_7B; break;

                default: type = LLM_TYPE_UNKNOWN;

            } break;

        case 32:

            switch (hparams.n_embd) {

                case 2560: type = LLM_TYPE_2_9B; break;

                case 4096: type = LLM_TYPE_7B; break;

                default: type = LLM_TYPE_UNKNOWN;

            } break;

        case 61:

            switch (hparams.n_embd) {

                case 4096: type = LLM_TYPE_14B; break;

                default: type = LLM_TYPE_UNKNOWN;

            } break;

        default: type = LLM_TYPE_UNKNOWN;

    }

}

void llama_model_arwkv7::load_arch_tensors(llama_model_loader &) {

    LLAMA_LOAD_LOCALS;

    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}, 0);

    const int n_lora_decay = hparams.n_lora_decay;

    const int n_lora_iclr = hparams.n_lora_iclr;

    const int n_lora_value_res_mix = hparams.n_lora_value_res_mix;

    const int n_lora_gate = hparams.n_lora_gate;

    const int attn_hidden_size = n_embd;

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

        auto & layer = layers[i];

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

        layer.time_mix_w0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W0, "weight", i), {n_embd}, 0);

        layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, n_lora_decay}, 0);

        layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {n_lora_decay, n_embd}, 0);

        layer.time_mix_a0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A0, "weight", i), {n_embd}, 0);

        layer.time_mix_a1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A1, "weight", i), {n_embd, n_lora_iclr}, 0);

        layer.time_mix_a2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A2, "weight", i), {n_lora_iclr, n_embd}, 0);

        if (i == 0) {

            // actually not used

            layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);

            layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_iclr}, 0);

            layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_iclr, n_embd}, 0);

        } else {

            layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);

            layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_value_res_mix}, 0);

            layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_value_res_mix, n_embd}, 0);

        }

        layer.time_mix_g1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_G1, "weight", i), {n_embd, n_lora_gate}, TENSOR_NOT_REQUIRED);

        layer.time_mix_g2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_G2, "weight", i), {n_lora_gate, n_embd}, TENSOR_NOT_REQUIRED);

        try {

            layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 6}, 0);

        } catch(std::runtime_error & e) {

            // ARWKV models may not have gate tensors

            layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 5}, 0);

        }

        layer.time_mix_k_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_K_K, "weight", i), {attn_hidden_size}, 0);

        layer.time_mix_k_a = create_tensor(tn(LLM_TENSOR_TIME_MIX_K_A, "weight", i), {attn_hidden_size}, 0);

        layer.time_mix_r_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_R_K, "weight", i), {attn_hidden_size}, 0);

        layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd}, 0);

        layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd}, 0);

        layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);

        layer.time_mix_ln = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);

        layer.time_mix_ln_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);

        layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);

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

        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_arwkv7::build_arch_graph(const llm_graph_params & params) const {

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

}

llama_model_arwkv7::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_build_rwkv7_base(model, params) {

    GGML_ASSERT(n_embd == hparams.n_embd_r());

    ggml_tensor * cur;

    ggml_tensor * inpL;

    ggml_tensor * v_first = nullptr;

    inpL = build_inp_embd(model.tok_embd);

    auto * rs_inp = build_rs_inp();

    const auto n_embd = hparams.n_embd;

    const auto n_seq_tokens = ubatch.n_seq_tokens;

    const auto n_seqs = ubatch.n_seqs;

    ggml_tensor * inp_out_ids = build_inp_out_ids();

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

        const llama_layer * layer = &model.layers[il];

        inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);

        ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, ubatch, il);

        ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM_RMS, il);

        cb(att_norm, "attn_norm", il);

        ggml_tensor * x_prev = ggml_concat(

                ctx0,

                token_shift,

                ggml_view_3d(ctx0, att_norm, n_embd, n_seq_tokens - 1, n_seqs, att_norm->nb[1], att_norm->nb[2], 0),

                1

                );

        cur = build_rwkv7_time_mix(rs_inp, att_norm, x_prev, v_first, ubatch, il);

        token_shift = ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(att_norm));

        ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il));

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

        cb(ffn_inp, "ffn_inp", il);

        cur     = ggml_reshape_2d(ctx0, cur,     n_embd, n_tokens);

        ffn_inp = ggml_reshape_2d(ctx0, ffn_inp, n_embd, n_tokens);

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

            cur     = ggml_get_rows(ctx0, cur,     inp_out_ids);

            ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);

        }

        // feed-forward network

        cur = build_norm(ffn_inp,

                model.layers[il].ffn_norm, NULL,

                LLM_NORM_RMS, il);

        cb(cur, "ffn_norm", il);

        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);

        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, model.output_norm_b, LLM_NORM_RMS, -1);

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

    res->t_embd = cur;

    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);

}