#include "models.h"

void llama_model_phi3::load_arch_hparams(llama_model_loader & ml) {

    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);

    switch (hparams.n_layer()) {

        case 24: type = LLM_TYPE_1B; break;

        case 32: type = LLM_TYPE_3B; break;

        case 40: type = LLM_TYPE_14B; break;

        default: type = LLM_TYPE_UNKNOWN;

    }

    const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);

    if (found_swa && hparams.n_swa > 0) {

        LLAMA_LOG_WARN("%s: Phi SWA is currently disabled - results might be suboptimal for some models (see %s)\n",

                __func__, "https://github.com/ggml-org/llama.cpp/pull/13676");

        // TODO: fix conversion scripts to correctly populate `n_swa` and `n_swa_pattern`

        hparams.swa_type = LLAMA_SWA_TYPE_NONE;

        hparams.n_swa         = 0;

        hparams.set_swa_pattern(1);

    }

}

void llama_model_phi3::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}, 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);

    }

    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);

        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, TENSOR_NOT_REQUIRED);

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

        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 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, 2 * n_ff }, 0);

        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));

        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));

    }

}

std::unique_ptr<llm_graph_context> llama_model_phi3::build_arch_graph(const llm_graph_params & params) const {

    if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {

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

    } else {

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

    }

}

template<bool iswa>

llama_model_phi3::graph<iswa>::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);

    // inp_pos - contains the positions

    ggml_tensor * inp_pos = build_inp_pos();

    using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa, llm_graph_input_attn_kv>;

    inp_attn_type * inp_attn = nullptr;

    if constexpr (iswa) {

        inp_attn = build_attn_inp_kv_iswa();

    } else {

        inp_attn = build_attn_inp_kv();

    }

    ggml_tensor * inp_out_ids = build_inp_out_ids();

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

        auto * residual = inpL;

        // self-attention

        {

            // rope freq factors for 128k context

            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);

            ggml_tensor* attn_norm_output = build_norm(inpL,

                    model.layers[il].attn_norm,

                    model.layers[il].attn_norm_b,

                    LLM_NORM_RMS, il);

            cb(attn_norm_output, "attn_norm", il);

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

                    n_embd_head, n_head, n_head_kv, il);

            Qcur = ggml_rope_ext(

                    ctx0, Qcur, inp_pos, rope_factors,

                    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, rope_factors,

                    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);

            Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head)));

            cb(Qcur, "Qcur", il);

            cur = build_attn(inp_attn,

                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,

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

        }

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

            cur      = ggml_get_rows(ctx0, cur,      inp_out_ids);

            residual = ggml_get_rows(ctx0, residual, inp_out_ids);

        }

        cur = ggml_add(ctx0, cur, residual);

        residual = cur;

        cur = build_norm(cur,

                model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,

                LLM_NORM_RMS, il);

        cb(cur, "ffn_norm", il);

        // feed-forward network

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

            cur = build_ffn(cur,

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

                    NULL,                      NULL, NULL,

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

                    NULL,

                    LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);

            cb(cur, "ffn_out", il);

        } else {

            // MoE branch

            cur = 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,

                    nullptr,

                    n_expert, n_expert_used,

                    LLM_FFN_SILU, true,

                    hparams.expert_weights_scale,

                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,

                    il);

            cb(cur, "ffn_moe_out", il);

        }

        cur = ggml_add(ctx0, residual, cur);

        cur = build_cvec(cur, il);

        cb(cur, "l_out", il);

        // input for next layer

        inpL = cur;

    }

    cur = build_norm(inpL,

            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);

    if (model.output_b != nullptr) {

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

        cur = ggml_add(ctx0, cur, model.output_b);

    }

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

    res->t_logits = cur;

    ggml_build_forward_expand(gf, cur);

}

Unexecuted instantiation: llama_model_phi3::graph<false>::graph(llama_model const&, llm_graph_params const&)

Unexecuted instantiation: llama_model_phi3::graph<true>::graph(llama_model const&, llm_graph_params const&)

// Explicit template instantiations

template struct llama_model_phi3::graph<false>;

template struct llama_model_phi3::graph<true>;