#include "models.h"

void llama_model_phi2::load_arch_hparams(llama_model_loader & ml) {

    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);

    switch (hparams.n_layer()) {

        case 24: type = LLM_TYPE_1B; break;

        case 32: type = LLM_TYPE_3B; break;

        default: type = LLM_TYPE_UNKNOWN;

    }

}

void llama_model_phi2::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_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);

    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

    output_b      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "bias"),   {n_vocab}, 0);

    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.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);

        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);

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

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

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

        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);

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

        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);

    }

}

std::unique_ptr<llm_graph_context> llama_model_phi2::build_arch_graph(const llm_graph_params & params) const {

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

}

llama_model_phi2::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 * attn_norm_output;

    ggml_tensor * ffn_output;

    ggml_tensor * inpL;

    inpL = build_inp_embd(model.tok_embd);

    // inp_pos - contains the positions

    ggml_tensor * inp_pos = build_inp_pos();

    auto * inp_attn = build_attn_inp_kv();

    ggml_tensor * inp_out_ids = build_inp_out_ids();

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

        attn_norm_output = build_norm(inpL,

                model.layers[il].attn_norm,

                model.layers[il].attn_norm_b,

                LLM_NORM, il);

        cb(attn_norm_output, "attn_norm", il);

        // self-attention

        {

            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, 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);

            // with phi2, we scale the Q to avoid precision issues

            // ref: https://github.com/ml-explore/mlx-examples/blob/08e862336ade809bc37d1035f94b359e7d1a5152/phi2/phi2.py#L64-L66

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

            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);

            inpL             = ggml_get_rows(ctx0,             inpL, inp_out_ids);

            attn_norm_output = ggml_get_rows(ctx0, attn_norm_output, inp_out_ids);

        }

        // FF

        {

            ffn_output = build_ffn(attn_norm_output,

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

                    NULL,                      NULL,                        NULL,

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

                    NULL,

                    LLM_FFN_GELU, LLM_FFN_SEQ, il);

            cb(ffn_output, "ffn_out", il);

        }

        cur = ggml_add(ctx0, cur, ffn_output);

        cur = ggml_add(ctx0, cur, inpL);

        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, -1);

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

    res->t_embd = cur;

    cur = build_lora_mm(model.output, cur, model.output_s);

    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);

}