#include "models.h"

template<bool iswa>

llm_build_phi3<iswa>::llm_build_phi3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {

    const int64_t n_embd_head = hparams.n_embd_head_v;

    const int64_t n_embd_gqa = hparams.n_embd_v_gqa();

    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);

            ggml_tensor * Qcur = nullptr;

            ggml_tensor * Kcur = nullptr;

            ggml_tensor * Vcur = nullptr;

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

                cur = build_lora_mm(model.layers[il].wqkv, attn_norm_output);

                cb(cur, "wqkv", il);

                Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head * sizeof(float), cur->nb[1], 0 * sizeof(float) * (n_embd));

                Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float), cur->nb[1], 1 * sizeof(float) * (n_embd));

                Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float), cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa));

                }

                else {

                Qcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wq, attn_norm_output), model.layers[il].bq);

                Kcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wk, attn_norm_output), model.layers[il].bk);

                Vcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wv, attn_norm_output), model.layers[il].bv);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);

                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);

                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);

            }

            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].bo,

                    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,

                    false, 0.0,

                    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);

    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: llm_build_phi3<false>::llm_build_phi3(llama_model const&, llm_graph_params const&)

Unexecuted instantiation: llm_build_phi3<true>::llm_build_phi3(llama_model const&, llm_graph_params const&)

// Explicit template instantiations

template struct llm_build_phi3<false>;

template struct llm_build_phi3<true>;