llama.cpp/src/models/llm_build_phi3.cpp

#include "../llama-model.h"
#include "../llama-graph.h"

#include "llm_build_phi3.h"

#include <cmath>

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);
    }

// Explicit template instantiations
template struct llm_build_phi3<false>;
template struct llm_build_phi3<true>;