Felipe97/llama-cpp-compiled

1#include "models.h"2 3std::unique_ptr<llm_graph_context> llama_model_ernie4_5_moe::build_arch_graph(const llm_graph_params & params) const {4    return std::make_unique<graph>(*this, params);5}6 7llama_model_ernie4_5_moe::graph::graph(const llama_model & model, const llm_graph_params & params) :8    llm_graph_context(params) {9    const int64_t n_embd_head = hparams.n_embd_head_v();10 11    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());12    GGML_ASSERT(n_embd_head == n_rot);13 14    ggml_tensor * cur;15    ggml_tensor * inpL;16 17    inpL = build_inp_embd(model.tok_embd);18 19    // inp_pos - contains the positions20    ggml_tensor * inp_pos = build_inp_pos();21 22    auto * inp_attn = build_attn_inp_kv();23 24    ggml_tensor * inp_out_ids = build_inp_out_ids();25 26    GGML_ASSERT(hparams.n_moe_layer_step > 0 && "Ernie 4.5 MoE requires n_moe_layer_step > 0");27    for (int il = 0; il < n_layer; ++il) {28        ggml_tensor * inpSA = inpL;29        // norm30        {31            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);32            cb(cur, "attn_norm", il);33        }34        // self-attention35        {36            // compute Q and K and RoPE them37            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,38                    n_embd_head, n_head, n_head_kv, il);39 40            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,41                                 ext_factor, attn_factor, beta_fast, beta_slow);42 43            Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,44                                 ext_factor, attn_factor, beta_fast, beta_slow);45 46            cb(Qcur, "Qcur", il);47            cb(Kcur, "Kcur", il);48            cb(Vcur, "Vcur", il);49 50            cur = build_attn(inp_attn,51                    model.layers[il].wo, NULL, model.layers[il].wo_s,52                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);53            cb(cur, "attn_out", il);54        }55        if (il == n_layer - 1 && inp_out_ids) {56            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);57            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);58        }59        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);60        cb(ffn_inp, "ffn_inp", il);61 62        // feed-forward network63        bool is_moe_layer =64            static_cast<uint32_t>(il) >= hparams.n_layer_dense_lead && (il + 1) % hparams.n_moe_layer_step == 0;65 66        if (!is_moe_layer) {67            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);68            cb(cur, "ffn_norm", il);69 70            cur = build_ffn(cur,71                    model.layers[il].ffn_up, NULL, NULL,72                    model.layers[il].ffn_gate, NULL, NULL,73                    model.layers[il].ffn_down, NULL, NULL,74                    NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);75            cb(cur, "ffn_out", il);76        } else {77            // MoE branch78            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);79            cb(cur, "ffn_norm", il);80 81            ggml_tensor * moe_out = build_moe_ffn(cur,82                                        model.layers[il].ffn_gate_inp,83                                        model.layers[il].ffn_up_exps,84                                        model.layers[il].ffn_gate_exps,85                                        model.layers[il].ffn_down_exps,86                                        model.layers[il].ffn_exp_probs_b,87                                        n_expert, n_expert_used,88                                        LLM_FFN_SILU, true,89                                        hparams.expert_weights_scale,90                                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,91                                        il);92            cb(moe_out, "ffn_moe_out", il);93 94            // Shared expert (if present)95            if (hparams.n_ff_shexp > 0) {96                ggml_tensor * ffn_shexp =97                    build_ffn(cur,98                        model.layers[il].ffn_up_shexp, NULL, NULL,99                        model.layers[il].ffn_gate_shexp, NULL, NULL,100                        model.layers[il].ffn_down_shexp, NULL, NULL,101                        NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);102                cb(ffn_shexp, "ffn_shexp", il);103 104                cur = ggml_add(ctx0, moe_out, ffn_shexp);105            } else {106                cur = moe_out;107            }108            cb(cur, "ffn_out", il);109        }110        cur = ggml_add(ctx0, cur, ffn_inp);111        cb(cur, "ffn_out", il);112 113        cur = build_cvec(cur, il);114        cb(cur, "l_out", il);115 116        // input for next layer117        inpL = cur;118    }119    cur = inpL;120 121    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);122 123    cb(cur, "result_norm", -1);124    res->t_embd = cur;125 126    // lm_head127    cur = build_lora_mm(model.output, cur, model.output_s);128 129    cb(cur, "result_output", -1);130    res->t_logits = cur;131 132    ggml_build_forward_expand(gf, cur);133}134