Felipe97/llama-cpp-compiled

1#include "models.h"2 3std::unique_ptr<llm_graph_context> llama_model_paddleocr::build_arch_graph(const llm_graph_params & params) const {4    return std::make_unique<graph>(*this, params);5}6 7llama_model_paddleocr::graph::graph(const llama_model & model, const llm_graph_params & params) :8    llm_graph_context(params) {9 10    // NOTE: same with qwen2vl.cpp, but bias tensors are optional11 12    const int64_t n_embd_head = hparams.n_embd_head_v();13 14    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());15    GGML_ASSERT(n_embd_head == n_rot);16 17    ggml_tensor * cur;18    ggml_tensor * inpL;19 20    inpL = build_inp_embd(model.tok_embd);21 22    int sections[4];23    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);24 25    // inp_pos - contains the positions26    ggml_tensor * inp_pos = build_inp_pos();27 28    auto * inp_attn = build_attn_inp_kv();29 30    ggml_tensor * inp_out_ids = build_inp_out_ids();31 32    for (int il = 0; il < n_layer; ++il) {33        ggml_tensor * inpSA = inpL;34 35        // norm36        {37            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);38            cb(cur, "attn_norm", il);39        }40        // self-attention41        {42            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,43                    n_embd_head, n_head, n_head_kv, il);44 45            Qcur = ggml_rope_multi(46                    ctx0, Qcur, inp_pos, nullptr,47                    n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,48                    ext_factor, attn_factor, beta_fast, beta_slow49                    );50 51            Kcur = ggml_rope_multi(52                    ctx0, Kcur, inp_pos, nullptr,53                    n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,54                    ext_factor, attn_factor, beta_fast, beta_slow55                    );56 57            cb(Qcur, "Qcur", il);58            cb(Kcur, "Kcur", il);59            cb(Vcur, "Vcur", il);60 61            cur = build_attn(inp_attn,62                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,63                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);64        }65        if (il == n_layer - 1) {66            // skip computing output for unused tokens67            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);68            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);69        }70        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);71        cb(ffn_inp, "ffn_inp", il);72 73        // feed-forward network74        {75            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);76            cb(cur, "ffn_norm", il);77 78            cur = build_ffn(cur,79                    model.layers[il].ffn_up, NULL, NULL,80                    model.layers[il].ffn_gate, NULL, NULL,81                    model.layers[il].ffn_down, NULL, NULL,82                    NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);83            cb(cur, "ffn_out", il);84        }85        cur = ggml_add(ctx0, cur, ffn_inp);86 87        cur = build_cvec(cur, il);88        cb(cur, "l_out", il);89 90        // input for next layer91        inpL = cur;92    }93    cur = inpL;94 95    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);96 97    cb(cur, "result_norm", -1);98    res->t_embd = cur;99 100    // lm_head101    cur = build_lora_mm(model.output, cur, model.output_s);102 103    cb(cur, "result_output", -1);104    res->t_logits = cur;105 106    ggml_build_forward_expand(gf, cur);107}108