Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_phi3::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);5 6    switch (hparams.n_layer()) {7        case 24: type = LLM_TYPE_1B; break;8        case 32: type = LLM_TYPE_3B; break;9        case 40: type = LLM_TYPE_14B; break;10        default: type = LLM_TYPE_UNKNOWN;11    }12 13    const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);14 15    if (found_swa && hparams.n_swa > 0) {16        LLAMA_LOG_WARN("%s: Phi SWA is currently disabled - results might be suboptimal for some models (see %s)\n",17                __func__, "https://github.com/ggml-org/llama.cpp/pull/13676");18 19        // TODO: fix conversion scripts to correctly populate `n_swa` and `n_swa_pattern`20        hparams.swa_type = LLAMA_SWA_TYPE_NONE;21 22        hparams.n_swa         = 0;23        hparams.set_swa_pattern(1);24    }25}26 27void llama_model_phi3::load_arch_tensors(llama_model_loader &) {28    LLAMA_LOAD_LOCALS;29 30    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);31 32    // output33    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);34    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);35 36    // if output is NULL, init from the input tok embed37    if (output == NULL) {38        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);39    }40 41    for (int i = 0; i < n_layer; ++i) {42        auto & layer = layers[i];43 44        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);45 46        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, TENSOR_NOT_REQUIRED);47        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);48 49        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);50 51        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);52        layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff }, 0);53 54        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));55        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));56    }57}58 59std::unique_ptr<llm_graph_context> llama_model_phi3::build_arch_graph(const llm_graph_params & params) const {60    if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {61        return std::make_unique<graph<true>> (*this, params);62    } else {63        return std::make_unique<graph<false>>(*this, params);64    }65}66 67template<bool iswa>68llama_model_phi3::graph<iswa>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {69    const int64_t n_embd_head = hparams.n_embd_head_v();70 71    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());72 73    ggml_tensor * cur;74    ggml_tensor * inpL;75 76    inpL = build_inp_embd(model.tok_embd);77 78    // inp_pos - contains the positions79    ggml_tensor * inp_pos = build_inp_pos();80 81    using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa, llm_graph_input_attn_kv>;82    inp_attn_type * inp_attn = nullptr;83 84    if constexpr (iswa) {85        inp_attn = build_attn_inp_kv_iswa();86    } else {87        inp_attn = build_attn_inp_kv();88    }89    ggml_tensor * inp_out_ids = build_inp_out_ids();90 91    for (int il = 0; il < n_layer; ++il) {92        auto * residual = inpL;93 94        // self-attention95        {96            // rope freq factors for 128k context97            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);98 99            ggml_tensor* attn_norm_output = build_norm(inpL,100                    model.layers[il].attn_norm,101                    model.layers[il].attn_norm_b,102                    LLM_NORM_RMS, il);103            cb(attn_norm_output, "attn_norm", il);104 105            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], attn_norm_output,106                    n_embd_head, n_head, n_head_kv, il);107            Qcur = ggml_rope_ext(108                    ctx0, Qcur, inp_pos, rope_factors,109                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,110                    ext_factor, attn_factor, beta_fast, beta_slow111                    );112 113            Kcur = ggml_rope_ext(114                    ctx0, Kcur, inp_pos, rope_factors,115                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,116                    ext_factor, attn_factor, beta_fast, beta_slow117                    );118 119            cb(Qcur, "Qcur", il);120            cb(Kcur, "Kcur", il);121            cb(Vcur, "Vcur", il);122 123            Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head)));124            cb(Qcur, "Qcur", il);125 126            cur = build_attn(inp_attn,127                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,128                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);129        }130        if (il == n_layer - 1 && inp_out_ids) {131            cur      = ggml_get_rows(ctx0, cur,      inp_out_ids);132            residual = ggml_get_rows(ctx0, residual, inp_out_ids);133        }134        cur = ggml_add(ctx0, cur, residual);135        residual = cur;136 137        cur = build_norm(cur,138                model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,139                LLM_NORM_RMS, il);140        cb(cur, "ffn_norm", il);141 142        // feed-forward network143        if (model.layers[il].ffn_gate_inp == nullptr) {144            cur = build_ffn(cur,145                    model.layers[il].ffn_up,   NULL, NULL,146                    NULL,                      NULL, NULL,147                    model.layers[il].ffn_down, NULL, NULL,148                    NULL,149                    LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);150            cb(cur, "ffn_out", il);151        } else {152            // MoE branch153            cur = build_moe_ffn(cur,154                    model.layers[il].ffn_gate_inp,155                    model.layers[il].ffn_up_exps,156                    model.layers[il].ffn_gate_exps,157                    model.layers[il].ffn_down_exps,158                    nullptr,159                    n_expert, n_expert_used,160                    LLM_FFN_SILU, true,161                    hparams.expert_weights_scale,162                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,163                    il);164            cb(cur, "ffn_moe_out", il);165        }166        cur = ggml_add(ctx0, residual, cur);167 168        cur = build_cvec(cur, il);169        cb(cur, "l_out", il);170 171        // input for next layer172        inpL = cur;173    }174    cur = build_norm(inpL,175            model.output_norm,176            model.output_norm_b,177            LLM_NORM_RMS, -1);178 179    cb(cur, "result_norm", -1);180    res->t_embd = cur;181 182    cur = build_lora_mm(model.output, cur, model.output_s);183 184    if (model.output_b != nullptr) {185        cb(cur, "result_output_no_bias", -1);186        cur = ggml_add(ctx0, cur, model.output_b);187    }188    cb(cur, "result_output", -1);189    res->t_logits = cur;190 191    ggml_build_forward_expand(gf, cur);192}193 194// Explicit template instantiations195template struct llama_model_phi3::graph<false>;196template struct llama_model_phi3::graph<true>;197