Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_ernie4_5::load_arch_hparams(llama_model_loader & ml) {4    // paddleocr need mrope_section5    ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);6 7    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);8    if (arch == LLM_ARCH_ERNIE4_5_MOE) {9        ml.get_key_or_arr(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_arr, hparams.n_layer_all);10        ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);11        ml.get_key(LLM_KV_INTERLEAVE_MOE_LAYER_STEP,         hparams.n_moe_layer_step);12        ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,         hparams.n_layer_dense_lead, false);13    }14 15    switch (hparams.n_layer()) {16        case 18: type = LLM_TYPE_0_3B; break;17        case 28: type = LLM_TYPE_21B_A3B; break;18        case 54: type = LLM_TYPE_300B_A47B; break;19        default: type = LLM_TYPE_UNKNOWN;20    }21}22 23void llama_model_ernie4_5::load_arch_tensors(llama_model_loader &) {24    LLAMA_LOAD_LOCALS;25 26    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);27 28    // output29    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);30    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);31    // if output is NULL, init from the input tok embed32    if (output == NULL) {33        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);34    }35 36    for (int i = 0; i < n_layer; ++i) {37        auto & layer = layers[i];38 39        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);40 41        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);42        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);43 44        // optional bias tensors45        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);46 47        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);48 49        if (arch == LLM_ARCH_ERNIE4_5_MOE && static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead) { // MoE layers50            int n_ff_exp = hparams.n_ff_exp();51 52            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);53            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);54            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED);55            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff_exp, n_embd, n_expert}, 0);56            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff_exp, n_expert}, 0);57 58            // Shared expert (if present)59            if (hparams.n_ff_shexp > 0) {60                layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {    n_embd, hparams.n_ff_shexp}, 0);61                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd    }, 0);62                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {    n_embd, hparams.n_ff_shexp}, 0);63            }64        } else { // Dense layers65            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);66            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);67            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);68        }69    }70}71 72std::unique_ptr<llm_graph_context> llama_model_ernie4_5::build_arch_graph(const llm_graph_params & params) const {73    return std::make_unique<graph>(*this, params);74}75 76llama_model_ernie4_5::graph::graph(const llama_model & model, const llm_graph_params & params) :77    llm_graph_context(params) {78    const int64_t n_embd_head = hparams.n_embd_head_v();79 80    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());81    GGML_ASSERT(n_embd_head == n_rot);82 83    ggml_tensor * cur;84    ggml_tensor * inpL;85 86    inpL = build_inp_embd(model.tok_embd);87 88    // inp_pos - contains the positions89    ggml_tensor * inp_pos = build_inp_pos();90 91    auto * inp_attn = build_attn_inp_kv();92 93    ggml_tensor * inp_out_ids = build_inp_out_ids();94 95    for (int il = 0; il < n_layer; ++il) {96        ggml_tensor * inpSA = inpL;97 98        // norm99        {100            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);101            cb(cur, "attn_norm", il);102        }103        // self-attention104        {105            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,106                    n_embd_head, n_head, n_head_kv, il);107 108            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,109                                 ext_factor, attn_factor, beta_fast, beta_slow);110 111            Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,112                                 ext_factor, attn_factor, beta_fast, beta_slow);113 114            cb(Qcur, "Qcur", il);115            cb(Kcur, "Kcur", il);116            cb(Vcur, "Vcur", il);117 118            cur = build_attn(inp_attn,119                    model.layers[il].wo, NULL, model.layers[il].wo_s,120                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);121        }122        if (il == n_layer - 1) {123            // skip computing output for unused tokens124            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);125            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);126        }127        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);128        cb(ffn_inp, "ffn_inp", il);129 130        // feed-forward network131        {132            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);133            cb(cur, "ffn_norm", il);134 135            cur = build_ffn(cur,136                    model.layers[il].ffn_up, NULL, NULL,137                    model.layers[il].ffn_gate, NULL, NULL,138                    model.layers[il].ffn_down, NULL, NULL,139                    NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);140            cb(cur, "ffn_out", il);141        }142        cur = ggml_add(ctx0, cur, ffn_inp);143 144        cur = build_cvec(cur, il);145        cb(cur, "l_out", il);146 147        // input for next layer148        inpL = cur;149    }150    cur = inpL;151 152    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);153 154    cb(cur, "result_norm", -1);155    res->t_embd = cur;156 157    // lm_head158    cur = build_lora_mm(model.output, cur, model.output_s);159 160    cb(cur, "result_output", -1);161    res->t_logits = cur;162 163    ggml_build_forward_expand(gf, cur);164}165