Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_mistral3::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);5    ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale, false);6 7    ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_FAST, hparams.yarn_beta_fast,    false);8    ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, hparams.yarn_beta_slow,    false);9    ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL,   hparams.rope_yarn_log_mul, false);10 11    hparams.f_attn_temp_offset = 0.0f;12 13    // TODO: maybe add n_attn_temp_floor_scale as a separate KV?14    if (hparams.f_attn_temp_scale != 0.0f) {15        hparams.n_attn_temp_floor_scale = hparams.n_ctx_orig_yarn;16        if (hparams.n_attn_temp_floor_scale == 0) {17            throw std::runtime_error("invalid n_ctx_orig_yarn for attention temperature scaling");18        }19    }20 21    switch (hparams.n_layer()) {22        case 26: type = LLM_TYPE_3B; break;23        case 34: type = LLM_TYPE_8B; break;24        case 40: type = LLM_TYPE_14B; break;25        default: type = LLM_TYPE_UNKNOWN;26    }27}28 29void llama_model_mistral3::load_arch_tensors(llama_model_loader &) {30    LLAMA_LOAD_LOCALS;31 32    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);33 34    // output35    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);36    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);37 38    // if output is NULL, init from the input tok embed39    if (output == NULL) {40        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);41    }42 43    for (int i = 0; i < n_layer; ++i) {44        auto & layer = layers[i];45 46        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);47 48        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);49        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);50 51        // optional bias tensors52        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);53 54        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);55 56        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {57            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));58            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));59        }60        else {61            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));62        }63 64        if (n_expert == 0) {65            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            // optional MLP bias70            layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);71            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);72            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);73        } else {74            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);75            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, TENSOR_NOT_REQUIRED);76            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);77            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);78 79            // For Granite MoE Shared80            if (hparams.n_ff_shexp > 0) {81                layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);82                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, hparams.n_ff_shexp}, 0);83                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);84            }85        }86    }87}88 89std::unique_ptr<llm_graph_context> llama_model_mistral3::build_arch_graph(const llm_graph_params & params) const {90    return std::make_unique<graph>(*this, params);91}92 93llama_model_mistral3::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {94    const int64_t n_embd_head = hparams.n_embd_head_v();95 96    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());97    GGML_ASSERT(n_embd_head == n_rot);98 99    ggml_tensor * cur;100    ggml_tensor * inpL;101 102    inpL = build_inp_embd(model.tok_embd);103 104    // inp_pos - contains the positions105    ggml_tensor * inp_pos = build_inp_pos();106 107    // (optional) temperature tuning108    ggml_tensor * inp_attn_scale = nullptr;109    if (hparams.f_attn_temp_scale != 0.0f) {110        inp_attn_scale = build_inp_attn_scale();111    }112 113    auto * inp_attn = build_attn_inp_kv();114 115    const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;116 117    ggml_tensor * inp_out_ids = build_inp_out_ids();118 119    for (int il = 0; il < n_layer; ++il) {120        ggml_tensor * inpSA = inpL;121 122        // norm123        cur = build_norm(inpL,124                model.layers[il].attn_norm, NULL,125                LLM_NORM_RMS, il);126        cb(cur, "attn_norm", il);127 128        // self-attention129        {130            // rope freq factors for llama3; may return nullptr for llama2 and other models131            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);132 133            // compute Q and K and RoPE them134            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,135                    n_embd_head, n_head, n_head_kv, il);136 137            Qcur = ggml_rope_ext(138                    ctx0, Qcur, inp_pos, rope_factors,139                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,140                    ext_factor, attn_factor, beta_fast, beta_slow141                    );142 143            Kcur = ggml_rope_ext(144                    ctx0, Kcur, inp_pos, rope_factors,145                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,146                    ext_factor, attn_factor, beta_fast, beta_slow147                    );148 149            cb(Qcur, "Qcur", il);150            cb(Kcur, "Kcur", il);151            cb(Vcur, "Vcur", il);152 153            if (inp_attn_scale) {154                // apply llama 4 temperature scaling155                Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale);156                cb(Qcur, "Qcur_attn_temp_scaled", il);157            }158 159            cur = build_attn(inp_attn,160                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,161                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);162            cb(cur, "attn_out", il);163        }164        if (il == n_layer - 1 && inp_out_ids) {165            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);166            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);167        }168        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);169        cb(ffn_inp, "ffn_inp", il);170 171        // feed-forward network (non-MoE)172        if (model.layers[il].ffn_gate_inp == nullptr) {173 174            cur = build_norm(ffn_inp,175                    model.layers[il].ffn_norm, NULL,176                    LLM_NORM_RMS, il);177            cb(cur, "ffn_norm", il);178 179            cur = build_ffn(cur,180                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   model.layers[il].ffn_up_s,181                    model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, model.layers[il].ffn_gate_s,182                    model.layers[il].ffn_down, model.layers[il].ffn_down_b, model.layers[il].ffn_down_s,183                    NULL,184                    LLM_FFN_SILU, LLM_FFN_PAR, il);185            cb(cur, "ffn_out", il);186        } else {187            // MoE branch188            cur = build_norm(ffn_inp,189                    model.layers[il].ffn_norm, NULL,190                    LLM_NORM_RMS, il);191            cb(cur, "ffn_norm", il);192 193            cur = build_moe_ffn(cur,194                    model.layers[il].ffn_gate_inp,195                    model.layers[il].ffn_up_exps,196                    model.layers[il].ffn_gate_exps,197                    model.layers[il].ffn_down_exps,198                    nullptr,199                    n_expert, n_expert_used,200                    LLM_FFN_SILU, true,201                    hparams.expert_weights_scale,202                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,203                    il,204                    nullptr, nullptr,205                    model.layers[il].ffn_up_exps_s,206                    model.layers[il].ffn_gate_exps_s,207                    model.layers[il].ffn_down_exps_s);208            cb(cur, "ffn_moe_out", il);209        }210        cur = ggml_add(ctx0, cur, ffn_inp);211        cb(cur, "ffn_out", il);212 213        cur = build_cvec(cur, il);214        cb(cur, "l_out", il);215 216        // input for next layer217        inpL = cur;218    }219    cur = inpL;220 221    cur = build_norm(cur,222            model.output_norm, NULL,223            LLM_NORM_RMS, -1);224 225    cb(cur, "result_norm", -1);226    res->t_embd = cur;227 228    // lm_head229    cur = build_lora_mm(model.output, cur, model.output_s);230 231    cb(cur, "result_output", -1);232    res->t_logits = cur;233 234    ggml_build_forward_expand(gf, cur);235}236