Felipe97/llama-cpp-compiled

1#include "models.h"2 3#include <sstream>4 5void llama_model_granite_swa::load_arch_hparams(llama_model_loader & ml) {6    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);7    ml.get_key(LLM_KV_LOGIT_SCALE,                 hparams.f_logit_scale);8    ml.get_key(LLM_KV_RESIDUAL_SCALE,              hparams.f_residual_scale, false);9    ml.get_key(LLM_KV_EMBEDDING_SCALE,             hparams.f_embedding_scale, false);10    ml.get_key(LLM_KV_ATTENTION_SCALE,             hparams.f_attention_scale, false);11 12    // MoE expert configuration13    ml.get_key(LLM_KV_EXPERT_COUNT,                hparams.n_expert, false);14    ml.get_key_or_arr(LLM_KV_EXPERT_USED_COUNT,    hparams.n_expert_used_arr, hparams.n_layer_all, false);15 16     // iSWA configuration17    ml.get_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl);18    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);19    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;20 21    // Granite4 Vision uses array deepstack_mapping22    ml.get_arr(LLM_KV_DEEPSTACK_MAPPING, hparams.deepstack_mapping_arr, false);23 24    // Count the unique deepstack input indices25    std::unordered_set<uint32_t> unique_deepstack_idxs;26    for (const auto val : hparams.deepstack_mapping_arr) {27        if (val >= 0) {28            unique_deepstack_idxs.insert(val);29        }30    }31    hparams.n_deepstack_layers = unique_deepstack_idxs.size();32 33    // Ensure all values are valid (avoid overflow attacks)34    for (const auto val : unique_deepstack_idxs) {35        if (val > hparams.n_deepstack_layers) {36            std::stringstream ss;37            ss << "Invalid deepstack index: " << val << " > " << hparams.n_deepstack_layers;38            throw std::runtime_error(ss.str());39        }40    }41 42    // Per-layer RoPE pattern (optional)43    ml.get_arr(LLM_KV_ATTENTION_ROPE_PATTERN, hparams.rope_pattern, false);44 45    switch (hparams.n_layer()) {46        case 32: type = LLM_TYPE_3B; break;47        case 40: type = LLM_TYPE_3B; break;48        // Add additional layer/vocab/etc checks here for other model sizes49        default: type = LLM_TYPE_UNKNOWN;50    }51 52    // For Granite MoE Shared53    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, /* required */ false);54}55 56void llama_model_granite_swa::load_arch_tensors(llama_model_loader &) {57    LLAMA_LOAD_LOCALS;58 59    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);60 61    // output62    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);63    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);64 65    // if output is NULL, init from the input tok embed66    if (output == NULL) {67        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);68    }69 70    for (int i = 0; i < n_layer; ++i) {71        auto & layer = layers[i];72 73        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);74 75        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);76        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);77 78        // optional bias tensors79        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);80 81        // Per-layer attention sinks for iSWA82        layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, 0);83 84        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);85 86        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {87            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));88            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));89        }90        else {91            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));92        }93 94        if (n_expert == 0) {95            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);96            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);97            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);98 99            // optional MLP bias100            layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);101            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);102            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);103        } else {104            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);105            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);106            create_tensor_gate_up_exps(layer, i, n_embd, n_ff, n_expert, 0);107 108            // For Granite MoE Shared - gate+up kept fused in ffn_up_shexp (see LLM_FFN_SWIGLU below)109            if (hparams.n_ff_shexp > 0) {110                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, 2*hparams.n_ff_shexp}, 0);111                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);112            }113        }114    }115}116 117std::unique_ptr<llm_graph_context> llama_model_granite_swa::build_arch_graph(const llm_graph_params & params) const {118    return std::make_unique<graph>(*this, params);119}120 121llama_model_granite_swa::graph::graph(122    const llama_model & model,123    const llm_graph_params & params)124    : llm_graph_context(params) {125 126    const int64_t n_embd_head = hparams.n_embd_head_v();127 128    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());129    GGML_ASSERT(n_embd_head == n_rot);130 131    ggml_tensor * cur;132    ggml_tensor * inpL;133 134    inpL = build_inp_embd(model.tok_embd);135 136    // inp_pos - built only if rope enabled137    ggml_tensor * inp_pos = build_inp_pos();138    auto * inp_attn = build_attn_inp_kv_iswa();139 140    ggml_tensor * inp_out_ids = build_inp_out_ids();141 142    for (int il = 0; il < n_layer; ++il) {143 144        // Granite Vision 4.1 deepstack: inject the projector stream that145        // targets decoder layer `il` before the decoder runs.146        // NOTE: skip the first deepstack layer since that's inpL147        const auto & deepstack_emb_idx = hparams.deepstack_mapping_arr[il];148        if (il > 0 && deepstack_emb_idx >= 0) {149            ggml_tensor * ds = ggml_view_2d(ctx0,150                res->t_inp_embd, n_embd, n_tokens,151                res->t_inp_embd->nb[1],152                deepstack_emb_idx * n_embd * sizeof(float));153            inpL = ggml_add(ctx0, inpL, ds);154            cb(inpL, "deepstack_in", il);155        }156 157        ggml_tensor * inpSA = inpL;158 159        // norm160        cur = build_norm(inpL,161                model.layers[il].attn_norm, NULL,162                LLM_NORM_RMS, il);163        cb(cur, "attn_norm", il);164 165        // self-attention166        cur = build_attention_layer(167            cur, inp_pos, inp_attn,168            model, n_embd_head, il);169 170        if (il == n_layer - 1 && inp_out_ids) {171            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);172            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);173        }174        // ffn175        cur = build_layer_ffn(cur, inpSA, model, il);176 177        // input for next layer178        inpL = cur;179    }180    cur = inpL;181 182    cur = build_norm(cur,183            model.output_norm, NULL,184            LLM_NORM_RMS, -1);185 186    cb(cur, "result_norm", -1);187    res->t_embd = cur;188 189    // lm_head190    cur = build_lora_mm(model.output, cur, model.output_s);191 192    // For Granite architectures - scale logits193    cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);194    cb(cur, "result_output", -1);195    res->t_logits = cur;196 197    ggml_build_forward_expand(gf, cur);198}199 200ggml_tensor * llama_model_granite_swa::graph::build_attention_layer(201          ggml_tensor                  * cur,202          ggml_tensor                  * inp_pos,203          llm_graph_input_attn_kv_iswa * inp_attn,204    const llama_model                  & model,205    const int64_t                        n_embd_head,206    const int                            il) {207 208    auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,209            n_embd_head, hparams.n_head(il), hparams.n_head_kv(il), il);210 211    const bool use_rope = hparams.has_rope(il);212    if (use_rope) {213        ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);214        Qcur = ggml_rope_ext(215                ctx0, Qcur, inp_pos, rope_factors,216                n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,217                ext_factor, attn_factor, beta_fast, beta_slow218                );219 220        Kcur = ggml_rope_ext(221                ctx0, Kcur, inp_pos, rope_factors,222                n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,223                ext_factor, attn_factor, beta_fast, beta_slow224                );225    }226 227    cb(Qcur, "Qcur", il);228    cb(Kcur, "Kcur", il);229    cb(Vcur, "Vcur", il);230 231    const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;232 233    // Pass layer.attn_sinks to build_attn for sink-based attention modulation234    cur = build_attn(inp_attn,235            model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,236            Qcur, Kcur, Vcur, nullptr, model.layers[il].attn_sinks, nullptr, kq_scale, il);237    cb(cur, "attn_out", il);238    return cur;239}240 241ggml_tensor * llama_model_granite_swa::graph::build_layer_ffn(242          ggml_tensor * cur,243          ggml_tensor * inpSA,244    const llama_model & model,245    const int           il) {246 247    // For Granite architectures - scale residual248    if (hparams.f_residual_scale) {249        cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);250    }251    ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);252    cb(ffn_inp, "ffn_inp", il);253 254    // feed-forward network (non-MoE)255    if (model.layers[il].ffn_gate_inp == nullptr) {256 257        cur = build_norm(ffn_inp,258                model.layers[il].ffn_norm, NULL,259                LLM_NORM_RMS, il);260        cb(cur, "ffn_norm", il);261 262        cur = build_ffn(cur,263                model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,264                model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,265                model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,266                NULL,267                LLM_FFN_SILU, LLM_FFN_PAR, il);268        cb(cur, "ffn_out", il);269 270    } else {271        // MoE branch272        cur = build_norm(ffn_inp,273                model.layers[il].ffn_norm, NULL,274                LLM_NORM_RMS, il);275        cb(cur, "ffn_norm", il);276 277        ggml_tensor * moe_out = build_moe_ffn(cur,278                model.layers[il].ffn_gate_inp,279                model.layers[il].ffn_up_exps,280                model.layers[il].ffn_gate_exps,281                model.layers[il].ffn_down_exps,282                nullptr,283                n_expert, n_expert_used,284                LLM_FFN_SILU, true,285                hparams.expert_weights_scale,286                LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,287                il,288                nullptr, model.layers[il].ffn_gate_up_exps);289        cb(moe_out, "ffn_moe_out", il);290 291        // For Granite MoE Shared - gate+up kept fused in ffn_up_shexp292        if (hparams.n_ff_shexp > 0) {293            ggml_tensor * ffn_shexp = build_ffn(cur,294                model.layers[il].ffn_up_shexp,   NULL, NULL,295                NULL,                            NULL, NULL,296                model.layers[il].ffn_down_shexp, NULL, NULL,297                NULL,298                LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);299            cb(ffn_shexp, "ffn_shexp", il);300 301            cur = ggml_add(ctx0, moe_out, ffn_shexp);302            cb(cur, "ffn_out", il);303        } else {304            cur = moe_out;305        }306    }307 308    // For Granite architectures - scale residual309    if (hparams.f_residual_scale) {310        cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);311    }312    cur = ggml_add(ctx0, cur, ffn_inp);313    cb(cur, "ffn_out", il);314 315    cur = build_cvec(cur, il);316    cb(cur, "l_out", il);317 318    return cur;319}320