Brunobkr/llama.cpp_AlgMor24_github

1#include "models.h"2 3void llama_model_llama4::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_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);6    ml.get_key(LLM_KV_INTERLEAVE_MOE_LAYER_STEP,   hparams.n_moe_layer_step);7 8    const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);9    if (found_swa && hparams.n_swa == 0) {10        hparams.swa_type             = LLAMA_SWA_TYPE_NONE;11        hparams.n_no_rope_layer_step = hparams.n_layer(); // always use rope12    } else {13        hparams.swa_type                = LLAMA_SWA_TYPE_CHUNKED;14        hparams.n_swa                   = 8192;15        hparams.n_attn_temp_floor_scale = 8192;16        hparams.f_attn_temp_scale       = 0.1f;17        hparams.f_attn_temp_offset      = 1.0f;18 19        uint32_t swa_period = 4; // pattern: 3 chunked - 1 full20        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);21        hparams.set_swa_pattern(swa_period);22 23        hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;24        hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;25        ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);26    }27 28    switch (hparams.n_expert) {29        case 0: {30            // MobileLLM (no MoE)31            switch (hparams.n_embd) {32                case 2048: type = LLM_TYPE_140M; break;33                case 4096: type = LLM_TYPE_360M; break;34                case 6144: type = LLM_TYPE_950M; break;35                default:   type = LLM_TYPE_UNKNOWN;36            }37        } break;38        case 16:  type = LLM_TYPE_17B_16E; break;39        case 128: type = LLM_TYPE_17B_128E; break;40        default:  type = LLM_TYPE_UNKNOWN;41    }42 43    hparams.use_kq_norm = type != LLM_TYPE_17B_128E;44}45 46void llama_model_llama4::load_arch_tensors(llama_model_loader &) {47    LLAMA_LOAD_LOCALS;48 49    if (n_expert == 0) {50        throw std::runtime_error(arch_name() + " model cannot have zero experts");51    }52    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);53 54    // output55    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);56    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);57 58    // if output is NULL, init from the input tok embed59    if (output == NULL) {60        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);61    }62 63    for (int i = 0; i < n_layer; ++i) {64        const bool is_moe_layer = hparams.n_moe_layer_step > 0 && (i + 1) % hparams.n_moe_layer_step == 0;65 66        auto & layer = layers[i];67 68        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);69 70        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);71        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);72 73        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);74 75        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));76 77        if (is_moe_layer) {78            const int64_t n_ff_exp = hparams.n_ff_exp;79 80            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);81            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff_exp, n_expert}, 0);82            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff_exp, n_embd, n_expert}, 0);83            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff_exp, n_expert}, 0);84 85            // Shared expert86            const int64_t n_ff_shexp = n_ff_exp;87            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {    n_embd, n_ff_shexp}, 0);88            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd    }, 0);89            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {    n_embd, n_ff_shexp}, 0);90        } else {91            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);92            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);93            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);94        }95    }96}97 98std::unique_ptr<llm_graph_context> llama_model_llama4::build_arch_graph(const llm_graph_params & params) const {99    if (hparams.swa_type == LLAMA_SWA_TYPE_NONE) {100        return std::make_unique<graph<false>>(*this, params);101    } else {102        return std::make_unique<graph<true>>(*this, params);103    }104}105 106template <bool iswa>107llama_model_llama4::graph<iswa>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {108    const int64_t n_embd_head = hparams.n_embd_head_v();109 110    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());111    GGML_ASSERT(n_embd_head == n_rot);112 113    ggml_tensor * cur;114    ggml_tensor * inpL;115 116    inpL = build_inp_embd(model.tok_embd);117 118    // inp_pos - contains the positions119    ggml_tensor * inp_pos = build_inp_pos();120 121    // temperature tuning122    ggml_tensor * inp_attn_scale = nullptr;123    inp_attn_scale = build_inp_attn_scale();124 125    using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa, llm_graph_input_attn_kv>;126    inp_attn_type * inp_attn = nullptr;127 128    if constexpr (iswa) {129        inp_attn = build_attn_inp_kv_iswa();130    } else {131        inp_attn = build_attn_inp_kv();132    }133 134    const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;135 136    ggml_tensor * inp_out_ids = build_inp_out_ids();137 138    for (int il = 0; il < n_layer; ++il) {139        const float freq_base_l  = model.get_rope_freq_base (cparams, il);140        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);141 142        ggml_tensor * inpSA = inpL;143 144        // This overlaps with SWA layers in current models, so get_rope_freq_base/scale may be superfluous145        const bool use_rope = hparams.n_no_rope_layer_step > 0 &&146                              (il + 1) % hparams.n_no_rope_layer_step != 0;147 148        // norm149        cur = build_norm(inpL,150                model.layers[il].attn_norm, NULL,151                LLM_NORM_RMS, il);152        cb(cur, "attn_norm", il);153 154        // self-attention155        {156            // rope freq factors for llama3; may return nullptr for llama2 and other models157            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);158 159            // compute Q and K and RoPE them160            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,161                    n_embd_head, n_head, n_head_kv, il);162 163            if (use_rope) {164                Qcur = ggml_rope_ext(165                        ctx0, Qcur, inp_pos, rope_factors,166                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,167                        ext_factor, attn_factor, beta_fast, beta_slow168                        );169 170                Kcur = ggml_rope_ext(171                        ctx0, Kcur, inp_pos, rope_factors,172                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,173                        ext_factor, attn_factor, beta_fast, beta_slow174                        );175            } else if (inp_attn_scale) {176                Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale);177            }178            cb(Qcur, "Qcur", il);179            cb(Kcur, "Kcur", il);180            cb(Vcur, "Vcur", il);181 182            if (use_rope && hparams.use_kq_norm) {183                // Llama4TextL2Norm184                Qcur = ggml_rms_norm(ctx0, Qcur, hparams.f_norm_rms_eps);185                Kcur = ggml_rms_norm(ctx0, Kcur, hparams.f_norm_rms_eps);186                cb(Qcur, "Qcur_normed", il);187                cb(Kcur, "Kcur_normed", il);188            }189            cur = build_attn(inp_attn,190                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,191                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);192            cb(cur, "attn_out", il);193        }194        if (il == n_layer - 1 && inp_out_ids) {195            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);196            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);197        }198        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);199        cb(ffn_inp, "ffn_inp", il);200 201        // feed-forward network (non-MoE)202        if (model.layers[il].ffn_gate_inp == nullptr) {203            cur = build_norm(ffn_inp,204                    model.layers[il].ffn_norm, NULL,205                    LLM_NORM_RMS, il);206            cb(cur, "ffn_norm", il);207 208            cur = build_ffn(cur,209                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,210                    model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,211                    model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,212                    NULL,213                    LLM_FFN_SILU, LLM_FFN_PAR, il);214            cb(cur, "ffn_out", il);215        } else {216            ggml_tensor * ffn_inp_normed = build_norm(ffn_inp,217                    model.layers[il].ffn_norm, NULL,218                    LLM_NORM_RMS, il);219            cb(cur, "ffn_norm", il);220 221            ggml_tensor * moe_out = build_moe_ffn(ffn_inp_normed,222                    model.layers[il].ffn_gate_inp,223                    model.layers[il].ffn_up_exps,224                    model.layers[il].ffn_gate_exps,225                    model.layers[il].ffn_down_exps,226                    nullptr,227                    n_expert, n_expert_used,228                    LLM_FFN_SILU, false,229                    hparams.expert_weights_scale,230                    LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID,231                    il);232 233            // Shared experts234            ggml_tensor * shexp_out = build_ffn(ffn_inp_normed,235                model.layers[il].ffn_up_shexp,   NULL, NULL,236                model.layers[il].ffn_gate_shexp, NULL, NULL,237                model.layers[il].ffn_down_shexp, NULL, NULL,238                NULL,239                LLM_FFN_SILU, LLM_FFN_PAR, il);240            cb(shexp_out, "ffn_moe_shexp", il);241 242            cur = ggml_add(ctx0, moe_out, shexp_out);243            cb(cur, "ffn_moe_out_merged", il);244        }245        cur = ggml_add(ctx0, cur, ffn_inp);246        cb(cur, "ffn_out", il);247 248        cur = build_cvec(cur, il);249        cb(cur, "l_out", il);250 251        // input for next layer252        inpL = cur;253    }254    cur = inpL;255 256    cur = build_norm(cur,257            model.output_norm, NULL,258            LLM_NORM_RMS, -1);259 260    cb(cur, "result_norm", -1);261    res->t_embd = cur;262 263    // lm_head264    cur = build_lora_mm(model.output, cur, model.output_s);265 266    cb(cur, "result_output", -1);267    res->t_logits = cur;268 269    ggml_build_forward_expand(gf, cur);270}271 272// Explicit template instantiations273template struct llama_model_llama4::graph<false>;274template struct llama_model_llama4::graph<true>;275