Brunobkr/llama.cpp_AlgMor24_github

1#include "models.h"2 3void llama_model_gemma2::load_arch_hparams(llama_model_loader & ml) {4    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;5    hparams.n_swa = 4096; // default value of gemma 26    uint32_t swa_period = 2;7    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);8    hparams.set_swa_pattern(swa_period);9    hparams.attn_soft_cap = true;10    hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;11    hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;12 13    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,          hparams.rope_freq_base_train_swa, false);14    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa, false);15    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);16    ml.get_key(LLM_KV_ATTN_LOGIT_SOFTCAPPING,      hparams.f_attn_logit_softcapping, false);17    ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING,     hparams.f_final_logit_softcapping, false);18 19    switch (hparams.n_layer()) {20        case 26: type = LLM_TYPE_2B; break;21        case 42: type = LLM_TYPE_9B; break;22        case 46: type = LLM_TYPE_27B; break;23        default: type = LLM_TYPE_UNKNOWN;24   }25 26    // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/config.py#L17327    hparams.f_attention_scale = type == LLM_TYPE_27B28        ? 1.0f / std::sqrt(float(hparams.n_embd / hparams.n_head(0)))29        : 1.0f / std::sqrt(float(hparams.n_embd_head_k()));30}31 32void llama_model_gemma2::load_arch_tensors(llama_model_loader &) {33    LLAMA_LOAD_LOCALS;34 35    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);36 37    // output38    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);39    output      = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading40 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_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);47        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);48        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);49 50        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);51        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);52        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);53        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);54        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);55    }56}57 58std::unique_ptr<llm_graph_context> llama_model_gemma2::build_arch_graph(const llm_graph_params & params) const {59    return std::make_unique<graph>(*this, params);60}61 62llama_model_gemma2::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {63    const int64_t n_embd_head = hparams.n_embd_head_k();64 65    ggml_tensor * cur;66    ggml_tensor * inpL;67 68    inpL = build_inp_embd(model.tok_embd);69 70    inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));71    cb(inpL, "inp_scaled", -1);72 73    // inp_pos - contains the positions74    ggml_tensor * inp_pos = build_inp_pos();75 76    auto * inp_attn = build_attn_inp_kv_iswa();77 78    ggml_tensor * inp_out_ids = build_inp_out_ids();79 80    for (int il = 0; il < n_layer; ++il) {81        const float freq_base_l  = model.get_rope_freq_base (cparams, il);82        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);83 84        // norm85        cur = build_norm(inpL,86                model.layers[il].attn_norm, NULL,87                LLM_NORM_RMS, il);88        cb(cur, "attn_norm", il);89 90        // self-attention91        {92            // compute Q and K and RoPE them93            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,94                    n_embd_head, n_head, n_head_kv, il);95 96            Qcur = ggml_rope_ext(97                    ctx0, Qcur, inp_pos, nullptr,98                    n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,99                    ext_factor, attn_factor, beta_fast, beta_slow);100 101            Kcur = ggml_rope_ext(102                    ctx0, Kcur, inp_pos, nullptr,103                    n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,104                    ext_factor, attn_factor, beta_fast, beta_slow);105 106            cb(Qcur, "Qcur", il);107            cb(Kcur, "Kcur", il);108            cb(Vcur, "Vcur", il);109 110            Qcur = ggml_scale(ctx0, Qcur, hparams.f_attention_scale);111 112            cur = build_attn(inp_attn,113                    model.layers[il].wo, NULL, model.layers[il].wo_s,114                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);115        }116        if (il == n_layer - 1 && inp_out_ids) {117            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);118            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);119        }120        cur = build_norm(cur,121                model.layers[il].attn_post_norm, NULL,122                LLM_NORM_RMS, il);123        cb(cur, "attn_post_norm", il);124 125        ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);126        cb(sa_out, "sa_out", il);127 128        cur = build_norm(sa_out,129                model.layers[il].ffn_norm, NULL,130                LLM_NORM_RMS, il);131        cb(cur, "ffn_norm", il);132 133        // feed-forward network134        {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,140                    LLM_FFN_GELU, LLM_FFN_PAR, il);141            cb(cur, "ffn_out", il);142        }143        cur = build_norm(cur,144                model.layers[il].ffn_post_norm, NULL,145                LLM_NORM_RMS, -1);146        cb(cur, "ffn_post_norm", -1);147 148        cur = ggml_add(ctx0, cur, sa_out);149 150        cur = build_cvec(cur, il);151        cb(cur, "l_out", il);152 153        // input for next layer154        inpL = cur;155    }156    cur = inpL;157 158    cur = build_norm(cur,159            model.output_norm, NULL,160            LLM_NORM_RMS, -1);161 162    cb(cur, "result_norm", -1);163    res->t_embd = cur;164 165    // lm_head166    cur = build_lora_mm(model.output, cur, model.output_s);167 168    // final logit soft-capping169    cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_final_logit_softcapping);170    cur = ggml_tanh(ctx0, cur);171    cur = ggml_scale(ctx0, cur, hparams.f_final_logit_softcapping);172 173    cb(cur, "result_output", -1);174    res->t_logits = cur;175 176    ggml_build_forward_expand(gf, cur);177}178