Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_dots1::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_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);6    ml.get_key_or_arr(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_arr, hparams.n_layer_all);7    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);8    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);9    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);10    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);11 12    switch (hparams.n_layer()) {13        case 62: type = LLM_TYPE_142B; break;14        default: type = LLM_TYPE_UNKNOWN;15    }16}17 18void llama_model_dots1::load_arch_tensors(llama_model_loader &) {19    LLAMA_LOAD_LOCALS;20    const int64_t n_expert_shared = hparams.n_expert_shared;21 22    const int64_t n_ff_exp        = hparams.n_ff_exp();23 24    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);25 26    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);27    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);28 29    for (int i = 0; i < n_layer; ++i) {30        auto & layer = layers[i];31 32        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);33 34        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_head_k * n_head, n_embd_head_k * n_head, 0);35        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);36 37        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);38        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);39 40        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);41 42        if (i < (int) hparams.n_layer_dense_lead) {43            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);44            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);45            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);46        } else {47            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);48            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);49 50            if (n_expert == 0) {51                throw std::runtime_error("n_expert must be > 0");52            }53            if (n_expert_used == 0) {54                throw std::runtime_error("n_expert_used must be > 0");55            }56 57            // MoE branch58            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);59            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);60            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);61 62            // Shared expert branch63            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);64            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);65            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);66        }67    }68}69 70std::unique_ptr<llm_graph_context> llama_model_dots1::build_arch_graph(const llm_graph_params & params) const {71    return std::make_unique<graph>(*this, params);72}73 74llama_model_dots1::graph::graph(const llama_model & model, const llm_graph_params & params) :75    llm_graph_context(params) {76    const int64_t n_embd_head = hparams.n_embd_head_v();77 78    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());79    GGML_ASSERT(n_embd_head == n_rot);80 81    ggml_tensor * cur;82    ggml_tensor * inpL;83 84    inpL = build_inp_embd(model.tok_embd);85 86    // inp_pos - contains the positions87    ggml_tensor * inp_pos = build_inp_pos();88 89    auto * inp_attn = build_attn_inp_kv();90 91    ggml_tensor * inp_out_ids = build_inp_out_ids();92 93    for (int il = 0; il < n_layer; ++il) {94        ggml_tensor * inpSA = inpL;95 96        // norm97        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);98        cb(cur, "attn_norm", il);99 100        // self_attention101        {102            // compute Q and K and RoPE them103            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,104                    n_embd_head, n_head, n_head_kv, il);105 106            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);107            cb(Qcur, "Qcur_normed", il);108 109            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,110                                 ext_factor, attn_factor, beta_fast, beta_slow);111 112            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);113            cb(Kcur, "Kcur_normed", il);114 115            Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,116                                 ext_factor, attn_factor, beta_fast, beta_slow);117 118            cb(Qcur, "Qcur", il);119            cb(Kcur, "Kcur", il);120            cb(Vcur, "Vcur", il);121 122            cur = build_attn(inp_attn,123                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,124                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);125        }126        if (il == n_layer - 1 && inp_out_ids) {127            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);128            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);129        }130        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);131        cb(ffn_inp, "ffn_inp", il);132 133        // MoE branch134        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);135        cb(cur, "ffn_norm", il);136 137        if ((uint32_t) il < hparams.n_layer_dense_lead) {138            cur = build_ffn(cur,139                    model.layers[il].ffn_up, NULL, NULL,140                    model.layers[il].ffn_gate, NULL, NULL,141                    model.layers[il].ffn_down, NULL, NULL,142                    NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);143            cb(cur, "ffn_out", il);144        } else {145            ggml_tensor * moe_out = build_moe_ffn(cur,146                model.layers[il].ffn_gate_inp,147                model.layers[il].ffn_up_exps,148                model.layers[il].ffn_gate_exps,149                model.layers[il].ffn_down_exps,150                model.layers[il].ffn_exp_probs_b,151                n_expert, n_expert_used,152                LLM_FFN_SILU, hparams.expert_weights_norm,153                hparams.expert_weights_scale,154                (llama_expert_gating_func_type) hparams.expert_gating_func,155                il);156            cb(moe_out, "ffn_moe_out", il);157 158            {159                ggml_tensor * ffn_shexp =160                    build_ffn(cur,161                        model.layers[il].ffn_up_shexp, NULL, NULL,162                        model.layers[il].ffn_gate_shexp, NULL, NULL,163                        model.layers[il].ffn_down_shexp, NULL, NULL,164                        NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);165                cb(ffn_shexp, "ffn_shexp", il);166 167                cur = ggml_add(ctx0, moe_out, ffn_shexp);168                cb(cur, "ffn_out", il);169            }170        }171        cur = ggml_add(ctx0, cur, ffn_inp);172 173        cur = build_cvec(cur, il);174        cb(cur, "l_out", il);175 176        // input for next layer177        inpL = cur;178    }179    cur = inpL;180 181    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);182 183    cb(cur, "result_norm", -1);184    res->t_embd = cur;185 186    // lm_head187    cur = build_lora_mm(model.output, cur, model.output_s);188 189    cb(cur, "result_output", -1);190    res->t_logits = cur;191 192    ggml_build_forward_expand(gf, cur);193}194