Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_deci::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);5 6    switch (hparams.n_layer()) {7        case 32: type = LLM_TYPE_7B; break;8        case 80: type = LLM_TYPE_70B; break;9        case 162: type = LLM_TYPE_405B; break;10        default: type = LLM_TYPE_UNKNOWN;11    }12}13 14void llama_model_deci::load_arch_tensors(llama_model_loader &) {15    LLAMA_LOAD_LOCALS;16 17    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);18 19    // output20    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);21    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);22 23    // if output is NULL, init from the input tok embed24    if (output == NULL) {25        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);26    }27 28    for (int i = 0; i < n_layer; ++i) {29        auto & layer = layers[i];30        const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa(i);31        const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa(i);32        const int64_t n_ff          = hparams.n_ff(i);33        const int64_t n_head        = hparams.n_head(i);34        const int64_t n_head_kv     = hparams.n_head_kv(i);35 36        if (n_head_kv == 0 && n_head > 0) {37            // linear attention for DeciLMCausalModel38            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);39            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);40        }41        else if (n_head_kv > 0) {42            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);43 44            create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);45            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);46        }47 48        // optional bias tensors49        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);50 51        if (n_ff > 0) {52            layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);53        }54 55        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {56            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));57            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));58        }59        else {60            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));61        }62 63        if (n_ff > 0) {64            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);65            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);66            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);67        }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    }74}75 76std::unique_ptr<llm_graph_context> llama_model_deci::build_arch_graph(const llm_graph_params & params) const {77    return std::make_unique<graph>(*this, params);78}79 80llama_model_deci::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {81    const int64_t n_embd_head = hparams.n_embd_head_v();82 83    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());84    GGML_ASSERT(n_embd_head == n_rot);85 86    ggml_tensor * cur;87    ggml_tensor * inpL;88 89    inpL = build_inp_embd(model.tok_embd);90 91    // inp_pos - contains the positions92    ggml_tensor * inp_pos = build_inp_pos();93 94    auto * inp_attn = build_attn_inp_kv();95 96    const float kq_scale =97        hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;98 99    ggml_tensor * inp_out_ids = build_inp_out_ids();100 101    for (int il = 0; il < n_layer; ++il) {102        ggml_tensor * inpSA     = inpL;103        const int64_t n_head_kv = hparams.n_head_kv(il);104        const int64_t n_head    = hparams.n_head(il);105        const int64_t n_ff      = hparams.n_ff(il);106 107        if (n_head == 0) {108            // attention-free layer of Llama-3_1-Nemotron-51B109            cur = inpL;110        } else {111            // norm112            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);113            cb(cur, "attn_norm", il);114        }115        if (n_head > 0 && n_head_kv == 0) {116            // "linear attention" of Llama-3_1-Nemotron-51B117            cur = build_lora_mm(model.layers[il].wo, cur);118            cb(cur, "wo", il);119        } else if (n_head > 0) {120            // self-attention121            // rope freq factors for llama3; may return nullptr for llama2 and other models122            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);123 124            // compute Q and K and RoPE them125            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,126                    n_embd_head, n_head, n_head_kv, il);127 128            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,129                                 ext_factor, attn_factor, beta_fast, beta_slow);130 131            Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,132                                 ext_factor, attn_factor, beta_fast, beta_slow);133 134            cb(Qcur, "Qcur", il);135            cb(Kcur, "Kcur", il);136            cb(Vcur, "Vcur", il);137 138            cur = build_attn(inp_attn,139                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,140                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);141        }142        if (il == n_layer - 1 && inp_out_ids) {143            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);144            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);145        }146        // FFN-free layer of Llama-3_1-Nemotron-Ultra-253B147        if (n_ff == 0) {148            continue;149        }150        // modified to support attention-free layer of Llama-3_1-Nemotron-51B151        ggml_tensor * ffn_inp = cur;152        if (n_head > 0) {153            ffn_inp = ggml_add(ctx0, cur, inpSA);154            cb(ffn_inp, "ffn_inp", il);155        }156        // feed-forward network157        if (model.layers[il].ffn_gate_inp == nullptr) {158            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);159            cb(cur, "ffn_norm", il);160 161            cur = build_ffn(cur,162                model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,163                model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,164                model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,165                NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);166            cb(cur, "ffn_out", il);167        }168        cur = ggml_add(ctx0, cur, ffn_inp);169        cb(cur, "ffn_out", il);170 171        cur = build_cvec(cur, il);172        cb(cur, "l_out", il);173 174        // input for next layer175        inpL = cur;176    }177    cur = inpL;178 179    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);180 181    cb(cur, "result_norm", -1);182    res->t_embd = cur;183 184    // lm_head185    cur = build_lora_mm(model.output, cur, model.output_s);186 187    cb(cur, "result_output", -1);188    res->t_logits = cur;189 190    ggml_build_forward_expand(gf, cur);191}192