Brunobkr/llama.cpp_AlgMor24_github

1#include "models.h"2 3void llama_model_openai_moe::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_ATTENTION_SLIDING_WINDOW,    hparams.n_swa);7 8    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;9    uint32_t swa_period = 2;10    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);11    hparams.set_swa_pattern(swa_period);12 13    hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;14    hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;15    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);16 17    switch (hparams.n_layer()) {18        case 24: type = LLM_TYPE_20B; break;19        case 36: type = LLM_TYPE_120B; break;20        default: type = LLM_TYPE_UNKNOWN;21    }22}23 24void llama_model_openai_moe::load_arch_tensors(llama_model_loader &) {25    LLAMA_LOAD_LOCALS;26 27    const int64_t n_ff_exp = hparams.n_ff_exp;28 29    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);30 31    // output32    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);33    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);34 35    for (int i = 0; i < n_layer; ++i) {36        auto & layer = layers[i];37 38        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), {n_embd}, 0);39        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);40 41        create_tensor_qkv(layer, i, n_embd, n_head * n_rot, n_head_kv * n_rot, n_head_kv * n_rot, 0);42        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_rot, n_embd}, 0);43 44        layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, 0);45 46        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {  n_embd, n_expert}, 0);47        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);48        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);49        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);50 51        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);52 53        layer.ffn_gate_inp_b  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "bias", i), {n_expert}, 0);54        layer.ffn_gate_exps_b = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "bias", i), {n_ff_exp, n_expert}, 0);55        layer.ffn_down_exps_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "bias", i), {  n_embd, n_expert}, 0);56        layer.ffn_up_exps_b   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "bias", i), {n_ff_exp, n_expert}, 0);57    }58}59 60std::unique_ptr<llm_graph_context> llama_model_openai_moe::build_arch_graph(const llm_graph_params & params) const {61    return std::make_unique<graph>(*this, params);62}63 64llama_model_openai_moe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {65    ggml_tensor * cur;66    ggml_tensor * inpL;67 68    inpL = build_inp_embd(model.tok_embd);69 70    // inp_pos - contains the positions71    ggml_tensor * inp_pos = build_inp_pos();72 73    auto * inp_attn = build_attn_inp_kv_iswa();74 75    ggml_tensor * inp_out_ids = build_inp_out_ids();76 77    for (int il = 0; il < n_layer; ++il) {78        res->t_layer_inp[il] = inpL;79 80        const float freq_base_l  = model.get_rope_freq_base (cparams, il);81        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);82 83        ggml_tensor * inpSA = inpL;84 85        // norm86        cur = build_norm(inpL,87                model.layers[il].attn_norm, nullptr,88                LLM_NORM_RMS, il);89        cb(cur, "attn_norm", il);90 91        // self-attention92        {93            // compute Q and K and RoPE them94            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,95                    n_rot, n_head, n_head_kv, il);96 97            Qcur = ggml_rope_ext(98                    ctx0, Qcur, inp_pos, nullptr,99                    n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,100                    ext_factor, attn_factor, beta_fast, beta_slow101                    );102 103            Kcur = ggml_rope_ext(104                    ctx0, Kcur, inp_pos, nullptr,105                    n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,106                    ext_factor, attn_factor, beta_fast, beta_slow107                    );108 109            cb(Qcur, "Qcur", il);110            cb(Kcur, "Kcur", il);111            cb(Vcur, "Vcur", il);112 113            cur = build_attn(inp_attn,114                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,115                    Qcur, Kcur, Vcur, nullptr, model.layers[il].attn_sinks, nullptr, 1.0f/sqrtf(float(n_rot)), il);116 117            cb(cur, "attn_out", il);118        }119        if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {120            // skip computing output for unused tokens121            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);122            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);123        }124        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);125        cb(ffn_inp, "ffn_inp", il);126 127        cur = ffn_inp;128        cur = build_norm(cur,129                model.layers[il].attn_post_norm, nullptr,130                LLM_NORM_RMS, il);131        cb(cur, "attn_post_norm", il);132 133        // MoE branch134        cur = build_moe_ffn(cur,135                model.layers[il].ffn_gate_inp,  model.layers[il].ffn_gate_inp_b,136                model.layers[il].ffn_up_exps,   model.layers[il].ffn_up_exps_b,137                model.layers[il].ffn_gate_exps, model.layers[il].ffn_gate_exps_b,138                model.layers[il].ffn_down_exps, model.layers[il].ffn_down_exps_b,139                nullptr,140                n_expert, n_expert_used,141                LLM_FFN_SWIGLU_OAI_MOE, false,142                hparams.expert_weights_scale,143                LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX_WEIGHT,144                il);145        cb(cur, "ffn_moe_out", il);146 147        cur = ggml_add(ctx0, cur, ffn_inp);148 149        cur = build_cvec(cur, il);150        cb(cur, "l_out", il);151 152        // input for next layer153        inpL = cur;154    }155    cur = inpL;156 157    res->t_h_nextn = cur;158 159    if (!cparams.embeddings_nextn_masked && inp_out_ids) {160        cur = ggml_get_rows(ctx0, cur, inp_out_ids);161    }162 163    cur = build_norm(cur,164            model.output_norm, NULL,165            LLM_NORM_RMS, -1);166 167    cb(cur, "result_norm", -1);168    res->t_embd = cur;169 170    // lm_head171    cur = build_lora_mm(model.output, cur, model.output_s);172 173    cb(cur, "result_output", -1);174    res->t_logits = cur;175 176    ggml_build_forward_expand(gf, cur);177}178