Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_deepseek::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 10    switch (hparams.n_ff_exp()) {11        case 1408: type = LLM_TYPE_16B; break;12        case 1792: type = LLM_TYPE_20B; break;13        default: type = LLM_TYPE_UNKNOWN;14    }15}16 17void llama_model_deepseek::load_arch_tensors(llama_model_loader &) {18    LLAMA_LOAD_LOCALS;19    const int64_t n_expert_shared = hparams.n_expert_shared;20 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    // output27    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);28    // try to load output.weight, if not found, use token_embd (tied embeddings)29    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);30    if (!output) {31        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);32    }33 34    for (int i = 0; i < n_layer; ++i) {35        auto & layer = layers[i];36 37        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);38 39        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);40        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);41        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);42 43        if (i < (int) hparams.n_layer_dense_lead) {44            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);45            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);46            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);47        } else {48            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);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_deepseek::build_arch_graph(const llm_graph_params & params) const {71    return std::make_unique<graph>(*this, params);72}73 74llama_model_deepseek::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    const float kq_scale =92        hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;93 94    ggml_tensor * inp_out_ids = build_inp_out_ids();95 96    for (int il = 0; il < n_layer; ++il) {97        ggml_tensor * inpSA = inpL;98 99        // norm100        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);101        cb(cur, "attn_norm", il);102 103        // self-attention104        {105            // rope freq factors for llama3; may return nullptr for llama2 and other models106            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);107 108            // compute Q and K and RoPE them109            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,110                    n_embd_head, n_head, n_head_kv, il);111 112            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,113                                 ext_factor, attn_factor, beta_fast, beta_slow);114 115            Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, rope_factors, 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, kq_scale, 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        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);134        cb(cur, "ffn_norm", il);135 136        if ((uint32_t) il < hparams.n_layer_dense_lead) {137            cur = build_ffn(cur,138                    model.layers[il].ffn_up, NULL, NULL,139                    model.layers[il].ffn_gate, NULL, NULL,140                    model.layers[il].ffn_down, NULL, NULL,141                    NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);142            cb(cur, "ffn_out", il);143        } else {144            // MoE branch145            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                nullptr,151                n_expert, n_expert_used,152                LLM_FFN_SILU, false,153                hparams.expert_weights_scale,154                LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,155                il);156            cb(moe_out, "ffn_moe_out", il);157 158            // FFN shared expert159            {160                ggml_tensor * ffn_shexp =161                    build_ffn(cur,162                        model.layers[il].ffn_up_shexp, NULL, NULL,163                        model.layers[il].ffn_gate_shexp, NULL, NULL,164                        model.layers[il].ffn_down_shexp, NULL, NULL,165                        NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);166                cb(ffn_shexp, "ffn_shexp", il);167 168                cur = ggml_add(ctx0, moe_out, ffn_shexp);169                cb(cur, "ffn_out", il);170            }171        }172        cur = ggml_add(ctx0, cur, ffn_inp);173 174        cur = build_cvec(cur, il);175        cb(cur, "l_out", il);176 177        // input for next layer178        inpL = cur;179    }180    cur = inpL;181 182    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);183 184    cb(cur, "result_norm", -1);185    res->t_embd = cur;186 187    // lm_head188    cur = build_lora_mm(model.output, cur, model.output_s);189 190    cb(cur, "result_output", -1);191    res->t_logits = cur;192 193    ggml_build_forward_expand(gf, cur);194}195