Brunobkr/llama.cpp_AlgMor24_github

1#include "models.h"2 3void llama_model_smallthinker::load_arch_hparams(llama_model_loader & ml) {4    const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);5 6    if (found_swa && hparams.n_swa > 0) {7        hparams.swa_type    = LLAMA_SWA_TYPE_STANDARD;8        hparams.n_swa       = 4096;9        uint32_t swa_period = 4;10        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);11        hparams.set_swa_pattern(swa_period, true);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    } else {17        hparams.swa_type             = LLAMA_SWA_TYPE_NONE;18        hparams.n_no_rope_layer_step = hparams.n_layer();19    }20 21    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp, false);22    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);23    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);24 25    switch (hparams.n_layer()) {26        case 32: type = LLM_TYPE_4B;  break;27        case 52: type = LLM_TYPE_20B; break;28        default: type = LLM_TYPE_UNKNOWN;29    }30}31 32void llama_model_smallthinker::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_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);40 41    // if output is NULL, init from the input tok embed42    if (output == NULL) {43        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);44    }45 46    for (int i = 0; i < n_layer; ++i) {47        auto & layer = layers[i];48 49        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);50 51        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);52        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);53 54        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);55 56        GGML_ASSERT(n_expert > 0 && "n_expert must be > 0 for SMALLTHINKER");57        GGML_ASSERT(n_expert_used > 0 && "n_expert_used must be > 0 for SMALLTHINKER");58 59        // MoE branch60        const int64_t n_ff_exp = hparams.n_ff_exp;61        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, 0);62        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);63        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);64        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);65    }66}67 68std::unique_ptr<llm_graph_context> llama_model_smallthinker::build_arch_graph(const llm_graph_params & params) const {69    if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {70        return std::make_unique<graph<true>> (*this, params);71    } else {72        return std::make_unique<graph<false>>(*this, params);73    }74}75 76template <bool iswa>77llama_model_smallthinker::graph<iswa>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params){78    const int64_t n_embd_head = hparams.n_embd_head_v();79 80    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());81    GGML_ASSERT(n_embd_head == n_rot);82 83    ggml_tensor * cur;84    ggml_tensor * inpL;85 86    inpL = build_inp_embd(model.tok_embd);87 88    // inp_pos - contains the positions89    ggml_tensor * inp_pos = build_inp_pos();90 91    using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa, llm_graph_input_attn_kv>;92    inp_attn_type * inp_attn = nullptr;93 94    if constexpr (iswa) {95        inp_attn = build_attn_inp_kv_iswa();96    } else {97        inp_attn = build_attn_inp_kv();98    }99    ggml_tensor * inp_out_ids = build_inp_out_ids();100 101    for (int il = 0; il < n_layer; ++il) {102        const float freq_base_l  = model.get_rope_freq_base (cparams, il);103        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);104 105        ggml_tensor * inpSA  = inpL;106 107        // This overlaps with SWA layers in current models, so get_rope_freq_base/scale may be superfluous108        const bool use_rope = hparams.n_no_rope_layer_step == n_layer ||109                              il % hparams.n_no_rope_layer_step != 0;110 111        ggml_tensor * probs = build_lora_mm(model.layers[il].ffn_gate_inp, inpL);  // [n_expert, n_tokens]112        cb(probs, "ffn_moe_logits", il);113 114        // norm115        cur = build_norm(inpL,model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);116        cb(cur, "attn_norm", il);117 118        // self_attention119        {120            // compute Q and K and RoPE them121            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,122                    n_embd_head, n_head, n_head_kv, il);123 124            if (use_rope) {125                Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,126                                    ext_factor, attn_factor, beta_fast, beta_slow);127 128                Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,129                                    ext_factor, attn_factor, beta_fast, beta_slow);130            }131            cb(Qcur, "Qcur", il);132            cb(Kcur, "Kcur", il);133 134            cur = build_attn(inp_attn,135                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,136                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);137        }138        if (il == n_layer - 1 && inp_out_ids) {139            cur = ggml_get_rows(ctx0, cur, inp_out_ids);140            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);141            probs = ggml_get_rows(ctx0, probs, inp_out_ids);142        }143        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);144        cb(ffn_inp, "ffn_inp", il);145 146        // MoE branch147        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);148        cb(cur, "ffn_norm", il);149 150        ggml_tensor * ffn_out =151            build_moe_ffn(cur,152                    nullptr,153                    model.layers[il].ffn_up_exps,154                    model.layers[il].ffn_gate_exps,155                    model.layers[il].ffn_down_exps,156                    nullptr,157                    n_expert, n_expert_used,158                    LLM_FFN_RELU, true,159                    hparams.expert_weights_scale,160                    static_cast<llama_expert_gating_func_type>(hparams.expert_gating_func),161                    il, probs);162 163        cb(ffn_out, "ffn_out", il);164        cur = ffn_out;165 166        cur = ggml_add(ctx0, cur, ffn_inp);167 168        cur = build_cvec(cur, il);169        cb(cur, "l_out", il);170 171        // input for next layer172        inpL = cur;173    }174    cur = inpL;175 176    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);177    cb(cur, "result_norm", -1);178    res->t_embd = cur;179 180    // lm_head181    cur = build_lora_mm(model.output, cur, model.output_s);182    cb(cur, "result_output", -1);183    res->t_logits = cur;184 185    ggml_build_forward_expand(gf, cur);186}187 188// Explicit template instantiations189template struct llama_model_smallthinker::graph<false>;190template struct llama_model_smallthinker::graph<true>;191