Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_jamba::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);5    ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);6    ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);7    ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);8 9    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);10 11    for (uint32_t i = 0; i < hparams.n_layer(); ++i) {12        hparams.is_recr_impl[i] = hparams.n_head_kv(i) == 0;13    }14 15    switch (hparams.n_layer()) {16        // TODO: Jamba layers are a bit heterogeneous, so naming this is hard.17        case 12: // 900M  8x???M18        case 32: // 51B  16x?B19        default: type = LLM_TYPE_UNKNOWN;20    }21}22 23void llama_model_jamba::load_arch_tensors(llama_model_loader &) {24    LLAMA_LOAD_LOCALS;25 26    const int64_t d_conv  = hparams.ssm_d_conv;27    const int64_t d_inner = hparams.ssm_d_inner;28    const int64_t d_state = hparams.ssm_d_state;29    const int64_t dt_rank = hparams.ssm_dt_rank;30 31    // only an expansion factor of 2 is supported for now32    GGML_ASSERT(2 * n_embd == d_inner);33 34    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);35 36    // output37    {38        output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);39 40        output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);41        // if output is NULL, init from the input tok embed, duplicated to allow offloading42        if (output == NULL) {43            output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);44        }45    }46 47    for (int i = 0; i < n_layer; ++i) {48        const int64_t n_head_kv = hparams.n_head_kv(i);49        const int64_t n_embd_gqa = hparams.n_embd_v_gqa(i);50 51        auto & layer = layers[i];52 53        // norm54        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);55 56        if (n_head_kv == 0) {57            // Mamba layer58            layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner}, 0);59 60            layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner}, 0);61            layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner}, 0);62 63            layer.ssm_x = create_tensor(tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state}, 0);64 65            layer.ssm_dt_norm = create_tensor(tn(LLM_TENSOR_SSM_DT_NORM, "weight", i), {dt_rank}, 0);66 67            layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner}, 0);68            layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}, 0);69 70            layer.ssm_b_norm = create_tensor(tn(LLM_TENSOR_SSM_B_NORM, "weight", i), {d_state}, 0);71            layer.ssm_c_norm = create_tensor(tn(LLM_TENSOR_SSM_C_NORM, "weight", i), {d_state}, 0);72 73            // no "weight" suffix for these74            layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner}, 0);75            layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {d_inner}, 0);76 77            // out_proj78            layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);79        } else {80            // Attention layers81 82            create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);83            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);84        }85 86        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);87 88        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED);89 90        if (layer.ffn_gate_inp) {91            // MoE92            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);93            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert}, 0);94            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff, n_expert}, 0);95        } else {96            // FFN (no MoE)97            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);98            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);99            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);100        }101    }102}103 104std::unique_ptr<llm_graph_context> llama_model_jamba::build_arch_graph(const llm_graph_params & params) const {105    return std::make_unique<graph>(*this, params);106}107 108llama_model_jamba::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_build_mamba_base(params) {109    const int64_t n_embd_head = hparams.n_embd_head_v();110 111    ggml_tensor * cur;112    ggml_tensor * inpL;113 114    // {n_embd, n_tokens}115    inpL = build_inp_embd(model.tok_embd);116 117    auto * inp_hybrid = build_inp_mem_hybrid();118 119    ggml_tensor * inp_out_ids = build_inp_out_ids();120 121    for (int il = 0; il < n_layer; ++il) {122        const int64_t n_head_kv = hparams.n_head_kv(il);123 124        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);125        cb(cur, "attn_norm", il);126 127        if (n_head_kv == 0) {128            cur = build_mamba_layer(inp_hybrid->get_recr(), cur, model, ubatch, il);129        } else {130            // Attention131 132            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,133                    n_embd_head, n_head, n_head_kv, il);134 135            // No RoPE :)136            cur = build_attn(inp_hybrid->get_attn(),137                    model.layers[il].wo, NULL, model.layers[il].wo_s,138                    Qcur, Kcur, Vcur, NULL, NULL, NULL, 1.0f/sqrtf(float(n_embd_head)), il);139        }140        if (il == n_layer - 1 && inp_out_ids) {141            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);142            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);143        }144        // residual145        struct ggml_tensor * ffn_inp = ggml_add(ctx0, inpL, cur);146        cb(cur, "ffn_inp", il);147 148        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);149        cb(cur, "ffn_norm", il);150 151        // feed-forward network152        if (model.layers[il].ffn_gate_inp == nullptr) {153            // FFN154            cur = build_ffn(cur,155                    model.layers[il].ffn_up,   NULL, NULL,156                    model.layers[il].ffn_gate, NULL, NULL,157                    model.layers[il].ffn_down, NULL, NULL,158                    NULL,159                    LLM_FFN_SILU, LLM_FFN_PAR, il);160            cb(cur, "ffn_out", il);161        } else {162            // MoE branch163            cur = build_moe_ffn(cur,164                    model.layers[il].ffn_gate_inp,165                    model.layers[il].ffn_up_exps,166                    model.layers[il].ffn_gate_exps,167                    model.layers[il].ffn_down_exps,168                    nullptr,169                    n_expert, n_expert_used,170                    LLM_FFN_SILU, false,171                    hparams.expert_weights_scale,172                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,173                    il);174            cb(cur, "ffn_moe_out", il);175        }176        // residual177        cur = ggml_add(ctx0, ffn_inp, cur);178 179        cur = build_cvec(cur, il);180        cb(cur, "l_out", il);181 182        // input for next layer183        inpL = cur;184    }185    // final rmsnorm186    cur = build_norm(inpL, model.output_norm, NULL, LLM_NORM_RMS, -1);187 188    cb(cur, "result_norm", -1);189    res->t_embd = cur;190 191    // lm_head192    cur = build_lora_mm(model.output, cur, model.output_s);193 194    cb(cur, "result_output", -1);195    res->t_logits = cur;196 197    ggml_build_forward_expand(gf, cur);198}199