1
  2#include "models.h"
  3
  4llm_build_minimax_m2::llm_build_minimax_m2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
  5    const int64_t n_embd_head = hparams.n_embd_head_v;
  6
  7    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
  8    // GGML_ASSERT(n_embd_head == hparams.n_rot); this is wrong in case of minimax, head_dim = 128, n_rot = 64
  9
 10    ggml_tensor * cur;
 11    ggml_tensor * inpL;
 12
 13    inpL = build_inp_embd(model.tok_embd);
 14
 15    ggml_tensor * inp_pos = build_inp_pos();
 16    auto inp_attn = build_attn_inp_kv();
 17    ggml_tensor * inp_out_ids = build_inp_out_ids();
 18
 19    for (int il = 0; il < n_layer; ++il) {
 20        ggml_tensor * inpSA = inpL;
 21
 22        cur = inpL;
 23
 24        // self_attention
 25        {
 26            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
 27            cb(cur, "attn_norm", il);
 28
 29            // compute Q and K and RoPE them
 30            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
 31            cb(Qcur, "Qcur", il);
 32
 33            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
 34            cb(Kcur, "Kcur", il);
 35
 36            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
 37            cb(Vcur, "Vcur", il);
 38
 39            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL,
 40                    LLM_NORM_RMS, il);
 41            cb(Qcur, "Qcur_normed", il);
 42
 43            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL,
 44                    LLM_NORM_RMS, il);
 45            cb(Kcur, "Kcur_normed", il);
 46
 47            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
 48            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 49            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
 50
 51            Qcur = ggml_rope_ext(
 52                ctx0, Qcur, inp_pos, nullptr,
 53                n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
 54                ext_factor, attn_factor, beta_fast, beta_slow
 55                );
 56
 57            Kcur = ggml_rope_ext(
 58                ctx0, Kcur, inp_pos, nullptr,
 59                n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
 60                ext_factor, attn_factor, beta_fast, beta_slow
 61                );
 62
 63            cb(Qcur, "Qcur", il);
 64            cb(Kcur, "Kcur", il);
 65            cb(Vcur, "Vcur", il);
 66
 67            cur = build_attn(inp_attn,
 68                    model.layers[il].wo, NULL,
 69                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
 70        }
 71
 72        if (il == n_layer - 1 && inp_out_ids) {
 73            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
 74            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
 75        }
 76
 77        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
 78        cb(ffn_inp, "ffn_inp", il);
 79
 80        // MoE branch
 81        cur = build_norm(ffn_inp,
 82                model.layers[il].ffn_norm, NULL,
 83                LLM_NORM_RMS, il);
 84        cb(cur, "ffn_norm", il);
 85
 86        cur = build_moe_ffn(cur,
 87                model.layers[il].ffn_gate_inp,
 88                model.layers[il].ffn_up_exps,
 89                model.layers[il].ffn_gate_exps,
 90                model.layers[il].ffn_down_exps,
 91                model.layers[il].ffn_exp_probs_b,
 92                n_expert, n_expert_used,
 93                LLM_FFN_SILU, true,
 94                false, 0.0,
 95                (llama_expert_gating_func_type) hparams.expert_gating_func,
 96                il);
 97        cb(cur, "ffn_moe_out", il);
 98
 99        cur = ggml_add(ctx0, cur, ffn_inp);
100
101        cur = build_cvec(cur, il);
102        cb(cur, "l_out", il);
103
104        // input for next layer
105        inpL = cur;
106    }
107
108    cur = inpL;
109
110    cur = build_norm(cur,
111            model.output_norm, NULL,
112            LLM_NORM_RMS, -1);
113
114    cb(cur, "result_norm", -1);
115    res->t_embd = cur;
116
117    // lm_head
118    cur = build_lora_mm(model.output, cur);
119
120    cb(cur, "result_output", -1);
121    res->t_logits = cur;
122
123    ggml_build_forward_expand(gf, cur);
124}