#include "models.h" llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B bool is_lite = (hparams.n_layer == 27 || hparams.n_layer == 26); const bool is_mla = (hparams.n_embd_head_k_mla != 0 && hparams.n_embd_head_v_mla != 0); // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA const int64_t n_embd_head_k = is_mla ? hparams.n_embd_head_k_mla : hparams.n_embd_head_k; const int64_t n_embd_head_v = is_mla ? hparams.n_embd_head_v_mla : hparams.n_embd_head_v; const int64_t n_embd_head_qk_rope = hparams.n_rot; const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope; const uint32_t kv_lora_rank = hparams.n_lora_kv; // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly. // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation. const float mscale = attn_factor * (1.0f + hparams.rope_yarn_log_mul * logf(1.0f / freq_scale)); const float kq_scale = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k)); const float attn_factor = 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale)); ggml_tensor * cur; ggml_tensor * inpL; // {n_embd, n_tokens} inpL = build_inp_embd(model.tok_embd); // (optional) temperature tuning - used by mistral-large ggml_tensor * inp_attn_scale = nullptr; if (hparams.f_attn_temp_scale != 0.0f) { inp_attn_scale = build_inp_attn_scale(); } // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); auto * inp_attn = build_attn_inp_kv(); ggml_tensor * inp_out_ids = build_inp_out_ids(); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; // norm cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); cb(cur, "attn_norm", il); // self_attention { ggml_tensor * q = NULL; if (!is_lite) { q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur); cb(q, "q", il); q = build_norm(q, model.layers[il].attn_q_a_norm, nullptr, LLM_NORM_RMS, il); cb(q, "q", il); q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q); cb(q, "q", il); } else { q = ggml_mul_mat(ctx0, model.layers[il].wq, cur); cb(q, "q", il); } // split into {n_embd_head_qk_nope, n_head, n_tokens} ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k), ggml_row_size(q->type, n_embd_head_k) * n_head, 0); cb(q_nope, "q_nope", il); // and {n_embd_head_qk_rope, n_head, n_tokens} ggml_tensor * q_pe = ggml_view_3d( ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k), ggml_row_size(q->type, n_embd_head_k) * n_head, ggml_row_size(q->type, n_embd_head_qk_nope)); cb(q_pe, "q_pe", il); ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur); cb(kv_cmpr_pe, "kv_cmpr_pe", il); // split into {kv_lora_rank, n_tokens} ggml_tensor * kv_cmpr = ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens, ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0); cb(kv_cmpr, "kv_cmpr", il); // and {n_embd_head_qk_rope, 1, n_tokens} ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens, ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), ggml_row_size(kv_cmpr_pe->type, kv_lora_rank)); cb(k_pe, "k_pe", il); q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(q_pe, "q_pe", il); k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(k_pe, "k_pe", il); kv_cmpr = build_norm(kv_cmpr, model.layers[il].attn_kv_a_norm, nullptr, LLM_NORM_RMS, il); cb(kv_cmpr, "kv_cmpr", il); if (is_mla) { // {n_embd_head_qk_nope, n_tokens, n_head} q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3); cb(q_nope, "q_nope_perm", il); // {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head} ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, model.layers[il].wk_b, q_nope); cb(q_nope_absorbed, "q_nope_absorbed", il); // {kv_lora_rank, n_head, n_tokens} q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3); cb(q_nope_absorbed, "q_nope_absorbed_perm", il); // {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens} // note: rope must go first for in-place context shifting in build_rope_shift() ggml_tensor * Qcur = ggml_concat(ctx0, q_pe, q_nope_absorbed, 0); cb(Qcur, "Qcur", il); kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens); cb(kv_cmpr, "kv_cmpr_reshape", il); // {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens} ggml_tensor * Kcur = ggml_concat(ctx0, k_pe, kv_cmpr, 0); cb(Kcur, "Kcur", il); // {kv_lora_rank, 1, n_tokens} ggml_tensor * Vcur = kv_cmpr; cb(Vcur, "Vcur", il); if (inp_attn_scale) { // apply llama 4 temperature scaling Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale); cb(Qcur, "Qcur_attn_temp_scaled", il); } // note: MLA with the absorption optimzation converts into MQA (ie: GQA with 1 group) cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, kq_scale, il); } else { ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_cmpr); cb(kv, "kv", il); // split into {n_embd_head_qk_nope, n_head, n_tokens} ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v), ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v) * n_head, 0); cb(k_nope, "k_nope_view", il); // and {n_embd_head_v, n_head, n_tokens} ggml_tensor * Vcur = ggml_view_3d(ctx0, kv, n_embd_head_v, n_head, n_tokens, ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v), ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v) * n_head, ggml_row_size(kv->type, n_embd_head_qk_nope)); cb(Vcur, "Vcur_view", il); Vcur = ggml_cont(ctx0, Vcur); cb(Vcur, "Vcur_cont", il); // note: rope must go first for in-place context shifting in build_rope_shift() ggml_tensor * Qcur = ggml_concat(ctx0, q_pe, q_nope, 0); cb(Qcur, "Qcur", il); ggml_tensor * Kcur = ggml_concat(ctx0, ggml_repeat(ctx0, k_pe, q_pe), k_nope, 0); cb(Kcur, "Kcur", il); if (inp_attn_scale) { // apply llama 4 temperature scaling Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale); cb(Qcur, "Qcur_attn_temp_scaled", il); } // note: MLA without the absorption optimization converts into MHA (ie: GQA with full n_head groups) cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il); } } if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); cb(ffn_inp, "ffn_inp", il); cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il); cb(cur, "ffn_norm", il); if ((uint32_t) il < hparams.n_layer_dense_lead) { cur = build_ffn(cur, model.layers[il].ffn_up, NULL, NULL, model.layers[il].ffn_gate, NULL, NULL, model.layers[il].ffn_down, NULL, NULL, NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); cb(cur, "ffn_out", il); } else { // MoE branch ggml_tensor * moe_out = build_moe_ffn(cur, model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps, model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps, model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, hparams.expert_weights_norm, true, hparams.expert_weights_scale, (llama_expert_gating_func_type) hparams.expert_gating_func, il); cb(moe_out, "ffn_moe_out", il); // FFN shared expert { ggml_tensor * ffn_shexp = build_ffn(cur, model.layers[il].ffn_up_shexp, NULL, NULL, model.layers[il].ffn_gate_shexp, NULL, NULL, model.layers[il].ffn_down_shexp, NULL, NULL, NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); cb(ffn_shexp, "ffn_shexp", il); cur = ggml_add(ctx0, moe_out, ffn_shexp); cb(cur, "ffn_out", il); } } cur = ggml_add(ctx0, cur, ffn_inp); cur = build_cvec(cur, il); cb(cur, "l_out", il); // input for next layer inpL = cur; } cur = inpL; cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); cb(cur, "result_norm", -1); res->t_embd = cur; // lm_head cur = ggml_mul_mat(ctx0, model.output, cur); cb(cur, "result_output", -1); res->t_logits = cur; ggml_build_forward_expand(gf, cur); }