| | #include "models.h"
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| | llm_build_bailingmoe2::llm_build_bailingmoe2(const llama_model & model, const llm_graph_params & params) :
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| | llm_graph_context(params) {
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| | const int64_t n_embd_head = hparams.n_embd_head_v;
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| | const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
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| | GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
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| | ggml_tensor * cur;
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| | ggml_tensor * inpL;
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| | inpL = build_inp_embd(model.tok_embd);
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| | ggml_tensor * inp_pos = build_inp_pos();
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| | auto * inp_attn = build_attn_inp_kv();
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| | ggml_tensor * inp_out_ids = build_inp_out_ids();
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| | const int n_transformer_layers = n_layer - hparams.nextn_predict_layers;
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| | for (int il = 0; il < n_transformer_layers; ++il) {
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| | ggml_tensor * inpSA = inpL;
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| | cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
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| | cb(cur, "attn_norm", il);
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| | {
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| | cur = build_lora_mm(model.layers[il].wqkv, cur);
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| | cb(cur, "wqkv", il);
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| | ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, n_embd_head * sizeof(float),
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| | cur->nb[1], 0 * sizeof(float) * (n_embd));
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| | ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float),
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| | cur->nb[1], 1 * sizeof(float) * (n_embd));
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| | ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float),
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| | cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa));
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| | Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
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| | cb(Qcur, "Qcur_normed", il);
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| | Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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| | ext_factor, attn_factor, beta_fast, beta_slow);
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| | Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
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| | cb(Kcur, "Kcur_normed", il);
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| | Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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| | ext_factor, attn_factor, beta_fast, beta_slow);
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| | cb(Qcur, "Qcur", il);
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| | cb(Kcur, "Kcur", il);
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| | cb(Vcur, "Vcur", il);
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| | cur = build_attn(inp_attn,
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| | model.layers[il].wo, model.layers[il].bo,
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| | Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
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| | }
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| | if (il == n_transformer_layers - 1 && inp_out_ids) {
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| | cur = ggml_get_rows(ctx0, cur, inp_out_ids);
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| | inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
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| | }
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| | ggml_tensor * sa_out = ggml_add(ctx0, cur, inpSA);
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| | cb(sa_out, "sa_out", il);
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| | cur = build_norm(sa_out, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
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| | cb(cur, "ffn_norm", il);
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| | if (static_cast<uint32_t>(il) < hparams.n_layer_dense_lead) {
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| | cur = build_ffn(cur,
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| | model.layers[il].ffn_up, NULL, NULL,
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| | model.layers[il].ffn_gate, NULL, NULL,
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| | model.layers[il].ffn_down, NULL, NULL,
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| | NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
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| | cb(cur, "ffn_out", il);
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| | } else {
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| | ggml_tensor * moe_out = build_moe_ffn(cur,
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| | model.layers[il].ffn_gate_inp,
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| | model.layers[il].ffn_up_exps,
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| | model.layers[il].ffn_gate_exps,
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| | model.layers[il].ffn_down_exps,
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| | model.layers[il].ffn_exp_probs_b,
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| | n_expert, n_expert_used,
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| | LLM_FFN_SILU, hparams.expert_weights_norm,
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| | true, hparams.expert_weights_scale,
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| | (llama_expert_gating_func_type) hparams.expert_gating_func,
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| | il);
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| | cb(moe_out, "ffn_moe_out", il);
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| |
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| | {
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| | ggml_tensor * ffn_shexp =
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| | build_ffn(cur,
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| | model.layers[il].ffn_up_shexp, NULL, NULL,
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| | model.layers[il].ffn_gate_shexp, NULL, NULL,
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| | model.layers[il].ffn_down_shexp, NULL, NULL,
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| | NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
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| | cb(ffn_shexp, "ffn_shexp", il);
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| | cur = ggml_add(ctx0, moe_out, ffn_shexp);
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| | cb(cur, "ffn_out", il);
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| | }
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| | }
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| | cur = ggml_add(ctx0, cur, sa_out);
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| | cur = build_cvec(cur, il);
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| | cb(cur, "l_out", il);
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| | inpL = cur;
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| | }
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| |
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| | cur = inpL;
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| | cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
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| | cb(cur, "result_norm", -1);
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| | res->t_embd = cur;
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| | cur = build_lora_mm(model.output, cur);
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| | cb(cur, "result_output", -1);
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| | res->t_logits = cur;
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| | ggml_build_forward_expand(gf, cur);
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| | }
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