// Laguna (poolside): sigmoid-routed MoE with a score-correction bias, one shared // expert, a softplus attention output gate, QK-norm, and per-layer-type RoPE // (YaRN on full-attention layers, plain RoPE on sliding-window layers). XS.2 is // hybrid full/SWA with a per-head gate; M.1 is full-attention with a per-element // gate. Shares the MoE/gate structure with afmoe. #include "models.h" void llama_model_laguna::load_arch_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false); ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false); // Laguna ships one shared expert and stores its size directly (routed and // shared experts may differ), so read the size from expert_shared_feed_forward_length. // The count is not in the config; default to 1 but read the key if present. hparams.n_expert_shared = 1; ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared, false); ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false); if (hparams.n_ff_shexp == 0) { // Weightless fixtures (test-llama-archs) omit this key; derive a nonzero // size so the shared expert is still built. Real GGUFs always carry the // exact value (routed and shared FF lengths may differ). hparams.n_ff_shexp = hparams.n_ff_exp * hparams.n_expert_shared; } // Sliding-window attention is OPTIONAL. XS.2 is hybrid (full / SWA / SWA / // SWA repeating, period 4 starting with full); M.1 has no sliding window // (all layers full attention). When sliding_window is absent or zero we // leave swa_type = NONE and skip the SWA-specific per-layer-type RoPE. hparams.n_swa = 0; ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false); if (hparams.n_swa > 0) { hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; uint32_t swa_period = 4; ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false); hparams.set_swa_pattern(swa_period, /*dense_first=*/true); // XS.2: FULL at il%4==0 // Per-layer-type RoPE: full layers use YaRN θ=500000 over 64 dims; // SWA layers use default RoPE θ=10000 over 128 dims. Base load_hparams // already reads ROPE_FREQ_BASE and ROPE_DIMENSION_COUNT into the // non-SWA fields; we explicitly pull the SWA mirrors here. hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train; hparams.rope_freq_scale_train_swa = 1.0f; // SWA uses plain RoPE (no YaRN scaling); do NOT inherit full layers 1/factor ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false); ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT_SWA, hparams.n_rot_swa, false); } // Default the expert gating function to SIGMOID when the key is absent // (matches the HF reference). if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) { hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID; } switch (hparams.n_layer()) { case 40: type = LLM_TYPE_30B_A3B; break; // Laguna-XS.2 case 48: type = LLM_TYPE_118B_A8B; break; // Laguna-S.2 case 70: type = LLM_TYPE_230B_A10B; break; // Laguna-M.1 default: type = LLM_TYPE_UNKNOWN; } } void llama_model_laguna::load_arch_tensors(llama_model_loader & ml) { LLAMA_LOAD_LOCALS; tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED); if (output == NULL) { // tied embeddings fallback output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); } const int64_t n_ff_exp = hparams.n_ff_exp; const int64_t n_ff_shexp = hparams.n_ff_shexp; for (int i = 0; i < n_layer; ++i) { auto & layer = layers[i]; // Per-layer head count — Laguna varies n_head between full and SWA // layers (48 vs 64 in XS.2). KV head count is uniform. const int64_t n_head_il = hparams.n_head(i); const int64_t n_head_kv_il = hparams.n_head_kv(i); const int64_t n_embd_q_il = n_embd_head_k * n_head_il; const int64_t n_embd_k_il = n_embd_head_k * n_head_kv_il; const int64_t n_embd_v_il = n_embd_head_v * n_head_kv_il; layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); create_tensor_qkv(layer, i, n_embd, n_embd_q_il, n_embd_k_il, n_embd_v_il, 0); layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_q_il, n_embd}, 0); layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0); layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0); // Attention output gate. XS.2 is per-head (g_proj -> n_head, one scalar // per head broadcast over head_dim at multiply time); M.1 is per-element // (g_proj -> n_head*head_dim, like afmoe). Detect from the stored tensor // shape so a single arch handles both; the graph mirrors this check. // Gate width selects per-head vs per-element. Real GGUFs always carry the // gate tensor, so read the width from it and require EXACTLY one of the two // valid widths -- never guess between them. Weightless fixtures // (test-llama-archs) have no gate tensor; fall back to the per-head layout so // the per-head reshape path is still exercised. const int64_t n_gate_per_head = n_head_il; const int64_t n_gate_per_elem = n_embd_head_k * n_head_il; const ggml_tensor * gate_meta = ml.get_tensor_meta(tn(LLM_TENSOR_ATTN_GATE, "weight", i).str().c_str()); int64_t n_gate_out; if (gate_meta != nullptr) { n_gate_out = gate_meta->ne[1]; if (n_gate_out != n_gate_per_head && n_gate_out != n_gate_per_elem) { GGML_ABORT("Laguna: unexpected attention gate width %lld at layer %d " "(expected %lld per-head or %lld per-element)", (long long) n_gate_out, i, (long long) n_gate_per_head, (long long) n_gate_per_elem); } } else { n_gate_out = n_gate_per_head; } layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_gate_out}, 0); layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); if ((uint32_t)i >= hparams.n_layer_dense_lead) { // MoE layer layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0); layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0); layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0); layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0); // Always-on shared expert. layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0); layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0); layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0); } else { // Dense layer (the leading n_layer_dense_lead layers) layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); } } } std::unique_ptr llama_model_laguna::build_arch_graph(const llm_graph_params & params) const { return std::make_unique(*this, params); } llama_model_laguna::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v(); GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; inpL = build_inp_embd(model.tok_embd); // No MuP embedding scale (laguna omits this; afmoe scales by sqrt(hidden)). ggml_tensor * inp_pos = build_inp_pos(); // XS.2 is hybrid SWA -> interleaved-SWA KV input; M.1 is all-full -> plain // KV input. Pick the matching input (and build_attn overload) per swa_type. const bool has_swa = hparams.swa_type != LLAMA_SWA_TYPE_NONE; llm_graph_input_attn_kv * inp_attn_kv = has_swa ? nullptr : build_attn_inp_kv(); llm_graph_input_attn_kv_iswa * inp_attn_iswa = has_swa ? build_attn_inp_kv_iswa() : nullptr; ggml_tensor * inp_out_ids = build_inp_out_ids(); const float kq_scale = 1.0f / sqrtf(float(n_embd_head)); for (int il = 0; il < n_layer; ++il) { const bool is_swa_il = hparams.is_swa(il); const int64_t n_head_il = hparams.n_head(il); const int64_t n_head_kv_il = hparams.n_head_kv(il); // Per-layer-type RoPE config. SWA layers run plain rope (no YaRN), // achieved by zeroing the YaRN ext/beta params for those layers. const int n_rot_l = is_swa_il ? hparams.n_rot_swa : n_rot; const float freq_base_l = is_swa_il ? hparams.rope_freq_base_train_swa : freq_base; const float freq_scale_l = is_swa_il ? hparams.rope_freq_scale_train_swa : freq_scale; const float ext_factor_l = is_swa_il ? 0.0f : ext_factor; // YaRN magnitude scaling (mscale) is already handled by the framework: // llama_context pre-divides cparams.yarn_attn_factor by (1 + 0.1*ln(factor)) // to cancel ggml rope_yarn's internal mscale *= 1 + 0.1*ln(1/freq_scale). // Pass attn_factor straight through (like every other arch); SWA layers run // plain RoPE (ext_factor 0, no mscale) so force 1.0 there. const float attn_factor_l = is_swa_il ? 1.0f : attn_factor; const float beta_fast_l = is_swa_il ? 0.0f : beta_fast; const float beta_slow_l = is_swa_il ? 0.0f : beta_slow; const int n_ctx_orig_l = is_swa_il ? hparams.n_ctx_train : n_ctx_orig; ggml_tensor * inpSA = inpL; // Pre-norm cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); cb(cur, "attn_norm", il); // Self-attention { ggml_tensor * attn_inp = cur; // saved for the gate projection auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, n_embd_head, n_head_il, n_head_kv_il, il); // g_proj on the *pre-attention* hidden state (matches HF // reference: gate is computed from the same `hidden_states` // input as q/k/v, not from the attn output). ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, attn_inp); cb(gate, "attn_gate_proj", il); // QK RMSNorm at head_dim level (Qwen3 style) Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il); Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il); cb(Qcur, "Qcur_normed", il); cb(Kcur, "Kcur_normed", il); Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot_l, rope_type, n_ctx_orig_l, freq_base_l, freq_scale_l, ext_factor_l, attn_factor_l, beta_fast_l, beta_slow_l); Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot_l, rope_type, n_ctx_orig_l, freq_base_l, freq_scale_l, ext_factor_l, attn_factor_l, beta_fast_l, beta_slow_l); cb(Qcur, "Qcur_rope", il); cb(Kcur, "Kcur_rope", il); cur = has_swa ? build_attn(inp_attn_iswa, NULL, NULL, NULL, // o_proj deferred until after gating Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il) : build_attn(inp_attn_kv, NULL, NULL, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il); cb(cur, "attn_out", il); // Softplus output gate (the unary kernel computes softplus in fp32 // and casts back). Two shapes, distinguished by the g_proj output // dim (matching the load-time detection): // XS.2 per-head : gate [n_head_il, n_tokens] -> reshape to // [1, n_head_il, n_tokens] and broadcast over // head_dim against cur [head_dim, n_head, T]. // M.1 per-element : gate [n_head_il*head_dim, n_tokens] spans the // full attention output -> direct ggml_mul. gate = ggml_softplus(ctx0, gate); cb(gate, "attn_gate_softplus", il); const int64_t n_tokens = cur->ne[1]; if (model.layers[il].wqkv_gate->ne[1] == n_head_il) { cur = ggml_reshape_3d(ctx0, cur, n_embd_head, n_head_il, n_tokens); gate = ggml_reshape_3d(ctx0, gate, 1, n_head_il, n_tokens); cur = ggml_mul(ctx0, cur, gate); cur = ggml_reshape_2d(ctx0, cur, n_embd_head * n_head_il, n_tokens); } else { cur = ggml_mul(ctx0, cur, gate); } cb(cur, "attn_gated", il); cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s); cb(cur, "attn_o_proj", 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); // Pre-norm only (no post-attn norm) 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) { // MoE: sigmoid routing + score-correction bias + sum-norm + // routed_scaling_factor (all handled by build_moe_ffn). 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, hparams.expert_weights_scale, (llama_expert_gating_func_type) hparams.expert_gating_func, il); cb(moe_out, "ffn_moe_out", il); // Always-on shared expert, summed in parallel. 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); } else { // Dense FFN for the leading n_layer_dense_lead layers (XS.2: 1, M.1: 3) 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); } // No post-ffn norm cur = ggml_add(ctx0, cur, ffn_inp); cur = build_cvec(cur, il); cb(cur, "l_out", il); 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; cur = build_lora_mm(model.output, cur); cb(cur, "result_output", -1); res->t_logits = cur; ggml_build_forward_expand(gf, cur); }