mirror of
https://github.com/ggml-org/whisper.cpp.git
synced 2026-08-12 22:29:04 +04:00
334 lines
17 KiB
C++
334 lines
17 KiB
C++
// Laguna (poolside): sigmoid-routed MoE with a score-correction bias, one shared
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// expert, a softplus attention output gate, QK-norm, and per-layer-type RoPE
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// (YaRN on full-attention layers, plain RoPE on sliding-window layers). XS.2 is
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// hybrid full/SWA with a per-head gate; M.1 is full-attention with a per-element
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// gate. Shares the MoE/gate structure with afmoe.
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#include "models.h"
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void llama_model_laguna::load_arch_hparams(llama_model_loader & ml) {
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
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ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
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ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
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ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
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ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
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// Laguna ships one shared expert and stores its size directly (routed and
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// shared experts may differ), so read the size from expert_shared_feed_forward_length.
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// The count is not in the config; default to 1 but read the key if present.
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hparams.n_expert_shared = 1;
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ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared, false);
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ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
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if (hparams.n_ff_shexp == 0) {
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// Weightless fixtures (test-llama-archs) omit this key; derive a nonzero
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// size so the shared expert is still built. Real GGUFs always carry the
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// exact value (routed and shared FF lengths may differ).
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hparams.n_ff_shexp = hparams.n_ff_exp * hparams.n_expert_shared;
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}
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// Sliding-window attention is OPTIONAL. XS.2 is hybrid (full / SWA / SWA /
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// SWA repeating, period 4 starting with full); M.1 has no sliding window
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// (all layers full attention). When sliding_window is absent or zero we
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// leave swa_type = NONE and skip the SWA-specific per-layer-type RoPE.
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hparams.n_swa = 0;
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ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
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if (hparams.n_swa > 0) {
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hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
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uint32_t swa_period = 4;
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ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
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hparams.set_swa_pattern(swa_period, /*dense_first=*/true); // XS.2: FULL at il%4==0
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// Per-layer-type RoPE: full layers use YaRN θ=500000 over 64 dims;
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// SWA layers use default RoPE θ=10000 over 128 dims. Base load_hparams
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// already reads ROPE_FREQ_BASE and ROPE_DIMENSION_COUNT into the
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// non-SWA fields; we explicitly pull the SWA mirrors here.
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hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
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hparams.rope_freq_scale_train_swa = 1.0f; // SWA uses plain RoPE (no YaRN scaling); do NOT inherit full layers 1/factor
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ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
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ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT_SWA, hparams.n_rot_swa, false);
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}
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// Default the expert gating function to SIGMOID when the key is absent
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// (matches the HF reference).
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if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
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hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
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}
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switch (hparams.n_layer()) {
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case 40: type = LLM_TYPE_30B_A3B; break; // Laguna-XS.2
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case 48: type = LLM_TYPE_118B_A8B; break; // Laguna-S.2
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case 70: type = LLM_TYPE_230B_A10B; break; // Laguna-M.1
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default: type = LLM_TYPE_UNKNOWN;
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}
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}
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void llama_model_laguna::load_arch_tensors(llama_model_loader & ml) {
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LLAMA_LOAD_LOCALS;
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tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
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output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
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output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
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if (output == NULL) {
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// tied embeddings fallback
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output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
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}
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const int64_t n_ff_exp = hparams.n_ff_exp;
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const int64_t n_ff_shexp = hparams.n_ff_shexp;
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = layers[i];
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// Per-layer head count — Laguna varies n_head between full and SWA
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// layers (48 vs 64 in XS.2). KV head count is uniform.
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const int64_t n_head_il = hparams.n_head(i);
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const int64_t n_head_kv_il = hparams.n_head_kv(i);
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const int64_t n_embd_q_il = n_embd_head_k * n_head_il;
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const int64_t n_embd_k_il = n_embd_head_k * n_head_kv_il;
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const int64_t n_embd_v_il = n_embd_head_v * n_head_kv_il;
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
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create_tensor_qkv(layer, i, n_embd, n_embd_q_il, n_embd_k_il, n_embd_v_il, 0);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_q_il, n_embd}, 0);
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layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
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layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
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// Attention output gate. XS.2 is per-head (g_proj -> n_head, one scalar
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// per head broadcast over head_dim at multiply time); M.1 is per-element
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// (g_proj -> n_head*head_dim, like afmoe). Detect from the stored tensor
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// shape so a single arch handles both; the graph mirrors this check.
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// Gate width selects per-head vs per-element. Real GGUFs always carry the
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// gate tensor, so read the width from it and require EXACTLY one of the two
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// valid widths -- never guess between them. Weightless fixtures
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// (test-llama-archs) have no gate tensor; fall back to the per-head layout so
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// the per-head reshape path is still exercised.
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const int64_t n_gate_per_head = n_head_il;
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const int64_t n_gate_per_elem = n_embd_head_k * n_head_il;
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const ggml_tensor * gate_meta = ml.get_tensor_meta(tn(LLM_TENSOR_ATTN_GATE, "weight", i).str().c_str());
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int64_t n_gate_out;
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if (gate_meta != nullptr) {
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n_gate_out = gate_meta->ne[1];
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if (n_gate_out != n_gate_per_head && n_gate_out != n_gate_per_elem) {
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GGML_ABORT("Laguna: unexpected attention gate width %lld at layer %d "
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"(expected %lld per-head or %lld per-element)",
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(long long) n_gate_out, i, (long long) n_gate_per_head, (long long) n_gate_per_elem);
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}
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} else {
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n_gate_out = n_gate_per_head;
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}
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layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_gate_out}, 0);
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layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
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if ((uint32_t)i >= hparams.n_layer_dense_lead) {
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// MoE layer
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layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
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layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
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layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
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layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
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layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
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// Always-on shared expert.
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layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0);
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layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0);
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layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
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} else {
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// Dense layer (the leading n_layer_dense_lead layers)
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layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
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layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
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}
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}
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}
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std::unique_ptr<llm_graph_context> llama_model_laguna::build_arch_graph(const llm_graph_params & params) const {
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return std::make_unique<graph>(*this, params);
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}
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llama_model_laguna::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
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const int64_t n_embd_head = hparams.n_embd_head_v();
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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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// No MuP embedding scale (laguna omits this; afmoe scales by sqrt(hidden)).
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ggml_tensor * inp_pos = build_inp_pos();
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// XS.2 is hybrid SWA -> interleaved-SWA KV input; M.1 is all-full -> plain
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// KV input. Pick the matching input (and build_attn overload) per swa_type.
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const bool has_swa = hparams.swa_type != LLAMA_SWA_TYPE_NONE;
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llm_graph_input_attn_kv * inp_attn_kv = has_swa ? nullptr : build_attn_inp_kv();
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llm_graph_input_attn_kv_iswa * inp_attn_iswa = has_swa ? build_attn_inp_kv_iswa() : nullptr;
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ggml_tensor * inp_out_ids = build_inp_out_ids();
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const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
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for (int il = 0; il < n_layer; ++il) {
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const bool is_swa_il = hparams.is_swa(il);
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const int64_t n_head_il = hparams.n_head(il);
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const int64_t n_head_kv_il = hparams.n_head_kv(il);
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// Per-layer-type RoPE config. SWA layers run plain rope (no YaRN),
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// achieved by zeroing the YaRN ext/beta params for those layers.
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const int n_rot_l = is_swa_il ? hparams.n_rot_swa : n_rot;
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const float freq_base_l = is_swa_il ? hparams.rope_freq_base_train_swa : freq_base;
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const float freq_scale_l = is_swa_il ? hparams.rope_freq_scale_train_swa : freq_scale;
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const float ext_factor_l = is_swa_il ? 0.0f : ext_factor;
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// YaRN magnitude scaling (mscale) is already handled by the framework:
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// llama_context pre-divides cparams.yarn_attn_factor by (1 + 0.1*ln(factor))
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// to cancel ggml rope_yarn's internal mscale *= 1 + 0.1*ln(1/freq_scale).
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// Pass attn_factor straight through (like every other arch); SWA layers run
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// plain RoPE (ext_factor 0, no mscale) so force 1.0 there.
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const float attn_factor_l = is_swa_il ? 1.0f : attn_factor;
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const float beta_fast_l = is_swa_il ? 0.0f : beta_fast;
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const float beta_slow_l = is_swa_il ? 0.0f : beta_slow;
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const int n_ctx_orig_l = is_swa_il ? hparams.n_ctx_train : n_ctx_orig;
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ggml_tensor * inpSA = inpL;
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// Pre-norm
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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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// Self-attention
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{
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ggml_tensor * attn_inp = cur; // saved for the gate projection
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auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
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n_embd_head, n_head_il, n_head_kv_il, il);
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// g_proj on the *pre-attention* hidden state (matches HF
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// reference: gate is computed from the same `hidden_states`
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// input as q/k/v, not from the attn output).
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ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, attn_inp);
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cb(gate, "attn_gate_proj", il);
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// QK RMSNorm at head_dim level (Qwen3 style)
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Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
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Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
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cb(Qcur, "Qcur_normed", il);
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cb(Kcur, "Kcur_normed", il);
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Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr,
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n_rot_l, rope_type, n_ctx_orig_l, freq_base_l, freq_scale_l,
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ext_factor_l, attn_factor_l, beta_fast_l, beta_slow_l);
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Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr,
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n_rot_l, rope_type, n_ctx_orig_l, freq_base_l, freq_scale_l,
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ext_factor_l, attn_factor_l, beta_fast_l, beta_slow_l);
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cb(Qcur, "Qcur_rope", il);
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cb(Kcur, "Kcur_rope", il);
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cur = has_swa
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? build_attn(inp_attn_iswa,
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NULL, NULL, NULL, // o_proj deferred until after gating
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Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il)
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: build_attn(inp_attn_kv,
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NULL, NULL, NULL,
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Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
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cb(cur, "attn_out", il);
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// Softplus output gate (the unary kernel computes softplus in fp32
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// and casts back). Two shapes, distinguished by the g_proj output
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// dim (matching the load-time detection):
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// XS.2 per-head : gate [n_head_il, n_tokens] -> reshape to
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// [1, n_head_il, n_tokens] and broadcast over
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// head_dim against cur [head_dim, n_head, T].
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// M.1 per-element : gate [n_head_il*head_dim, n_tokens] spans the
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// full attention output -> direct ggml_mul.
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gate = ggml_softplus(ctx0, gate);
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cb(gate, "attn_gate_softplus", il);
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const int64_t n_tokens = cur->ne[1];
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if (model.layers[il].wqkv_gate->ne[1] == n_head_il) {
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cur = ggml_reshape_3d(ctx0, cur, n_embd_head, n_head_il, n_tokens);
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gate = ggml_reshape_3d(ctx0, gate, 1, n_head_il, n_tokens);
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cur = ggml_mul(ctx0, cur, gate);
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cur = ggml_reshape_2d(ctx0, cur, n_embd_head * n_head_il, n_tokens);
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} else {
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cur = ggml_mul(ctx0, cur, gate);
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}
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cb(cur, "attn_gated", il);
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cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s);
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cb(cur, "attn_o_proj", il);
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}
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if (il == n_layer - 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 * ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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// Pre-norm only (no post-attn norm)
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cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
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cb(cur, "ffn_norm", il);
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if ((uint32_t)il >= hparams.n_layer_dense_lead) {
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// MoE: sigmoid routing + score-correction bias + sum-norm +
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// routed_scaling_factor (all handled by build_moe_ffn).
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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,
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hparams.expert_weights_norm,
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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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// Always-on shared expert, summed in parallel.
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ggml_tensor * ffn_shexp = 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,
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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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} else {
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// Dense FFN for the leading n_layer_dense_lead layers (XS.2: 1, M.1: 3)
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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,
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LLM_FFN_SILU, LLM_FFN_PAR, il);
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cb(cur, "ffn_out", il);
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}
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// No post-ffn norm
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cur = ggml_add(ctx0, cur, ffn_inp);
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cur = build_cvec(cur, il);
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cb(cur, "l_out", il);
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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);
|
|
}
|