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llama.cpp fork for Ternary Bonsai 2 27B: MTP speculation, suffix cache reuse (SCR), q8 KV, two-slot fairness, fused DeltaNet kernels
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25 kB · 480 lines
C++
at main
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#include "llama-kv-cache.h"#include "llama-kv-cache-dsa.h"
// note: code adapted from deepseek32.cpp (DSA indexer + absorbed MLA) and step35.cpp (head-wise output gate)
void llama_model_dots3note::load_arch_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); hparams.f_norm_eps = 1e-6; // eps for the indexer k_norm layer norm
// TODO: use MTP layer ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false); GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_all");
// MoE parameters ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); 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); ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
// MLA parameters of the full-attention layers ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q); ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv); ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_mla_impl); ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl);
// MLA parameters of the sliding-window layers ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK_SWA, hparams.n_lora_kv_swa); ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA_SWA, hparams.n_embd_head_k_mla_swa); ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA_SWA, hparams.n_embd_head_v_mla_swa);
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa); ml.get_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl);
// DSA parameters ml.get_key(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head); ml.get_key(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size); ml.get_key(LLM_KV_ATTENTION_INDEXER_TOP_K, hparams.indexer_top_k); ml.get_arr(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl);
switch (hparams.n_layer()) { case 46: type = LLM_TYPE_288B_A19B; break; default: type = LLM_TYPE_UNKNOWN; }}
void llama_model_dots3note::load_arch_tensors(llama_model_loader & ml) { LLAMA_LOAD_LOCALS; GGML_UNUSED(ml);
if (!hparams.is_mla()) { throw std::runtime_error("DOTS3NOTE architecture requires MLA"); }
const int64_t n_embd_head_qk_rope = hparams.n_rot();
const int64_t q_lora_rank = hparams.n_lora_q; const int64_t n_ff_exp = hparams.n_ff_exp; const int64_t n_expert_shared = hparams.n_expert_shared;
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) { output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); }
for (int i = 0; i < n_layer_all; ++i) { auto & layer = layers[i];
const bool is_mtp = i >= n_layer; // the NextN/MTP block uses the sliding-attention geometry const bool is_swa = is_mtp || hparams.is_swa(i);
// MTP tensors are preserved in the GGUF but there is no MTP graph yet const int flags = is_mtp ? TENSOR_SKIP | TENSOR_NOT_REQUIRED : 0;
const int64_t n_head_l = hparams.n_head(i);
const int64_t kv_lora_rank = is_swa ? hparams.n_lora_kv_swa : hparams.n_lora_kv; const int64_t n_embd_head_k_mla = is_swa ? hparams.n_embd_head_k_mla_swa : hparams.n_embd_head_k_mla(); const int64_t n_embd_head_v_mla = is_swa ? hparams.n_embd_head_v_mla_swa : hparams.n_embd_head_v_mla(); const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags); layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, flags); layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, flags); // norm applied on the shared rope key before rope layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_qk_rope}, flags);
layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, flags); layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head_l * n_embd_head_k_mla}, flags);
layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}, flags);
layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head_l}, flags); layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head_l}, flags);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head_l * n_embd_head_v_mla, n_embd}, flags);
// head-wise sigmoid output gate layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head_l}, flags);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
// DSA indexer if (!is_mtp && hparams.is_indexer_full(i)) { layer.indexer_k_norm = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "weight", i), {hparams.indexer_head_size}, flags); layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "bias", i), {hparams.indexer_head_size}, flags); layer.indexer_proj = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ, "weight", i), {n_embd, hparams.indexer_n_head}, flags); layer.indexer_attn_k = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K, "weight", i), {n_embd, hparams.indexer_head_size}, flags); layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * hparams.indexer_head_size}, flags); }
if (is_mtp || i < (int) hparams.n_layer_dense_lead) { layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, flags); layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, flags); layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, flags); } else { if (n_expert == 0 || n_expert_used == 0) { throw std::runtime_error("n_expert and n_expert_used must be > 0"); }
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags); layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, flags);
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, flags); layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, flags); layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, flags);
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags); layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, flags); layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags); }
if (is_mtp) { layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags); layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags); layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags); layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags); layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags); } }}
std::unique_ptr<llm_graph_context> llama_model_dots3note::build_arch_graph(const llm_graph_params & params) const { return std::make_unique<graph>(*this, params);}
llama_model_dots3note::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { GGML_ASSERT(hparams.is_mla());
const int64_t n_embd_head_qk_rope = hparams.n_rot();
const int64_t n_indexer_head = hparams.indexer_n_head; const int64_t n_embd_indexer_head = hparams.indexer_head_size; const uint32_t n_indexer_top_k = hparams.indexer_top_k;
// the indexer head layout is [rope | nope] GGML_ASSERT(hparams.n_rot() <= n_embd_indexer_head);
ggml_tensor * cur; ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd);
ggml_tensor * inp_pos = build_inp_pos();
llm_graph_input_attn_k_dsa_iswa * inp_attn = build_attn_inp_k_dsa_iswa();
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL;
const bool is_swa = hparams.is_swa(il);
const int64_t n_head_l = hparams.n_head(il);
const int64_t kv_lora_rank = is_swa ? hparams.n_lora_kv_swa : hparams.n_lora_kv; const int64_t n_embd_head_k_mla = is_swa ? hparams.n_embd_head_k_mla_swa : hparams.n_embd_head_k_mla(); const int64_t n_embd_head_v_mla = is_swa ? hparams.n_embd_head_v_mla_swa : hparams.n_embd_head_v_mla(); const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
const float kq_scale = 1.0f/sqrtf(float(n_embd_head_k_mla)); const float freq_base_l = model.get_rope_freq_base(cparams, il);
// 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;
ggml_tensor * qr = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur); cb(qr, "qr", il);
qr = build_norm(qr, model.layers[il].attn_q_a_norm, nullptr, LLM_NORM_RMS, il); cb(qr, "qr", il);
ggml_tensor * top_k = nullptr;
// lightning indexer (full-attention layers only) if (!is_swa) { ggml_tensor * indexer_q = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_q_b, qr); cb(indexer_q, "indexer_q", il);
// {n_embd_indexer_head, n_indexer_head, n_tokens} indexer_q = ggml_reshape_3d(ctx0, indexer_q, n_embd_indexer_head, n_indexer_head, n_tokens); indexer_q = ggml_rope_ext(ctx0, indexer_q, inp_pos, nullptr, n_rot, LLAMA_ROPE_TYPE_NEOX, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(indexer_q, "indexer_q", il);
ggml_tensor * indexer_k = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_k, cur); cb(indexer_k, "indexer_k", il);
indexer_k = build_norm(indexer_k, model.layers[il].indexer_k_norm, model.layers[il].indexer_k_norm_b, LLM_NORM, il); cb(indexer_k, "indexer_k", il);
// {n_embd_indexer_head, 1, n_tokens} indexer_k = ggml_reshape_3d(ctx0, indexer_k, n_embd_indexer_head, 1, n_tokens); indexer_k = ggml_rope_ext(ctx0, indexer_k, inp_pos, nullptr, n_rot, LLAMA_ROPE_TYPE_NEOX, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(indexer_k, "indexer_k", il);
// perform Hadamard transform on indexer q and k indexer_q = ggml_mul_mat(ctx0, inp_attn->get_dsa()->self_k_rot_lid, indexer_q); cb(indexer_q, "indexer_q", il); indexer_k = ggml_mul_mat(ctx0, inp_attn->get_dsa()->self_k_rot_lid, indexer_k); cb(indexer_k, "indexer_k", il);
// store indexer keys to KV cache const auto * mctx_lid = inp_attn->get_dsa()->mctx->get_lid(); const auto & k_idxs_lid = inp_attn->get_dsa()->get_k_idxs_lid(); ggml_build_forward_expand(gf, mctx_lid->cpy_k(ctx0, indexer_k, k_idxs_lid, il));
ggml_tensor * indexer_weights = ggml_mul_mat(ctx0, model.layers[il].indexer_proj, cur); cb(indexer_weights, "indexer_weights", il);
indexer_k = mctx_lid->get_k(ctx0, il);
// split the batch into streams if needed const auto n_stream = indexer_k->ne[3]; indexer_q = ggml_view_4d(ctx0, indexer_q, indexer_q->ne[0], indexer_q->ne[1], indexer_q->ne[2]/n_stream, n_stream, indexer_q->nb[1], indexer_q->nb[2], indexer_q->nb[3]/n_stream, 0); indexer_weights = ggml_view_4d(ctx0, indexer_weights, indexer_weights->ne[0], indexer_weights->ne[1]/n_stream, indexer_weights->ne[2], n_stream, indexer_weights->nb[1], indexer_weights->nb[2]/n_stream, indexer_weights->nb[3]/n_stream, 0);
// pre-scale weights to avoid scaling operations on huge indexer_score tensor indexer_weights = ggml_scale(ctx0, indexer_weights, 1.0f / sqrtf(float(n_embd_indexer_head * n_indexer_head))); cb(indexer_weights, "indexer_weights", il);
ggml_tensor * indexer_score = nullptr; if (cparams.fused_lid) { indexer_score = ggml_lightning_indexer(ctx0, indexer_q, indexer_k, indexer_weights, inp_attn->get_dsa()->get_kq_mask_lid()); cb(indexer_score, "indexer_score", il); res->add_fused_node({LLM_FUSED_OP_LIGHTNING_INDEXER, indexer_score, il}); } else { indexer_q = ggml_permute(ctx0, indexer_q, 0, 2, 1, 3); cb(indexer_q, "indexer_q", il); indexer_k = ggml_permute(ctx0, indexer_k, 0, 2, 1, 3); cb(indexer_k, "indexer_k", il);
ggml_tensor * indexer_kq = ggml_mul_mat(ctx0, indexer_k, indexer_q); cb(indexer_kq, "indexer_kq", il);
// ReLU requires contiguous tensors indexer_kq = ggml_cont(ctx0, ggml_permute(ctx0, indexer_kq, 2, 1, 0, 3)); cb(indexer_kq, "indexer_kq", il);
indexer_score = ggml_relu(ctx0, indexer_kq); cb(indexer_score, "indexer_score", il);
indexer_score = ggml_mul(ctx0, indexer_score, indexer_weights); cb(indexer_score, "indexer_score", il);
// sum by q n_indexer_head dimension indexer_score = ggml_sum_rows(ctx0, indexer_score); cb(indexer_score, "indexer_score", il);
// permute result to match KQ mask indexer_score = ggml_cont(ctx0, ggml_permute(ctx0, indexer_score, 2, 1, 0, 3)); cb(indexer_score, "indexer_score", il);
ggml_tensor * indexer_kq_mask = inp_attn->get_dsa()->get_kq_mask_lid(); indexer_score = ggml_add(ctx0, indexer_score, indexer_kq_mask); cb(indexer_score, "indexer_score", il); }
// get indices of top k indexer scores uint32_t n_top_k = indexer_score->ne[0] < n_indexer_top_k ? indexer_score->ne[0] : n_indexer_top_k; top_k = ggml_cont(ctx0, ggml_top_k(ctx0, indexer_score, n_top_k)); cb(top_k, "top_k", il); }
ggml_tensor * q = ggml_mul_mat(ctx0, model.layers[il].wq_b, qr); cb(q, "q", il);
// split into {n_embd_head_qk_nope, n_head_l, n_tokens} ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head_l, n_tokens, ggml_row_size(q->type, n_embd_head_k_mla), ggml_row_size(q->type, n_embd_head_k_mla) * n_head_l, 0); cb(q_nope, "q_nope", il);
// and {n_embd_head_qk_rope, n_head_l, n_tokens} ggml_tensor * q_pe = ggml_view_3d( ctx0, q, n_embd_head_qk_rope, n_head_l, n_tokens, ggml_row_size(q->type, n_embd_head_k_mla), ggml_row_size(q->type, n_embd_head_k_mla) * n_head_l, 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);
// norm on the shared rope key, applied before rope k_pe = build_norm(k_pe, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il); 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_l, 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_l, 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);
// MLA attention with the absorption optimization { // {n_embd_head_qk_nope, n_tokens, n_head_l} 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_l} x {n_embd_head_qk_nope, n_tokens, n_head_l} 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_l, 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_l, n_tokens} ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 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, kv_cmpr, k_pe, 0); cb(Kcur, "Kcur", il);
// {kv_lora_rank, 1, n_tokens} ggml_tensor * Vcur = kv_cmpr; cb(Vcur, "Vcur", il);
// apply the head-wise output gate before o_proj, so wo stays out of build_attn if (is_swa) { cur = build_attn(inp_attn->get_swa(), nullptr, nullptr, nullptr, Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, kq_scale, il); } else { cur = build_attn(inp_attn->get_dsa(), nullptr, nullptr, nullptr, Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, top_k, kq_scale, il); } cb(cur, "attn_out", il);
ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, attn_inp); cb(gate, "attn_gate", il);
gate = ggml_sigmoid(ctx0, gate); cb(gate, "attn_gate_sigmoid", il);
// broadcast the per-head gate over the head dimension ggml_tensor * attn_3d = ggml_reshape_3d(ctx0, cur, n_embd_head_v_mla, n_head_l, n_tokens); ggml_tensor * gate_3d = ggml_reshape_3d(ctx0, gate, 1, n_head_l, n_tokens); attn_3d = ggml_mul(ctx0, attn_3d, gate_3d); cb(attn_3d, "attn_gated", il);
cur = ggml_reshape_2d(ctx0, attn_3d, n_embd_head_v_mla * n_head_l, n_tokens);
cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s); cb(cur, "attn_output", 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, model.layers[il].ffn_up_s, model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_s, model.layers[il].ffn_down, NULL, model.layers[il].ffn_down_s, NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); cb(cur, "ffn_out", il); } else { 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, nullptr, model.layers[il].ffn_gate_up_exps, model.layers[il].ffn_up_exps_s, model.layers[il].ffn_gate_exps_s, model.layers[il].ffn_down_exps_s); cb(moe_out, "ffn_moe_out", il);
ggml_tensor * ffn_shexp = build_ffn(cur, model.layers[il].ffn_up_shexp, NULL, model.layers[il].ffn_up_shexp_s, model.layers[il].ffn_gate_shexp, NULL, model.layers[il].ffn_gate_shexp_s, model.layers[il].ffn_down_shexp, NULL, model.layers[il].ffn_down_shexp_s, 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);
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 = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1); res->t_logits = cur;
ggml_build_forward_expand(gf, cur);}