#include "models.h" #include "llama-kv-cache.h" #include "llama-kv-cache-dsa.h" void llama_model_deepseek32::load_arch_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); hparams.f_norm_eps = 1e-6; // eps for layer norm ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false); // MoE parameters ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert); ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used); ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, 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); // deepseek MLA parameters 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, false); ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl, false); ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); // 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); // Expert gating function ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func); if (ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, 0.0f)) { // [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX] // cancel the factor from the convert script hparams.rope_yarn_log_mul /= 0.1f; } // NextN/MTP parameters 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"); switch (hparams.n_layer()) { case 62: type = LLM_TYPE_685B_A37B; break; default: type = LLM_TYPE_UNKNOWN; } } void llama_model_deepseek32::load_arch_tensors(llama_model_loader &) { LLAMA_LOAD_LOCALS; const bool is_mla = hparams.is_mla(); if (!is_mla) { throw std::runtime_error("DEEPSEEK32 architecture requires MLA"); } // 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_mla = hparams.n_embd_head_k_mla(); const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla(); const int64_t n_embd_head_qk_rope = hparams.n_rot(); const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope; const int64_t q_lora_rank = hparams.n_lora_q; const int64_t kv_lora_rank = hparams.n_lora_kv; 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 output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); // try to load output.weight, if not found, use token_embd (tied embeddings) 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) { int flags = 0; if (i >= n_layer) { // skip all tensors in the NextN layers // TODO @ngxson : TENSOR_NOT_REQUIRED was a hack, need to remove it later flags |= TENSOR_SKIP | TENSOR_NOT_REQUIRED; } auto & layer = layers[i]; 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); 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 * 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); // note: only old legacy GGUF files will have the unsplit wkv_b tensor in layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head}, flags); layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, flags); layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, flags); layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags); // DSA indexer 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 (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 { 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}, TENSOR_NOT_REQUIRED); if (n_expert == 0) { throw std::runtime_error("n_expert must be > 0"); } if (n_expert_used == 0) { throw std::runtime_error("n_expert_used must be > 0"); } // MoE branch 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); // Shared expert branch 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); } // NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers if (i >= n_layer) { 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); // Optional tensors layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED); layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED); layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags | TENSOR_NOT_REQUIRED); } } } std::unique_ptr llama_model_deepseek32::build_arch_graph(const llm_graph_params & params) const { return std::make_unique(*this, params); } llama_model_deepseek32::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const bool is_mla = hparams.is_mla(); GGML_ASSERT(is_mla); // 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 = hparams.n_embd_head_k_mla(); const int64_t n_embd_head_v = hparams.n_embd_head_v_mla(); GGML_UNUSED(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 int64_t n_indexer_head = hparams.indexer_n_head; const int64_t n_embd_indexer_head = hparams.indexer_head_size; const int64_t n_embd_indexer_head_rope = hparams.n_rot(); const int64_t n_embd_indexer_head_nope = n_embd_indexer_head - n_embd_indexer_head_rope; const uint32_t n_indexer_top_k = hparams.indexer_top_k; 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/ggml-org/llama.cpp/discussions/7416 for detailed explanation. // And also: https://github.com/ggml-org/llama.cpp/pull/17945 [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX] // first cancel the adjustment from llama_hparams::yarn_attn_factor_adjust to get the original attn_factor GGML_ASSERT(ext_factor >= 0.0f); const float attn_factor_org = attn_factor * (1.0f + 0.1f * logf(1.0f / freq_scale)); // use the original attn_factor to pre-scale the kq_scale const float mscale = attn_factor_org * (1.0f + 0.1f * hparams.rope_yarn_log_mul * logf(1.0f / freq_scale)); const float kq_scale = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k)); ggml_tensor * cur; ggml_tensor * inpL; // {n_embd, n_tokens} inpL = build_inp_embd(model.tok_embd); // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); llm_graph_input_attn_k_dsa * inp_attn_dsa = build_attn_inp_k_dsa(); 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 * 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 { ggml_tensor * indexer_q = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_q_b, qr); cb(indexer_q, "indexer_q", il); // split into {n_embd_indexer_head_rope, n_indexer_head, n_tokens} ggml_tensor * indexer_q_pe = ggml_view_3d(ctx0, indexer_q, n_embd_indexer_head_rope, n_indexer_head, n_tokens, ggml_row_size(indexer_q->type, n_embd_indexer_head), ggml_row_size(indexer_q->type, n_embd_indexer_head) * n_indexer_head, 0); cb(indexer_q_pe, "indexer_q_pe", il); // and {n_embd_indexer_head_nope, n_indexer_head, n_tokens} ggml_tensor * indexer_q_nope = ggml_view_3d(ctx0, indexer_q, n_embd_indexer_head_nope, n_indexer_head, n_tokens, ggml_row_size(indexer_q->type, n_embd_indexer_head), ggml_row_size(indexer_q->type, n_embd_indexer_head) * n_indexer_head, ggml_row_size(indexer_q->type, n_embd_indexer_head_nope)); cb(indexer_q_nope, "indexer_q_nope", il); indexer_q_pe = ggml_rope_ext(ctx0, indexer_q_pe, 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_pe, "indexer_q_pe", il); // {n_embd_indexer_head_rope + n_embd_indexer_head_nope, n_head, n_tokens} indexer_q = ggml_concat(ctx0, indexer_q_pe, indexer_q_nope, 0); 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); // split into {n_embd_indexer_head_rope, 1, n_tokens} ggml_tensor * indexer_k_pe = ggml_view_3d(ctx0, indexer_k, n_embd_indexer_head_rope, 1, n_tokens, ggml_row_size(indexer_k->type, n_embd_indexer_head), ggml_row_size(indexer_k->type, n_embd_indexer_head) * 1, 0); cb(indexer_k_pe, "indexer_k_pe", il); // and {n_embd_indexer_head_nope, 1, n_tokens} ggml_tensor * indexer_k_nope = ggml_view_3d(ctx0, indexer_k, n_embd_indexer_head_nope, 1, n_tokens, ggml_row_size(indexer_k->type, n_embd_indexer_head), ggml_row_size(indexer_k->type, n_embd_indexer_head) * 1, ggml_row_size(indexer_k->type, n_embd_indexer_head_nope)); cb(indexer_k_nope, "indexer_k_nope", il); indexer_k_pe = ggml_rope_ext(ctx0, indexer_k_pe, 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_pe, "indexer_k_pe", il); // {n_embd_indexer_head_rope + n_embd_indexer_head_nope, 1, n_tokens} indexer_k = ggml_concat(ctx0, indexer_k_pe, indexer_k_nope, 0); cb(indexer_k, "indexer_k", il); // perform Hadamard transform on indexer q and k indexer_q = ggml_mul_mat(ctx0, inp_attn_dsa->self_k_rot_lid, indexer_q); cb(indexer_q, "indexer_q", il); indexer_k = ggml_mul_mat(ctx0, inp_attn_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_dsa->mctx->get_lid(); const auto & k_idxs_lid = inp_attn_dsa->get_k_idxs_lid(); ggml_build_forward_expand(gf, mctx_lid->cpy_k(ctx0, indexer_k, k_idxs_lid, il)); // prepare indexer weights ggml_tensor * indexer_weights = ggml_mul_mat(ctx0, model.layers[il].indexer_proj, cur); cb(indexer_weights, "indexer_weights", il); // get cached indexer keys 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); // calculate indexer kq 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); // apply ReLU ggml_tensor * indexer_score = ggml_relu(ctx0, indexer_kq); cb(indexer_score, "indexer_score", il); // 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); // multiply scores by indexer weights 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); // mask indexer scores ggml_tensor * indexer_kq_mask = inp_attn_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, 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); // MLA attention { // {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_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); // note: MLA with the absorption optimization converts into MQA (ie: GQA with 1 group) cur = build_attn(inp_attn_dsa, model.layers[il].wo, NULL, model.layers[il].wo_s, Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, top_k, 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, 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 { // 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, 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); // FFN shared expert { 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); // 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); }