Felipe97/llama-cpp-compiled
01.1k
1#include "models.h"2 3void llama_model_deepseek2::load_arch_hparams(llama_model_loader & ml) {4 uint32_t n_vocab = 0;5 ml.get_key(LLM_KV_VOCAB_SIZE, n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, n_vocab, false);6 7 // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B, Kanana-2-30B-A3B8 const bool is_lite = (hparams.n_layer() == 27 || hparams.n_layer() == 26 || (hparams.n_layer() == 48 && n_vocab == 128256));9 10 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);11 ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);12 if (!is_lite) {13 ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);14 }15 ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);16 ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_mla_impl, false);17 ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl, false);18 ml.get_key_or_arr(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_arr, hparams.n_layer_all);19 ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);20 ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);21 ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);22 ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);23 if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {24 // for compatibility with existing DeepSeek V2 and V2.5 GGUFs25 // that have no expert_gating_func model parameter set26 if ((hparams.n_layer() == 47 || hparams.n_layer() == 48) && n_vocab == 154880) {27 // GLM 4.7 Lite28 hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;29 } else {30 hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX;31 }32 }33 34 if (ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, false)) {35 // [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]36 // cancel the factor from the convert script37 hparams.rope_yarn_log_mul /= 0.1f;38 }39 40 // (optional) temperature tuning - used by mistral-large41 ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale, false);42 ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH, hparams.n_attn_temp_floor_scale, false); // FIXME why not use temperature_length?43 44 hparams.f_attn_temp_offset = 0.0f;45 46 switch (hparams.n_layer()) {47 case 27: type = LLM_TYPE_16B; break;48 case 47: type = LLM_TYPE_30B_A3B; break;49 case 60: type = LLM_TYPE_236B; break;50 case 61: type = LLM_TYPE_671B; break;51 default: type = LLM_TYPE_UNKNOWN;52 }53}54 55void llama_model_deepseek2::load_arch_tensors(llama_model_loader & ml) {56 LLAMA_LOAD_LOCALS;57 const int64_t n_expert_shared = hparams.n_expert_shared;58 59 const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);60 const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";61 const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);62 const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;63 int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;64 65 if (!ml.load_mtp) {66 mtp_flags |= TENSOR_SKIP;67 }68 69 const bool is_mla = hparams.is_mla();70 71 // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA72 const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();73 const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla();74 75 const int64_t n_embd_head_qk_rope = hparams.n_rot();76 const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;77 GGML_ASSERT(n_embd_head_qk_nope >= 1);78 79 const int64_t q_lora_rank = hparams.n_lora_q;80 const int64_t kv_lora_rank = hparams.n_lora_kv;81 82 const int64_t n_ff_exp = hparams.n_ff_exp();83 84 tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);85 86 // output87 output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);88 // try to load output.weight, if not found, use token_embd (tied embeddings)89 output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);90 if (!output) {91 output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);92 }93 94 for (int i = 0; i < n_layer_all; ++i) {95 auto & layer = layers[i];96 const int flags = i < n_layer ? trunk_flags : mtp_flags;97 98 layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);99 if (q_lora_rank > 0) {100 layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, flags);101 }102 103 layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, flags);104 105 if (q_lora_rank > 0) {106 layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, flags);107 layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, flags);108 } else {109 layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_head * n_embd_head_k_mla}, flags);110 }111 112 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);113 114 // note: only old legacy GGUF files will have the unsplit wkv_b tensor in115 if (is_mla) {116 layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head}, flags);117 layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, flags);118 } else {119 layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v_mla)}, flags);120 }121 122 layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, flags);123 124 layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);125 126 if (i < (int) hparams.n_layer_dense_lead) {127 layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, flags);128 layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, flags);129 layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, flags);130 } else {131 layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags);132 layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | flags);133 134 if (n_expert == 0) {135 throw std::runtime_error("n_expert must be > 0");136 }137 if (n_expert_used == 0) {138 throw std::runtime_error("n_expert_used must be > 0");139 }140 141 // MoE branch142 layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, flags);143 create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, flags);144 145 // Shared expert branch146 layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);147 layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, flags);148 layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);149 }150 151 // NextN/MTP tensors152 if (i >= n_layer) {153 layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, mtp_flags);154 layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, mtp_flags);155 layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, mtp_flags);156 layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags);157 layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags);158 layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, TENSOR_NOT_REQUIRED | flags);159 }160 }161}162 163std::unique_ptr<llm_graph_context> llama_model_deepseek2::build_arch_graph(const llm_graph_params & params) const {164 if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {165 return std::make_unique<graph_mtp>(*this, params);166 }167 return std::make_unique<graph>(*this, params);168}169 170llama_model_deepseek2::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params) :171 llm_graph_context(params) {172 GGML_ASSERT(hparams.n_layer_nextn > 0 && "GLM4 MTP requires n_layer_nextn > 0");173 GGML_ASSERT(hparams.n_layer_nextn == 1 && "GLM4 MTP currently only supports a single MTP block");174 GGML_ASSERT(hparams.is_mla() && "GLM4 MTP requires MLA");175 GGML_ASSERT(hparams.f_attn_temp_scale == 0.0f && "GLM4 MTP does not support attention temperature scaling");176 177 // The appended MTP block is stored immediately after the main decoder layers.178 const int il = hparams.n_layer();179 const auto & layer = model.layers[il];180 181 GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");182 GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm");183 GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm");184 185 GGML_ASSERT((uint32_t) il >= hparams.n_layer_dense_lead && "GLM4 MTP block expected to use MoE FFN");186 187 const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla();188 const int64_t n_embd_head_qk_rope = hparams.n_rot();189 const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;190 const int64_t kv_lora_rank = hparams.n_lora_kv;191 192 GGML_ASSERT(n_embd_head_qk_nope >= 1);193 GGML_ASSERT(hparams.n_lora_q > 0);194 GGML_ASSERT(layer.wq_a);195 GGML_ASSERT(layer.attn_q_a_norm);196 GGML_ASSERT(layer.wq_b);197 GGML_ASSERT(layer.wkv_a_mqa);198 GGML_ASSERT(layer.attn_kv_a_norm);199 GGML_ASSERT(layer.wk_b);200 201 const bool has_split_exps =202 layer.ffn_up_exps != nullptr &&203 layer.ffn_gate_exps != nullptr;204 205 const bool has_fused_exps = layer.ffn_gate_up_exps != nullptr;206 207 GGML_ASSERT(has_split_exps || has_fused_exps);208 GGML_ASSERT(layer.ffn_norm);209 GGML_ASSERT(layer.ffn_gate_inp);210 GGML_ASSERT(layer.ffn_down_exps);211 GGML_ASSERT(layer.ffn_gate_shexp);212 GGML_ASSERT(layer.ffn_down_shexp);213 GGML_ASSERT(layer.ffn_up_shexp);214 215 auto inp = std::make_unique<llm_graph_input_embd_h>(hparams.n_embd);216 217 inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);218 ggml_set_input(inp->tokens);219 220 inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens);221 ggml_set_input(inp->embd);222 223 ggml_tensor * tok_embd;224 if (ubatch.token) {225 ggml_tensor * tok_embd_w = layer.nextn.embed_tokens226 ? layer.nextn.embed_tokens227 : model.tok_embd;228 229 tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);230 } else {231 tok_embd = inp->embd;232 }233 cb(tok_embd, "mtp_tok_embd", il);234 235 inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);236 ggml_set_input(inp->h);237 ggml_set_name(inp->h, "mtp_h_input");238 239 ggml_tensor * h_embd = inp->h;240 241 res->add_input(std::move(inp));242 243 ggml_tensor * inp_pos = build_inp_pos();244 ggml_tensor * inp_out_ids = build_inp_out_ids();245 246 auto * inp_attn_k = build_attn_inp_k();247 248 ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);249 cb(h_norm, "mtp_hnorm", il);250 251 ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il);252 cb(e_norm, "mtp_enorm", il);253 254 ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, 0);255 cb(concat, "mtp_concat", il);256 257 ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s);258 cb(cur, "mtp_eh_proj", il);259 260 ggml_tensor * inpSA = cur;261 262 cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);263 cb(cur, "mtp_attn_norm", il);264 265 ggml_tensor * q = ggml_mul_mat(ctx0, layer.wq_a, cur);266 cb(q, "mtp_q_a", il);267 268 q = build_norm(q, layer.attn_q_a_norm, nullptr, LLM_NORM_RMS, il);269 cb(q, "mtp_q_a_norm", il);270 271 q = ggml_mul_mat(ctx0, layer.wq_b, q);272 cb(q, "mtp_q_b", il);273 274 ggml_tensor * q_nope =275 ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,276 ggml_row_size(q->type, n_embd_head_k_mla),277 ggml_row_size(q->type, n_embd_head_k_mla) * n_head, 0);278 cb(q_nope, "mtp_q_nope", il);279 280 ggml_tensor * q_pe =281 ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,282 ggml_row_size(q->type, n_embd_head_k_mla),283 ggml_row_size(q->type, n_embd_head_k_mla) * n_head,284 ggml_row_size(q->type, n_embd_head_qk_nope));285 cb(q_pe, "mtp_q_pe", il);286 287 ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur);288 cb(kv_cmpr_pe, "mtp_kv_cmpr_pe", il);289 290 ggml_tensor * kv_cmpr =291 ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,292 ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);293 cb(kv_cmpr, "mtp_kv_cmpr", il);294 295 ggml_tensor * k_pe =296 ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,297 ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),298 ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),299 ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));300 cb(k_pe, "mtp_k_pe", il);301 302 kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);303 cb(kv_cmpr, "mtp_kv_cmpr_norm", il);304 305 GGML_ASSERT(ext_factor >= 0.0f);306 307 const float attn_factor_org =308 attn_factor * (1.0f + 0.1f * logf(1.0f / freq_scale));309 310 const float mscale =311 attn_factor_org * (1.0f + 0.1f * hparams.rope_yarn_log_mul * logf(1.0f / freq_scale));312 313 const float kq_scale =314 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k_mla));315 316 q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr,317 n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,318 ext_factor, attn_factor, beta_fast, beta_slow);319 cb(q_pe, "mtp_q_pe_rope", il);320 321 k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr,322 n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,323 ext_factor, attn_factor, beta_fast, beta_slow);324 cb(k_pe, "mtp_k_pe_rope", il);325 326 q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);327 cb(q_nope, "mtp_q_nope_perm", il);328 329 ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope);330 cb(q_nope_absorbed, "mtp_q_nope_absorbed", il);331 332 q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);333 cb(q_nope_absorbed, "mtp_q_nope_absorbed_perm", il);334 335 ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);336 cb(Qcur, "mtp_Qcur", il);337 338 kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, hparams.n_lora_kv, 1, n_tokens);339 cb(kv_cmpr, "mtp_kv_cmpr_reshape", il);340 341 ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);342 cb(Kcur, "mtp_Kcur", il);343 344 ggml_tensor * Vcur = kv_cmpr;345 cb(Vcur, "mtp_Vcur", il);346 347 cur = build_attn(inp_attn_k,348 layer.wo, nullptr, layer.wo_s,349 Qcur, Kcur, Vcur, nullptr, nullptr, layer.wv_b, kq_scale, il);350 cb(cur, "mtp_attn_out", il);351 352 ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);353 cb(ffn_inp, "mtp_ffn_inp", il);354 355 cur = build_norm(ffn_inp, layer.ffn_norm, nullptr, LLM_NORM_RMS, il);356 cb(cur, "mtp_ffn_norm", il);357 358 ggml_tensor * moe_out = build_moe_ffn(cur,359 layer.ffn_gate_inp,360 layer.ffn_up_exps,361 layer.ffn_gate_exps,362 layer.ffn_down_exps,363 layer.ffn_exp_probs_b,364 n_expert, n_expert_used,365 LLM_FFN_SILU, hparams.expert_weights_norm,366 hparams.expert_weights_scale,367 (llama_expert_gating_func_type) hparams.expert_gating_func,368 il,369 nullptr,370 layer.ffn_gate_up_exps);371 cb(moe_out, "mtp_ffn_moe_out", il);372 373 ggml_tensor * ffn_shexp = build_ffn(cur,374 layer.ffn_up_shexp, nullptr, nullptr,375 layer.ffn_gate_shexp, nullptr, nullptr,376 layer.ffn_down_shexp, nullptr, nullptr,377 nullptr, LLM_FFN_SILU, LLM_FFN_PAR, il);378 cb(ffn_shexp, "mtp_ffn_shexp", il);379 380 cur = ggml_add(ctx0, moe_out, ffn_shexp);381 cb(cur, "mtp_ffn_out", il);382 383 cur = ggml_add(ctx0, cur, ffn_inp);384 cb(cur, "mtp_post_ffn", il);385 386 ggml_tensor * head_norm_w = layer.nextn.shared_head_norm387 ? layer.nextn.shared_head_norm388 : model.output_norm;389 GGML_ASSERT(head_norm_w && "GLM4 MTP: missing both nextn.shared_head_norm and output_norm");390 391 cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1);392 cb(cur, "h_nextn", -1);393 res->t_h_nextn = cur;394 395 if (inp_out_ids) {396 cur = ggml_get_rows(ctx0, cur, inp_out_ids);397 }398 cb(cur, "mtp_shared_head_norm", -1);399 400 ggml_tensor * head_w = layer.nextn.shared_head_head401 ? layer.nextn.shared_head_head402 : model.output;403 404 ggml_tensor * head_s = layer.nextn.shared_head_head405 ? layer.nextn.shared_head_head_s406 : model.output_s;407 408 GGML_ASSERT(head_w && "GLM4 MTP: missing LM head (nextn.shared_head_head or model.output)");409 410 cur = build_lora_mm(head_w, cur, head_s);411 cb(cur, "result_output", -1);412 413 res->t_logits = cur;414 ggml_build_forward_expand(gf, cur);415}416 417llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_params & params) :418 llm_graph_context(params) {419 // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B420 bool is_ocr = model.arch == LLM_ARCH_DEEPSEEK2OCR;421 422 const bool is_mla = hparams.is_mla();423 424 // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA425 const int64_t n_embd_head_k = hparams.n_embd_head_k_mla();426 const int64_t n_embd_head_v = hparams.n_embd_head_v_mla();427 428 const int64_t n_embd_head_qk_rope = hparams.n_rot();429 const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope;430 431 const uint32_t kv_lora_rank = hparams.n_lora_kv;432 433 // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.434 // See https://github.com/ggml-org/llama.cpp/discussions/7416 for detailed explanation.435 // And also: https://github.com/ggml-org/llama.cpp/pull/17945 [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]436 437 // first cancel the adjustment from llama_hparams::yarn_attn_factor_adjust to get the original attn_factor438 GGML_ASSERT(ext_factor >= 0.0f);439 const float attn_factor_org = attn_factor * (1.0f + 0.1f * logf(1.0f / freq_scale));440 441 // use the original attn_factor to pre-scale the kq_scale442 const float mscale = attn_factor_org * (1.0f + 0.1f * hparams.rope_yarn_log_mul * logf(1.0f / freq_scale));443 const float kq_scale = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k));444 445 ggml_tensor * cur;446 ggml_tensor * inpL;447 448 // {n_embd, n_tokens}449 inpL = build_inp_embd(model.tok_embd);450 451 // (optional) temperature tuning - used by mistral-large452 ggml_tensor * inp_attn_scale = nullptr;453 if (hparams.f_attn_temp_scale != 0.0f) {454 inp_attn_scale = build_inp_attn_scale();455 }456 457 // inp_pos - contains the positions458 ggml_tensor * inp_pos = build_inp_pos();459 460 auto * inp_attn_kv = !is_mla ? build_attn_inp_kv() : nullptr;461 auto * inp_attn_k = is_mla ? build_attn_inp_k() : nullptr;462 463 ggml_tensor * inp_out_ids = build_inp_out_ids();464 465 for (int il = 0; il < n_layer; ++il) {466 ggml_tensor * inpSA = inpL;467 468 // norm469 cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);470 cb(cur, "attn_norm", il);471 472 // self_attention473 if (is_ocr) {474 const int n_embed_head = hparams.n_embd / hparams.n_head();475 const int ocr_rope_type = GGML_ROPE_TYPE_NEOX;476 GGML_ASSERT(n_embed_head == n_embd_head_k && n_embed_head == n_embd_head_v);477 478 auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,479 n_embed_head, n_head, n_head, il);480 cb(Qcur, "q", il);481 cb(Kcur, "k", il);482 cb(Vcur, "v", il);483 484 GGML_ASSERT(fabs(freq_base - 10000.0) < 1e-4);485 Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_embed_head, ocr_rope_type, 0, freq_base, 1, 0, 1, 0, 0);486 Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_embed_head, ocr_rope_type, 0, freq_base, 1, 0, 1, 0, 0);487 cb(Qcur, "q_pe", il);488 cb(Kcur, "k_pe", il);489 490 cur = build_attn(inp_attn_kv,491 model.layers[il].wo, NULL, model.layers[il].wo_s,492 Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);493 cb(cur, "attn_out", il);494 }495 else {496 ggml_tensor * q = NULL;497 498 const bool is_lite = model.layers[il].wq;499 500 if (!is_lite) {501 q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);502 cb(q, "q", il);503 504 q = build_norm(q, model.layers[il].attn_q_a_norm, nullptr, LLM_NORM_RMS, il);505 cb(q, "q", il);506 507 q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q);508 cb(q, "q", il);509 } else {510 q = ggml_mul_mat(ctx0, model.layers[il].wq, cur);511 cb(q, "q", il);512 }513 // {n_embd_head_k, n_head, n_tokens}514 q = ggml_reshape_3d(ctx0, q, n_embd_head_k, n_head, n_tokens);515 cb(q, "q", il);516 517 ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);518 cb(kv_cmpr_pe, "kv_cmpr_pe", il);519 520 // split into {kv_lora_rank, n_tokens}521 ggml_tensor * kv_cmpr =522 ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,523 ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);524 cb(kv_cmpr, "kv_cmpr", il);525 526 // and {n_embd_head_qk_rope, 1, n_tokens}527 ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,528 ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),529 ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),530 ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));531 cb(k_pe, "k_pe", il);532 533 k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,534 ext_factor, attn_factor, beta_fast, beta_slow);535 cb(k_pe, "k_pe", il);536 537 kv_cmpr = build_norm(kv_cmpr, model.layers[il].attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);538 cb(kv_cmpr, "kv_cmpr", il);539 540 if (is_mla) {541 // split into {n_embd_head_qk_nope, n_head, n_tokens}542 ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,543 q->nb[1], q->nb[2], 0);544 cb(q_nope, "q_nope", il);545 546 // and {n_embd_head_qk_rope, n_head, n_tokens}547 ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,548 q->nb[1], q->nb[2], ggml_row_size(q->type, n_embd_head_qk_nope));549 cb(q_pe, "q_pe", il);550 551 q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,552 ext_factor, attn_factor, beta_fast, beta_slow);553 cb(q_pe, "q_pe", il);554 555 // {n_embd_head_qk_nope, n_tokens, n_head}556 q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);557 cb(q_nope, "q_nope_perm", il);558 559 // {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head}560 ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, model.layers[il].wk_b, q_nope);561 cb(q_nope_absorbed, "q_nope_absorbed", il);562 563 // {kv_lora_rank, n_head, n_tokens}564 q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);565 cb(q_nope_absorbed, "q_nope_absorbed_perm", il);566 567 // {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens}568 // note: rope must go first for in-place context shifting in build_rope_shift()569 ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);570 cb(Qcur, "Qcur", il);571 572 kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);573 cb(kv_cmpr, "kv_cmpr_reshape", il);574 575 // {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens}576 ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);577 cb(Kcur, "Kcur", il);578 579 // {kv_lora_rank, 1, n_tokens}580 ggml_tensor * Vcur = kv_cmpr;581 cb(Vcur, "Vcur", il);582 583 if (inp_attn_scale) {584 // apply llama 4 temperature scaling585 Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale);586 cb(Qcur, "Qcur_attn_temp_scaled", il);587 }588 589 // note: MLA with the absorption optimization converts into MQA (ie: GQA with 1 group)590 cur = build_attn(inp_attn_k,591 model.layers[il].wo, NULL, model.layers[il].wo_s,592 Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, kq_scale, il);593 } else {594 ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_cmpr);595 cb(kv, "kv", il);596 597 // split into {n_embd_head_qk_nope, n_head, n_tokens}598 ggml_tensor * k_nope =599 ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,600 ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v),601 ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v) * n_head, 0);602 cb(k_nope, "k_nope_view", il);603 604 // and {n_embd_head_v, n_head, n_tokens}605 ggml_tensor * Vcur = ggml_view_3d(ctx0, kv, n_embd_head_v, n_head, n_tokens,606 ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v),607 ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v) * n_head,608 ggml_row_size(kv->type, n_embd_head_qk_nope));609 cb(Vcur, "Vcur_view", il);610 611 Vcur = ggml_cont(ctx0, Vcur);612 cb(Vcur, "Vcur_cont", il);613 614 // RoPE is applied to the trailing dims only615 ggml_tensor * Qcur = ggml_rope_ext(ctx0, q, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,616 freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);617 Qcur = ggml_rope_set_offset(Qcur, n_embd_head_qk_nope);618 cb(Qcur, "Qcur", il);619 620 ggml_tensor * Kcur = ggml_concat(ctx0, k_nope,621 ggml_repeat_4d(ctx0, k_pe, n_embd_head_qk_rope, n_head, n_tokens, 1), 0);622 cb(Kcur, "Kcur", il);623 624 if (inp_attn_scale) {625 // apply llama 4 temperature scaling626 Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale);627 cb(Qcur, "Qcur_attn_temp_scaled", il);628 }629 630 // note: MLA without the absorption optimization converts into MHA (ie: GQA with full n_head groups)631 cur = build_attn(inp_attn_kv,632 model.layers[il].wo, NULL, model.layers[il].wo_s,633 Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);634 }635 }636 if (il == n_layer - 1 && inp_out_ids && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked)) {637 cur = ggml_get_rows(ctx0, cur, inp_out_ids);638 inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);639 }640 ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);641 cb(ffn_inp, "ffn_inp", il);642 643 cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);644 cb(cur, "ffn_norm", il);645 646 if ((uint32_t) il < hparams.n_layer_dense_lead) {647 cur = build_ffn(cur,648 model.layers[il].ffn_up, NULL, NULL,649 model.layers[il].ffn_gate, NULL, NULL,650 model.layers[il].ffn_down, NULL, NULL,651 NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);652 cb(cur, "ffn_out", il);653 } else {654 // MoE branch655 ggml_tensor * moe_out = build_moe_ffn(cur,656 model.layers[il].ffn_gate_inp,657 model.layers[il].ffn_up_exps,658 model.layers[il].ffn_gate_exps,659 model.layers[il].ffn_down_exps,660 model.layers[il].ffn_exp_probs_b,661 n_expert, n_expert_used,662 LLM_FFN_SILU, hparams.expert_weights_norm,663 hparams.expert_weights_scale,664 (llama_expert_gating_func_type) hparams.expert_gating_func,665 il,666 nullptr,667 model.layers[il].ffn_gate_up_exps);668 cb(moe_out, "ffn_moe_out", il);669 670 // FFN shared expert671 {672 ggml_tensor * ffn_shexp =673 build_ffn(cur,674 model.layers[il].ffn_up_shexp, NULL, NULL,675 model.layers[il].ffn_gate_shexp, NULL, NULL,676 model.layers[il].ffn_down_shexp, NULL, NULL,677 NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);678 cb(ffn_shexp, "ffn_shexp", il);679 680 cur = ggml_add(ctx0, moe_out, ffn_shexp);681 cb(cur, "ffn_out", il);682 }683 }684 cur = ggml_add(ctx0, cur, ffn_inp);685 686 cur = build_cvec(cur, il);687 cb(cur, "l_out", il);688 689 // input for next layer690 inpL = cur;691 }692 cur = inpL;693 694 cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);695 696 cb(cur, "h_nextn", -1);697 res->t_h_nextn = cur;698 699 if (cparams.embeddings_nextn && !cparams.embeddings_nextn_masked && inp_out_ids) {700 cur = ggml_get_rows(ctx0, cur, inp_out_ids);701 }702 703 cb(cur, "result_norm", -1);704 res->t_embd = cur;705 706 // lm_head707 cur = ggml_mul_mat(ctx0, model.output, cur);708 709 cb(cur, "result_output", -1);710 res->t_logits = cur;711 712 ggml_build_forward_expand(gf, cur);713}714