diff --git a/conversion/glm.py b/conversion/glm.py index d85268a62149..cc34cddbf843 100644 --- a/conversion/glm.py +++ b/conversion/glm.py @@ -1,6 +1,8 @@ from __future__ import annotations -from typing import Iterable, TYPE_CHECKING +import re + +from typing import Callable, Iterable, TYPE_CHECKING import torch @@ -213,12 +215,47 @@ def set_vocab(self): class GlmMoeDsaModel(DeepseekV2Model): model_arch = gguf.MODEL_ARCH.GLM_DSA skip_mtp = False + supports_mtp_export = True + + # Trunk layer count, stashed before indexing so the classmethod + # filter_tensors can identify the appended NextN/MTP block (mirrors + # HYV3Model / Step35Model). + _n_main_layers: int | None = None def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) - self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0) + self.block_count = self.hparams["num_hidden_layers"] + if not self.no_mtp: + self.block_count += self.hparams.get("num_nextn_predict_layers", 0) self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) + def index_tensors(self, remote_hf_model_id: str | None = None): + type(self)._n_main_layers = self.hparams["num_hidden_layers"] + return super().index_tensors(remote_hf_model_id=remote_hf_model_id) + + @classmethod + def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None: + if (titem := super().filter_tensors(item)) is None: + return None + name, gen = titem + + # GLM-5.2 appends the NextN/MTP block past num_hidden_layers + # (model.layers.78 -> blk.78 in the 79-block file). + assert cls._n_main_layers is not None + is_mtp = (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None and int(m.group(1)) >= cls._n_main_layers + + # --no-mtp: drop the appended NextN block entirely. + if is_mtp and cls.no_mtp: + return None + # --mtp: keep ONLY NextN-block tensors plus the shared embeddings/ + # norm/lm_head (so the resulting GGUF carries just the draft head). + if cls.mtp_only and not is_mtp and name not in ( + "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight", + ): + return None + + return name, gen + def set_vocab(self): return self._set_vocab_glm() @@ -230,7 +267,7 @@ def set_gguf_parameters(self): self.gguf_writer.add_rope_dimension_count(int(rope_dim * partial_rotary_factor)) # NextN/MTP prediction layers - if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None: + if not self.no_mtp and (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None: self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers) # DSA indexer parameters diff --git a/src/llama-model.cpp b/src/llama-model.cpp index b100f6018150..4580482c9474 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -2066,7 +2066,6 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params, res = nullptr; } break; case LLM_ARCH_DEEPSEEK32: - case LLM_ARCH_GLM_DSA: { res = new llama_kv_cache_dsa( *this, @@ -2083,6 +2082,56 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params, nullptr, nullptr); } break; + case LLM_ARCH_GLM_DSA: + { + if (params.ctx_type == LLAMA_CONTEXT_TYPE_MTP && hparams.n_layer_nextn > 0) { + // The NextN/MTP draft head runs dense MLA (no DSA indexer), so the + // MTP context uses a plain attention KV cache holding only the + // nextn layer(s) - same pattern as the hybrid Qwen3.5 MTP context. + llama_kv_cache::layer_filter_cb filter = + [&](uint32_t il) { return il >= hparams.n_layer(); }; + + res = new llama_kv_cache( + *this, + hparams, + params.type_k, + params.type_v, + !cparams.flash_attn, + cparams.offload_kqv, + cparams.kv_unified, + cparams.n_ctx_seq, + cparams.n_seq_max, + 1, + hparams.n_swa, + hparams.swa_type, + nullptr, + filter, + nullptr, + nullptr); + } else { + // Main context: DSA cache for the trunk layers only - the nextn + // layer(s) are never attended by the trunk graph. + llama_kv_cache::layer_filter_cb filter = nullptr; + if (hparams.n_layer_nextn > 0) { + filter = [&](uint32_t il) { return il < hparams.n_layer(); }; + } + + res = new llama_kv_cache_dsa( + *this, + params.type_k, + params.type_v, + !cparams.flash_attn, + cparams.offload_kqv, + cparams.kv_unified, + cparams.n_ctx_seq, + cparams.n_seq_max, + 1, + hparams.n_swa, + hparams.swa_type, + filter, + nullptr); + } + } break; // Models that need standard caching should rely on recurrent/hybrid // checks default: @@ -2188,7 +2237,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params, filter = [&](uint32_t il) { return il >= hparams.n_layer(); }; } - if ((arch == LLM_ARCH_STEP35 || arch == LLM_ARCH_HY_V3) && hparams.n_layer_nextn > 0) { + if ((arch == LLM_ARCH_STEP35 || arch == LLM_ARCH_HY_V3 || arch == LLM_ARCH_GLM_DSA) && hparams.n_layer_nextn > 0) { if (params.ctx_type == LLAMA_CONTEXT_TYPE_MTP) { filter = [&](uint32_t il) { return il >= hparams.n_layer(); }; } else { diff --git a/src/models/glm-dsa.cpp b/src/models/glm-dsa.cpp index df190e1f634b..bd1c4df21281 100644 --- a/src/models/glm-dsa.cpp +++ b/src/models/glm-dsa.cpp @@ -72,15 +72,27 @@ void llama_model_glm_dsa::load_arch_hparams(llama_model_loader & ml) { ml.get_key_or_arr(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl, hparams.n_layer(), false); switch (hparams.n_layer()) { - case 78: type = LLM_TYPE_744B_A40B; break; + case 78: // GGUF with NextN/MTP metadata: n_layer() excludes the nextn layer + case 79: + type = LLM_TYPE_744B_A40B; break; default: type = LLM_TYPE_UNKNOWN; } } -void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) { +void llama_model_glm_dsa::load_arch_tensors(llama_model_loader & ml) { LLAMA_LOAD_LOCALS; const int64_t n_expert_shared = hparams.n_expert_shared; + // MTP-only: the GGUF carries only the NextN/MTP block(s) (user split target/draft). + const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr); + // Trunk-only: the GGUF declares MTP layers in metadata but the actual MTP + // tensors live in a separate file (or were stripped at conversion). Mark + // MTP tensors NOT_REQUIRED so the trunk loads cleanly. + const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight"; + const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr); + const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0; + const int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0; + const bool is_mla = hparams.is_mla(); if (!is_mla) { throw std::runtime_error("GLM_DSA architecture requires MLA"); @@ -109,12 +121,9 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) { } 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; - } + // NextN/MTP layers (i >= n_layer) are full decoder blocks used by the + // LLM_GRAPH_TYPE_DECODER_MTP draft head; load them like qwen35moe/step35/hy_v3. + const int flags = (i >= n_layer) ? mtp_flags : trunk_flags; auto & layer = layers[i]; @@ -167,7 +176,7 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) { 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 n_layer_nextn + // NextN/MTP tensors - the NextN-specific wiring around the extra decoder block 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); @@ -182,6 +191,9 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) { } std::unique_ptr llama_model_glm_dsa::build_arch_graph(const llm_graph_params & params) const { + if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) { + return std::make_unique(*this, params); + } return std::make_unique(*this, params); } @@ -469,7 +481,9 @@ llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_par Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, top_k, kq_scale, il); } } - if (il == n_layer - 1 && inp_out_ids) { + // when unmasked nextn embeddings are requested, t_h_nextn must keep all rows, + // so the early output masking has to be skipped (it is applied after the final norm instead) + if (il == n_layer - 1 && inp_out_ids && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked)) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -532,6 +546,14 @@ llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_par cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); + // post-norm hidden state feeds the NextN/MTP draft head + cb(cur, "h_nextn", -1); + res->t_h_nextn = cur; + + if (cparams.embeddings_nextn && !cparams.embeddings_nextn_masked && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + } + cb(cur, "result_norm", -1); res->t_embd = cur; @@ -543,3 +565,242 @@ llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_par ggml_build_forward_expand(gf, cur); } + +// LLM_GRAPH_TYPE_DECODER_MTP draft head for GLM-5.2 (GLM_DSA). +// Semantics mirror the deepseek-family NextN/MTP layer: +// enorm(embed) + hnorm(prev_hidden) -> concat(e, h) -> eh_proj -> +// full glm_dsa decoder block (dense MLA attention + sigmoid-gated MoE FFN +// with shared expert, exactly as the trunk deepseek2 graph builds it) -> +// shared_head_norm (fallback output_norm) -> shared LM head. +// The DSA indexer is not used at runtime (same as the trunk graph). +llama_model_glm_dsa::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params) + : llm_graph_context(params) { + GGML_ASSERT(hparams.n_layer_nextn > 0 && "GLM_DSA MTP requires n_layer_nextn > 0"); + GGML_ASSERT(hparams.n_layer_nextn == 1 && "GLM_DSA MTP currently only supports a single MTP block"); + GGML_ASSERT(hparams.is_mla() && "GLM_DSA MTP requires MLA"); + + const int il = hparams.n_layer() + cparams.nextn_layer_offset; + GGML_ASSERT(cparams.nextn_layer_offset >= 0 && + cparams.nextn_layer_offset < (int) hparams.n_layer_nextn && + "nextn_layer_offset out of range [0, n_layer_nextn)"); + const auto & layer = model.layers[il]; + + GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj"); + GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm"); + GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm"); + GGML_ASSERT(layer.ffn_gate_inp && "MTP block missing ffn_gate_inp"); + + // 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_qk_rope = hparams.n_rot(); + const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope; + + 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 the deepseek2 trunk graph for the detailed explanation - this must match it EXACTLY. + GGML_ASSERT(ext_factor >= 0.0f); + const float attn_factor_org = attn_factor * (1.0f + 0.1f * logf(1.0f / freq_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)); + + // TODO: extract in a common llm_graph_context::build_inp_embd_h() + auto inp = std::make_unique(hparams.n_embd); + + inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + ggml_set_input(inp->tokens); + + inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens); + ggml_set_input(inp->embd); + + ggml_tensor * tok_embd; + if (ubatch.token) { + ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd; + + tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens); + } else { + tok_embd = inp->embd; + } + cb(tok_embd, "mtp_tok_embd", il); + + inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens); + ggml_set_input(inp->h); + ggml_set_name(inp->h, "mtp_h_input"); + + ggml_tensor * h_embd = inp->h; + + res->add_input(std::move(inp)); + + ggml_tensor * inp_pos = build_inp_pos(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + // MLA with the absorption optimization uses a K-only cache (V is a view of K) + auto * inp_attn = build_attn_inp_k(); + + ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il); + cb(h_norm, "mtp_hnorm", il); + + ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il); + cb(e_norm, "mtp_enorm", il); + + ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0); + cb(concat, "mtp_concat", il); + + ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s); + cb(cur, "mtp_eh_proj", il); + + ggml_tensor * inpSA = cur; + + cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "mtp_attn_norm", il); + + // self-attention: dense MLA, same construction as the deepseek2 trunk graph + { + ggml_tensor * q = ggml_mul_mat(ctx0, layer.wq_a, cur); + cb(q, "mtp_q", il); + + q = build_norm(q, layer.attn_q_a_norm, nullptr, LLM_NORM_RMS, il); + cb(q, "mtp_q", il); + + q = ggml_mul_mat(ctx0, layer.wq_b, q); + cb(q, "mtp_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, "mtp_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, "mtp_q_pe", il); + + ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur); + cb(kv_cmpr_pe, "mtp_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, "mtp_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, "mtp_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, "mtp_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, "mtp_k_pe", il); + + kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il); + cb(kv_cmpr, "mtp_kv_cmpr", il); + + // {n_embd_head_qk_nope, n_tokens, n_head} + q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3); + cb(q_nope, "mtp_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, layer.wk_b, q_nope); + cb(q_nope_absorbed, "mtp_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, "mtp_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, "mtp_Qcur", il); + + kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens); + cb(kv_cmpr, "mtp_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, "mtp_Kcur", il); + + // {kv_lora_rank, 1, n_tokens} + ggml_tensor * Vcur = kv_cmpr; + cb(Vcur, "mtp_Vcur", il); + + // note: MLA with the absorption optimization converts into MQA (ie: GQA with 1 group) + cur = build_attn(inp_attn, + layer.wo, NULL, layer.wo_s, + Qcur, Kcur, Vcur, nullptr, nullptr, layer.wv_b, kq_scale, il); + cb(cur, "mtp_attn_out", il); + } + + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "mtp_ffn_inp", il); + + cur = build_norm(ffn_inp, layer.ffn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "mtp_ffn_norm", il); + + // MoE FFN with shared expert - same construction as the deepseek2 trunk graph + ggml_tensor * moe_out = build_moe_ffn(cur, + layer.ffn_gate_inp, + layer.ffn_up_exps, + layer.ffn_gate_exps, + layer.ffn_down_exps, + layer.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, + layer.ffn_gate_up_exps, + layer.ffn_up_exps_s, + layer.ffn_gate_exps_s, + layer.ffn_down_exps_s); + cb(moe_out, "mtp_ffn_moe_out", il); + + // FFN shared expert + ggml_tensor * ffn_shexp = + build_ffn(cur, + layer.ffn_up_shexp, NULL, layer.ffn_up_shexp_s, + layer.ffn_gate_shexp, NULL, layer.ffn_gate_shexp_s, + layer.ffn_down_shexp, NULL, layer.ffn_down_shexp_s, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(ffn_shexp, "mtp_ffn_shexp", il); + + cur = ggml_add(ctx0, moe_out, ffn_shexp); + cb(cur, "mtp_ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "mtp_post_ffn", il); + + // shared_head_norm applied after the decoder block, before the shared LM head. + // The post-norm hidden state seeds the next MTP step. + ggml_tensor * head_norm_w = layer.nextn.shared_head_norm + ? layer.nextn.shared_head_norm + : model.output_norm; + GGML_ASSERT(head_norm_w && "GLM_DSA MTP: missing both nextn.shared_head_norm and output_norm"); + cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1); + + cb(cur, "h_nextn", -1); + res->t_h_nextn = cur; + + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + cb(cur, "mtp_shared_head_norm", -1); + + ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output; + ggml_tensor * head_s = layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : model.output_s; + GGML_ASSERT(head_w && "GLM_DSA MTP: missing LM head (nextn.shared_head_head or model.output)"); + cur = build_lora_mm(head_w, cur, head_s); + cb(cur, "result_output", -1); + + res->t_logits = cur; + ggml_build_forward_expand(gf, cur); +} diff --git a/src/models/models.h b/src/models/models.h index 76daa8cc1994..0e1b2e2928a1 100644 --- a/src/models/models.h +++ b/src/models/models.h @@ -1221,6 +1221,10 @@ struct llama_model_glm_dsa : public llama_model_base { graph(const llama_model & model, const llm_graph_params & params); }; + struct graph_mtp : public llm_graph_context { + graph_mtp(const llama_model & model, const llm_graph_params & params); + }; + std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; };