From 58bec51a8a1a4ea92c577703e9263b61d989bd2f Mon Sep 17 00:00:00 2001 From: Pedro Cuenca Date: Sun, 5 Jul 2026 19:17:04 +0200 Subject: [PATCH 1/6] Start building graph - reuse deepseek32 --- src/models/glm-dsa.cpp | 347 ++++++++++++++++++++++++++++++++++++++++- src/models/models.h | 4 +- 2 files changed, 348 insertions(+), 3 deletions(-) diff --git a/src/models/glm-dsa.cpp b/src/models/glm-dsa.cpp index 32fe6def6f3c..2b58ac1be2a9 100644 --- a/src/models/glm-dsa.cpp +++ b/src/models/glm-dsa.cpp @@ -1,5 +1,7 @@ #include "models.h" +#include "llama-kv-cache-dsa.h" + void llama_model_glm_dsa::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); @@ -34,10 +36,10 @@ void llama_model_glm_dsa::load_arch_hparams(llama_model_loader & ml) { // 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_impl"); + GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_all"); switch (hparams.n_layer()) { - case 79: type = LLM_TYPE_744B_A40B; break; + case 78: type = LLM_TYPE_744B_A40B; break; default: type = LLM_TYPE_UNKNOWN; } } @@ -150,3 +152,344 @@ std::unique_ptr llama_model_glm_dsa::build_arch_graph(const l return std::make_unique(*this, params); } +llama_model_glm_dsa::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); +} diff --git a/src/models/models.h b/src/models/models.h index 7a52e7bc1ab7..78984a3effda 100644 --- a/src/models/models.h +++ b/src/models/models.h @@ -1216,7 +1216,9 @@ struct llama_model_glm_dsa : public llama_model_base { void load_arch_hparams(llama_model_loader & ml) override; void load_arch_tensors(llama_model_loader & ml) override; - using graph = llama_model_deepseek2::graph; + struct graph : public llm_graph_context { + graph(const llama_model & model, const llm_graph_params & params); + }; std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; }; From ab2dc715c333155c59a8a16093081ce115317fae Mon Sep 17 00:00:00 2001 From: Pedro Cuenca Date: Sun, 5 Jul 2026 19:18:28 +0200 Subject: [PATCH 2/6] Enable kv cache and rotation for glm_dsa architecture Just follow Deepseek 3.2 for now. --- src/llama-kv-cache.cpp | 2 +- src/llama-model.cpp | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp index 12bf5c37914d..f11e2e71478f 100644 --- a/src/llama-kv-cache.cpp +++ b/src/llama-kv-cache.cpp @@ -339,7 +339,7 @@ llama_kv_cache::llama_kv_cache( hparams.n_embd_head_k() % 64 == 0; // always create Hadamard rotation tensors for DeepSeek lightning indexers - if ((model.arch == LLM_ARCH_DEEPSEEK32 || model.arch == LLM_ARCH_DEEPSEEK4) && + if ((model.arch == LLM_ARCH_DEEPSEEK32 || model.arch == LLM_ARCH_DEEPSEEK4 || model.arch == LLM_ARCH_GLM_DSA) && hparams.n_embd_head_k_full == hparams.indexer_head_size) { attn_rot_k = true; } diff --git a/src/llama-model.cpp b/src/llama-model.cpp index e07f6e986363..4514e3d1183e 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -2039,6 +2039,7 @@ 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, From 37edc79f6c71602db4296aa9b90e12a7970d1f78 Mon Sep 17 00:00:00 2001 From: Pedro Cuenca Date: Sun, 5 Jul 2026 19:44:10 +0200 Subject: [PATCH 3/6] Reuse prev_top_k for "shared" indexer layers --- src/models/glm-dsa.cpp | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/src/models/glm-dsa.cpp b/src/models/glm-dsa.cpp index 2b58ac1be2a9..2b364183293b 100644 --- a/src/models/glm-dsa.cpp +++ b/src/models/glm-dsa.cpp @@ -198,6 +198,9 @@ llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_par ggml_tensor * inp_out_ids = build_inp_out_ids(); + // Difference vs Deepseek 3.2: shared indexer layers reuse the top_k from the previous full indexer layers + // See https://huggingface.co/zai-org/GLM-5.2/blob/main/config.json#L30 + ggml_tensor * prev_top_k = nullptr; for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -216,7 +219,8 @@ llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_par ggml_tensor * top_k = nullptr; // lightning indexer - { + if (model.layers[il].indexer_attn_q_b != nullptr) { + // "full" layer ggml_tensor * indexer_q = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_q_b, qr); cb(indexer_q, "indexer_q", il); @@ -338,6 +342,11 @@ llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_par // 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)); + prev_top_k = top_k; + cb(top_k, "top_k", il); + } else { + // "shared" indexer layer - reuse from previous + top_k = prev_top_k; cb(top_k, "top_k", il); } From 9d72ffff2dd32683f9353e77162730befa3c4abb Mon Sep 17 00:00:00 2001 From: Pedro Cuenca Date: Sun, 5 Jul 2026 20:41:35 +0200 Subject: [PATCH 4/6] GLM 5.2 uses LLAMA_ROPE_TYPE_NORM for the indexer. This is transformers' `apply_rotary_pos_emb_interleave` --- src/models/glm-dsa.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/models/glm-dsa.cpp b/src/models/glm-dsa.cpp index 2b364183293b..afa1062e8361 100644 --- a/src/models/glm-dsa.cpp +++ b/src/models/glm-dsa.cpp @@ -240,7 +240,7 @@ llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_par 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, + LLAMA_ROPE_TYPE_NORM, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(indexer_q_pe, "indexer_q_pe", il); @@ -270,7 +270,7 @@ llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_par 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, + LLAMA_ROPE_TYPE_NORM, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(indexer_k_pe, "indexer_k_pe", il); From a056a18765a28bdc84d6b6a486652863a4a3f36f Mon Sep 17 00:00:00 2001 From: Pedro Cuenca Date: Tue, 7 Jul 2026 00:12:48 +0200 Subject: [PATCH 5/6] Default indexer types to GLM pattern Previous converted GGUFs like https://huggingface.co/unsloth/GLM-5.2-GGUF write indexer weights to _all_ layers, even if they are only required for "full" types. This PR relies on a new key "%s.attention.indexer.types"; if absent, it will use the default GLM 5.2 schedule as defined in https://huggingface.co/zai-org/GLM-5.2/blob/main/config.json#L26. Note that conversion is not saving this key yet. --- src/llama-arch.cpp | 1 + src/llama-arch.h | 1 + src/llama-hparams.cpp | 8 ++++++++ src/llama-hparams.h | 6 ++++++ src/llama-model-saver.cpp | 1 + src/models/glm-dsa.cpp | 35 +++++++++++++++++++++++++++++++---- 6 files changed, 48 insertions(+), 4 deletions(-) diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index b890e66fcf6e..58bd043d25fe 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -251,6 +251,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, "%s.attention.indexer.head_count" }, { LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, "%s.attention.indexer.key_length" }, { LLM_KV_ATTENTION_INDEXER_TOP_K, "%s.attention.indexer.top_k" }, + { LLM_KV_ATTENTION_INDEXER_TYPES, "%s.attention.indexer.types" }, { LLM_KV_ATTENTION_OUTPUT_GROUP_COUNT, "%s.attention.output_group_count" }, { LLM_KV_ATTENTION_OUTPUT_LORA_RANK, "%s.attention.output_lora_rank" }, { LLM_KV_ATTENTION_COMPRESS_ROPE_FREQ_BASE, "%s.attention.compress_rope_freq_base" }, diff --git a/src/llama-arch.h b/src/llama-arch.h index a4f5091e7170..081f1a391e37 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -256,6 +256,7 @@ enum llm_kv { LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, LLM_KV_ATTENTION_INDEXER_TOP_K, + LLM_KV_ATTENTION_INDEXER_TYPES, LLM_KV_ATTENTION_OUTPUT_GROUP_COUNT, LLM_KV_ATTENTION_OUTPUT_LORA_RANK, LLM_KV_ATTENTION_COMPRESS_ROPE_FREQ_BASE, diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp index 9d0683d2fec4..846d4c69a626 100644 --- a/src/llama-hparams.cpp +++ b/src/llama-hparams.cpp @@ -248,6 +248,14 @@ bool llama_hparams::is_mla() const { return n_embd_head_k_mla_impl != 0 && n_embd_head_v_mla_impl != 0; } +bool llama_hparams::is_indexer_full(uint32_t il) const { + if (il < n_layer()) { + return is_indexer_full_impl[il]; + } + + GGML_ABORT("%s: il (%u) out of bounds (n_layer: %u)\n", __func__, il, n_layer()); +} + uint32_t llama_hparams::n_embd_head_k_mla() const { return is_mla() ? n_embd_head_k_mla_impl : n_embd_head_k(); } diff --git a/src/llama-hparams.h b/src/llama-hparams.h index 8be5f28f39e6..747754fc0d0b 100644 --- a/src/llama-hparams.h +++ b/src/llama-hparams.h @@ -227,6 +227,10 @@ struct llama_hparams { uint32_t indexer_head_size = 0; uint32_t indexer_top_k = 0; + // Indexer is "full" (1) or "shared" (0) + // Shared indexers reuse top-k from previous full layer + std::array is_indexer_full_impl; + // DeepSeek-V4 uint32_t dsv4_o_group_count = 0; uint32_t dsv4_o_lora_rank = 0; @@ -302,6 +306,8 @@ struct llama_hparams { bool is_swa(uint32_t il) const; + bool is_indexer_full(uint32_t il) const; + void set_recr_pattern(uint32_t n_pattern, bool dense_first = false); // whether or not the given layer is recurrent (for hybrid models) diff --git a/src/llama-model-saver.cpp b/src/llama-model-saver.cpp index a3928523ba8d..87e20ab537ae 100644 --- a/src/llama-model-saver.cpp +++ b/src/llama-model-saver.cpp @@ -280,6 +280,7 @@ void llama_model_saver::add_kv_from_model() { add_kv(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head); add_kv(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size); add_kv(LLM_KV_ATTENTION_INDEXER_TOP_K, hparams.indexer_top_k); + add_kv(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl, true); add_kv(LLM_KV_ATTENTION_RECURRENT_LAYERS, hparams.is_recr_impl, true); const float rope_scaling_factor = hparams.rope_freq_scale_train == 1.0f ? 0.0f : 1.0f/hparams.rope_freq_scale_train; diff --git a/src/models/glm-dsa.cpp b/src/models/glm-dsa.cpp index afa1062e8361..e76cbf5c4d43 100644 --- a/src/models/glm-dsa.cpp +++ b/src/models/glm-dsa.cpp @@ -2,6 +2,30 @@ #include "llama-kv-cache-dsa.h" +// https://huggingface.co/zai-org/GLM-5.2/blob/main/config.json#L26 +const std::array GLM_DSA_DEFAULT_INDEXER_TYPES = { + 1, 1, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, +}; + void llama_model_glm_dsa::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); @@ -24,9 +48,12 @@ void llama_model_glm_dsa::load_arch_hparams(llama_model_loader & ml) { 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); + 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); + + hparams.is_indexer_full_impl = GLM_DSA_DEFAULT_INDEXER_TYPES; + ml.get_key_or_arr(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl, hparams.n_layer(), false); // Expert gating function (GLM-4.5 uses sigmoid) ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false); @@ -219,7 +246,7 @@ llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_par ggml_tensor * top_k = nullptr; // lightning indexer - if (model.layers[il].indexer_attn_q_b != nullptr) { + if (hparams.is_indexer_full(il)) { // "full" layer ggml_tensor * indexer_q = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_q_b, qr); cb(indexer_q, "indexer_q", il); From 8dedd06415f36f10fc6091241a39b23c1bf0ee11 Mon Sep 17 00:00:00 2001 From: Pedro Cuenca Date: Tue, 7 Jul 2026 14:06:53 +0200 Subject: [PATCH 6/6] Save indexer types to gguf, restore on load --- conversion/glm.py | 3 +++ gguf-py/gguf/constants.py | 1 + gguf-py/gguf/gguf_writer.py | 4 ++++ src/models/glm-dsa.cpp | 6 +++--- 4 files changed, 11 insertions(+), 3 deletions(-) diff --git a/conversion/glm.py b/conversion/glm.py index 895cefc22b89..d85268a62149 100644 --- a/conversion/glm.py +++ b/conversion/glm.py @@ -237,6 +237,9 @@ def set_gguf_parameters(self): self.gguf_writer.add_indexer_head_count(self.hparams["index_n_heads"]) self.gguf_writer.add_indexer_key_length(self.hparams["index_head_dim"]) self.gguf_writer.add_indexer_top_k(self.hparams["index_topk"]) + if (indexer_types := self.hparams.get("indexer_types")) is not None: + indexer_types = [t == "full" for t in indexer_types] + self.gguf_writer.add_indexer_types(indexer_types) @ModelBase.register("SolarOpenForCausalLM") diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index cd4cdef8991f..b67f7504b122 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -200,6 +200,7 @@ class Indexer: HEAD_COUNT = "{arch}.attention.indexer.head_count" KEY_LENGTH = "{arch}.attention.indexer.key_length" TOP_K = "{arch}.attention.indexer.top_k" + TYPES = "{arch}.attention.indexer.types" class HyperConnection: COUNT = "{arch}.hyper_connection.count" diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 1e277f0687c5..bb21596701d4 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -793,6 +793,10 @@ def add_indexer_key_length(self, length: int) -> None: def add_indexer_top_k(self, top_k: int) -> None: self.add_uint32(Keys.Attention.Indexer.TOP_K.format(arch=self.arch), top_k) + def add_indexer_types(self, value: Sequence[bool]) -> None: + key = Keys.Attention.Indexer.TYPES.format(arch=self.arch) + self.add_array(key, value) + def add_max_alibi_bias(self, bias: float) -> None: self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias) diff --git a/src/models/glm-dsa.cpp b/src/models/glm-dsa.cpp index e76cbf5c4d43..f7109cbd4da5 100644 --- a/src/models/glm-dsa.cpp +++ b/src/models/glm-dsa.cpp @@ -52,9 +52,6 @@ void llama_model_glm_dsa::load_arch_hparams(llama_model_loader & ml) { 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); - hparams.is_indexer_full_impl = GLM_DSA_DEFAULT_INDEXER_TYPES; - ml.get_key_or_arr(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl, hparams.n_layer(), false); - // Expert gating function (GLM-4.5 uses sigmoid) ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false); if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) { @@ -65,6 +62,9 @@ void llama_model_glm_dsa::load_arch_hparams(llama_model_loader & ml) { 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"); + hparams.is_indexer_full_impl = GLM_DSA_DEFAULT_INDEXER_TYPES; + 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; default: type = LLM_TYPE_UNKNOWN;