From 35bee031e17ed2b2e8e7278b284a6c8cd120d9f8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Sun, 8 Mar 2026 18:58:28 +0100 Subject: [PATCH 01/22] graph : remove redundant scale_w parameter (#20235) --- src/llama-graph.cpp | 6 +----- src/llama-graph.h | 2 -- src/llama-model.cpp | 2 ++ src/models/afmoe.cpp | 1 - src/models/arctic.cpp | 3 +-- src/models/bailingmoe.cpp | 3 +-- src/models/bailingmoe2.cpp | 4 +--- src/models/bert.cpp | 16 +++++++++++----- src/models/dbrx.cpp | 3 +-- src/models/deepseek.cpp | 4 +--- src/models/deepseek2.cpp | 2 +- src/models/dots1.cpp | 4 +--- src/models/ernie4-5-moe.cpp | 4 +--- src/models/exaone-moe.cpp | 3 +-- src/models/glm4-moe.cpp | 2 +- src/models/granite-hybrid.cpp | 3 +-- src/models/granite.cpp | 3 +-- src/models/grok.cpp | 2 +- src/models/grovemoe.cpp | 6 ++---- src/models/hunyuan-moe.cpp | 3 +-- src/models/jamba.cpp | 2 +- src/models/kimi-linear.cpp | 3 +-- src/models/lfm2.cpp | 14 ++++++++++---- src/models/llada-moe.cpp | 2 +- src/models/llama-iswa.cpp | 2 +- src/models/llama.cpp | 2 +- src/models/mimo2-iswa.cpp | 16 +++++++++++----- src/models/minimax-m2.cpp | 3 +-- src/models/mistral3.cpp | 2 +- src/models/nemotron-h.cpp | 2 +- src/models/olmoe.cpp | 2 +- src/models/openai-moe-iswa.cpp | 2 +- src/models/phi3.cpp | 2 +- src/models/qwen2moe.cpp | 2 +- src/models/qwen35moe.cpp | 12 ++++++++---- src/models/qwen3moe.cpp | 2 +- src/models/qwen3next.cpp | 12 ++++++++---- src/models/qwen3vl-moe.cpp | 2 +- src/models/rnd1.cpp | 2 +- src/models/smallthinker.cpp | 2 +- src/models/step35-iswa.cpp | 7 ++----- 41 files changed, 85 insertions(+), 86 deletions(-) diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index 99bd6796b..f4cb7dce1 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -1151,7 +1151,6 @@ ggml_tensor * llm_graph_context::build_ffn( return cur; } -// TODO remove redundant scale_w argument ggml_tensor * llm_graph_context::build_moe_ffn( ggml_tensor * cur, ggml_tensor * gate_inp, @@ -1163,7 +1162,6 @@ ggml_tensor * llm_graph_context::build_moe_ffn( int64_t n_expert_used, llm_ffn_op_type type_op, bool norm_w, - bool scale_w, float w_scale, llama_expert_gating_func_type gating_op, int il, @@ -1180,7 +1178,6 @@ ggml_tensor * llm_graph_context::build_moe_ffn( n_expert_used, type_op, norm_w, - scale_w, w_scale, gating_op, il, @@ -1204,7 +1201,6 @@ ggml_tensor * llm_graph_context::build_moe_ffn( int64_t n_expert_used, llm_ffn_op_type type_op, bool norm_w, - bool scale_w, float w_scale, llama_expert_gating_func_type gating_op, int il, @@ -1332,7 +1328,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn( weights = ggml_reshape_3d(ctx0, weights, 1, n_expert_used, n_tokens); } - if (scale_w) { + if (w_scale != 0.0f && w_scale != 1.0f) { weights = ggml_scale(ctx0, weights, w_scale); cb(weights, "ffn_moe_weights_scaled", il); } diff --git a/src/llama-graph.h b/src/llama-graph.h index e8f006977..7f6c9e963 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -810,7 +810,6 @@ struct llm_graph_context { int64_t n_expert_used, llm_ffn_op_type type_op, bool norm_w, - bool scale_w, float w_scale, llama_expert_gating_func_type gating_op, int il, @@ -832,7 +831,6 @@ struct llm_graph_context { int64_t n_expert_used, llm_ffn_op_type type_op, bool norm_w, - bool scale_w, float w_scale, llama_expert_gating_func_type gating_op, int il, diff --git a/src/llama-model.cpp b/src/llama-model.cpp index ef9c2dfc5..e18cca052 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -1570,6 +1570,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, 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); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); switch (hparams.n_ff_exp) { case 1408: type = LLM_TYPE_16B; break; @@ -2076,6 +2077,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, 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); + 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); switch (hparams.n_layer) { diff --git a/src/models/afmoe.cpp b/src/models/afmoe.cpp index 6a752a403..11f5ea2c2 100644 --- a/src/models/afmoe.cpp +++ b/src/models/afmoe.cpp @@ -127,7 +127,6 @@ llm_build_afmoe::llm_build_afmoe(const llama_model & model, const llm_graph_para n_expert, n_expert_used, LLM_FFN_SILU, hparams.expert_weights_norm, // norm_w (route_norm=True) - hparams.expert_weights_scale, // scale_w hparams.expert_weights_scale, // w_scale (route_scale=2.826) (llama_expert_gating_func_type) hparams.expert_gating_func, il); diff --git a/src/models/arctic.cpp b/src/models/arctic.cpp index e8f028a72..d7db06de1 100644 --- a/src/models/arctic.cpp +++ b/src/models/arctic.cpp @@ -1,6 +1,5 @@ #include "models.h" - llm_build_arctic::llm_build_arctic(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; @@ -104,7 +103,7 @@ llm_build_arctic::llm_build_arctic(const llama_model & model, const llm_graph_pa nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(cur, "ffn_moe_out", il); diff --git a/src/models/bailingmoe.cpp b/src/models/bailingmoe.cpp index ed56b9c47..25e3369c3 100644 --- a/src/models/bailingmoe.cpp +++ b/src/models/bailingmoe.cpp @@ -1,6 +1,5 @@ #include "models.h" - llm_build_bailingmoe::llm_build_bailingmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { ggml_tensor * cur; ggml_tensor * inpL; @@ -97,7 +96,7 @@ llm_build_bailingmoe::llm_build_bailingmoe(const llama_model & model, const llm_ nullptr, n_expert, n_expert_used, LLM_FFN_SILU, hparams.expert_weights_norm, - false, hparams.expert_weights_scale, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/bailingmoe2.cpp b/src/models/bailingmoe2.cpp index a72a5a7ca..81906cecb 100644 --- a/src/models/bailingmoe2.cpp +++ b/src/models/bailingmoe2.cpp @@ -1,7 +1,5 @@ #include "models.h" - - llm_build_bailingmoe2::llm_build_bailingmoe2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; @@ -90,7 +88,7 @@ llm_build_bailingmoe2::llm_build_bailingmoe2(const llama_model & model, const ll model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, hparams.expert_weights_norm, - hparams.expert_weights_scale, hparams.expert_weights_scale, + hparams.expert_weights_scale, (llama_expert_gating_func_type) hparams.expert_gating_func, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/bert.cpp b/src/models/bert.cpp index bca0e254f..17efdafec 100644 --- a/src/models/bert.cpp +++ b/src/models/bert.cpp @@ -1,7 +1,5 @@ #include "models.h" - - llm_build_bert::llm_build_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); @@ -129,9 +127,17 @@ llm_build_bert::llm_build_bert(const llama_model & model, const llm_graph_params // feed-forward network if (hparams.moe_every_n_layers > 0 && il % hparams.moe_every_n_layers == 1) { // MoE branch - cur = build_moe_ffn(cur, model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps, nullptr, - model.layers[il].ffn_down_exps, nullptr, hparams.n_expert, hparams.n_expert_used, - LLM_FFN_GELU, false, false, 0.0f, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); + cur = build_moe_ffn(cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + nullptr, + model.layers[il].ffn_down_exps, + nullptr, + hparams.n_expert, hparams.n_expert_used, + LLM_FFN_GELU, false, + hparams.expert_weights_scale, + LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, + il); cb(cur, "ffn_moe_out", il); } else if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE || model.arch == LLM_ARCH_JINA_BERT_V3) { diff --git a/src/models/dbrx.cpp b/src/models/dbrx.cpp index 6d2a0ebf1..5c7f10843 100644 --- a/src/models/dbrx.cpp +++ b/src/models/dbrx.cpp @@ -1,6 +1,5 @@ #include "models.h" - llm_build_dbrx::llm_build_dbrx(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); @@ -89,7 +88,7 @@ llm_build_dbrx::llm_build_dbrx(const llama_model & model, const llm_graph_params nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(cur, "ffn_moe_out", il); diff --git a/src/models/deepseek.cpp b/src/models/deepseek.cpp index 17866c0d8..57cb1724f 100644 --- a/src/models/deepseek.cpp +++ b/src/models/deepseek.cpp @@ -1,7 +1,5 @@ #include "models.h" - - llm_build_deepseek::llm_build_deepseek(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; @@ -100,7 +98,7 @@ llm_build_deepseek::llm_build_deepseek(const llama_model & model, const llm_grap nullptr, n_expert, n_expert_used, LLM_FFN_SILU, false, - false, hparams.expert_weights_scale, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/deepseek2.cpp b/src/models/deepseek2.cpp index be81709c5..abd54b765 100644 --- a/src/models/deepseek2.cpp +++ b/src/models/deepseek2.cpp @@ -216,7 +216,7 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, hparams.expert_weights_norm, - hparams.expert_weights_scale, hparams.expert_weights_scale, + hparams.expert_weights_scale, (llama_expert_gating_func_type) hparams.expert_gating_func, il, nullptr, diff --git a/src/models/dots1.cpp b/src/models/dots1.cpp index bcbd9af50..0bcf3fe0d 100644 --- a/src/models/dots1.cpp +++ b/src/models/dots1.cpp @@ -1,7 +1,5 @@ #include "models.h" - - llm_build_dots1::llm_build_dots1(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; @@ -91,7 +89,7 @@ llm_build_dots1::llm_build_dots1(const llama_model & model, const llm_graph_para model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, hparams.expert_weights_norm, - hparams.expert_weights_scale, hparams.expert_weights_scale, + hparams.expert_weights_scale, (llama_expert_gating_func_type) hparams.expert_gating_func, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/ernie4-5-moe.cpp b/src/models/ernie4-5-moe.cpp index 0d96d14e6..ed781d507 100644 --- a/src/models/ernie4-5-moe.cpp +++ b/src/models/ernie4-5-moe.cpp @@ -1,7 +1,5 @@ #include "models.h" - - llm_build_ernie4_5_moe::llm_build_ernie4_5_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; @@ -103,7 +101,7 @@ llm_build_ernie4_5_moe::llm_build_ernie4_5_moe(const llama_model & model, const model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/exaone-moe.cpp b/src/models/exaone-moe.cpp index efc31d694..a7396829c 100644 --- a/src/models/exaone-moe.cpp +++ b/src/models/exaone-moe.cpp @@ -1,6 +1,5 @@ #include "models.h" - llm_build_exaone_moe::llm_build_exaone_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_k; @@ -100,7 +99,7 @@ llm_build_exaone_moe::llm_build_exaone_moe(const llama_model & model, const llm_ model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, hparams.expert_weights_norm, - hparams.expert_weights_scale, hparams.expert_weights_scale, + hparams.expert_weights_scale, (llama_expert_gating_func_type) hparams.expert_gating_func, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/glm4-moe.cpp b/src/models/glm4-moe.cpp index d51cf0741..97a65f411 100644 --- a/src/models/glm4-moe.cpp +++ b/src/models/glm4-moe.cpp @@ -128,7 +128,7 @@ llm_build_glm4_moe::llm_build_glm4_moe(const llama_model & model, const llm_grap model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, hparams.expert_weights_norm, - hparams.expert_weights_scale, hparams.expert_weights_scale, + hparams.expert_weights_scale, (llama_expert_gating_func_type) hparams.expert_gating_func, il); cb(routed_out, "ffn_moe_out", il); diff --git a/src/models/granite-hybrid.cpp b/src/models/granite-hybrid.cpp index 726ecdcca..d9b1c06da 100644 --- a/src/models/granite-hybrid.cpp +++ b/src/models/granite-hybrid.cpp @@ -1,6 +1,5 @@ #include "models.h" - llm_build_granite_hybrid::llm_build_granite_hybrid(const llama_model & model, const llm_graph_params & params) : llm_build_mamba_base(params) { const int64_t n_embd_head = hparams.n_embd_head_v; @@ -160,7 +159,7 @@ ggml_tensor * llm_build_granite_hybrid::build_layer_ffn(ggml_tensor * cur, nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/granite.cpp b/src/models/granite.cpp index 18748e9c2..fd97116ed 100644 --- a/src/models/granite.cpp +++ b/src/models/granite.cpp @@ -1,6 +1,5 @@ #include "models.h" - llm_build_granite::llm_build_granite( const llama_model & model, const llm_graph_params & params) @@ -175,7 +174,7 @@ ggml_tensor * llm_build_granite::build_layer_ffn( nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/grok.cpp b/src/models/grok.cpp index 3c54dfee6..24232604b 100644 --- a/src/models/grok.cpp +++ b/src/models/grok.cpp @@ -99,7 +99,7 @@ llm_build_grok::llm_build_grok(const llama_model & model, const llm_graph_params nullptr, n_expert, n_expert_used, LLM_FFN_GELU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/grovemoe.cpp b/src/models/grovemoe.cpp index 56b6db9a3..2081f0685 100644 --- a/src/models/grovemoe.cpp +++ b/src/models/grovemoe.cpp @@ -1,7 +1,5 @@ #include "models.h" - - llm_build_grovemoe::llm_build_grovemoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; @@ -90,7 +88,7 @@ llm_build_grovemoe::llm_build_grovemoe(const llama_model & model, const llm_grap nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il, probs); @@ -106,7 +104,7 @@ llm_build_grovemoe::llm_build_grovemoe(const llama_model & model, const llm_grap nullptr, n_chunk_expert, n_expert_used > n_chunk_expert ? n_chunk_expert : n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il, probs); diff --git a/src/models/hunyuan-moe.cpp b/src/models/hunyuan-moe.cpp index 77e39de5b..cee2b541b 100644 --- a/src/models/hunyuan-moe.cpp +++ b/src/models/hunyuan-moe.cpp @@ -119,8 +119,7 @@ llm_build_hunyuan_moe::llm_build_hunyuan_moe(const llama_model & model, const ll n_expert, n_expert_used, LLM_FFN_SILU, true, // norm_topk_prob - false, - 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(cur_moe, "ffn_moe_out", il); diff --git a/src/models/jamba.cpp b/src/models/jamba.cpp index ceab58174..1d482e425 100644 --- a/src/models/jamba.cpp +++ b/src/models/jamba.cpp @@ -76,7 +76,7 @@ llm_build_jamba::llm_build_jamba(const llama_model & model, const llm_graph_para nullptr, n_expert, n_expert_used, LLM_FFN_SILU, false, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(cur, "ffn_moe_out", il); diff --git a/src/models/kimi-linear.cpp b/src/models/kimi-linear.cpp index d178ca8b7..8d4b95e51 100644 --- a/src/models/kimi-linear.cpp +++ b/src/models/kimi-linear.cpp @@ -1,5 +1,4 @@ #include "models.h" -#include "ggml.h" #include "llama-memory-recurrent.h" @@ -341,7 +340,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll hparams.n_expert, hparams.n_expert_used, LLM_FFN_SILU, true, - hparams.expert_weights_scale, hparams.expert_weights_scale, + hparams.expert_weights_scale, (llama_expert_gating_func_type) hparams.expert_gating_func, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/lfm2.cpp b/src/models/lfm2.cpp index cf01ad625..8ca8e6c8e 100644 --- a/src/models/lfm2.cpp +++ b/src/models/lfm2.cpp @@ -23,10 +23,16 @@ llm_build_lfm2::llm_build_lfm2(const llama_model & model, const llm_graph_ }; auto build_moe_feed_forward = [&model, this](ggml_tensor * cur, int il) -> ggml_tensor * { return 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, true, false, 0.0, - static_cast(hparams.expert_gating_func), il); + 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, true, + hparams.expert_weights_scale, + static_cast(hparams.expert_gating_func), + il); }; auto build_attn_block = [&model, this](ggml_tensor * cur, ggml_tensor * inp_pos, diff --git a/src/models/llada-moe.cpp b/src/models/llada-moe.cpp index 5f64686f5..3bb9943f4 100644 --- a/src/models/llada-moe.cpp +++ b/src/models/llada-moe.cpp @@ -90,7 +90,7 @@ llm_build_llada_moe::llm_build_llada_moe(const llama_model & model, const llm_gr nullptr, n_expert, n_expert_used, LLM_FFN_SILU, false, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(cur, "ffn_moe_out", il); diff --git a/src/models/llama-iswa.cpp b/src/models/llama-iswa.cpp index 61dd2c179..40dc2427a 100644 --- a/src/models/llama-iswa.cpp +++ b/src/models/llama-iswa.cpp @@ -134,7 +134,7 @@ llm_build_llama_iswa::llm_build_llama_iswa(const llama_model & model, const llm_ nullptr, n_expert, n_expert_used, LLM_FFN_SILU, false, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID, il); diff --git a/src/models/llama.cpp b/src/models/llama.cpp index 42b5fcdf4..279f2e301 100644 --- a/src/models/llama.cpp +++ b/src/models/llama.cpp @@ -130,7 +130,7 @@ llm_build_llama::llm_build_llama(const llama_model & model, const llm_gra nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(cur, "ffn_moe_out", il); diff --git a/src/models/mimo2-iswa.cpp b/src/models/mimo2-iswa.cpp index edc87cc9f..06956915e 100644 --- a/src/models/mimo2-iswa.cpp +++ b/src/models/mimo2-iswa.cpp @@ -1,4 +1,3 @@ - #include "models.h" llm_build_mimo2_iswa::llm_build_mimo2_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { @@ -88,10 +87,17 @@ llm_build_mimo2_iswa::llm_build_mimo2_iswa(const llama_model & model, const llm_ cb(cur, "ffn_out", il); } else { // MoE branch - cur = 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, true, false, - 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID, il); + cur = 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, true, + hparams.expert_weights_scale, + LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID, + il); cb(cur, "ffn_moe_out", il); } diff --git a/src/models/minimax-m2.cpp b/src/models/minimax-m2.cpp index f7001badf..fbeed8eab 100644 --- a/src/models/minimax-m2.cpp +++ b/src/models/minimax-m2.cpp @@ -1,4 +1,3 @@ - #include "models.h" llm_build_minimax_m2::llm_build_minimax_m2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { @@ -91,7 +90,7 @@ llm_build_minimax_m2::llm_build_minimax_m2(const llama_model & model, const llm_ model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, (llama_expert_gating_func_type) hparams.expert_gating_func, il); cb(cur, "ffn_moe_out", il); diff --git a/src/models/mistral3.cpp b/src/models/mistral3.cpp index 0b6722359..49734989d 100644 --- a/src/models/mistral3.cpp +++ b/src/models/mistral3.cpp @@ -127,7 +127,7 @@ llm_build_mistral3::llm_build_mistral3(const llama_model & model, const llm_grap nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(cur, "ffn_moe_out", il); diff --git a/src/models/nemotron-h.cpp b/src/models/nemotron-h.cpp index 347f28948..fa2b55a28 100644 --- a/src/models/nemotron-h.cpp +++ b/src/models/nemotron-h.cpp @@ -124,7 +124,7 @@ ggml_tensor * llm_build_nemotron_h::build_ffn_layer(ggml_tensor * cur, const lla model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_RELU_SQR, hparams.expert_weights_norm, - hparams.expert_weights_scale, hparams.expert_weights_scale, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/olmoe.cpp b/src/models/olmoe.cpp index b8b6988f8..a05b892c7 100644 --- a/src/models/olmoe.cpp +++ b/src/models/olmoe.cpp @@ -92,7 +92,7 @@ llm_build_olmoe::llm_build_olmoe(const llama_model & model, const llm_graph_para nullptr, n_expert, n_expert_used, LLM_FFN_SILU, false, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(cur, "ffn_moe_out", il); diff --git a/src/models/openai-moe-iswa.cpp b/src/models/openai-moe-iswa.cpp index dbe3ca185..403f130bc 100644 --- a/src/models/openai-moe-iswa.cpp +++ b/src/models/openai-moe-iswa.cpp @@ -95,7 +95,7 @@ llm_build_openai_moe_iswa::llm_build_openai_moe_iswa(const llama_model & model, nullptr, n_expert, n_expert_used, LLM_FFN_SWIGLU_OAI_MOE, false, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX_WEIGHT, il); cb(cur, "ffn_moe_out", il); diff --git a/src/models/phi3.cpp b/src/models/phi3.cpp index c8e5da33d..803e374aa 100644 --- a/src/models/phi3.cpp +++ b/src/models/phi3.cpp @@ -114,7 +114,7 @@ llm_build_phi3::llm_build_phi3(const llama_model & model, const llm_graph_ nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(cur, "ffn_moe_out", il); diff --git a/src/models/qwen2moe.cpp b/src/models/qwen2moe.cpp index 49142b712..e19061334 100644 --- a/src/models/qwen2moe.cpp +++ b/src/models/qwen2moe.cpp @@ -94,7 +94,7 @@ llm_build_qwen2moe::llm_build_qwen2moe(const llama_model & model, const llm_grap nullptr, n_expert, n_expert_used, LLM_FFN_SILU, false, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/qwen35moe.cpp b/src/models/qwen35moe.cpp index 17291ec23..fe081af21 100644 --- a/src/models/qwen35moe.cpp +++ b/src/models/qwen35moe.cpp @@ -375,11 +375,15 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_ffn(ggml_tensor * cur, const int 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_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, nullptr, - n_expert, n_expert_used, LLM_FFN_SILU, - true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il, + n_expert, n_expert_used, + LLM_FFN_SILU, true, + hparams.expert_weights_scale, + LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il, nullptr, model.layers[il].ffn_gate_up_exps); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/qwen3moe.cpp b/src/models/qwen3moe.cpp index 888534fb3..9cf1ec03c 100644 --- a/src/models/qwen3moe.cpp +++ b/src/models/qwen3moe.cpp @@ -91,7 +91,7 @@ llm_build_qwen3moe::llm_build_qwen3moe(const llama_model & model, const llm_grap nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/qwen3next.cpp b/src/models/qwen3next.cpp index f2621200f..9b8164ddf 100644 --- a/src/models/qwen3next.cpp +++ b/src/models/qwen3next.cpp @@ -475,11 +475,15 @@ ggml_tensor * llm_build_qwen3next::build_layer_ffn(ggml_tensor * cur, const int // 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_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, nullptr, - n_expert, n_expert_used, LLM_FFN_SILU, - true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il, + n_expert, n_expert_used, + LLM_FFN_SILU, true, + hparams.expert_weights_scale, + LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il, nullptr, model.layers[il].ffn_gate_up_exps); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/qwen3vl-moe.cpp b/src/models/qwen3vl-moe.cpp index e5e1a2150..f347c5d6f 100644 --- a/src/models/qwen3vl-moe.cpp +++ b/src/models/qwen3vl-moe.cpp @@ -99,7 +99,7 @@ llm_build_qwen3vlmoe::llm_build_qwen3vlmoe(const llama_model & model, const llm_ nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/rnd1.cpp b/src/models/rnd1.cpp index 46b3dc3ef..de9ab1c65 100644 --- a/src/models/rnd1.cpp +++ b/src/models/rnd1.cpp @@ -93,7 +93,7 @@ llm_build_rnd1::llm_build_rnd1(const llama_model & model, const llm_graph_params nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, - false, 0.0, + hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/models/smallthinker.cpp b/src/models/smallthinker.cpp index 4c497ca76..8723905e8 100644 --- a/src/models/smallthinker.cpp +++ b/src/models/smallthinker.cpp @@ -93,7 +93,7 @@ llm_build_smallthinker::llm_build_smallthinker(const llama_model & model, nullptr, n_expert, n_expert_used, LLM_FFN_RELU, true, - false, 0.0, + hparams.expert_weights_scale, static_cast(hparams.expert_gating_func), il, probs); diff --git a/src/models/step35-iswa.cpp b/src/models/step35-iswa.cpp index f8737815a..aa8e98b73 100644 --- a/src/models/step35-iswa.cpp +++ b/src/models/step35-iswa.cpp @@ -119,9 +119,6 @@ llm_build_step35_iswa::llm_build_step35_iswa(const llama_model & model, const ll cb(cur, "ffn_out", il); } else { // MoE routed experts - const bool norm_w = hparams.expert_weights_norm; - const float w_scale = hparams.expert_weights_scale; - const bool scale_w = w_scale != 0.0f; ggml_tensor * moe_out = build_moe_ffn(cur, model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps, @@ -129,8 +126,8 @@ llm_build_step35_iswa::llm_build_step35_iswa(const llama_model & model, const ll model.layers[il].ffn_down_exps, model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, - LLM_FFN_SILU, - norm_w, scale_w, w_scale, + LLM_FFN_SILU, hparams.expert_weights_norm, + hparams.expert_weights_scale, (llama_expert_gating_func_type) hparams.expert_gating_func, il); cb(moe_out, "ffn_moe_out", il); From d417bc43dd29eab006a0da73afc7d610c9ebae7d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 8 Mar 2026 22:16:46 +0200 Subject: [PATCH 02/22] server : do not create checkpoints right after mtmd chunks (#20232) --- tools/server/server-context.cpp | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index 9dbd6d798..3790308d0 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -2438,6 +2438,8 @@ private: slot.n_prompt_tokens_cache = 0; } + bool do_checkpoint = params_base.n_ctx_checkpoints > 0; + // check if we should process the image if (slot.prompt.n_tokens() < slot.task->n_tokens() && input_tokens[slot.prompt.n_tokens()] == LLAMA_TOKEN_NULL) { // process the image @@ -2457,6 +2459,8 @@ private: const auto & chunk = input_tokens.find_chunk(slot.prompt.n_tokens()); slot.prompt.tokens.push_back(chunk.get()); // copy } + + do_checkpoint = false; // do not checkpoint right after an image chunk } // If using an alora, there may be uncached tokens that come @@ -2473,8 +2477,6 @@ private: alora_disabled_id = enabled_loras[0]; } - bool do_checkpoint = params_base.n_ctx_checkpoints > 0; - // make checkpoints only for completion tasks do_checkpoint = do_checkpoint && slot.task->type == SERVER_TASK_TYPE_COMPLETION; From 97c64fbdbdf6662a0f31c97918fec7d480e1ecf5 Mon Sep 17 00:00:00 2001 From: "Piotr Wilkin (ilintar)" Date: Mon, 9 Mar 2026 01:11:22 +0100 Subject: [PATCH 03/22] PEG parser for LFM2 (#20251) * PEG parser for LFM2 * Simplify using python_value() --- common/chat-peg-parser.cpp | 68 ++++++++++++++++++++++ common/chat-peg-parser.h | 5 ++ common/chat.cpp | 84 +++++++++++++++++++++++++++ models/templates/LFM2-8B-A1B.jinja | 14 +---- models/templates/llama-cpp-lfm2.jinja | 37 ------------ tests/test-chat.cpp | 72 +++++++++++++++++++++++ 6 files changed, 231 insertions(+), 49 deletions(-) delete mode 100644 models/templates/llama-cpp-lfm2.jinja diff --git a/common/chat-peg-parser.cpp b/common/chat-peg-parser.cpp index 3448c4f5b..e055447e0 100644 --- a/common/chat-peg-parser.cpp +++ b/common/chat-peg-parser.cpp @@ -476,6 +476,74 @@ common_peg_parser common_chat_peg_builder::standard_constructed_tools( return force_tool_calls ? section : optional(section); } +// Python-style tool calls: name(arg1="value1", arg2=123) +// Used only by LFM2 for now, so we don't merge it into autoparser +common_peg_parser common_chat_peg_builder::python_style_tool_calls( + const nlohmann::json & tools, + bool parallel_tool_calls) { + if (!tools.is_array() || tools.empty()) { + return eps(); + } + + auto tool_choices = choice(); + + for (const auto & tool_def : tools) { + if (!tool_def.contains("function")) { + continue; + } + const auto & function = tool_def.at("function"); + std::string name = function.at("name"); + nlohmann::json params = function.contains("parameters") ? function.at("parameters") : nlohmann::json::object(); + + auto args = eps(); + if (params.contains("properties") && !params["properties"].empty()) { + auto arg_choice = choice(); + for (const auto & el : params["properties"].items()) { + const std::string & prop_name = el.key(); + const auto & prop_def = el.value(); + bool is_string_type = (prop_def.contains("type") && prop_def["type"] == "string"); + + auto arg_name_parser = literal(prop_name); + + common_peg_parser arg_value_parser = eps(); + auto string_value_parser = choice({ + literal("\"") + tool_arg_string_value(json_string_content()) + literal("\""), + literal("'") + tool_arg_string_value(json_string_content()) + literal("'") + }); + + if (is_string_type) { + arg_value_parser = string_value_parser; + } else { + arg_value_parser = tool_arg_value(python_value()); + } + + // Full argument: name="value" or name=value + auto arg_rule = tool_arg( + tool_arg_open(eps()) + + tool_arg_name(arg_name_parser) + + literal("=") + + arg_value_parser + + tool_arg_close(eps()) + ); + arg_choice |= arg_rule; + } + + args = arg_choice + zero_or_more("," + space() + arg_choice); + } + + auto tool_parser = tool(tool_open(tool_name(literal(name)) + literal("(")) + + space() + tool_args(args) + space() + tool_close(literal(")")) + ); + + tool_choices |= rule("tool-" + name, tool_parser); + } + + if (parallel_tool_calls) { + return "[" + space() + tool_choices + zero_or_more("," + space() + tool_choices) + space() + "]"; + } + return "[" + space() + tool_choices + space() + "]"; +} + // Helper: Parse dot notation key into prefix and field name static std::pair parse_key_spec(const std::string & key) { auto dot_pos = key.find('.'); diff --git a/common/chat-peg-parser.h b/common/chat-peg-parser.h index fe4c1b648..5ea14be03 100644 --- a/common/chat-peg-parser.h +++ b/common/chat-peg-parser.h @@ -112,6 +112,11 @@ class common_chat_peg_builder : public common_peg_parser_builder { bool parallel_tool_calls, bool force_tool_calls); + // Helper for Python-style function call format: name(arg1="value1", arg2=123) + // Used by LFM2 and similar templates + common_peg_parser python_style_tool_calls(const nlohmann::json & tools, + bool parallel_tool_calls); + private: // Implementation helpers for standard_json_tools — one per JSON tool call layout mode common_peg_parser build_json_tools_function_is_key(const nlohmann::json & tools, diff --git a/common/chat.cpp b/common/chat.cpp index d12802bd7..29d2e5fd1 100644 --- a/common/chat.cpp +++ b/common/chat.cpp @@ -1274,6 +1274,82 @@ static common_chat_params common_chat_params_init_kimi_k2(const common_chat_temp return data; } +// LFM2 format: +// - Reasoning: {reasoning} (optional, only if enable_thinking is true) +// - Content: text after reasoning (optional) +// - Tool calls: <|tool_call_start|>[function_name(arg1="value1", arg2="value2")]<|tool_call_end|> +// Tool calls can appear multiple times (parallel tool calls) +static common_chat_params common_chat_params_init_lfm2(const common_chat_template & tmpl, + const autoparser::templates_params & inputs) { + common_chat_params data; + + data.prompt = common_chat_template_direct_apply(tmpl, inputs); + data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; + data.supports_thinking = true; + data.preserved_tokens = { + "<|tool_list_start|>", + "<|tool_list_end|>", + "<|tool_call_start|>", + "<|tool_call_end|>", + "", + "", + }; + + auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); + auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE; + auto include_grammar = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE; + + + const std::string TOOL_CALL_START = "<|tool_call_start|>"; + const std::string TOOL_CALL_END = "<|tool_call_end|>"; + const std::string THINK_START = ""; + const std::string THINK_END = ""; + auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { + + auto end = p.end(); + + auto reasoning = p.eps(); + if (extract_reasoning && inputs.enable_thinking) { + reasoning = p.optional(THINK_START + p.reasoning(p.until(THINK_END)) + THINK_END); + } + + if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) { + return reasoning + p.content(p.rest()) + end; + } + + auto tool_calls = p.rule("tool-calls", + p.trigger_rule("tool-call", p.literal(TOOL_CALL_START) + + p.python_style_tool_calls(inputs.tools, inputs.parallel_tool_calls) + + p.literal(TOOL_CALL_END) + ) + ); + + auto content = p.content(p.until(TOOL_CALL_START)); + + return reasoning + content + tool_calls + end; + }); + + data.parser = parser.save(); + + if (include_grammar) { + data.grammar_lazy = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + auto schema = function.at("parameters"); + builder.resolve_refs(schema); + }); + parser.build_grammar(builder, data.grammar_lazy); + }); + + data.grammar_triggers = { + { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, TOOL_CALL_START } + }; + } + + return data; +} + namespace workaround { // if first message is system and template does not support it, merge it with next message @@ -1422,6 +1498,14 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_ return common_chat_params_init_kimi_k2(tmpl, params); } + // LFM2 - uses <|tool_list_start|>/<|tool_list_end|> markers and <|tool_call_start|>[name(args)]<|tool_call_end|> format + // Detection: template has "<|tool_list_start|>" and "<|tool_list_end|>" markers + if (src.find("<|tool_list_start|>") != std::string::npos && + src.find("<|tool_list_end|>") != std::string::npos) { + LOG_DBG("Using specialized template: LFM2\n"); + return common_chat_params_init_lfm2(tmpl, params); + } + try { LOG_DBG("Using differential autoparser\n"); struct autoparser::autoparser autoparser; diff --git a/models/templates/LFM2-8B-A1B.jinja b/models/templates/LFM2-8B-A1B.jinja index 3738b3d14..fab22e952 100644 --- a/models/templates/LFM2-8B-A1B.jinja +++ b/models/templates/LFM2-8B-A1B.jinja @@ -6,7 +6,7 @@ {%- set messages = messages[1:] -%} {%- endif -%} {%- if tools -%} - {%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "You can use the following tools: <|tool_list_start|>[" -%} + {%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: <|tool_list_start|>[" -%} {%- for tool in tools -%} {%- if tool is not string -%} {%- set tool = tool | tojson -%} @@ -17,7 +17,6 @@ {%- endif -%} {%- endfor -%} {%- set ns.system_prompt = ns.system_prompt + "]<|tool_list_end|>" -%} - {{- '**IMPORTANT**: The syntax for calling the tools is: <|tool_call_start|>JSON tool call goes here<|tool_call_end|>. Please only call tools in the specified manner.' -}} {%- endif -%} {%- if ns.system_prompt -%} {{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}} @@ -30,18 +29,9 @@ {%- endif -%} {%- if message["role"] == "tool" -%} {%- set content = "<|tool_response_start|>" + content + "<|tool_response_end|>" -%} - {%- elif message["role"] == "assistant" -%} - {%- if message.tool_calls %} - {%- for tool_call in message.tool_calls %} - {%- if tool_call.function %} - {%- set tool_call = tool_call.function %} - {%- endif %} - {{- '\n<|tool_call_start|>\n{"name": "' + tool_call.name + '", "arguments": ' + (tool_call.arguments if tool_call.arguments is string else tool_call.arguments | tojson) + '}\n<|tool_call_end|>\n' }} - {%- endfor %} - {%- endif %} {%- endif -%} {{- content + "<|im_end|>\n" -}} {%- endfor -%} {%- if add_generation_prompt -%} {{- "<|im_start|>assistant\n" -}} -{%- endif -%} +{%- endif -%} \ No newline at end of file diff --git a/models/templates/llama-cpp-lfm2.jinja b/models/templates/llama-cpp-lfm2.jinja deleted file mode 100644 index b7921120b..000000000 --- a/models/templates/llama-cpp-lfm2.jinja +++ /dev/null @@ -1,37 +0,0 @@ -{{- bos_token -}} -{%- set system_prompt = "" -%} -{%- set ns = namespace(system_prompt="") -%} -{%- if messages[0]["role"] == "system" -%} - {%- set ns.system_prompt = messages[0]["content"] -%} - {%- set messages = messages[1:] -%} -{%- endif -%} -{%- if tools -%} - {%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: <|tool_list_start|>[" -%} - {%- for tool in tools -%} - {%- if tool is not string -%} - {%- set tool = tool | tojson -%} - {%- endif -%} - {%- set ns.system_prompt = ns.system_prompt + tool -%} - {%- if not loop.last -%} - {%- set ns.system_prompt = ns.system_prompt + ", " -%} - {%- endif -%} - {%- endfor -%} - {%- set ns.system_prompt = ns.system_prompt + "]<|tool_list_end|>" -%} -{%- endif -%} -{%- if ns.system_prompt -%} - {{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}} -{%- endif -%} -{%- for message in messages -%} - {{- "<|im_start|>" + message["role"] + "\n" -}} - {%- set content = message["content"] -%} - {%- if content is not string -%} - {%- set content = content | tojson -%} - {%- endif -%} - {%- if message["role"] == "tool" -%} - {%- set content = "<|tool_response_start|>" + content + "<|tool_response_end|>" -%} - {%- endif -%} - {{- content + "<|im_end|>\n" -}} -{%- endfor -%} -{%- if add_generation_prompt -%} - {{- "<|im_start|>assistant\n" -}} -{%- endif -%} diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp index 7b4477671..2f83d7c0b 100644 --- a/tests/test-chat.cpp +++ b/tests/test-chat.cpp @@ -2387,6 +2387,78 @@ static void test_template_output_peg_parsers(bool detailed_debug) { .run(); } + // LFM2-8B-A1B tests - uses <|tool_list_start|>/<|tool_list_end|> and <|tool_call_start|>[name(args)]<|tool_call_end|> + { + auto tst = peg_tester("models/templates/LFM2-8B-A1B.jinja", detailed_debug); + + // Basic content only + tst.test("Hello, world!\nWhat's up?").expect(message_assist).run(); + + // Single tool call without reasoning + tst.test("<|tool_call_start|>[special_function(arg1=1)]<|tool_call_end|>") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + + // Tool call with string argument + tst.test("<|tool_call_start|>[get_time(city=\"XYZCITY\")]<|tool_call_end|>") + .tools({ get_time_tool }) + .expect(message_with_tool_calls("get_time", "{\"city\":\"XYZCITY\"}")) + .run(); + + // Tool call with reasoning (enable_thinking=true) + tst.test("I'm\nthinking<|tool_call_start|>[special_function(arg1=1)]<|tool_call_end|>") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ special_function_tool }) + .expect(message_assist_call_thoughts) + .run(); + + // Multiple tool calls (parallel) + tst.test("<|tool_call_start|>[special_function(arg1=1), special_function_with_opt(arg1=1, arg2=2)]<|tool_call_end|>") + .parallel_tool_calls(true) + .tools({ + special_function_tool, special_function_tool_with_optional_param + }) + .expect_tool_calls({ + { "special_function", R"({"arg1": 1})", {} }, + { "special_function_with_opt", R"({"arg1": 1, "arg2": 2})", {} }, + }) + .run(); + + // Tool call with reasoning and content + tst.test("I need to call a function" + "Let me check the time.<|tool_call_start|>[get_time(city=\"Paris\")]<|tool_call_end|>") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ get_time_tool }) + .expect(message_with_reasoning_content_and_multiple_tool_calls( + "I need to call a function", "Let me check the time.", { { "get_time", "{\"city\":\"Paris\"}" } } + )) + .run(); + + // Python tool with multiline code in string + tst.test("<|tool_call_start|>[python(code=\"def hello():\\n print('hey')\")]<|tool_call_end|>") + .tools({ python_tool }) + .expect_tool_calls({ + { "python", R"#({"code": "def hello():\\n print('hey')"})#", "" } + }) + .run(); + + // Partial tool call (streaming) + tst.test("<|tool_call_start|>[special_function(arg1=") + .tools({ special_function_tool }) + .is_partial(true) + .expect(simple_assist_msg("", "", "special_function", "{\"arg1\": ")) + .run(); + + // Tool call with empty arguments + tst.test("<|tool_call_start|>[empty_args()]<|tool_call_end|>") + .tools({ empty_args_tool }) + .expect(simple_assist_msg("", "", "empty_args", "{}")) + .run(); + } + // Apertus-8B-Instruct tests - FUNC_NAME_AS_KEY format // Format: <|tools_prefix|>[{"function_name": {...arguments...}}]<|tools_suffix|> { From ae87863dc10f659a0094450655cccae96609dd33 Mon Sep 17 00:00:00 2001 From: Aaron Teo Date: Mon, 9 Mar 2026 09:05:44 +0800 Subject: [PATCH 04/22] llama-bench: introduce `-hf` and `-hff` flags & use `--mmap 1` by default (#20211) --- tools/llama-bench/llama-bench.cpp | 180 +++++++++++++++++++----------- 1 file changed, 116 insertions(+), 64 deletions(-) diff --git a/tools/llama-bench/llama-bench.cpp b/tools/llama-bench/llama-bench.cpp index 7a7502655..b0f1d6b93 100644 --- a/tools/llama-bench/llama-bench.cpp +++ b/tools/llama-bench/llama-bench.cpp @@ -20,6 +20,7 @@ #include #include "common.h" +#include "download.h" #include "ggml.h" #include "llama.h" @@ -312,6 +313,9 @@ static std::vector parse_int_range(const std::string & s) { struct cmd_params { std::vector model; + std::vector hf_repo; + std::vector hf_file; + std::string hf_token; std::vector n_prompt; std::vector n_gen; std::vector> n_pg; @@ -351,6 +355,9 @@ struct cmd_params { static const cmd_params cmd_params_defaults = { /* model */ { "models/7B/ggml-model-q4_0.gguf" }, + /* hf_repo */ {}, + /* hf_file */ {}, + /* hf_token */ "", /* n_prompt */ { 512 }, /* n_gen */ { 128 }, /* n_pg */ {}, @@ -372,7 +379,7 @@ static const cmd_params cmd_params_defaults = { /* devices */ { {} }, /* tensor_split */ { std::vector(llama_max_devices(), 0.0f) }, /* tensor_buft_overrides*/ { std::vector{ { nullptr, nullptr } } }, - /* use_mmap */ { false }, + /* use_mmap */ { true }, /* use_direct_io */ { false }, /* embeddings */ { false }, /* no_op_offload */ { false }, @@ -393,74 +400,57 @@ static void print_usage(int /* argc */, char ** argv) { printf("\n"); printf("options:\n"); printf(" -h, --help\n"); - printf(" --numa numa mode (default: disabled)\n"); - printf(" -r, --repetitions number of times to repeat each test (default: %d)\n", - cmd_params_defaults.reps); - printf(" --prio <-1|0|1|2|3> process/thread priority (default: %d)\n", - cmd_params_defaults.prio); - printf(" --delay <0...N> (seconds) delay between each test (default: %d)\n", - cmd_params_defaults.delay); - printf(" -o, --output output format printed to stdout (default: %s)\n", - output_format_str(cmd_params_defaults.output_format)); - printf(" -oe, --output-err output format printed to stderr (default: %s)\n", - output_format_str(cmd_params_defaults.output_format_stderr)); - printf(" --list-devices list available devices and exit\n"); - printf(" -v, --verbose verbose output\n"); - printf(" --progress print test progress indicators\n"); - printf(" --no-warmup skip warmup runs before benchmarking\n"); + printf(" --numa numa mode (default: disabled)\n"); + printf(" -r, --repetitions number of times to repeat each test (default: %d)\n", cmd_params_defaults.reps); + printf(" --prio <-1|0|1|2|3> process/thread priority (default: %d)\n", cmd_params_defaults.prio); + printf(" --delay <0...N> (seconds) delay between each test (default: %d)\n", cmd_params_defaults.delay); + printf(" -o, --output output format printed to stdout (default: %s)\n", output_format_str(cmd_params_defaults.output_format)); + printf(" -oe, --output-err output format printed to stderr (default: %s)\n", output_format_str(cmd_params_defaults.output_format_stderr)); + printf(" --list-devices list available devices and exit\n"); + printf(" -v, --verbose verbose output\n"); + printf(" --progress print test progress indicators\n"); + printf(" --no-warmup skip warmup runs before benchmarking\n"); if (llama_supports_rpc()) { - printf(" -rpc, --rpc register RPC devices (comma separated)\n"); + printf(" -rpc, --rpc register RPC devices (comma separated)\n"); } printf("\n"); printf("test parameters:\n"); - printf(" -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); - printf(" -p, --n-prompt (default: %s)\n", - join(cmd_params_defaults.n_prompt, ",").c_str()); - printf(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); - printf(" -pg (default: %s)\n", - join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str()); - printf(" -d, --n-depth (default: %s)\n", - join(cmd_params_defaults.n_depth, ",").c_str()); - printf(" -b, --batch-size (default: %s)\n", - join(cmd_params_defaults.n_batch, ",").c_str()); - printf(" -ub, --ubatch-size (default: %s)\n", - join(cmd_params_defaults.n_ubatch, ",").c_str()); - printf(" -ctk, --cache-type-k (default: %s)\n", - join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); - printf(" -ctv, --cache-type-v (default: %s)\n", - join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); - printf(" -t, --threads (default: %s)\n", - join(cmd_params_defaults.n_threads, ",").c_str()); - printf(" -C, --cpu-mask (default: %s)\n", - join(cmd_params_defaults.cpu_mask, ",").c_str()); - printf(" --cpu-strict <0|1> (default: %s)\n", - join(cmd_params_defaults.cpu_strict, ",").c_str()); - printf(" --poll <0...100> (default: %s)\n", join(cmd_params_defaults.poll, ",").c_str()); - printf(" -ngl, --n-gpu-layers (default: %s)\n", - join(cmd_params_defaults.n_gpu_layers, ",").c_str()); - printf(" -ncmoe, --n-cpu-moe (default: %s)\n", - join(cmd_params_defaults.n_cpu_moe, ",").c_str()); - printf(" -sm, --split-mode (default: %s)\n", - join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); - printf(" -mg, --main-gpu (default: %s)\n", - join(cmd_params_defaults.main_gpu, ",").c_str()); - printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", - join(cmd_params_defaults.no_kv_offload, ",").c_str()); - printf(" -fa, --flash-attn <0|1> (default: %s)\n", - join(cmd_params_defaults.flash_attn, ",").c_str()); - printf(" -dev, --device (default: auto)\n"); - printf(" -mmp, --mmap <0|1> (default: %s)\n", - join(cmd_params_defaults.use_mmap, ",").c_str()); - printf(" -dio, --direct-io <0|1> (default: %s)\n", - join(cmd_params_defaults.use_direct_io, ",").c_str()); - printf(" -embd, --embeddings <0|1> (default: %s)\n", - join(cmd_params_defaults.embeddings, ",").c_str()); - printf(" -ts, --tensor-split (default: 0)\n"); + printf(" -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); + printf(" -hf, -hfr, --hf-repo /[:quant] Hugging Face model repository; quant is optional, case-insensitive\n"); + printf(" default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.\n"); + printf(" example: unsloth/phi-4-GGUF:Q4_K_M\n"); + printf(" (default: unused)\n"); + printf(" -hff, --hf-file Hugging Face model file. If specified, it will override the quant in --hf-repo\n"); + printf(" (default: unused)\n"); + printf(" -hft, --hf-token Hugging Face access token\n"); + printf(" (default: value from HF_TOKEN environment variable)\n"); + printf(" -p, --n-prompt (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str()); + printf(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); + printf(" -pg (default: %s)\n", join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str()); + printf(" -d, --n-depth (default: %s)\n", join(cmd_params_defaults.n_depth, ",").c_str()); + printf(" -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); + printf(" -ub, --ubatch-size (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str()); + printf(" -ctk, --cache-type-k (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); + printf(" -ctv, --cache-type-v (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); + printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); + printf(" -C, --cpu-mask (default: %s)\n", join(cmd_params_defaults.cpu_mask, ",").c_str()); + printf(" --cpu-strict <0|1> (default: %s)\n", join(cmd_params_defaults.cpu_strict, ",").c_str()); + printf(" --poll <0...100> (default: %s)\n", join(cmd_params_defaults.poll, ",").c_str()); + printf(" -ngl, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); + printf(" -ncmoe, --n-cpu-moe (default: %s)\n", join(cmd_params_defaults.n_cpu_moe, ",").c_str()); + printf(" -sm, --split-mode (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); + printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); + printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); + printf(" -fa, --flash-attn <0|1> (default: %s)\n", join(cmd_params_defaults.flash_attn, ",").c_str()); + printf(" -dev, --device (default: auto)\n"); + printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str()); + printf(" -dio, --direct-io <0|1> (default: %s)\n", join(cmd_params_defaults.use_direct_io, ",").c_str()); + printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str()); + printf(" -ts, --tensor-split (default: 0)\n"); printf(" -ot --override-tensor =;...\n"); - printf(" (default: disabled)\n"); - printf(" -nopo, --no-op-offload <0|1> (default: 0)\n"); - printf(" --no-host <0|1> (default: %s)\n", - join(cmd_params_defaults.no_host, ",").c_str()); + printf(" (default: disabled)\n"); + printf(" -nopo, --no-op-offload <0|1> (default: 0)\n"); + printf(" --no-host <0|1> (default: %s)\n", join(cmd_params_defaults.no_host, ",").c_str()); printf("\n"); printf( "Multiple values can be given for each parameter by separating them with ','\n" @@ -514,6 +504,10 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { params.progress = cmd_params_defaults.progress; params.no_warmup = cmd_params_defaults.no_warmup; + if (const char * env = getenv("HF_TOKEN")) { + params.hf_token = env; + } + for (int i = 1; i < argc; i++) { arg = argv[i]; if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { @@ -531,6 +525,26 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } auto p = string_split(argv[i], split_delim); params.model.insert(params.model.end(), p.begin(), p.end()); + } else if (arg == "-hf" || arg == "-hfr" || arg == "--hf-repo") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + params.hf_repo.insert(params.hf_repo.end(), p.begin(), p.end()); + } else if (arg == "-hff" || arg == "--hf-file") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + params.hf_file.insert(params.hf_file.end(), p.begin(), p.end()); + } else if (arg == "-hft" || arg == "--hf-token") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.hf_token = argv[i]; } else if (arg == "-p" || arg == "--n-prompt") { if (++i >= argc) { invalid_param = true; @@ -961,6 +975,44 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { exit(1); } + if (!params.hf_repo.empty()) { + for (size_t i = 0; i < params.hf_repo.size(); i++) { + common_params_model model; + + // step 1: no `-hff` provided, we auto-detect based on the `-hf` flag + if (params.hf_file.empty() || params.hf_file[i].empty()) { + auto auto_detected = common_get_hf_file(params.hf_repo[i], params.hf_token, false); + if (auto_detected.repo.empty() || auto_detected.ggufFile.empty()) { + exit(1); + } + + model.name = params.hf_repo[i]; + model.hf_repo = auto_detected.repo; + model.hf_file = auto_detected.ggufFile; + } else { + model.hf_file = params.hf_file[i]; + } + + // step 2: construct the model cache path + std::string clean_fname = model.hf_repo + "_" + model.hf_file; + string_replace_all(clean_fname, "\\", "_"); + string_replace_all(clean_fname, "/", "_"); + model.path = fs_get_cache_file(clean_fname); + + // step 3: download the model if not exists + std::string model_endpoint = get_model_endpoint(); + model.url = model_endpoint + model.hf_repo + "/resolve/main/" + model.hf_file; + + bool ok = common_download_model(model, params.hf_token, false); + if (!ok) { + fprintf(stderr, "error: failed to download model from %s\n", model.url.c_str()); + exit(1); + } + + params.model.push_back(model.path); + } + } + // set defaults if (params.model.empty()) { params.model = cmd_params_defaults.model; From 5f4cdac3857ec3915c069caf3dec4f35af1691a1 Mon Sep 17 00:00:00 2001 From: Michael Huang <15768500+tehsiuhuang@users.noreply.github.com> Date: Sun, 8 Mar 2026 21:45:43 -0700 Subject: [PATCH 05/22] cuda : display total and free VRAM capacity during device initialization (#20185) --- ggml/src/ggml-cuda/ggml-cuda.cu | 30 +++++++++++++++++++++++------- 1 file changed, 23 insertions(+), 7 deletions(-) diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index a8007a063..0fafaf009 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -205,7 +205,14 @@ static ggml_cuda_device_info ggml_cuda_init() { GGML_ASSERT(info.device_count <= GGML_CUDA_MAX_DEVICES); int64_t total_vram = 0; - GGML_LOG_INFO("%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count); + for (int id = 0; id < info.device_count; ++id) { + cudaDeviceProp prop; + CUDA_CHECK(cudaGetDeviceProperties(&prop, id)); + total_vram += prop.totalGlobalMem; + } + GGML_LOG_INFO("%s: found %d " GGML_CUDA_NAME " devices (Total VRAM: %zu MiB):\n", + __func__, info.device_count, (size_t)(total_vram / (1024 * 1024))); + total_vram = 0; std::vector> turing_devices_without_mma; for (int id = 0; id < info.device_count; ++id) { @@ -243,6 +250,12 @@ static ggml_cuda_device_info ggml_cuda_init() { #else info.devices[id].supports_cooperative_launch = false; #endif // !(GGML_USE_MUSA) + + // cudaMemGetInfo returns info for the current device + size_t free_mem; + CUDA_CHECK(cudaSetDevice(id)); + CUDA_CHECK(cudaMemGetInfo(&free_mem, NULL)); + #if defined(GGML_USE_HIP) info.devices[id].smpbo = prop.sharedMemPerBlock; @@ -257,22 +270,25 @@ static ggml_cuda_device_info ggml_cuda_init() { info.devices[id].cc += prop.minor * 0x10; } } - GGML_LOG_INFO(" Device %d: %s, %s (0x%x), VMM: %s, Wave Size: %d\n", + GGML_LOG_INFO(" Device %d: %s, %s (0x%x), VMM: %s, Wave Size: %d, VRAM: %zu MiB (%zu MiB free)\n", id, prop.name, prop.gcnArchName, info.devices[id].cc & 0xffff, - device_vmm ? "yes" : "no", prop.warpSize); + device_vmm ? "yes" : "no", prop.warpSize, + (size_t)(prop.totalGlobalMem / (1024 * 1024)), free_mem / (1024 * 1024)); #elif defined(GGML_USE_MUSA) // FIXME: Ensure compatibility with varying warp sizes across different MUSA archs. info.devices[id].warp_size = 32; info.devices[id].smpbo = prop.sharedMemPerBlockOptin; info.devices[id].cc = GGML_CUDA_CC_OFFSET_MTHREADS + prop.major * 0x100; info.devices[id].cc += prop.minor * 0x10; - GGML_LOG_INFO(" Device %d: %s, compute capability %d.%d, VMM: %s\n", - id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no"); + GGML_LOG_INFO(" Device %d: %s, compute capability %d.%d, VMM: %s, VRAM: %zu MiB (%zu MiB free)\n", + id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no", + (size_t)(prop.totalGlobalMem / (1024 * 1024)), free_mem / (1024 * 1024)); #else info.devices[id].smpbo = prop.sharedMemPerBlockOptin; info.devices[id].cc = 100*prop.major + 10*prop.minor; - GGML_LOG_INFO(" Device %d: %s, compute capability %d.%d, VMM: %s\n", - id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no"); + GGML_LOG_INFO(" Device %d: %s, compute capability %d.%d, VMM: %s, VRAM: %zu MiB (%zu MiB free)\n", + id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no", + (size_t)(prop.totalGlobalMem / (1024 * 1024)), free_mem / (1024 * 1024)); std::string device_name(prop.name); if (device_name == "NVIDIA GeForce MX450") { turing_devices_without_mma.push_back({ id, device_name }); From b2f460bd3c75f36b8fbe01bd3a6ca006a9424f49 Mon Sep 17 00:00:00 2001 From: Ruben Ortlam Date: Mon, 9 Mar 2026 07:23:45 +0100 Subject: [PATCH 06/22] vulkan: skip zero size tensors in backend copies (#20233) --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 31 +++++++++++++++++++++++++++- 1 file changed, 30 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 0bf7d2e24..70e992ce2 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -13253,6 +13253,10 @@ static void ggml_backend_vk_buffer_memset_tensor(ggml_backend_buffer_t buffer, g ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context; vk_buffer buf = buf_ctx->dev_buffer; + if (size == 0) { + return; + } + uint32_t val32 = (uint32_t)value * 0x01010101; ggml_vk_buffer_memset(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, val32, size); } @@ -13262,6 +13266,10 @@ static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context; vk_buffer buf = buf_ctx->dev_buffer; + if (size == 0) { + return; + } + ggml_vk_buffer_write(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, size); } @@ -13269,12 +13277,20 @@ static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, cons VK_LOG_DEBUG("ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")"); ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context; + if (size == 0) { + return; + } + vk_buffer buf = buf_ctx->dev_buffer; ggml_vk_buffer_read(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, size); } static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { + if (ggml_nbytes(src) == 0) { + return true; + } + if (ggml_backend_buffer_is_vk(src->buffer)) { ggml_backend_vk_buffer_context * src_buf_ctx = (ggml_backend_vk_buffer_context *)src->buffer->context; ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; @@ -13464,6 +13480,10 @@ static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_get_default_buffer_type(backend) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); + if (size == 0) { + return; + } + ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context; vk_context cpy_ctx; @@ -13507,6 +13527,10 @@ static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_ ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_get_default_buffer_type(backend) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); + if (size == 0) { + return; + } + ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context; vk_context compute_ctx = ggml_vk_get_compute_ctx(ctx); @@ -13533,9 +13557,14 @@ static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_ } static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const ggml_tensor * src, ggml_tensor * dst) { - VK_LOG_DEBUG("ggml_backend_vk_cpy_tensor_async()"); + VK_LOG_DEBUG("ggml_backend_vk_cpy_tensor_async(" << src << " -> " << dst << ", size=" << ggml_nbytes(src) << ")"); ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend_dst->context; + // Skip zero-size tensors + if (ggml_nbytes(src) == 0) { + return true; + } + if (dst->buffer->buft != ggml_backend_vk_get_default_buffer_type(backend_dst)) { return false; } From 0beb8db3a0037b51f8247ac657b7655ab68fa9f0 Mon Sep 17 00:00:00 2001 From: Bertay Eren <39909689+bertaye@users.noreply.github.com> Date: Mon, 9 Mar 2026 09:24:16 +0300 Subject: [PATCH 07/22] ggml-vulkan: add SGN operator, auto-generate Vulkan.csv and ops.md (#20219) --- docs/ops.md | 3 ++- docs/ops/Vulkan.csv | 21 +++++++++++++++---- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 9 ++++++++ ggml/src/ggml-vulkan/vulkan-shaders/sgn.comp | 21 +++++++++++++++++++ .../vulkan-shaders/vulkan-shaders-gen.cpp | 2 ++ 5 files changed, 51 insertions(+), 5 deletions(-) create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/sgn.comp diff --git a/docs/ops.md b/docs/ops.md index cab0b37a7..019b087ce 100644 --- a/docs/ops.md +++ b/docs/ops.md @@ -47,6 +47,7 @@ Legend: | FILL | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | | FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | | FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | +| GATED_DELTA_NET | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | GATED_LINEAR_ATTN | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | | GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | | GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | @@ -92,7 +93,7 @@ Legend: | SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | SET | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ | | SET_ROWS | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | -| SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | +| SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | | SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | | SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | | SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | diff --git a/docs/ops/Vulkan.csv b/docs/ops/Vulkan.csv index 011dffcd8..e69315496 100644 --- a/docs/ops/Vulkan.csv +++ b/docs/ops/Vulkan.csv @@ -1,8 +1,8 @@ "backend_name","op_name","op_params","test_mode","supported","error_message","backend_reg_name" "Vulkan0","ABS","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" "Vulkan0","ABS","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan" -"Vulkan0","SGN","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","Vulkan" -"Vulkan0","SGN","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","Vulkan" +"Vulkan0","SGN","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" +"Vulkan0","SGN","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan" "Vulkan0","NEG","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" "Vulkan0","NEG","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan" "Vulkan0","STEP","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" @@ -85,8 +85,8 @@ "Vulkan0","TRUNC","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","Vulkan" "Vulkan0","ABS","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" "Vulkan0","ABS","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan" -"Vulkan0","SGN","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","Vulkan" -"Vulkan0","SGN","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","Vulkan" +"Vulkan0","SGN","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" +"Vulkan0","SGN","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan" "Vulkan0","NEG","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" "Vulkan0","NEG","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan" "Vulkan0","STEP","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan" @@ -13591,3 +13591,16 @@ "Vulkan0","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[30000,1,1,1]","support","0","no","Vulkan" "Vulkan0","OPT_STEP_ADAMW","type=f32,ne=[10,5,4,3]","support","1","yes","Vulkan" "Vulkan0","OPT_STEP_SGD","type=f32,ne=[10,5,4,3]","support","1","yes","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=32,head_size=128,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=16,head_size=64,n_seq_tokens=1,n_seqs=2,v_repeat=1,permuted=0,kda=0","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=0,kda=0","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=8,head_size=32,n_seq_tokens=4,n_seqs=2,v_repeat=2,permuted=0,kda=0","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=1,kda=0","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=1,kda=0","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=1","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=1,n_seqs=2,v_repeat=1,permuted=0,kda=1","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=32,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=0,kda=1","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=0,kda=1","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=8,head_size=32,n_seq_tokens=4,n_seqs=2,v_repeat=2,permuted=0,kda=1","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=1,kda=1","support","0","no","Vulkan" diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 70e992ce2..61d112c50 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -763,6 +763,7 @@ struct vk_device_struct { vk_pipeline pipeline_ceil[2]; vk_pipeline pipeline_floor[2]; vk_pipeline pipeline_trunc[2]; + vk_pipeline pipeline_sgn[2]; vk_pipeline pipeline_add1_f16_f16; vk_pipeline pipeline_add1_f16_f32; @@ -4393,6 +4394,7 @@ static void ggml_vk_load_shaders(vk_device& device) { CREATE_UNARY(ceil) CREATE_UNARY(floor) CREATE_UNARY(trunc) + CREATE_UNARY(sgn) #undef CREATE_UNARY #define CREATE_UNARY_RTE(name) \ @@ -9281,6 +9283,8 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return ctx->device->pipeline_floor[dst->type == GGML_TYPE_F16]; case GGML_UNARY_OP_TRUNC: return ctx->device->pipeline_trunc[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_SGN: + return ctx->device->pipeline_sgn[dst->type == GGML_TYPE_F16]; default: break; } @@ -12875,6 +12879,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr case GGML_UNARY_OP_CEIL: case GGML_UNARY_OP_FLOOR: case GGML_UNARY_OP_TRUNC: + case GGML_UNARY_OP_SGN: ggml_vk_unary(ctx, compute_ctx, src0, node); break; case GGML_UNARY_OP_XIELU: @@ -15004,6 +15009,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_UNARY_OP_CEIL: case GGML_UNARY_OP_FLOOR: case GGML_UNARY_OP_TRUNC: + case GGML_UNARY_OP_SGN: return ggml_is_contiguous(op->src[0]) && (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) && (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && @@ -16170,6 +16176,9 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph * case GGML_UNARY_OP_TRUNC: tensor_clone = ggml_trunc(ggml_ctx, src_clone[0]); break; + case GGML_UNARY_OP_SGN: + tensor_clone = ggml_sgn(ggml_ctx, src_clone[0]); + break; default: std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl; GGML_ABORT("fatal error"); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/sgn.comp b/ggml/src/ggml-vulkan/vulkan-shaders/sgn.comp new file mode 100644 index 000000000..a9c147bf9 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/sgn.comp @@ -0,0 +1,21 @@ +#version 450 + +#include "generic_head.glsl" +#include "types.glsl" + +#extension GL_EXT_control_flow_attributes : enable + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; + + if (i >= p.KX) { + return; + } + + data_d[i] = D_TYPE(sign(float(data_a[i]))); +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index ed077dfb6..fb8941232 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -871,6 +871,8 @@ void process_shaders() { string_to_spv("elu_f32", "elu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); string_to_spv("xielu_f16", "xielu.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); string_to_spv("xielu_f32", "xielu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("sgn_f16", "sgn.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("sgn_f32", "sgn.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); string_to_spv("tri_f16", "tri.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); string_to_spv("tri_f32", "tri.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); From e2763a6723848d14e992c43410e2afcbe86446c7 Mon Sep 17 00:00:00 2001 From: Aman Gupta Date: Mon, 9 Mar 2026 15:05:34 +0800 Subject: [PATCH 08/22] contributing: limit open PRs for new contributors to 1 (#20036) --- CONTRIBUTING.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 0fe627f4e..996f34ed8 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -39,6 +39,7 @@ Before submitting your PR: - For intricate features, consider opening a feature request first to discuss and align expectations - When adding support for a new model or feature, focus on **CPU support only** in the initial PR unless you have a good reason not to. Add support for other backends like CUDA in follow-up PRs - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly +- If you are a new contributor, limit your open PRs to 1. After submitting your PR: - Expect requests for modifications to ensure the code meets llama.cpp's standards for quality and long-term maintainability From b518195101fda6e3e636d997d487c83a629a0089 Mon Sep 17 00:00:00 2001 From: ddh0 Date: Mon, 9 Mar 2026 02:28:41 -0500 Subject: [PATCH 09/22] llama-quant : left-align tensor names in output (#20117) --- src/llama-quant.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp index c192e1ef0..3e87ac55b 100644 --- a/src/llama-quant.cpp +++ b/src/llama-quant.cpp @@ -778,7 +778,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: ml.load_data_for(tensor); } - LLAMA_LOG_INFO("[%4d/%4d] %36s - [%s], type = %6s, ", + LLAMA_LOG_INFO("[%4d/%4d] %-36s - [%s], type = %6s, ", ++idx, ml.n_tensors, ggml_get_name(tensor), llama_format_tensor_shape(tensor).c_str(), From e8bbc736cbc5d945a0b26dadbd6224d0aeba7faa Mon Sep 17 00:00:00 2001 From: Aman Gupta Date: Mon, 9 Mar 2026 16:15:36 +0800 Subject: [PATCH 10/22] ggml-cuda: disable gdn for musa (#20278) --- ggml/src/ggml-cuda/ggml-cuda.cu | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index 0fafaf009..cda275b8c 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -4992,9 +4992,15 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_LEAKY_RELU: case GGML_OP_RWKV_WKV6: case GGML_OP_GATED_LINEAR_ATTN: - case GGML_OP_GATED_DELTA_NET: case GGML_OP_RWKV_WKV7: return true; + case GGML_OP_GATED_DELTA_NET: + //TODO: enable once MUSA compiler is solved https://github.com/ggml-org/llama.cpp/pull/19504#issuecomment-4018634327 +#ifdef GGML_USE_MUSA + return false; +#else + return true; +#endif // GGML_USE_MUSA case GGML_OP_FLASH_ATTN_EXT: return ggml_cuda_flash_attn_ext_supported(dev_ctx->device, op); case GGML_OP_CROSS_ENTROPY_LOSS: From 107d5999520dd02195ebe05278752db9fd33c865 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 9 Mar 2026 10:33:12 +0200 Subject: [PATCH 11/22] server : add kill switch when server is stuck (#20277) --- tools/server/server-context.cpp | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index 3790308d0..b67190a46 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -562,7 +562,7 @@ private: llama_model_ptr model_dft; - bool add_bos_token = true; + bool add_bos_token = true; int32_t n_ctx; // total context for all clients / slots @@ -570,6 +570,7 @@ private: std::vector slots; int slots_debug = 0; + int n_empty_consequtive = 0; std::unique_ptr prompt_cache; @@ -2628,6 +2629,12 @@ private: if (batch.n_tokens == 0) { SRV_WRN("%s", "no tokens to decode\n"); + + if (++n_empty_consequtive > 3) { + GGML_ABORT("fatal error - please provide logs and repro in %s\n", "https://github.com/ggml-org/llama.cpp/pull/20277"); + } + } else { + n_empty_consequtive = 0; } int32_t i_next = 0; From 43e1cbd6c1b407fcb1fb0196276265e774986035 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 9 Mar 2026 13:15:15 +0200 Subject: [PATCH 12/22] models : fix assert in mamba2 graph (#20270) --- src/models/mamba-base.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/models/mamba-base.cpp b/src/models/mamba-base.cpp index 8aedbef84..8a79fe4b6 100644 --- a/src/models/mamba-base.cpp +++ b/src/models/mamba-base.cpp @@ -155,7 +155,6 @@ ggml_tensor * llm_build_mamba_base::build_mamba2_layer(llm_graph_input_rs * inp, const auto kv_head = mctx_cur->get_head(); - const int64_t n_embd = hparams.n_embd; const int64_t d_conv = hparams.ssm_d_conv; const int64_t d_inner = hparams.ssm_d_inner; const int64_t d_state = hparams.ssm_d_state; @@ -170,7 +169,7 @@ ggml_tensor * llm_build_mamba_base::build_mamba2_layer(llm_graph_input_rs * inp, GGML_ASSERT(ubatch.equal_seqs()); GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs); GGML_ASSERT(d_inner % n_head == 0); - GGML_ASSERT(d_inner % (n_group*n_embd) == 0); + GGML_ASSERT(d_inner % (n_group*d_state) == 0); ggml_tensor * conv_states_all = mctx_cur->get_r_l(il); ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il); From f76565db92272d98976d5b8b1809ebe1e079f8cd Mon Sep 17 00:00:00 2001 From: "Piotr Wilkin (ilintar)" Date: Mon, 9 Mar 2026 14:25:11 +0100 Subject: [PATCH 13/22] common: map developer role to system (#20215) * Map developer role to system * Simplify --- common/chat.cpp | 15 +++ tests/test-chat.cpp | 291 ++++++-------------------------------------- 2 files changed, 54 insertions(+), 252 deletions(-) diff --git a/common/chat.cpp b/common/chat.cpp index 29d2e5fd1..d86bad462 100644 --- a/common/chat.cpp +++ b/common/chat.cpp @@ -1352,6 +1352,17 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat namespace workaround { +static void map_developer_role_to_system(json & messages) { + for (auto & message : messages) { + if (message.contains("role")) { + if (message["role"] == "developer") { + message["role"] = "system"; + } + } + } +} + + // if first message is system and template does not support it, merge it with next message static void system_message_not_supported(json & messages) { if (!messages.empty() && messages.front().at("role") == "system") { @@ -1429,6 +1440,10 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_ params.add_bos = tmpls->add_bos; params.add_eos = tmpls->add_eos; + if (src.find("<|channel|>") == std::string::npos) { + // map developer to system for all models except for GPT-OSS + workaround::map_developer_role_to_system(params.messages); + } workaround::func_args_not_string(params.messages); if (!tmpl.original_caps().supports_system_role) { diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp index 2f83d7c0b..b46a34e93 100644 --- a/tests/test-chat.cpp +++ b/tests/test-chat.cpp @@ -800,258 +800,6 @@ const common_chat_msg message_assist_call_python_lines_unclosed = const common_chat_msg message_assist_json_content = simple_assist_msg("{\n \"response\": \"Hello, world!\\nWhat's up?\"\n}"); -struct delta_data { - std::string delta; - common_chat_params params; -}; - -static delta_data init_delta(const struct common_chat_templates * tmpls, - const std::vector & end_tokens, - const common_chat_msg & user_message, - const common_chat_msg & delta_message, - const std::vector & tools, - const common_chat_tool_choice & tool_choice) { - common_chat_templates_inputs inputs; - inputs.parallel_tool_calls = true; - inputs.messages.push_back(user_message); - inputs.tools = tools; - inputs.tool_choice = tool_choice; - auto params_prefix = common_chat_templates_apply(tmpls, inputs); - - inputs.messages.push_back(delta_message); - inputs.add_generation_prompt = false; - auto params_full = common_chat_templates_apply(tmpls, inputs); - - std::string prefix = params_prefix.prompt; - std::string full = params_full.prompt; - - if (full == prefix) { - throw std::runtime_error("Full message is the same as the prefix"); - } - - size_t common_prefix_length = 0; - for (size_t i = 0; i < prefix.size() && i < full.size(); ++i) { - if (prefix[i] != full[i]) { - break; - } - if (prefix[i] == '<') { - // DeepSeek R1's template (as of 20250209) adds a trailing if add_generation_prompt, - // but it removes thinking tags for past messages. - // The prefix and full strings diverge at vs. <｜tool▁calls▁begin｜>, we avoid consuming the leading <. - continue; - } - common_prefix_length = i + 1; - } - auto delta = full.substr(common_prefix_length); - - // Strip end tokens - for (const auto & end_token : end_tokens) { - // rfind to find the last occurrence - auto pos = delta.rfind(end_token); - if (pos != std::string::npos) { - delta = delta.substr(0, pos); - break; - } - } - return { delta, params_full }; -} - -/* - Applies the template to 1 user message w/ add_generation_prompt=true, then w/ the test message w/ add_generation_prompt=false, - gets the diff, removes any end tokens and parses the result w/ the grammar, checking that - the parsed message is the same as the test_message -*/ -static void test_templates(const struct common_chat_templates * tmpls, - const std::vector & end_tokens, - const common_chat_msg & test_message, - const std::vector & tools = {}, - const std::string & expected_delta = "", - bool expect_grammar_triggered = true, - bool test_grammar_if_triggered = true, - common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_NONE, - bool ignore_whitespace_differences = false) { - common_chat_msg user_message; - user_message.role = "user"; - user_message.content = "Hello, world!"; - - common_chat_templates_inputs inputs_tools; - inputs_tools.messages = { message_user }; - inputs_tools.tools = { special_function_tool }; - - common_chat_params params = common_chat_templates_apply(tmpls, inputs_tools); - - for (const auto & tool_choice : - std::vector{ COMMON_CHAT_TOOL_CHOICE_AUTO, COMMON_CHAT_TOOL_CHOICE_REQUIRED }) { - auto data = init_delta(tmpls, end_tokens, user_message, test_message, tools, tool_choice); - if (!expected_delta.empty()) { - if (ignore_whitespace_differences) { - assert_equals(string_strip(expected_delta), string_strip(data.delta)); - } else { - assert_equals(expected_delta, data.delta); - } - } - - if (expect_grammar_triggered) { - // TODO @ngxson : refactor common_chat_parse to avoid passing format/reasoning_format every time - common_chat_parser_params parser_params; - parser_params.format = data.params.format; - parser_params.reasoning_format = reasoning_format; - if (!parser_params.parser.empty()) { - parser_params.parser = common_peg_arena(); - parser_params.parser.load(params.parser); - } - const auto msg = common_chat_parse(data.delta, /* is_partial= */ false, parser_params); - assert_msg_equals(test_message, msg, ignore_whitespace_differences); - } - - if (!test_message.tool_calls.empty()) { - GGML_ASSERT(!data.params.grammar.empty()); - } - if (!data.params.grammar.empty()) { - auto grammar = build_grammar(data.params.grammar); - if (!grammar) { - throw std::runtime_error("Failed to build grammar"); - } - auto earliest_trigger_pos = std::string::npos; - auto constrained = data.delta; - for (const auto & trigger : data.params.grammar_triggers) { - size_t pos = std::string::npos; - std::smatch match; - switch (trigger.type) { - case COMMON_GRAMMAR_TRIGGER_TYPE_WORD: - { - const auto & word = trigger.value; - pos = constrained.find(word); - break; - } - case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN: - { - const auto & pattern = std::regex(trigger.value); - if (std::regex_search(constrained, match, pattern)) { - pos = match.position(pattern.mark_count()); - } - break; - } - case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL: - { - const auto & pattern = trigger.value; - if (std::regex_match(constrained, match, std::regex(pattern))) { - auto mpos = std::string::npos; - for (size_t i = 1; i < match.size(); ++i) { - if (match[i].length() > 0) { - mpos = match.position(i); - break; - } - } - if (mpos == std::string::npos) { - mpos = match.position(0); - } - pos = mpos; - } - break; - } - default: - throw std::runtime_error("Unknown trigger type"); - } - if (pos == std::string::npos) { - continue; - } - if (earliest_trigger_pos == std::string::npos || pos < earliest_trigger_pos) { - earliest_trigger_pos = pos; - } - } - auto grammar_triggered = false; - if (earliest_trigger_pos != std::string::npos) { - constrained = constrained.substr(earliest_trigger_pos); - grammar_triggered = true; - } - if (data.params.grammar_lazy) { - assert_equals(expect_grammar_triggered, grammar_triggered); - } - - if (grammar_triggered && test_grammar_if_triggered && !match_string(constrained, grammar.get())) { - throw std::runtime_error("Failed to match delta against grammar:\n\n" + data.delta + - "\n\nConstrained: " + constrained + "\n\nGrammar: " + data.params.grammar); - } - } - } -} - -/** - * Test if streaming=true is consistent with streaming=false for given partial parser - * Also test if there is any problem with partial message - */ -template -static void test_parser_with_streaming(const common_chat_msg & expected, const std::string & raw_message, T parse_msg) { - constexpr auto utf8_truncate_safe_len = [](const std::string_view s) -> size_t { - auto len = s.size(); - if (len == 0) { - return 0; - } - auto i = len; - for (size_t back = 0; back < 4 && i > 0; ++back) { - --i; - unsigned char c = s[i]; - if ((c & 0x80) == 0) { - return len; - } - if ((c & 0xC0) == 0xC0) { - size_t expected_len = 0; - if ((c & 0xE0) == 0xC0) { - expected_len = 2; - } else if ((c & 0xF0) == 0xE0) { - expected_len = 3; - } else if ((c & 0xF8) == 0xF0) { - expected_len = 4; - } else { - return i; - } - if (len - i >= expected_len) { - return len; - } - return i; - } - } - return len - std::min(len, size_t(3)); - }; - constexpr auto utf8_truncate_safe_view = [utf8_truncate_safe_len](const std::string_view s) { - return s.substr(0, utf8_truncate_safe_len(s)); - }; - - auto merged = simple_assist_msg(""); - auto last_msg = parse_msg(""); - for (size_t i = 1; i <= raw_message.size(); ++i) { - auto curr_msg = parse_msg(std::string(utf8_truncate_safe_view(std::string_view(raw_message).substr(0, i)))); - if (curr_msg == simple_assist_msg("")) { - continue; - } - LOG_INF("Streaming msg: %s\n", common_chat_msgs_to_json_oaicompat({ curr_msg }).dump().c_str()); - for (auto diff : common_chat_msg_diff::compute_diffs(last_msg, curr_msg)) { - LOG_INF("Streaming diff: %s\n", common_chat_msg_diff_to_json_oaicompat(diff).dump().c_str()); - if (!diff.reasoning_content_delta.empty()) { - merged.reasoning_content += diff.reasoning_content_delta; - } - if (!diff.content_delta.empty()) { - merged.content += diff.content_delta; - } - if (diff.tool_call_index != std::string::npos) { - if (!diff.tool_call_delta.name.empty()) { - merged.tool_calls.push_back({ diff.tool_call_delta.name, "", "" }); - } - if (!diff.tool_call_delta.arguments.empty()) { - GGML_ASSERT(!merged.tool_calls.empty()); - merged.tool_calls.back().arguments += diff.tool_call_delta.arguments; - } - } - LOG_INF("Streaming merged: %s\n", common_chat_msgs_to_json_oaicompat({ merged }).dump().c_str()); - } - assert_msg_equals(curr_msg, merged, true); - last_msg = curr_msg; - } - assert_msg_equals(expected, parse_msg(raw_message), true); - assert_msg_equals(expected, merged, true); -} - // Use for PEG parser implementations struct peg_test_case { common_chat_templates_inputs params; @@ -3019,6 +2767,44 @@ static void test_template_output_peg_parsers(bool detailed_debug) { } } +// Test the developer role to system workaround with a simple mock template +static void test_developer_role_to_system_workaround() { + LOG_DBG("%s\n", __func__); + + // Simple mock template that supports system role + const std::string mock_template = + "{%- for message in messages -%}\n" + " {{- '<|' + message.role + '|>' + message.content + '<|end|>' -}}\n" + "{%- endfor -%}\n" + "{%- if add_generation_prompt -%}\n" + " {{- '<|assistant|>' -}}\n" + "{%- endif -%}"; + + auto tmpls = common_chat_templates_ptr(common_chat_templates_init(/* model= */ nullptr, mock_template)); + + // Test case 1: Developer message - should be changed to system + // After simplification we only test this case + { + common_chat_templates_inputs inputs; + common_chat_msg developer_msg; + developer_msg.role = "developer"; + developer_msg.content = "You are a helpful developer assistant."; + inputs.messages = { developer_msg }; + inputs.add_generation_prompt = false; + + auto params = common_chat_templates_apply(tmpls.get(), inputs); + + // The developer role should have been changed to system + if (params.prompt.find("<|developer|>") != std::string::npos) { + throw std::runtime_error("Test failed: developer role was not changed to system"); + } + if (params.prompt.find("<|system|>You are a helpful developer assistant.<|end|>") == std::string::npos) { + throw std::runtime_error("Test failed: system message not found in output"); + } + LOG_ERR("Test 1 passed: developer role changed to system\n"); + } +} + static void test_msg_diffs_compute() { LOG_DBG("%s\n", __func__); { @@ -3155,6 +2941,7 @@ int main(int argc, char ** argv) { test_msg_diffs_compute(); test_msgs_oaicompat_json_conversion(); test_tools_oaicompat_json_conversion(); + test_developer_role_to_system_workaround(); test_template_output_peg_parsers(detailed_debug); std::cout << "\n[chat] All tests passed!" << '\n'; } From d6e1556499814da42424e39397a9964a1bebbf00 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 9 Mar 2026 16:43:38 +0200 Subject: [PATCH 14/22] server : fix off-by-1 in server_tokens::size_up_to_pos() (#20279) * server : fix off-by-1 in server_tokens::size_up_to_pos() * cont : fix typo [no ci] --- tools/server/server-common.cpp | 4 ++-- tools/server/server-common.h | 2 +- tools/server/server-context.cpp | 8 ++++---- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/tools/server/server-common.cpp b/tools/server/server-common.cpp index 13ea8c690..5b8895b34 100644 --- a/tools/server/server-common.cpp +++ b/tools/server/server-common.cpp @@ -276,7 +276,7 @@ llama_pos server_tokens::pos_next(int64_t n_tokens) const { size_t server_tokens::size_up_to_pos(llama_pos max_pos) const { if (!has_mtmd) { - return std::min((size_t)(max_pos + 1), tokens.size()); + return std::min((size_t)max_pos, tokens.size()); } size_t idx = 0; @@ -296,7 +296,7 @@ size_t server_tokens::size_up_to_pos(llama_pos max_pos) const { idx++; } - if (pos > max_pos) { + if (pos >= max_pos) { break; } } diff --git a/tools/server/server-common.h b/tools/server/server-common.h index 4fb9e488d..a234541e1 100644 --- a/tools/server/server-common.h +++ b/tools/server/server-common.h @@ -170,7 +170,7 @@ public: // the next position after n_tokens. if n_tokens < 0, return the next position after all tokens. llama_pos pos_next(int64_t n_tokens = -1) const; - // number of tokens with position <= max_pos + // number of tokens with position < max_pos size_t size_up_to_pos(llama_pos max_pos) const; const mtmd::input_chunk_ptr & find_chunk(size_t idx) const; diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index b67190a46..33bec85c2 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -570,7 +570,7 @@ private: std::vector slots; int slots_debug = 0; - int n_empty_consequtive = 0; + int n_empty_consecutive = 0; std::unique_ptr prompt_cache; @@ -2372,7 +2372,7 @@ private: } else { pos_next = std::min(pos_next, std::max(it->pos_min + 1, it->pos_max)); n_past = std::min(slot.prompt.tokens.size_up_to_pos(pos_next), (size_t) it->n_tokens); - SLT_WRN(slot, "restored context checkpoint (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", size = %.3f MiB)\n", it->pos_min, it->pos_max, it->n_tokens, (float) checkpoint_size / 1024 / 1024); + SLT_WRN(slot, "restored context checkpoint (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", n_past = %d, size = %.3f MiB)\n", it->pos_min, it->pos_max, it->n_tokens, n_past, (float) checkpoint_size / 1024 / 1024); } } @@ -2630,11 +2630,11 @@ private: if (batch.n_tokens == 0) { SRV_WRN("%s", "no tokens to decode\n"); - if (++n_empty_consequtive > 3) { + if (++n_empty_consecutive > 3) { GGML_ABORT("fatal error - please provide logs and repro in %s\n", "https://github.com/ggml-org/llama.cpp/pull/20277"); } } else { - n_empty_consequtive = 0; + n_empty_consecutive = 0; } int32_t i_next = 0; From 344ee2a38af98b80c476bd65fb749702012f7414 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 9 Mar 2026 16:44:25 +0200 Subject: [PATCH 15/22] server : warn swa-full is not supported for non-SWA models (#20291) --- tools/server/server-context.cpp | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index 33bec85c2..67ac5112e 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -729,6 +729,13 @@ private: } } + if (llama_model_n_swa(model) == 0) { + if (params_base.swa_full) { + params_base.swa_full = false; + SRV_WRN("%s\n", "swa_full is not supported by this model, it will be disabled"); + } + } + // Necessary similarity of prompt for slot selection slot_prompt_similarity = params_base.slot_prompt_similarity; From ed0007aa32b94f40e4f3ba0d0fe24d86a582232b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 9 Mar 2026 16:45:11 +0200 Subject: [PATCH 16/22] metal : add upscale (#20284) --- ggml/src/ggml-metal/ggml-metal-device.cpp | 23 +++- ggml/src/ggml-metal/ggml-metal-device.m | 2 +- ggml/src/ggml-metal/ggml-metal-impl.h | 2 + ggml/src/ggml-metal/ggml-metal-ops.cpp | 59 +++++---- ggml/src/ggml-metal/ggml-metal.metal | 154 +++++++++++++++++++++- 5 files changed, 211 insertions(+), 29 deletions(-) diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp index 06f3d8045..169c63dd7 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.cpp +++ b/ggml/src/ggml-metal/ggml-metal-device.cpp @@ -1717,12 +1717,29 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_upscale(ggml_met char base[256]; char name[256]; - snprintf(base, 256, "kernel_upscale_%s", ggml_type_name(op->src[0]->type)); - snprintf(name, 256, "%s", base); + const int32_t mode_flags = ggml_get_op_params_i32(op, 0); + const ggml_scale_mode mode = (ggml_scale_mode) (mode_flags & 0xFF); + + const bool antialias = (mode_flags & GGML_SCALE_FLAG_ANTIALIAS); + + if (mode == GGML_SCALE_MODE_BILINEAR) { + snprintf(base, 256, "kernel_upscale_bilinear_%s", ggml_type_name(op->src[0]->type)); + } else if (mode == GGML_SCALE_MODE_BICUBIC) { + snprintf(base, 256, "kernel_upscale_bicubic_%s", ggml_type_name(op->src[0]->type)); + } else { + snprintf(base, 256, "kernel_upscale_nearest_%s", ggml_type_name(op->src[0]->type)); + } + snprintf(name, 256, "%s_aa=%d", base, antialias); ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name); if (!res.pipeline) { - res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr); + ggml_metal_cv_t cv = ggml_metal_cv_init(); + + ggml_metal_cv_set_bool(cv, antialias, FC_UPSCALE + 0); + + res = ggml_metal_library_compile_pipeline(lib, base, name, cv); + + ggml_metal_cv_free(cv); } return res; diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m index 4cce414ab..23bd2b2ab 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.m +++ b/ggml/src/ggml-metal/ggml-metal-device.m @@ -1108,7 +1108,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te op->type == GGML_TYPE_F32 && (op->src[0]->type == GGML_TYPE_F16 || op->src[0]->type == GGML_TYPE_F32); case GGML_OP_UPSCALE: - return op->src[0]->type == GGML_TYPE_F32 && op->op_params[0] == GGML_SCALE_MODE_NEAREST && !(op->op_params[0] & GGML_SCALE_FLAG_ANTIALIAS); + return op->src[0]->type == GGML_TYPE_F32; case GGML_OP_POOL_1D: return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32; case GGML_OP_POOL_2D: diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h index 383e0d6e9..bf51055e3 100644 --- a/ggml/src/ggml-metal/ggml-metal-impl.h +++ b/ggml/src/ggml-metal/ggml-metal-impl.h @@ -83,6 +83,7 @@ #define FC_UNARY 1200 #define FC_BIN 1300 #define FC_SUM_ROWS 1400 +#define FC_UPSCALE 1500 // op-specific constants #define OP_FLASH_ATTN_EXT_NQPSG 8 @@ -890,6 +891,7 @@ typedef struct { float sf1; float sf2; float sf3; + float poffs; } ggml_metal_kargs_upscale; typedef struct { diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp index b3390352f..524e11166 100644 --- a/ggml/src/ggml-metal/ggml-metal-ops.cpp +++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp @@ -3729,32 +3729,43 @@ int ggml_metal_op_upscale(ggml_metal_op_t ctx, int idx) { GGML_TENSOR_LOCALS( int32_t, ne, op, ne); GGML_TENSOR_LOCALS(uint64_t, nb, op, nb); - const float sf0 = (float)ne0/op->src[0]->ne[0]; - const float sf1 = (float)ne1/op->src[0]->ne[1]; - const float sf2 = (float)ne2/op->src[0]->ne[2]; - const float sf3 = (float)ne3/op->src[0]->ne[3]; + float sf0 = (float)ne0/op->src[0]->ne[0]; + float sf1 = (float)ne1/op->src[0]->ne[1]; + float sf2 = (float)ne2/op->src[0]->ne[2]; + float sf3 = (float)ne3/op->src[0]->ne[3]; + + const int32_t mode_flags = ggml_get_op_params_i32(op, 0); + + float poffs = 0.5f; + + if (mode_flags & GGML_SCALE_FLAG_ALIGN_CORNERS) { + poffs = 0.0f; + sf0 = ne0 > 1 && ne00 > 1 ? (float)(ne0 - 1) / (ne00 - 1) : sf0; + sf1 = ne1 > 1 && ne01 > 1 ? (float)(ne1 - 1) / (ne01 - 1) : sf1; + } ggml_metal_kargs_upscale args = { - /*.ne00 =*/ ne00, - /*.ne01 =*/ ne01, - /*.ne02 =*/ ne02, - /*.ne03 =*/ ne03, - /*.nb00 =*/ nb00, - /*.nb01 =*/ nb01, - /*.nb02 =*/ nb02, - /*.nb03 =*/ nb03, - /*.ne0 =*/ ne0, - /*.ne1 =*/ ne1, - /*.ne2 =*/ ne2, - /*.ne3 =*/ ne3, - /*.nb0 =*/ nb0, - /*.nb1 =*/ nb1, - /*.nb2 =*/ nb2, - /*.nb3 =*/ nb3, - /*.sf0 =*/ sf0, - /*.sf1 =*/ sf1, - /*.sf2 =*/ sf2, - /*.sf3 =*/ sf3 + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.ne03 =*/ ne03, + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.ne2 =*/ ne2, + /*.ne3 =*/ ne3, + /*.nb0 =*/ nb0, + /*.nb1 =*/ nb1, + /*.nb2 =*/ nb2, + /*.nb3 =*/ nb3, + /*.sf0 =*/ sf0, + /*.sf1 =*/ sf1, + /*.sf2 =*/ sf2, + /*.sf3 =*/ sf3, + /*.poffs =*/ poffs, }; auto pipeline = ggml_metal_library_get_pipeline_upscale(lib, op); diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal index a58e641ad..5cfd69dd8 100644 --- a/ggml/src/ggml-metal/ggml-metal.metal +++ b/ggml/src/ggml-metal/ggml-metal.metal @@ -4530,7 +4530,9 @@ kernel void kernel_conv_transpose_2d( uint3 tpitg[[thread_position_in_threadgroup]], uint3 ntg[[threads_per_threadgroup]]); -kernel void kernel_upscale_f32( +constant bool FC_upscale_aa [[function_constant(FC_UPSCALE + 0)]]; + +kernel void kernel_upscale_nearest_f32( constant ggml_metal_kargs_upscale & args, device const char * src0, device char * dst, @@ -4556,6 +4558,156 @@ kernel void kernel_upscale_f32( } } +static inline float bilinear_tri(float x) { + return MAX(0.0f, 1.0f - fabs(x)); +} + +kernel void kernel_upscale_bilinear_f32( + constant ggml_metal_kargs_upscale & args, + device const char * src0, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + + const int64_t i3 = tgpig.z; + const int64_t i2 = tgpig.y; + const int64_t i1 = tgpig.x; + + const int64_t i03 = i3 / args.sf3; + const int64_t i02 = i2 / args.sf2; + + const float f01 = ((float)i1 + args.poffs) / args.sf1 - args.poffs; + const int64_t i01 = MAX(0, MIN(args.ne01 - 1, (int64_t)floor(f01))); + const int64_t i01p = MAX(0, MIN(args.ne01 - 1, i01 + 1)); + const float fd1 = MAX(0.0f, MIN(1.0f, f01 - (float)i01)); + + src0 += i03*args.nb03 + i02*args.nb02; + + device float * dst_ptr = (device float *)(dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1); + + if (FC_upscale_aa) { + const float support0 = MAX(1.0f, 1.0f / args.sf0); + const float invscale0 = 1.0f / support0; + const float support1 = MAX(1.0f, 1.0f / args.sf1); + const float invscale1 = 1.0f / support1; + + for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) { + const float f00 = ((float)i0 + args.poffs) / args.sf0 - args.poffs; + + int64_t x_min = MAX((int64_t)0, (int64_t)floor(f00 - support0 + args.poffs)); + int64_t x_max = MIN(args.ne00, (int64_t)ceil (f00 + support0 + args.poffs)); + + int64_t y_min = MAX((int64_t)0, (int64_t)floor(f01 - support1 + args.poffs)); + int64_t y_max = MIN(args.ne01, (int64_t)ceil (f01 + support1 + args.poffs)); + + float sum = 0.0f; + float wsum = 0.0f; + + for (int64_t sy = y_min; sy < y_max; ++sy) { + const float wy = MAX(0.0f, 1.0f - fabs((float)sy - f01) * invscale1); + for (int64_t sx = x_min; sx < x_max; ++sx) { + const float wx = MAX(0.0f, 1.0f - fabs((float)sx - f00) * invscale0); + const float w = wx * wy; + const device const float * src_ptr = (device const float *)(src0 + sy*args.nb01 + sx*args.nb00); + sum += (*src_ptr) * w; + wsum += w; + } + } + + const float v = (wsum > 0.0f) ? (sum / wsum) : 0.0f; + dst_ptr[i0] = v; + } + } else { + for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) { + const float f00 = ((float)i0 + args.poffs) / args.sf0 - args.poffs; + const int64_t i00 = MAX(0, MIN(args.ne00 - 1, (int64_t)floor(f00))); + const int64_t i00p = MAX(0, MIN(args.ne00 - 1, i00 + 1)); + const float fd0 = MAX(0.0f, MIN(1.0f, f00 - (float)i00)); + + device const float * src00 = (device const float *)(src0 + i01*args.nb01 + i00*args.nb00); + device const float * src10 = (device const float *)(src0 + i01*args.nb01 + i00p*args.nb00); + device const float * src01 = (device const float *)(src0 + i01p*args.nb01 + i00*args.nb00); + device const float * src11 = (device const float *)(src0 + i01p*args.nb01 + i00p*args.nb00); + + const float v = + (*src00) * (1.0f - fd0) * (1.0f - fd1) + + (*src10) * fd0 * (1.0f - fd1) + + (*src01) * (1.0f - fd0) * fd1 + + (*src11) * fd0 * fd1; + + dst_ptr[i0] = v; + } + } +} + +static inline float bicubic_weight1(float x) { + const float a = -0.75f; + return ((a + 2) * x - (a + 3)) * x * x + 1; +} + +static inline float bicubic_weight2(float x) { + const float a = -0.75f; + return ((a * x - 5 * a) * x + 8 * a) * x - 4 * a; +} + +kernel void kernel_upscale_bicubic_f32( + constant ggml_metal_kargs_upscale & args, + device const char * src0, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + + const int64_t i3 = tgpig.z; + const int64_t i2 = tgpig.y; + const int64_t i1 = tgpig.x; + + const int64_t i03 = i3 / args.sf3; + const int64_t i02 = i2 / args.sf2; + + const float f01 = ((float)i1 + args.poffs) / args.sf1 - args.poffs; + const int64_t i01 = (int64_t)floor(f01); + const float fd1 = f01 - (float)i01; + + const float w_y0 = bicubic_weight2(fd1 + 1.0f); + const float w_y1 = bicubic_weight1(fd1); + const float w_y2 = bicubic_weight1(1.0f - fd1); + const float w_y3 = bicubic_weight2(2.0f - fd1); + + const device const char * src_slice = src0 + i03 * args.nb03 + i02 * args.nb02; + + device float * dst_ptr = (device float *)(dst + i3 * args.nb3 + i2 * args.nb2 + i1 * args.nb1); + + for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) { + const float f00 = ((float)i0 + args.poffs) / args.sf0 - args.poffs; + const int64_t i00 = (int64_t)floor(f00); + const float fd0 = f00 - (float)i00; + + const float w_x0 = bicubic_weight2(fd0 + 1.0f); + const float w_x1 = bicubic_weight1(fd0); + const float w_x2 = bicubic_weight1(1.0f - fd0); + const float w_x3 = bicubic_weight2(2.0f - fd0); + + float sum = 0.0f; + + for (int dy = -1; dy <= 2; ++dy) { + const int64_t iy = MAX(0, MIN(args.ne01 - 1, i01 + dy)); + const float wy = (dy == -1) ? w_y0 : (dy == 0) ? w_y1 : (dy == 1) ? w_y2 : w_y3; + + for (int dx = -1; dx <= 2; ++dx) { + const int64_t ix = MAX(0, MIN(args.ne00 - 1, i00 + dx)); + const float wx = (dx == -1) ? w_x0 : (dx == 0) ? w_x1 : (dx == 1) ? w_x2 : w_x3; + + const device const float * src_ptr = (device const float *)(src_slice + iy * args.nb01 + ix * args.nb00); + sum += (*src_ptr) * wx * wy; + } + } + + dst_ptr[i0] = sum; + } +} + kernel void kernel_pad_f32( constant ggml_metal_kargs_pad & args, device const char * src0, From 96cfc4992c625984d99f3529d30bedab1e1ecf93 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 9 Mar 2026 16:47:06 +0200 Subject: [PATCH 17/22] server : fix checkpoints n_tokens calculation (#20287) --- tools/server/server-context.cpp | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index 67ac5112e..3541d910d 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -2141,6 +2141,9 @@ private: if (slot.state == SLOT_STATE_PROCESSING_PROMPT || slot.state == SLOT_STATE_STARTED) { const auto & input_tokens = slot.task->tokens; + // used to determine the number of tokens added to the batch for the current slot + const auto n_tokens_prev = batch.n_tokens; + // TODO: maybe move branch to outside of this loop in the future if (slot.state == SLOT_STATE_STARTED) { slot.t_start_process_prompt = ggml_time_us(); @@ -2533,6 +2536,9 @@ private: } } + // the number of tokens added to the batch for the current slot + const auto n_tokens_cur = batch.n_tokens - n_tokens_prev; + // entire prompt has been processed if (slot.prompt.n_tokens() == slot.task->n_tokens()) { slot.state = SLOT_STATE_DONE_PROMPT; @@ -2593,7 +2599,7 @@ private: auto & cur = slot.prompt.checkpoints.emplace_back(server_prompt_checkpoint{ /*.pos_min = */ pos_min, /*.pos_max = */ pos_max, - /*.n_tokens = */ slot.prompt.n_tokens() - batch.n_tokens, + /*.n_tokens = */ slot.prompt.n_tokens() - n_tokens_cur, /*.data = */ std::vector(checkpoint_size), }); From e22cd0aa155f0188eb9c605fd54daf3f2f38177b Mon Sep 17 00:00:00 2001 From: Paul Flynn Date: Mon, 9 Mar 2026 10:48:12 -0400 Subject: [PATCH 18/22] metal : extend mul_mv_ext to BF16, Q2_K, Q3_K (#20250) Enable mul_mv_ext small-batch kernels (BS 2-8) for BF16, Q2_K, and Q3_K quantization types. These types previously fell through to the slower single-row mul_mv path. BF16 uses the float4 dequantize path (like F16). Q2_K and Q3_K use the float4x4 K-quant path (like Q4_K/Q5_K/Q6_K). Co-authored-by: Claude Opus 4.6 --- ggml/src/ggml-metal/ggml-metal-ops.cpp | 3 +++ ggml/src/ggml-metal/ggml-metal.metal | 17 +++++++++++++++++ 2 files changed, 20 insertions(+) diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp index 524e11166..267755d08 100644 --- a/ggml/src/ggml-metal/ggml-metal-ops.cpp +++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp @@ -1963,6 +1963,7 @@ int ggml_metal_op_mul_mat(ggml_metal_op_t ctx, int idx) { ( op->src[0]->type == GGML_TYPE_F32 || // TODO: helper function op->src[0]->type == GGML_TYPE_F16 || + op->src[0]->type == GGML_TYPE_BF16 || op->src[0]->type == GGML_TYPE_Q4_0 || op->src[0]->type == GGML_TYPE_Q4_1 || op->src[0]->type == GGML_TYPE_Q5_0 || @@ -1977,6 +1978,8 @@ int ggml_metal_op_mul_mat(ggml_metal_op_t ctx, int idx) { op->src[0]->type == GGML_TYPE_Q4_K || op->src[0]->type == GGML_TYPE_Q5_K || op->src[0]->type == GGML_TYPE_Q6_K || + op->src[0]->type == GGML_TYPE_Q2_K || + op->src[0]->type == GGML_TYPE_Q3_K || false) && (ne11 >= 4 && ne11 <= 8) ) ) diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal index 5cfd69dd8..82ebbb4e4 100644 --- a/ggml/src/ggml-metal/ggml-metal.metal +++ b/ggml/src/ggml-metal/ggml-metal.metal @@ -3481,6 +3481,13 @@ template [[host_name("kernel_mul_mv_ext_f16_f32_r1_3")]] kernel mul_mv_ext_q4 template [[host_name("kernel_mul_mv_ext_f16_f32_r1_4")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, half4, 4, dequantize_f16_t4>; template [[host_name("kernel_mul_mv_ext_f16_f32_r1_5")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<5, half4, 4, dequantize_f16_t4>; +#if defined(GGML_METAL_HAS_BF16) +template [[host_name("kernel_mul_mv_ext_bf16_f32_r1_2")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<2, bfloat4, 4, dequantize_bf16_t4>; +template [[host_name("kernel_mul_mv_ext_bf16_f32_r1_3")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<3, bfloat4, 4, dequantize_bf16_t4>; +template [[host_name("kernel_mul_mv_ext_bf16_f32_r1_4")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, bfloat4, 4, dequantize_bf16_t4>; +template [[host_name("kernel_mul_mv_ext_bf16_f32_r1_5")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<5, bfloat4, 4, dequantize_bf16_t4>; +#endif + template [[host_name("kernel_mul_mv_ext_q4_0_f32_r1_2")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<2, block_q4_0, 32, dequantize_q4_0_t4>; template [[host_name("kernel_mul_mv_ext_q4_0_f32_r1_3")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<3, block_q4_0, 32, dequantize_q4_0_t4>; template [[host_name("kernel_mul_mv_ext_q4_0_f32_r1_4")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, block_q4_0, 32, dequantize_q4_0_t4>; @@ -3531,6 +3538,16 @@ template [[host_name("kernel_mul_mv_ext_q6_K_f32_r1_3")]] kernel mul_mv_ext_q4x4 template [[host_name("kernel_mul_mv_ext_q6_K_f32_r1_4")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<4, block_q6_K, 256, dequantize_q6_K>; template [[host_name("kernel_mul_mv_ext_q6_K_f32_r1_5")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<5, block_q6_K, 256, dequantize_q6_K>; +template [[host_name("kernel_mul_mv_ext_q2_K_f32_r1_2")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<2, block_q2_K, 256, dequantize_q2_K>; +template [[host_name("kernel_mul_mv_ext_q2_K_f32_r1_3")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<3, block_q2_K, 256, dequantize_q2_K>; +template [[host_name("kernel_mul_mv_ext_q2_K_f32_r1_4")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<4, block_q2_K, 256, dequantize_q2_K>; +template [[host_name("kernel_mul_mv_ext_q2_K_f32_r1_5")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<5, block_q2_K, 256, dequantize_q2_K>; + +template [[host_name("kernel_mul_mv_ext_q3_K_f32_r1_2")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<2, block_q3_K, 256, dequantize_q3_K>; +template [[host_name("kernel_mul_mv_ext_q3_K_f32_r1_3")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<3, block_q3_K, 256, dequantize_q3_K>; +template [[host_name("kernel_mul_mv_ext_q3_K_f32_r1_4")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<4, block_q3_K, 256, dequantize_q3_K>; +template [[host_name("kernel_mul_mv_ext_q3_K_f32_r1_5")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<5, block_q3_K, 256, dequantize_q3_K>; + template void kernel_mul_mv_t_t_impl( args_t args, From 23fbfcb1ad6c6f76b230e8895254de785000be46 Mon Sep 17 00:00:00 2001 From: Evan Huus Date: Mon, 9 Mar 2026 12:47:54 -0400 Subject: [PATCH 19/22] server: Parse port numbers from MCP server URLs in CORS proxy (#20208) * Parse port numbers from MCP server URLs * Pass scheme to http proxy for determining whether to use SSL * Fix download on non-standard port and re-add port to logging * add test --------- Co-authored-by: Xuan Son Nguyen --- common/http.h | 17 ++++++++++- tools/server/server-cors-proxy.h | 5 ++-- tools/server/server-models.cpp | 4 ++- tools/server/server-models.h | 1 + tools/server/tests/unit/test_proxy.py | 41 +++++++++++++++++++++++++++ tools/server/tests/utils.py | 3 ++ 6 files changed, 67 insertions(+), 4 deletions(-) create mode 100644 tools/server/tests/unit/test_proxy.py diff --git a/common/http.h b/common/http.h index e8ed56f95..d3daccd6b 100644 --- a/common/http.h +++ b/common/http.h @@ -7,6 +7,7 @@ struct common_http_url { std::string user; std::string password; std::string host; + int port; std::string path; }; @@ -47,6 +48,20 @@ static common_http_url common_http_parse_url(const std::string & url) { parts.host = rest; parts.path = "/"; } + + auto colon_pos = parts.host.find(':'); + + if (colon_pos != std::string::npos) { + parts.port = std::stoi(parts.host.substr(colon_pos + 1)); + parts.host = parts.host.substr(0, colon_pos); + } else if (parts.scheme == "http") { + parts.port = 80; + } else if (parts.scheme == "https") { + parts.port = 443; + } else { + throw std::runtime_error("unsupported URL scheme: " + parts.scheme); + } + return parts; } @@ -68,7 +83,7 @@ static std::pair common_http_client(const std: } #endif - httplib::Client cli(parts.scheme + "://" + parts.host); + httplib::Client cli(parts.scheme + "://" + parts.host + ":" + std::to_string(parts.port)); if (!parts.user.empty()) { cli.set_basic_auth(parts.user, parts.password); diff --git a/tools/server/server-cors-proxy.h b/tools/server/server-cors-proxy.h index bca50b53d..c412d4c25 100644 --- a/tools/server/server-cors-proxy.h +++ b/tools/server/server-cors-proxy.h @@ -30,12 +30,13 @@ static server_http_res_ptr proxy_request(const server_http_req & req, std::strin throw std::runtime_error("unsupported URL scheme in target URL: " + parsed_url.scheme); } - SRV_INF("proxying %s request to %s://%s%s\n", method.c_str(), parsed_url.scheme.c_str(), parsed_url.host.c_str(), parsed_url.path.c_str()); + SRV_INF("proxying %s request to %s://%s:%i%s\n", method.c_str(), parsed_url.scheme.c_str(), parsed_url.host.c_str(), parsed_url.port, parsed_url.path.c_str()); auto proxy = std::make_unique( method, + parsed_url.scheme, parsed_url.host, - parsed_url.scheme == "http" ? 80 : 443, + parsed_url.port, parsed_url.path, req.headers, req.body, diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp index 5f87ba9a2..c13d48a36 100644 --- a/tools/server/server-models.cpp +++ b/tools/server/server-models.cpp @@ -783,6 +783,7 @@ server_http_res_ptr server_models::proxy_request(const server_http_req & req, co } auto proxy = std::make_unique( method, + "http", CHILD_ADDR, meta->port, proxy_path, @@ -1079,6 +1080,7 @@ static bool should_strip_proxy_header(const std::string & header_name) { server_http_proxy::server_http_proxy( const std::string & method, + const std::string & scheme, const std::string & host, int port, const std::string & path, @@ -1092,7 +1094,7 @@ server_http_proxy::server_http_proxy( auto cli = std::make_shared(host, port); auto pipe = std::make_shared>(); - if (port == 443) { + if (scheme == "https") { #ifdef CPPHTTPLIB_OPENSSL_SUPPORT cli.reset(new httplib::SSLClient(host, port)); #else diff --git a/tools/server/server-models.h b/tools/server/server-models.h index 78abc8d72..2b392f299 100644 --- a/tools/server/server-models.h +++ b/tools/server/server-models.h @@ -180,6 +180,7 @@ struct server_http_proxy : server_http_res { std::function cleanup = nullptr; public: server_http_proxy(const std::string & method, + const std::string & scheme, const std::string & host, int port, const std::string & path, diff --git a/tools/server/tests/unit/test_proxy.py b/tools/server/tests/unit/test_proxy.py new file mode 100644 index 000000000..b7c332618 --- /dev/null +++ b/tools/server/tests/unit/test_proxy.py @@ -0,0 +1,41 @@ +import pytest +from utils import * + +server = ServerPreset.tinyllama2() + + +@pytest.fixture(autouse=True) +def create_server(): + global server + server = ServerPreset.tinyllama2() + + +def test_mcp_no_proxy(): + global server + server.webui_mcp_proxy = False + server.start() + + res = server.make_request("GET", "/cors-proxy") + assert res.status_code == 404 + + +def test_mcp_proxy(): + global server + server.webui_mcp_proxy = True + server.start() + + url = f"http://{server.server_host}:{server.server_port}/cors-proxy?url=http://example.com" + res = requests.get(url) + assert res.status_code == 200 + assert "Example Domain" in res.text + + +def test_mcp_proxy_custom_port(): + global server + server.webui_mcp_proxy = True + server.start() + + # try getting the server's models API via the proxy + res = server.make_request("GET", f"/cors-proxy?url=http://{server.server_host}:{server.server_port}/models") + assert res.status_code == 200 + assert "data" in res.body diff --git a/tools/server/tests/utils.py b/tools/server/tests/utils.py index 5002999d9..db357d876 100644 --- a/tools/server/tests/utils.py +++ b/tools/server/tests/utils.py @@ -102,6 +102,7 @@ class ServerProcess: mmproj_url: str | None = None media_path: str | None = None sleep_idle_seconds: int | None = None + webui_mcp_proxy: bool = False # session variables process: subprocess.Popen | None = None @@ -236,6 +237,8 @@ class ServerProcess: server_args.extend(["--media-path", self.media_path]) if self.sleep_idle_seconds is not None: server_args.extend(["--sleep-idle-seconds", self.sleep_idle_seconds]) + if self.webui_mcp_proxy: + server_args.append("--webui-mcp-proxy") args = [str(arg) for arg in [server_path, *server_args]] print(f"tests: starting server with: {' '.join(args)}") From 59db9a357d9a247009c70fda34050661b17a1a5c Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 9 Mar 2026 22:22:39 +0100 Subject: [PATCH 20/22] llama: dynamic head_dim and n_rot for SWA (#20301) * llama: dynamic head_dim and n_rot for SWA * also add gguf_writer wrappers * fix build * build_rope_shift arg reorder --- gguf-py/gguf/constants.py | 3 ++ gguf-py/gguf/gguf_writer.py | 9 ++++ src/llama-arch.cpp | 3 ++ src/llama-arch.h | 3 ++ src/llama-context.cpp | 20 +++++--- src/llama-graph.cpp | 6 +-- src/llama-hparams.cpp | 32 ++++++++++-- src/llama-hparams.h | 19 +++++-- src/llama-kv-cache.cpp | 30 ++++++------ src/llama-kv-cache.h | 3 +- src/llama-model-loader.cpp | 2 +- src/llama-model-saver.cpp | 9 ++-- src/llama-model.cpp | 90 +++++++++++++++++++--------------- src/models/afmoe.cpp | 4 +- src/models/apertus.cpp | 6 +-- src/models/arcee.cpp | 6 +-- src/models/arctic.cpp | 6 +-- src/models/baichuan.cpp | 6 +-- src/models/bailingmoe2.cpp | 4 +- src/models/bert.cpp | 4 +- src/models/bitnet.cpp | 4 +- src/models/bloom.cpp | 4 +- src/models/chameleon.cpp | 6 +-- src/models/chatglm.cpp | 4 +- src/models/codeshell.cpp | 6 +-- src/models/cogvlm.cpp | 6 +-- src/models/cohere2-iswa.cpp | 4 +- src/models/command-r.cpp | 4 +- src/models/dbrx.cpp | 6 +-- src/models/deci.cpp | 6 +-- src/models/deepseek.cpp | 6 +-- src/models/deepseek2.cpp | 2 +- src/models/dots1.cpp | 6 +-- src/models/dream.cpp | 6 +-- src/models/ernie4-5-moe.cpp | 6 +-- src/models/ernie4-5.cpp | 6 +-- src/models/eurobert.cpp | 4 +- src/models/exaone-moe.cpp | 6 +-- src/models/exaone.cpp | 6 +-- src/models/exaone4.cpp | 6 +-- src/models/falcon-h1.cpp | 2 +- src/models/falcon.cpp | 6 +-- src/models/gemma-embedding.cpp | 2 +- src/models/gemma.cpp | 2 +- src/models/gemma2-iswa.cpp | 2 +- src/models/gemma3.cpp | 2 +- src/models/gemma3n-iswa.cpp | 2 +- src/models/glm4-moe.cpp | 4 +- src/models/glm4.cpp | 4 +- src/models/gpt2.cpp | 4 +- src/models/gptneox.cpp | 4 +- src/models/granite-hybrid.cpp | 4 +- src/models/granite.cpp | 6 +-- src/models/grok.cpp | 6 +-- src/models/grovemoe.cpp | 6 +-- src/models/hunyuan-dense.cpp | 6 +-- src/models/hunyuan-moe.cpp | 6 +-- src/models/internlm2.cpp | 6 +-- src/models/jais.cpp | 4 +- src/models/jais2.cpp | 6 +-- src/models/jamba.cpp | 2 +- src/models/kimi-linear.cpp | 2 +- src/models/lfm2.cpp | 2 +- src/models/llada-moe.cpp | 6 +-- src/models/llada.cpp | 6 +-- src/models/llama-iswa.cpp | 6 +-- src/models/llama.cpp | 6 +-- src/models/maincoder.cpp | 6 +-- src/models/minicpm3.cpp | 26 +++++----- src/models/minimax-m2.cpp | 6 +-- src/models/mistral3.cpp | 6 +-- src/models/modern-bert.cpp | 4 +- src/models/mpt.cpp | 4 +- src/models/nemotron-h.cpp | 4 +- src/models/nemotron.cpp | 6 +-- src/models/neo-bert.cpp | 4 +- src/models/olmo.cpp | 6 +-- src/models/olmo2.cpp | 6 +-- src/models/olmoe.cpp | 6 +-- src/models/openelm.cpp | 4 +- src/models/orion.cpp | 6 +-- src/models/paddleocr.cpp | 6 +-- src/models/pangu-embedded.cpp | 6 +-- src/models/phi2.cpp | 4 +- src/models/phi3.cpp | 4 +- src/models/plamo.cpp | 6 +-- src/models/plamo2.cpp | 6 +-- src/models/plamo3.cpp | 4 +- src/models/plm.cpp | 28 +++++------ src/models/qwen.cpp | 4 +- src/models/qwen2.cpp | 6 +-- src/models/qwen2moe.cpp | 6 +-- src/models/qwen2vl.cpp | 6 +-- src/models/qwen3.cpp | 6 +-- src/models/qwen35.cpp | 8 +-- src/models/qwen35moe.cpp | 8 +-- src/models/qwen3moe.cpp | 6 +-- src/models/qwen3next.cpp | 4 +- src/models/qwen3vl-moe.cpp | 6 +-- src/models/qwen3vl.cpp | 6 +-- src/models/refact.cpp | 4 +- src/models/rnd1.cpp | 6 +-- src/models/seed-oss.cpp | 6 +-- src/models/smallthinker.cpp | 6 +-- src/models/smollm3.cpp | 6 +-- src/models/stablelm.cpp | 4 +- src/models/starcoder.cpp | 4 +- src/models/starcoder2.cpp | 6 +-- src/models/step35-iswa.cpp | 2 +- src/models/t5-dec.cpp | 4 +- src/models/t5-enc.cpp | 4 +- src/models/xverse.cpp | 6 +-- 112 files changed, 419 insertions(+), 346 deletions(-) diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 839c6e787..c5f546950 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -177,6 +177,8 @@ class Keys: TEMPERATURE_LENGTH = "{arch}.attention.temperature_length" KEY_LENGTH_MLA = "{arch}.attention.key_length_mla" VALUE_LENGTH_MLA = "{arch}.attention.value_length_mla" + KEY_LENGTH_SWA = "{arch}.attention.key_length_swa" + VALUE_LENGTH_SWA = "{arch}.attention.value_length_swa" SHARED_KV_LAYERS = "{arch}.attention.shared_kv_layers" SLIDING_WINDOW_PATTERN = "{arch}.attention.sliding_window_pattern" TEMPERATURE_SCALE = "{arch}.attention.temperature_scale" @@ -188,6 +190,7 @@ class Keys: class Rope: DIMENSION_COUNT = "{arch}.rope.dimension_count" + DIMENSION_COUNT_SWA = "{arch}.rope.dimension_count_swa" DIMENSION_SECTIONS = "{arch}.rope.dimension_sections" FREQ_BASE = "{arch}.rope.freq_base" FREQ_BASE_SWA = "{arch}.rope.freq_base_swa" diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 9ee3ac9e8..e790be953 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -773,6 +773,12 @@ class GGUFWriter: def add_value_length_mla(self, length: int) -> None: self.add_uint32(Keys.Attention.VALUE_LENGTH_MLA.format(arch=self.arch), length) + def add_key_length_swa(self, length: int) -> None: + self.add_uint32(Keys.Attention.KEY_LENGTH_SWA.format(arch=self.arch), length) + + def add_value_length_swa(self, length: int) -> None: + self.add_uint32(Keys.Attention.VALUE_LENGTH_SWA.format(arch=self.arch), length) + def add_indexer_head_count(self, count: int) -> None: self.add_uint32(Keys.Attention.Indexer.HEAD_COUNT.format(arch=self.arch), count) @@ -946,6 +952,9 @@ class GGUFWriter: def add_rope_dimension_count(self, count: int) -> None: self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count) + def add_rope_dimension_count_swa(self, count: int) -> None: + self.add_uint32(Keys.Rope.DIMENSION_COUNT_SWA.format(arch=self.arch), count) + def add_rope_dimension_sections(self, dims: Sequence[int]) -> None: self.add_array(Keys.Rope.DIMENSION_SECTIONS.format(arch=self.arch), dims) diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index 9d8eb88d0..ce49bbd98 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -230,11 +230,14 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ATTENTION_TEMPERATURE_SCALE, "%s.attention.temperature_scale" }, { LLM_KV_ATTENTION_KEY_LENGTH_MLA, "%s.attention.key_length_mla" }, { LLM_KV_ATTENTION_VALUE_LENGTH_MLA, "%s.attention.value_length_mla" }, + { LLM_KV_ATTENTION_KEY_LENGTH_SWA, "%s.attention.key_length_swa" }, + { LLM_KV_ATTENTION_VALUE_LENGTH_SWA, "%s.attention.value_length_swa" }, { 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_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" }, + { LLM_KV_ROPE_DIMENSION_COUNT_SWA, "%s.rope.dimension_count_swa" }, { LLM_KV_ROPE_DIMENSION_SECTIONS, "%s.rope.dimension_sections" }, { LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" }, { LLM_KV_ROPE_FREQ_BASE_SWA, "%s.rope.freq_base_swa" }, diff --git a/src/llama-arch.h b/src/llama-arch.h index 07aac40aa..28dd1ffac 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -234,11 +234,14 @@ enum llm_kv { LLM_KV_ATTENTION_TEMPERATURE_SCALE, LLM_KV_ATTENTION_KEY_LENGTH_MLA, LLM_KV_ATTENTION_VALUE_LENGTH_MLA, + LLM_KV_ATTENTION_KEY_LENGTH_SWA, + LLM_KV_ATTENTION_VALUE_LENGTH_SWA, LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, LLM_KV_ATTENTION_INDEXER_TOP_K, LLM_KV_ROPE_DIMENSION_COUNT, + LLM_KV_ROPE_DIMENSION_COUNT_SWA, LLM_KV_ROPE_DIMENSION_SECTIONS, LLM_KV_ROPE_FREQ_BASE, LLM_KV_ROPE_FREQ_BASE_SWA, diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 009d07e00..ee2669c15 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -2876,19 +2876,23 @@ llama_context * llama_init_from_model( if (params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_AUTO && ggml_is_quantized(params.type_k)) { const uint32_t blck_size = ggml_blck_size(params.type_k); - if (model->hparams.n_embd_head_k % blck_size != 0) { - LLAMA_LOG_ERROR("%s: K cache type %s with block size %u does not divide n_embd_head_k=%u\n", - __func__, ggml_type_name(params.type_k), blck_size, model->hparams.n_embd_head_k); - return nullptr; + for (uint32_t il = 0; il < model->hparams.n_layer; ++il) { + if (model->hparams.n_embd_head_k(il) % blck_size != 0) { + LLAMA_LOG_ERROR("%s: K cache type %s with block size %u does not divide n_embd_head_k=%u\n", + __func__, ggml_type_name(params.type_k), blck_size, model->hparams.n_embd_head_k(il)); + return nullptr; + } } } if (params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_AUTO && ggml_is_quantized(params.type_v)) { const uint32_t blck_size = ggml_blck_size(params.type_v); - if (model->hparams.n_embd_head_v % blck_size != 0) { - LLAMA_LOG_ERROR("%s: V cache type %s with block size %u does not divide n_embd_head_k=%u\n", - __func__, ggml_type_name(params.type_v), blck_size, model->hparams.n_embd_head_v); - return nullptr; + for (uint32_t il = 0; il < model->hparams.n_layer; ++il) { + if (model->hparams.n_embd_head_v(il) % blck_size != 0) { + LLAMA_LOG_ERROR("%s: V cache type %s with block size %u does not divide n_embd_head_v=%u\n", + __func__, ggml_type_name(params.type_v), blck_size, model->hparams.n_embd_head_v(il)); + return nullptr; + } } } diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index f4cb7dce1..5f875136a 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -849,13 +849,13 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) : ubatch (params.ubatch), n_embd (hparams.n_embd), n_layer (hparams.n_layer), - n_rot (hparams.n_rot), + n_rot (hparams.n_rot()), n_ctx (cparams.n_ctx), n_head (hparams.n_head()), n_head_kv (hparams.n_head_kv()), - n_embd_head_k (hparams.n_embd_head_k), + n_embd_head_k (hparams.n_embd_head_k()), n_embd_k_gqa (hparams.n_embd_k_gqa()), - n_embd_head_v (hparams.n_embd_head_v), + n_embd_head_v (hparams.n_embd_head_v()), n_embd_v_gqa (hparams.n_embd_v_gqa()), n_expert (hparams.n_expert), n_expert_used (cparams.warmup ? hparams.n_expert : hparams.n_expert_used), diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp index 756dda1a7..002d15d41 100644 --- a/src/llama-hparams.cpp +++ b/src/llama-hparams.cpp @@ -62,6 +62,14 @@ uint32_t llama_hparams::n_gqa(uint32_t il) const { return n_head/n_head_kv; } +uint32_t llama_hparams::n_rot(uint32_t il) const { + if (il < n_layer) { + return is_swa(il) ? n_rot_swa : n_rot_full; + } + + GGML_ABORT("fatal error"); +} + uint32_t llama_hparams::n_embd_inp() const { uint32_t n_embd_inp = n_embd; @@ -76,16 +84,32 @@ uint32_t llama_hparams::n_embd_out() const { return n_embd_out_impl > 0 ? n_embd_out_impl : n_embd; } +uint32_t llama_hparams::n_embd_head_k(uint32_t il) const { + if (il < n_layer) { + return is_swa(il) ? n_embd_head_k_swa : n_embd_head_k_full; + } + + GGML_ABORT("fatal error"); +} + +uint32_t llama_hparams::n_embd_head_v(uint32_t il) const { + if (il < n_layer) { + return is_swa(il) ? n_embd_head_v_swa : n_embd_head_v_full; + } + + GGML_ABORT("fatal error"); +} + uint32_t llama_hparams::n_embd_k_gqa(uint32_t il) const { const uint32_t n_head_kv = this->n_head_kv(il); - return n_embd_head_k * n_head_kv; + return n_embd_head_k(il) * n_head_kv; } uint32_t llama_hparams::n_embd_v_gqa(uint32_t il) const { const uint32_t n_head_kv = this->n_head_kv(il); - return n_embd_head_v * n_head_kv; + return n_embd_head_v(il) * n_head_kv; } bool llama_hparams::is_n_embd_k_gqa_variable() const { @@ -197,11 +221,11 @@ bool llama_hparams::is_mla() const { } uint32_t llama_hparams::n_embd_head_k_mla() const { - return is_mla() ? n_embd_head_k_mla_impl : n_embd_head_k; + return is_mla() ? n_embd_head_k_mla_impl : n_embd_head_k(); } uint32_t llama_hparams::n_embd_head_v_mla() const { - return is_mla() ? n_embd_head_v_mla_impl : n_embd_head_v; + return is_mla() ? n_embd_head_v_mla_impl : n_embd_head_v(); } bool llama_hparams::has_kv(uint32_t il) const { diff --git a/src/llama-hparams.h b/src/llama-hparams.h index c4b2a99da..abfd7f2c4 100644 --- a/src/llama-hparams.h +++ b/src/llama-hparams.h @@ -44,13 +44,20 @@ struct llama_hparams { uint32_t n_embd; uint32_t n_layer; int32_t n_layer_kv_from_start = -1; // if non-negative, the first n_layer_kv_from_start layers have KV cache - uint32_t n_rot; - uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads - uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head uint32_t n_expert = 0; uint32_t n_expert_used = 0; uint32_t n_rel_attn_bkts = 0; + // different head size for full_attention and SWA layers + uint32_t n_embd_head_k_full; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads + uint32_t n_embd_head_v_full; // dimension of values (d_v) aka n_embd_head + uint32_t n_embd_head_k_swa; + uint32_t n_embd_head_v_swa; + + // different RoPE dimensions for full_attention and SWA layers + uint32_t n_rot_full; + uint32_t n_rot_swa; + // note: deepseek2 using MLA converts into MQA with larger heads, then decompresses to MHA uint32_t n_embd_head_k_mla_impl = 0; uint32_t n_embd_head_v_mla_impl = 0; @@ -247,12 +254,18 @@ struct llama_hparams { uint32_t n_gqa(uint32_t il = 0) const; + uint32_t n_rot(uint32_t il = 0) const; + // dimension of main + auxiliary input embeddings uint32_t n_embd_inp() const; // dimension of output embeddings uint32_t n_embd_out() const; + // dimension of key/value embeddings for each head (per layer) + uint32_t n_embd_head_k(uint32_t il = 0) const; + uint32_t n_embd_head_v(uint32_t il = 0) const; + // dimension of key embeddings across all k-v heads uint32_t n_embd_k_gqa(uint32_t il = 0) const; diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp index d80e8a70b..82fe58fac 100644 --- a/src/llama-kv-cache.cpp +++ b/src/llama-kv-cache.cpp @@ -1033,8 +1033,8 @@ ggml_tensor * llama_kv_cache::get_k(ggml_context * ctx, int32_t il, uint32_t n_k const uint32_t ns = sinfo.s1 - sinfo.s0 + 1; return ggml_view_4d(ctx, k, - hparams.n_embd_head_k, hparams.n_head_kv(il), n_kv, ns, - ggml_row_size(k->type, hparams.n_embd_head_k), + hparams.n_embd_head_k(il), hparams.n_head_kv(il), n_kv, ns, + ggml_row_size(k->type, hparams.n_embd_head_k(il)), ggml_row_size(k->type, n_embd_k_gqa), ggml_row_size(k->type, n_embd_k_gqa*kv_size), ggml_row_size(k->type, n_embd_k_gqa*kv_size)*sinfo.s0); @@ -1056,8 +1056,8 @@ ggml_tensor * llama_kv_cache::get_v(ggml_context * ctx, int32_t il, uint32_t n_k if (!v_trans) { // note: v->nb[1] <= v->nb[2] return ggml_view_4d(ctx, v, - hparams.n_embd_head_v, hparams.n_head_kv(il), n_kv, ns, - ggml_row_size(v->type, hparams.n_embd_head_v), // v->nb[1] + hparams.n_embd_head_v(il), hparams.n_head_kv(il), n_kv, ns, + ggml_row_size(v->type, hparams.n_embd_head_v(il)), // v->nb[1] ggml_row_size(v->type, n_embd_v_gqa), // v->nb[2] ggml_row_size(v->type, n_embd_v_gqa*kv_size), // v->nb[3] ggml_row_size(v->type, n_embd_v_gqa*kv_size)*sinfo.s0); @@ -1065,8 +1065,8 @@ ggml_tensor * llama_kv_cache::get_v(ggml_context * ctx, int32_t il, uint32_t n_k // note: v->nb[1] > v->nb[2] return ggml_view_4d(ctx, v, - n_kv, hparams.n_head_kv(il), hparams.n_embd_head_v, ns, - ggml_row_size(v->type, kv_size*hparams.n_embd_head_v), // v->nb[1] + n_kv, hparams.n_head_kv(il), hparams.n_embd_head_v(il), ns, + ggml_row_size(v->type, kv_size*hparams.n_embd_head_v(il)), // v->nb[1] ggml_row_size(v->type, kv_size), // v->nb[2] ggml_row_size(v->type, kv_size*n_embd_v_gqa), // v->nb[3] ggml_row_size(v->type, kv_size*n_embd_v_gqa)*sinfo.s0); @@ -1544,7 +1544,8 @@ ggml_tensor * llama_kv_cache::build_rope_shift( ggml_tensor * shift, ggml_tensor * factors, float freq_base, - float freq_scale) const { + float freq_scale, + uint32_t il) const { const auto & n_ctx_orig = cparams.n_ctx_orig_yarn; const auto & yarn_ext_factor = cparams.yarn_ext_factor; @@ -1552,7 +1553,7 @@ ggml_tensor * llama_kv_cache::build_rope_shift( const auto & yarn_beta_slow = cparams.yarn_beta_slow; const auto & yarn_attn_factor = cparams.yarn_attn_factor; - const auto & n_rot = hparams.n_rot; + const auto & n_rot = hparams.n_rot(il); const auto & rope_type = hparams.rope_type == LLAMA_ROPE_TYPE_MROPE || hparams.rope_type == LLAMA_ROPE_TYPE_IMROPE // @ngxson : this is a workaround // for M-RoPE, we want to rotate the whole vector when doing KV shift @@ -1606,13 +1607,6 @@ ggml_cgraph * llama_kv_cache::build_graph_shift(llm_graph_result * res, llama_co auto * ctx = res->get_ctx(); auto * gf = res->get_gf(); - const auto & n_embd_head_k = hparams.n_embd_head_k; - //const auto & n_embd_head_v = hparams.n_embd_head_v; - - const auto & n_rot = hparams.n_rot; - - const auto n_embd_nope = hparams.n_lora_kv > 0 ? n_embd_head_k - n_rot : 0; - auto inp = std::make_unique(this); inp->k_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, (int64_t) get_size()*n_stream); @@ -1626,6 +1620,10 @@ ggml_cgraph * llama_kv_cache::build_graph_shift(llm_graph_result * res, llama_co const int64_t n_head_kv = hparams.n_head_kv(il); const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il); + const auto n_rot = hparams.n_rot(il); + const auto n_embd_head_k = hparams.n_embd_head_k(il); + const auto n_embd_nope = hparams.n_lora_kv > 0 ? n_embd_head_k - n_rot : 0; + const float freq_base_l = model.get_rope_freq_base (cparams, il); const float freq_scale_l = model.get_rope_freq_scale(cparams, il); @@ -1638,7 +1636,7 @@ ggml_cgraph * llama_kv_cache::build_graph_shift(llm_graph_result * res, llama_co ggml_row_size(layer.k->type, n_embd_k_gqa), ggml_row_size(layer.k->type, n_embd_nope)); - ggml_tensor * cur = build_rope_shift(cparams, ctx, k, inp->k_shift, rope_factors, freq_base_l, freq_scale_l); + ggml_tensor * cur = build_rope_shift(cparams, ctx, k, inp->k_shift, rope_factors, freq_base_l, freq_scale_l, il); ggml_build_forward_expand(gf, cur); } diff --git a/src/llama-kv-cache.h b/src/llama-kv-cache.h index e194bf3e2..33c78c5f2 100644 --- a/src/llama-kv-cache.h +++ b/src/llama-kv-cache.h @@ -264,7 +264,8 @@ private: ggml_tensor * shift, ggml_tensor * factors, float freq_base, - float freq_scale) const; + float freq_scale, + uint32_t il) const; ggml_cgraph * build_graph_shift( llm_graph_result * res, diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp index 2a6196eff..623a3455d 100644 --- a/src/llama-model-loader.cpp +++ b/src/llama-model-loader.cpp @@ -918,7 +918,7 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w } break; case GGML_OP_ROPE: { - const int n_embd_head = hparams.n_embd_head_v; + const int n_embd_head = hparams.n_embd_head_v(); const int n_head = hparams.n_head(); ggml_tensor * a = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_head, n_head, 512); ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512); diff --git a/src/llama-model-saver.cpp b/src/llama-model-saver.cpp index 9f677b40c..6f6538aec 100644 --- a/src/llama-model-saver.cpp +++ b/src/llama-model-saver.cpp @@ -186,8 +186,10 @@ void llama_model_saver::add_kv_from_model() { add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv_arr, true); add_kv(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias); add_kv(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv); - add_kv(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k); - add_kv(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v); + add_kv(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k_full); + add_kv(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v_full); + add_kv(LLM_KV_ATTENTION_KEY_LENGTH_SWA, hparams.n_embd_head_k_swa); + add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_SWA, hparams.n_embd_head_v_swa); add_kv(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); add_kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); add_kv(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); @@ -199,7 +201,8 @@ void llama_model_saver::add_kv_from_model() { const float rope_scaling_factor = hparams.rope_freq_scale_train == 1.0f ? 0.0f : 1.0f/hparams.rope_freq_scale_train; - add_kv(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot); + add_kv(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot_full); + add_kv(LLM_KV_ROPE_DIMENSION_COUNT_SWA, hparams.n_rot_swa); add_kv(LLM_KV_ROPE_FREQ_BASE, hparams.rope_freq_base_train); // add_kv(LLM_KV_ROPE_SCALE_LINEAR, rope_scaling_factor); // old name add_kv(LLM_KV_ROPE_SCALING_TYPE, llama_rope_scaling_type_name(hparams.rope_scaling_type_train)); diff --git a/src/llama-model.cpp b/src/llama-model.cpp index e18cca052..349f70a61 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -459,26 +459,37 @@ void llama_model::load_hparams(llama_model_loader & ml) { // gpt-neox n_rot = rotary_pct * (n_embd / n_head) // gpt-j n_rot = rotary_dim - hparams.n_embd_head_k = hparams.n_embd / hparams.n_head(); - ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false); + hparams.n_embd_head_k_full = hparams.n_embd / hparams.n_head(); + ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k_full, false); - hparams.n_embd_head_v = hparams.n_embd / hparams.n_head(); - ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false); + hparams.n_embd_head_v_full = hparams.n_embd / hparams.n_head(); + ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v_full, false); // sanity check for n_rot (optional) - hparams.n_rot = hparams.n_embd_head_k; + hparams.n_rot_full = hparams.n_embd_head_k_full; - ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false); + ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot_full, false); if (arch == LLM_ARCH_LLAMA || arch == LLM_ARCH_DECI || arch == LLM_ARCH_FALCON || arch == LLM_ARCH_LLAMA_EMBED) { - if (hparams.n_rot != hparams.n_embd_head_k) { - throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k)); + if (hparams.n_rot_full != hparams.n_embd_head_k_full) { + throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot_full, hparams.n_embd_head_k_full)); } } } else { - hparams.n_rot = 0; - hparams.n_embd_head_k = 0; - hparams.n_embd_head_v = 0; + hparams.n_rot_full = 0; + hparams.n_embd_head_k_full = 0; + hparams.n_embd_head_v_full = 0; + } + + // head size and n_rot for SWA layers + { + hparams.n_embd_head_k_swa = hparams.n_embd_head_k_full; + hparams.n_embd_head_v_swa = hparams.n_embd_head_v_full; + ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_SWA, hparams.n_embd_head_k_swa, false); + ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_SWA, hparams.n_embd_head_v_swa, false); + + hparams.n_rot_swa = hparams.n_rot_full; + ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT_SWA, hparams.n_rot_swa, false); } // for differentiating model types @@ -1114,10 +1125,6 @@ void llama_model::load_hparams(llama_model_loader & ml) { break; default: type = LLM_TYPE_UNKNOWN; } - - // Load attention parameters - ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false); - ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false); } break; case LLM_ARCH_PLAMO3: { @@ -1212,7 +1219,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/config.py#L173 hparams.f_attention_scale = type == LLM_TYPE_27B ? 1.0f / std::sqrt(float(hparams.n_embd / hparams.n_head(0))) - : 1.0f / std::sqrt(float(hparams.n_embd_head_k)); + : 1.0f / std::sqrt(float(hparams.n_embd_head_k())); } break; case LLM_ARCH_GEMMA3: { @@ -1245,7 +1252,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/config.py#L289 hparams.f_attention_scale = type == LLM_TYPE_27B ? 1.0f / std::sqrt(float(hparams.n_embd / hparams.n_head(0))) - : 1.0f / std::sqrt(float(hparams.n_embd_head_k)); + : 1.0f / std::sqrt(float(hparams.n_embd_head_k())); } break; case LLM_ARCH_GEMMA3N: { @@ -1294,7 +1301,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { case 24: type = LLM_TYPE_0_3B; break; default: type = LLM_TYPE_UNKNOWN; } - hparams.f_attention_scale = 1.0f / std::sqrt(float(hparams.n_embd_head_k)); + hparams.f_attention_scale = 1.0f / std::sqrt(float(hparams.n_embd_head_k())); } break; case LLM_ARCH_STARCODER2: @@ -2487,7 +2494,6 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_mla_impl); ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl); ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv); - ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot); ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv); ml.get_key(LLM_KV_KDA_HEAD_DIM, hparams.n_embd_head_kda); @@ -2518,6 +2524,9 @@ void llama_model::load_hparams(llama_model_loader & ml) { hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; + // full_attention layer only use half of the RoPE dimensions + hparams.n_rot_full = hparams.n_rot_full / 2; + // MoE + SWA parameters ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false); @@ -2661,13 +2670,13 @@ bool llama_model::load_tensors(llama_model_loader & ml) { const int64_t n_embd = hparams.n_embd; const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); - const int64_t n_embd_head_k = hparams.n_embd_head_k; - const int64_t n_embd_head_v = hparams.n_embd_head_v; + const int64_t n_embd_head_k = hparams.n_embd_head_k(); + const int64_t n_embd_head_v = hparams.n_embd_head_v(); const int64_t n_ff = hparams.n_ff(); const int64_t n_embd_gqa = n_embd_v_gqa; const int64_t n_vocab = vocab.n_tokens(); const int64_t n_token_types = vocab.n_token_types(); - const int64_t n_rot = hparams.n_rot; + const int64_t n_rot = hparams.n_rot(); const int64_t n_expert = hparams.n_expert; const int64_t n_expert_used = hparams.n_expert_used; const int64_t n_ctx_train = hparams.n_ctx_train; @@ -2967,8 +2976,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } break; case LLM_ARCH_MINICPM3: { - const int64_t n_embd_head_qk_rope = hparams.n_rot; - const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot; + const int64_t n_embd_head_qk_rope = hparams.n_rot(); + const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k() - hparams.n_rot(); const int64_t q_lora_rank = hparams.n_lora_q; const int64_t kv_lora_rank = hparams.n_lora_kv; @@ -3840,8 +3849,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) { const int64_t dt_dim = std::max(64, int(hparams.n_embd / 16)); // attention parameters - const uint32_t qk_dim = hparams.n_embd_head_k; - const uint32_t v_dim = hparams.n_embd_head_v; + const uint32_t qk_dim = hparams.n_embd_head_k(); + const uint32_t v_dim = hparams.n_embd_head_v(); tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); @@ -3901,8 +3910,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } break; case LLM_ARCH_PLAMO3: { - const int64_t head_dim_q = hparams.n_embd_head_k; - const int64_t head_dim_v = hparams.n_embd_head_v; + const int64_t head_dim_q = hparams.n_embd_head_k(); + const int64_t head_dim_v = hparams.n_embd_head_v(); tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); @@ -4649,7 +4658,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } break; case LLM_ARCH_SEED_OSS: { - const uint32_t head_dim = hparams.n_embd_head_k; + const uint32_t head_dim = hparams.n_embd_head_k(); const int64_t n_qo_dim = n_head * head_dim; const int64_t n_kv_dim = n_head_kv * head_dim; @@ -4878,7 +4887,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { 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_rope = hparams.n_rot(); const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope; GGML_ASSERT(n_embd_head_qk_nope >= 1); @@ -4957,8 +4966,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } break; case LLM_ARCH_PLM: { - const int64_t n_embd_head_qk_rope = hparams.n_rot; - const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot; + const int64_t n_embd_head_qk_rope = hparams.n_rot(); + const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k() - hparams.n_rot(); const int64_t kv_lora_rank = hparams.n_lora_kv; tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); @@ -5396,7 +5405,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { 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_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; @@ -5680,7 +5689,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { const int64_t n_expert = hparams.n_expert; const int64_t n_expert_used = hparams.n_expert_used; const int64_t n_ff_shexp = hparams.n_ff_shexp > 0 ? hparams.n_ff_shexp : n_ff_exp; - const int64_t head_dim = hparams.n_embd_head_k; + const int64_t head_dim = hparams.n_embd_head_k(); const int64_t n_qo_dim = n_head * head_dim; const int64_t n_kv_dim = n_head_kv * head_dim; @@ -6968,7 +6977,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { // Kimi: qk_rope_head_dim = 64 (actual RoPE dimension for MLA) // Note: hparams.n_rot may be 72 (from conversion) but actual is 64 - const int64_t qk_rope_head_dim = hparams.n_rot; // From config: qk_rope_head_dim + const int64_t qk_rope_head_dim = hparams.n_rot(); // From config: qk_rope_head_dim layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + qk_rope_head_dim}, 0); // Support Legacy GGUFs that don't split wkv_b (MLA KV cache disabled) layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), @@ -7339,7 +7348,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { // ("rope_freqs.weight") and ggml uses only the first (n_rot_l/2) entries per layer. uint32_t n_rot_max = 0; for (int i = 0; i < n_layer; ++i) { - n_rot_max = std::max(n_rot_max, hparams.n_rot); + n_rot_max = std::max(n_rot_max, hparams.n_rot()); } if (n_rot_max == 0) { n_rot_max = n_rot; @@ -7674,11 +7683,11 @@ void llama_model::print_info() const { LLAMA_LOG_INFO("%s: n_layer = %u\n", __func__, hparams.n_layer); LLAMA_LOG_INFO("%s: n_head = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_head(il); }, hparams.n_layer).c_str()); LLAMA_LOG_INFO("%s: n_head_kv = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_head_kv(il); }, hparams.n_layer).c_str()); - LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot); + LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot_full); LLAMA_LOG_INFO("%s: n_swa = %u\n", __func__, hparams.n_swa); LLAMA_LOG_INFO("%s: is_swa_any = %u\n", __func__, hparams.is_swa_any()); - LLAMA_LOG_INFO("%s: n_embd_head_k = %u\n", __func__, hparams.n_embd_head_k); - LLAMA_LOG_INFO("%s: n_embd_head_v = %u\n", __func__, hparams.n_embd_head_v); + LLAMA_LOG_INFO("%s: n_embd_head_k = %u\n", __func__, hparams.n_embd_head_k_full); + LLAMA_LOG_INFO("%s: n_embd_head_v = %u\n", __func__, hparams.n_embd_head_v_full); LLAMA_LOG_INFO("%s: n_gqa = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_gqa(il); }, hparams.n_layer).c_str()); LLAMA_LOG_INFO("%s: n_embd_k_gqa = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_embd_k_gqa(il); }, hparams.n_layer).c_str()); LLAMA_LOG_INFO("%s: n_embd_v_gqa = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_embd_v_gqa(il); }, hparams.n_layer).c_str()); @@ -7702,6 +7711,9 @@ void llama_model::print_info() const { if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) { LLAMA_LOG_INFO("%s: freq_base_swa = %.1f\n", __func__, hparams.rope_freq_base_train_swa); LLAMA_LOG_INFO("%s: freq_scale_swa = %g\n", __func__, hparams.rope_freq_scale_train_swa); + LLAMA_LOG_INFO("%s: n_embd_head_k_swa = %u\n", __func__, hparams.n_embd_head_k_swa); + LLAMA_LOG_INFO("%s: n_embd_head_v_swa = %u\n", __func__, hparams.n_embd_head_v_swa); + LLAMA_LOG_INFO("%s: n_rot_swa = %u\n", __func__, hparams.n_rot_swa); } LLAMA_LOG_INFO("%s: n_ctx_orig_yarn = %u\n", __func__, hparams.n_ctx_orig_yarn); LLAMA_LOG_INFO("%s: rope_yarn_log_mul = %.4f\n", __func__, hparams.rope_yarn_log_mul); diff --git a/src/models/afmoe.cpp b/src/models/afmoe.cpp index 11f5ea2c2..9aabe25c9 100644 --- a/src/models/afmoe.cpp +++ b/src/models/afmoe.cpp @@ -1,8 +1,8 @@ #include "models.h" llm_build_afmoe::llm_build_afmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + const int64_t n_embd_head = hparams.n_embd_head_v(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/apertus.cpp b/src/models/apertus.cpp index 9af19c1bf..4d65614e4 100644 --- a/src/models/apertus.cpp +++ b/src/models/apertus.cpp @@ -3,10 +3,10 @@ llm_build_apertus::llm_build_apertus(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/arcee.cpp b/src/models/arcee.cpp index aa6167dba..20b9ffd49 100644 --- a/src/models/arcee.cpp +++ b/src/models/arcee.cpp @@ -2,10 +2,10 @@ llm_build_arcee::llm_build_arcee(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/arctic.cpp b/src/models/arctic.cpp index d7db06de1..b712e08cb 100644 --- a/src/models/arctic.cpp +++ b/src/models/arctic.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_arctic::llm_build_arctic(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/baichuan.cpp b/src/models/baichuan.cpp index d5c652853..abd03cd0b 100644 --- a/src/models/baichuan.cpp +++ b/src/models/baichuan.cpp @@ -2,10 +2,10 @@ llm_build_baichuan::llm_build_baichuan(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/bailingmoe2.cpp b/src/models/bailingmoe2.cpp index 81906cecb..420986246 100644 --- a/src/models/bailingmoe2.cpp +++ b/src/models/bailingmoe2.cpp @@ -2,10 +2,10 @@ llm_build_bailingmoe2::llm_build_bailingmoe2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/bert.cpp b/src/models/bert.cpp index 17efdafec..873317914 100644 --- a/src/models/bert.cpp +++ b/src/models/bert.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_bert::llm_build_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/bitnet.cpp b/src/models/bitnet.cpp index 331a3f111..d47638498 100644 --- a/src/models/bitnet.cpp +++ b/src/models/bitnet.cpp @@ -2,9 +2,9 @@ llm_build_bitnet::llm_build_bitnet(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/bloom.cpp b/src/models/bloom.cpp index 2c552d1d1..b1c19bb58 100644 --- a/src/models/bloom.cpp +++ b/src/models/bloom.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_bloom::llm_build_bloom(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/chameleon.cpp b/src/models/chameleon.cpp index 184511aed..2f24105fa 100644 --- a/src/models/chameleon.cpp +++ b/src/models/chameleon.cpp @@ -3,10 +3,10 @@ #include llm_build_chameleon::llm_build_chameleon(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/chatglm.cpp b/src/models/chatglm.cpp index 2685d4fbc..5887ed22e 100644 --- a/src/models/chatglm.cpp +++ b/src/models/chatglm.cpp @@ -2,10 +2,10 @@ llm_build_chatglm::llm_build_chatglm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/codeshell.cpp b/src/models/codeshell.cpp index 0b3bdbff5..e8e13e143 100644 --- a/src/models/codeshell.cpp +++ b/src/models/codeshell.cpp @@ -1,11 +1,11 @@ #include "models.h" llm_build_codeshell::llm_build_codeshell(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/cogvlm.cpp b/src/models/cogvlm.cpp index 0ceae3aae..2ef2b6e38 100644 --- a/src/models/cogvlm.cpp +++ b/src/models/cogvlm.cpp @@ -2,11 +2,11 @@ llm_build_cogvlm::llm_build_cogvlm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const float kq_scale = 1.0f / sqrtf(float(n_embd_head)); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * inpL; ggml_tensor * cur; diff --git a/src/models/cohere2-iswa.cpp b/src/models/cohere2-iswa.cpp index 9334b5e42..7c71a59ae 100644 --- a/src/models/cohere2-iswa.cpp +++ b/src/models/cohere2-iswa.cpp @@ -1,9 +1,9 @@ #include "models.h" llm_build_cohere2_iswa::llm_build_cohere2_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); const float f_logit_scale = hparams.f_logit_scale; diff --git a/src/models/command-r.cpp b/src/models/command-r.cpp index 4d3b643b4..ba1230f04 100644 --- a/src/models/command-r.cpp +++ b/src/models/command-r.cpp @@ -4,9 +4,9 @@ llm_build_command_r::llm_build_command_r(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); const float f_logit_scale = hparams.f_logit_scale; diff --git a/src/models/dbrx.cpp b/src/models/dbrx.cpp index 5c7f10843..73eb5cd24 100644 --- a/src/models/dbrx.cpp +++ b/src/models/dbrx.cpp @@ -1,11 +1,11 @@ #include "models.h" llm_build_dbrx::llm_build_dbrx(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/deci.cpp b/src/models/deci.cpp index 7410a3a46..ac448bfca 100644 --- a/src/models/deci.cpp +++ b/src/models/deci.cpp @@ -3,10 +3,10 @@ llm_build_deci::llm_build_deci(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/deepseek.cpp b/src/models/deepseek.cpp index 57cb1724f..3432359e0 100644 --- a/src/models/deepseek.cpp +++ b/src/models/deepseek.cpp @@ -2,10 +2,10 @@ llm_build_deepseek::llm_build_deepseek(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/deepseek2.cpp b/src/models/deepseek2.cpp index abd54b765..d437fe29e 100644 --- a/src/models/deepseek2.cpp +++ b/src/models/deepseek2.cpp @@ -8,7 +8,7 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr 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(); - const int64_t n_embd_head_qk_rope = hparams.n_rot; + 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; diff --git a/src/models/dots1.cpp b/src/models/dots1.cpp index 0bcf3fe0d..07236dd27 100644 --- a/src/models/dots1.cpp +++ b/src/models/dots1.cpp @@ -2,10 +2,10 @@ llm_build_dots1::llm_build_dots1(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/dream.cpp b/src/models/dream.cpp index 2aafbae13..4edc8530c 100644 --- a/src/models/dream.cpp +++ b/src/models/dream.cpp @@ -5,10 +5,10 @@ llm_build_dream::llm_build_dream(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { //copied from qwen2 - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/ernie4-5-moe.cpp b/src/models/ernie4-5-moe.cpp index ed781d507..63baf152c 100644 --- a/src/models/ernie4-5-moe.cpp +++ b/src/models/ernie4-5-moe.cpp @@ -2,10 +2,10 @@ llm_build_ernie4_5_moe::llm_build_ernie4_5_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/ernie4-5.cpp b/src/models/ernie4-5.cpp index 99aead532..d548de054 100644 --- a/src/models/ernie4-5.cpp +++ b/src/models/ernie4-5.cpp @@ -2,10 +2,10 @@ llm_build_ernie4_5::llm_build_ernie4_5(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/eurobert.cpp b/src/models/eurobert.cpp index 86e3176ed..e8628d165 100644 --- a/src/models/eurobert.cpp +++ b/src/models/eurobert.cpp @@ -1,9 +1,9 @@ #include "models.h" llm_build_eurobert::llm_build_eurobert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/exaone-moe.cpp b/src/models/exaone-moe.cpp index a7396829c..ea75701c5 100644 --- a/src/models/exaone-moe.cpp +++ b/src/models/exaone-moe.cpp @@ -2,10 +2,10 @@ llm_build_exaone_moe::llm_build_exaone_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_k; + const int64_t n_embd_head = hparams.n_embd_head_k(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_v); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_v()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/exaone.cpp b/src/models/exaone.cpp index 62602b284..d4eea58e2 100644 --- a/src/models/exaone.cpp +++ b/src/models/exaone.cpp @@ -4,10 +4,10 @@ llm_build_exaone::llm_build_exaone(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/exaone4.cpp b/src/models/exaone4.cpp index 8b7e3dc06..755af3b74 100644 --- a/src/models/exaone4.cpp +++ b/src/models/exaone4.cpp @@ -4,10 +4,10 @@ template llm_build_exaone4::llm_build_exaone4(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_k; + const int64_t n_embd_head = hparams.n_embd_head_k(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_v); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_v()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/falcon-h1.cpp b/src/models/falcon-h1.cpp index 785a7e5e6..ff842d93a 100644 --- a/src/models/falcon-h1.cpp +++ b/src/models/falcon-h1.cpp @@ -2,7 +2,7 @@ llm_build_falcon_h1::llm_build_falcon_h1(const llama_model & model, const llm_graph_params & params) : llm_build_mamba_base(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/falcon.cpp b/src/models/falcon.cpp index db1ccdb50..9fcba5088 100644 --- a/src/models/falcon.cpp +++ b/src/models/falcon.cpp @@ -2,11 +2,11 @@ llm_build_falcon::llm_build_falcon(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/gemma-embedding.cpp b/src/models/gemma-embedding.cpp index 944c198bf..98110d45e 100644 --- a/src/models/gemma-embedding.cpp +++ b/src/models/gemma-embedding.cpp @@ -2,7 +2,7 @@ llm_build_gemma_embedding::llm_build_gemma_embedding(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_k; + const int64_t n_embd_head = hparams.n_embd_head_k(); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/gemma.cpp b/src/models/gemma.cpp index 4893d9af4..1869efd38 100644 --- a/src/models/gemma.cpp +++ b/src/models/gemma.cpp @@ -2,7 +2,7 @@ llm_build_gemma::llm_build_gemma(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/gemma2-iswa.cpp b/src/models/gemma2-iswa.cpp index 7a9198193..3927ddd29 100644 --- a/src/models/gemma2-iswa.cpp +++ b/src/models/gemma2-iswa.cpp @@ -1,7 +1,7 @@ #include "models.h" llm_build_gemma2_iswa::llm_build_gemma2_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_k; + const int64_t n_embd_head = hparams.n_embd_head_k(); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/gemma3.cpp b/src/models/gemma3.cpp index dec3fc4b8..bbb4d9a81 100644 --- a/src/models/gemma3.cpp +++ b/src/models/gemma3.cpp @@ -2,7 +2,7 @@ template llm_build_gemma3::llm_build_gemma3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_k; + const int64_t n_embd_head = hparams.n_embd_head_k(); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/gemma3n-iswa.cpp b/src/models/gemma3n-iswa.cpp index 7db6d3bf4..8ce2ae39c 100644 --- a/src/models/gemma3n-iswa.cpp +++ b/src/models/gemma3n-iswa.cpp @@ -3,7 +3,7 @@ llm_build_gemma3n_iswa::llm_build_gemma3n_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params), model(model), - n_embd_head(model.hparams.n_embd_head_k), + n_embd_head(model.hparams.n_embd_head_k()), n_embd_altup(model.hparams.n_embd_altup), n_altup(model.hparams.n_altup), i_altup_act(model.hparams.i_altup_act) { diff --git a/src/models/glm4-moe.cpp b/src/models/glm4-moe.cpp index 97a65f411..7938545ed 100644 --- a/src/models/glm4-moe.cpp +++ b/src/models/glm4-moe.cpp @@ -1,9 +1,9 @@ #include "models.h" llm_build_glm4_moe::llm_build_glm4_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); int sections[4]; std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections); diff --git a/src/models/glm4.cpp b/src/models/glm4.cpp index bcd837b30..b6ad8febe 100644 --- a/src/models/glm4.cpp +++ b/src/models/glm4.cpp @@ -3,10 +3,10 @@ llm_build_glm4::llm_build_glm4(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); int sections[4]; std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections); diff --git a/src/models/gpt2.cpp b/src/models/gpt2.cpp index 60761c8e7..cb1238f2d 100644 --- a/src/models/gpt2.cpp +++ b/src/models/gpt2.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_gpt2::llm_build_gpt2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * pos; diff --git a/src/models/gptneox.cpp b/src/models/gptneox.cpp index 2151b14e9..1c8fe6c83 100644 --- a/src/models/gptneox.cpp +++ b/src/models/gptneox.cpp @@ -2,10 +2,10 @@ llm_build_gptneox::llm_build_gptneox(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/granite-hybrid.cpp b/src/models/granite-hybrid.cpp index d9b1c06da..9b54a38c3 100644 --- a/src/models/granite-hybrid.cpp +++ b/src/models/granite-hybrid.cpp @@ -2,8 +2,8 @@ llm_build_granite_hybrid::llm_build_granite_hybrid(const llama_model & model, const llm_graph_params & params) : llm_build_mamba_base(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + const int64_t n_embd_head = hparams.n_embd_head_v(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/granite.cpp b/src/models/granite.cpp index fd97116ed..7a7e1664c 100644 --- a/src/models/granite.cpp +++ b/src/models/granite.cpp @@ -5,10 +5,10 @@ llm_build_granite::llm_build_granite( const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/grok.cpp b/src/models/grok.cpp index 24232604b..580d63e36 100644 --- a/src/models/grok.cpp +++ b/src/models/grok.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_grok::llm_build_grok(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/grovemoe.cpp b/src/models/grovemoe.cpp index 2081f0685..aa60d3e93 100644 --- a/src/models/grovemoe.cpp +++ b/src/models/grovemoe.cpp @@ -2,11 +2,11 @@ llm_build_grovemoe::llm_build_grovemoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_chunk_expert = n_expert / hparams.n_group_experts; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/hunyuan-dense.cpp b/src/models/hunyuan-dense.cpp index 7d5dcc782..6a51707c8 100644 --- a/src/models/hunyuan-dense.cpp +++ b/src/models/hunyuan-dense.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_hunyuan_dense::llm_build_hunyuan_dense(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/hunyuan-moe.cpp b/src/models/hunyuan-moe.cpp index cee2b541b..806c30b36 100644 --- a/src/models/hunyuan-moe.cpp +++ b/src/models/hunyuan-moe.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_hunyuan_moe::llm_build_hunyuan_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/internlm2.cpp b/src/models/internlm2.cpp index 387e82112..441d25026 100644 --- a/src/models/internlm2.cpp +++ b/src/models/internlm2.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_internlm2::llm_build_internlm2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/jais.cpp b/src/models/jais.cpp index 3e3376e6a..135bf288b 100644 --- a/src/models/jais.cpp +++ b/src/models/jais.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_jais::llm_build_jais(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/jais2.cpp b/src/models/jais2.cpp index a69fcaa3b..2cfe484eb 100644 --- a/src/models/jais2.cpp +++ b/src/models/jais2.cpp @@ -3,10 +3,10 @@ // JAIS-2 model graph builder // Uses: LayerNorm (not RMSNorm), relu2 activation, separate Q/K/V, RoPE embeddings llm_build_jais2::llm_build_jais2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/jamba.cpp b/src/models/jamba.cpp index 1d482e425..c0c89de18 100644 --- a/src/models/jamba.cpp +++ b/src/models/jamba.cpp @@ -1,7 +1,7 @@ #include "models.h" llm_build_jamba::llm_build_jamba(const llama_model & model, const llm_graph_params & params) : llm_build_mamba_base(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/kimi-linear.cpp b/src/models/kimi-linear.cpp index 8d4b95e51..063b17a2f 100644 --- a/src/models/kimi-linear.cpp +++ b/src/models/kimi-linear.cpp @@ -102,7 +102,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll const int64_t kv_lora_rank = hparams.n_lora_kv; // qk_rope_head_dim = 64 (from Kimi config) which is hparams.n_rot // Confirmed from tensor shape: wkv_a_mqa [2304, 576] = [n_embd, kv_lora_rank + qk_rope_head_dim] - const int64_t n_embd_head_qk_rope = hparams.n_rot; // config.qk_rope_head_dim + const int64_t n_embd_head_qk_rope = hparams.n_rot(); // config.qk_rope_head_dim const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope; // 192 - 64 = 128 // Attention scale for MLA const float kq_scale_mla = 1.0f / sqrtf((float)n_embd_head_k_mla); diff --git a/src/models/lfm2.cpp b/src/models/lfm2.cpp index 8ca8e6c8e..dfa322166 100644 --- a/src/models/lfm2.cpp +++ b/src/models/lfm2.cpp @@ -39,7 +39,7 @@ llm_build_lfm2::llm_build_lfm2(const llama_model & model, const llm_graph_ inp_attn_type * inp_attn, int il) -> ggml_tensor * { GGML_ASSERT(hparams.n_embd_v_gqa(il) == hparams.n_embd_k_gqa(il)); - const auto n_embd_head = hparams.n_embd_head_v; + const auto n_embd_head = hparams.n_embd_head_v(); const auto n_head_kv = hparams.n_head_kv(il); auto * q = build_lora_mm(model.layers[il].wq, cur); diff --git a/src/models/llada-moe.cpp b/src/models/llada-moe.cpp index 3bb9943f4..18de88fde 100644 --- a/src/models/llada-moe.cpp +++ b/src/models/llada-moe.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_llada_moe::llm_build_llada_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/llada.cpp b/src/models/llada.cpp index 857033660..0dac9d616 100644 --- a/src/models/llada.cpp +++ b/src/models/llada.cpp @@ -2,10 +2,10 @@ llm_build_llada::llm_build_llada(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { // LLaDA is similar to LLaMA but uses non-causal attention for diffusion - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/llama-iswa.cpp b/src/models/llama-iswa.cpp index 40dc2427a..67cb9a10e 100644 --- a/src/models/llama-iswa.cpp +++ b/src/models/llama-iswa.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_llama_iswa::llm_build_llama_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/llama.cpp b/src/models/llama.cpp index 279f2e301..ca4beac51 100644 --- a/src/models/llama.cpp +++ b/src/models/llama.cpp @@ -2,10 +2,10 @@ template llm_build_llama::llm_build_llama(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/maincoder.cpp b/src/models/maincoder.cpp index da5730816..a72b7790a 100644 --- a/src/models/maincoder.cpp +++ b/src/models/maincoder.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_maincoder::llm_build_maincoder(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/minicpm3.cpp b/src/models/minicpm3.cpp index 297cc34ba..89dd71051 100644 --- a/src/models/minicpm3.cpp +++ b/src/models/minicpm3.cpp @@ -5,10 +5,10 @@ llm_build_minicpm3::llm_build_minicpm3(const llama_model & model, const llm_grap const int64_t n_embd_base = 256; const float scale_embd = 12.0f; const float scale_depth = 1.4f; - const float kq_scale = 1.0f / sqrtf(float(hparams.n_embd_head_k)); + const float kq_scale = 1.0f / sqrtf(float(hparams.n_embd_head_k())); - const uint32_t n_embd_head_qk_rope = hparams.n_rot; - const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot; + const uint32_t n_embd_head_qk_rope = hparams.n_rot(); + const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k() - hparams.n_rot(); const uint32_t kv_lora_rank = hparams.n_lora_kv; @@ -51,21 +51,21 @@ llm_build_minicpm3::llm_build_minicpm3(const llama_model & model, const llm_grap LLM_NORM_RMS, il); cb(q, "q", il); - // {q_lora_rank, n_head * hparams.n_embd_head_k} * {q_lora_rank, n_tokens} -> {n_head * hparams.n_embd_head_k, n_tokens} + // {q_lora_rank, n_head * hparams.n_embd_head_k()} * {q_lora_rank, n_tokens} -> {n_head * hparams.n_embd_head_k(), n_tokens} q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q); cb(q, "q", il); // split into {n_head * n_embd_head_qk_nope, 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, hparams.n_embd_head_k), - ggml_row_size(q->type, hparams.n_embd_head_k * n_head), + ggml_row_size(q->type, hparams.n_embd_head_k()), + ggml_row_size(q->type, hparams.n_embd_head_k() * n_head), 0); cb(q_nope, "q_nope", il); // and {n_head * n_embd_head_qk_rope, 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, hparams.n_embd_head_k), - ggml_row_size(q->type, hparams.n_embd_head_k * n_head), + ggml_row_size(q->type, hparams.n_embd_head_k()), + ggml_row_size(q->type, hparams.n_embd_head_k() * n_head), ggml_row_size(q->type, n_embd_head_qk_nope)); cb(q_pe, "q_pe", il); @@ -97,15 +97,15 @@ llm_build_minicpm3::llm_build_minicpm3(const llama_model & model, const llm_grap // split into {n_head * n_embd_head_qk_nope, n_tokens} ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens, - ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v), - ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)), + ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v()), + ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v())), 0); cb(k_nope, "k_nope", il); // and {n_head * n_embd_head_v, n_tokens} - ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v, n_head, n_tokens, - ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)), - ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)*n_head), + ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v(), n_head, n_tokens, + ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v())), + ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v())*n_head), ggml_row_size(kv->type, (n_embd_head_qk_nope))); cb(v_states, "v_states", il); diff --git a/src/models/minimax-m2.cpp b/src/models/minimax-m2.cpp index fbeed8eab..83d0916c0 100644 --- a/src/models/minimax-m2.cpp +++ b/src/models/minimax-m2.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_minimax_m2::llm_build_minimax_m2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - // GGML_ASSERT(n_embd_head == hparams.n_rot); this is wrong in case of minimax, head_dim = 128, n_rot = 64 + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + // GGML_ASSERT(n_embd_head == n_rot); this is wrong in case of minimax, head_dim = 128, n_rot = 64 ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/mistral3.cpp b/src/models/mistral3.cpp index 49734989d..42a5117ff 100644 --- a/src/models/mistral3.cpp +++ b/src/models/mistral3.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_mistral3::llm_build_mistral3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/modern-bert.cpp b/src/models/modern-bert.cpp index 32066c712..26020584c 100644 --- a/src/models/modern-bert.cpp +++ b/src/models/modern-bert.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_modern_bert::llm_build_modern_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/mpt.cpp b/src/models/mpt.cpp index 2328e027a..ce44a805f 100644 --- a/src/models/mpt.cpp +++ b/src/models/mpt.cpp @@ -3,10 +3,10 @@ llm_build_mpt::llm_build_mpt(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * pos; diff --git a/src/models/nemotron-h.cpp b/src/models/nemotron-h.cpp index fa2b55a28..635821505 100644 --- a/src/models/nemotron-h.cpp +++ b/src/models/nemotron-h.cpp @@ -2,8 +2,8 @@ llm_build_nemotron_h::llm_build_nemotron_h(const llama_model & model, const llm_graph_params & params) : llm_build_mamba_base(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + const int64_t n_embd_head = hparams.n_embd_head_v(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/nemotron.cpp b/src/models/nemotron.cpp index fcead041f..34aa6fa5e 100644 --- a/src/models/nemotron.cpp +++ b/src/models/nemotron.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_nemotron::llm_build_nemotron(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - //GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + //GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/neo-bert.cpp b/src/models/neo-bert.cpp index 7c32bfca5..2fdf4a369 100644 --- a/src/models/neo-bert.cpp +++ b/src/models/neo-bert.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_neo_bert::llm_build_neo_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/olmo.cpp b/src/models/olmo.cpp index bbd623f11..26f4b6ee6 100644 --- a/src/models/olmo.cpp +++ b/src/models/olmo.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_olmo::llm_build_olmo(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/olmo2.cpp b/src/models/olmo2.cpp index 713552dab..5076359e3 100644 --- a/src/models/olmo2.cpp +++ b/src/models/olmo2.cpp @@ -2,10 +2,10 @@ template llm_build_olmo2::llm_build_olmo2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/olmoe.cpp b/src/models/olmoe.cpp index a05b892c7..83a56a0b3 100644 --- a/src/models/olmoe.cpp +++ b/src/models/olmoe.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_olmoe::llm_build_olmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/openelm.cpp b/src/models/openelm.cpp index fbf682ec8..5df6fe3e3 100644 --- a/src/models/openelm.cpp +++ b/src/models/openelm.cpp @@ -1,9 +1,9 @@ #include "models.h" llm_build_openelm::llm_build_openelm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/orion.cpp b/src/models/orion.cpp index bb02273bf..48c01efe3 100644 --- a/src/models/orion.cpp +++ b/src/models/orion.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_orion::llm_build_orion(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/paddleocr.cpp b/src/models/paddleocr.cpp index 39a368df5..340455c2d 100644 --- a/src/models/paddleocr.cpp +++ b/src/models/paddleocr.cpp @@ -5,10 +5,10 @@ llm_build_paddleocr::llm_build_paddleocr(const llama_model & model, const llm_gr // NOTE: same with qwen2vl.cpp, but bias tensors are optional - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/pangu-embedded.cpp b/src/models/pangu-embedded.cpp index 664572a50..1cf0938e6 100644 --- a/src/models/pangu-embedded.cpp +++ b/src/models/pangu-embedded.cpp @@ -2,10 +2,10 @@ llm_build_pangu_embedded::llm_build_pangu_embedded(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/phi2.cpp b/src/models/phi2.cpp index 22dbf6107..32d40d71f 100644 --- a/src/models/phi2.cpp +++ b/src/models/phi2.cpp @@ -2,10 +2,10 @@ llm_build_phi2::llm_build_phi2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * attn_norm_output; diff --git a/src/models/phi3.cpp b/src/models/phi3.cpp index 803e374aa..3d11a9459 100644 --- a/src/models/phi3.cpp +++ b/src/models/phi3.cpp @@ -2,10 +2,10 @@ template llm_build_phi3::llm_build_phi3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/plamo.cpp b/src/models/plamo.cpp index 04ff709f9..b7a712110 100644 --- a/src/models/plamo.cpp +++ b/src/models/plamo.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_plamo::llm_build_plamo(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/plamo2.cpp b/src/models/plamo2.cpp index 276d3829b..f02acbc18 100644 --- a/src/models/plamo2.cpp +++ b/src/models/plamo2.cpp @@ -106,9 +106,9 @@ ggml_tensor * llm_build_plamo2::build_plamo2_attn_layer(llm_graph_input_attn_kv cb(qkv, "wqkv", il); // split QKV tensor into Q, K, V - const int64_t n_embd_head_q = hparams.n_embd_head_k; - const int64_t n_embd_head_k = hparams.n_embd_head_k; - const int64_t n_embd_head_v = hparams.n_embd_head_v; + const int64_t n_embd_head_q = hparams.n_embd_head_k(); + const int64_t n_embd_head_k = hparams.n_embd_head_k(); + const int64_t n_embd_head_v = hparams.n_embd_head_v(); int32_t n_head = hparams.n_head(il); int32_t n_head_kv = hparams.n_head_kv(il); diff --git a/src/models/plamo3.cpp b/src/models/plamo3.cpp index 55c806467..32af6e046 100644 --- a/src/models/plamo3.cpp +++ b/src/models/plamo3.cpp @@ -3,8 +3,8 @@ template llm_build_plamo3::llm_build_plamo3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t head_dim_q = hparams.n_embd_head_k; - const int64_t head_dim_v = hparams.n_embd_head_v; + const int64_t head_dim_q = hparams.n_embd_head_k(); + const int64_t head_dim_v = hparams.n_embd_head_v(); ggml_tensor * cur; ggml_tensor * inpL = build_inp_embd(model.tok_embd); diff --git a/src/models/plm.cpp b/src/models/plm.cpp index 612a487c5..bcb651ce5 100644 --- a/src/models/plm.cpp +++ b/src/models/plm.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_plm::llm_build_plm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const float kq_scale = 1.0f/sqrtf(float(hparams.n_embd_head_k)); + const float kq_scale = 1.0f/sqrtf(float(hparams.n_embd_head_k())); - const uint32_t n_embd_head_qk_rope = hparams.n_rot; - const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot; + const uint32_t n_embd_head_qk_rope = hparams.n_rot(); + const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k() - hparams.n_rot(); const uint32_t kv_lora_rank = hparams.n_lora_kv; @@ -38,15 +38,15 @@ llm_build_plm::llm_build_plm(const llama_model & model, const llm_graph_params & // split into {n_head * n_embd_head_qk_nope, 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, hparams.n_embd_head_k), - ggml_row_size(q->type, hparams.n_embd_head_k * n_head), + ggml_row_size(q->type, hparams.n_embd_head_k()), + ggml_row_size(q->type, hparams.n_embd_head_k() * n_head), 0); cb(q_nope, "q_nope", il); // and {n_head * n_embd_head_qk_rope, 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, hparams.n_embd_head_k), - ggml_row_size(q->type, hparams.n_embd_head_k * n_head), + ggml_row_size(q->type, hparams.n_embd_head_k()), + ggml_row_size(q->type, hparams.n_embd_head_k() * n_head), ggml_row_size(q->type, n_embd_head_qk_nope)); cb(q_pe, "q_pe", il); @@ -78,23 +78,23 @@ llm_build_plm::llm_build_plm(const llama_model & model, const llm_graph_params & // split into {n_head * n_embd_head_qk_nope, n_tokens} ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens, - ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v), - ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)), + ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v()), + ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v())), 0); cb(k_nope, "k_nope", il); // and {n_head * n_embd_head_v, n_tokens} - ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v, n_head, n_tokens, - ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)), - ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)*n_head), + ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v(), n_head, n_tokens, + ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v())), + ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v())*n_head), ggml_row_size(kv->type, (n_embd_head_qk_nope))); cb(v_states, "v_states", il); v_states = ggml_cont(ctx0, v_states); cb(v_states, "v_states", il); - v_states = ggml_view_2d(ctx0, v_states, hparams.n_embd_head_v * n_head, n_tokens, - ggml_row_size(kv->type, hparams.n_embd_head_v * n_head), + v_states = ggml_view_2d(ctx0, v_states, hparams.n_embd_head_v() * n_head, n_tokens, + ggml_row_size(kv->type, hparams.n_embd_head_v() * n_head), 0); cb(v_states, "v_states", il); diff --git a/src/models/qwen.cpp b/src/models/qwen.cpp index 31fd9b737..7390f1320 100644 --- a/src/models/qwen.cpp +++ b/src/models/qwen.cpp @@ -2,9 +2,9 @@ llm_build_qwen::llm_build_qwen(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/qwen2.cpp b/src/models/qwen2.cpp index 3da4dea3c..58c106225 100644 --- a/src/models/qwen2.cpp +++ b/src/models/qwen2.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_qwen2::llm_build_qwen2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/qwen2moe.cpp b/src/models/qwen2moe.cpp index e19061334..60761789d 100644 --- a/src/models/qwen2moe.cpp +++ b/src/models/qwen2moe.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_qwen2moe::llm_build_qwen2moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/qwen2vl.cpp b/src/models/qwen2vl.cpp index 9be38675c..9004bab9d 100644 --- a/src/models/qwen2vl.cpp +++ b/src/models/qwen2vl.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_qwen2vl::llm_build_qwen2vl(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/qwen3.cpp b/src/models/qwen3.cpp index a5cfffa53..be4811aba 100644 --- a/src/models/qwen3.cpp +++ b/src/models/qwen3.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_qwen3::llm_build_qwen3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/qwen35.cpp b/src/models/qwen35.cpp index afc5a1aad..ba096a5a7 100644 --- a/src/models/qwen35.cpp +++ b/src/models/qwen35.cpp @@ -4,9 +4,9 @@ llm_build_qwen35::llm_build_qwen35(const llama_model & model, const llm_graph_params & params) : llm_build_delta_net_base(params), model(model) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); int sections[4]; std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections); @@ -117,8 +117,8 @@ ggml_tensor * llm_build_qwen35::build_layer_attn( ggml_tensor * inp_pos, int * sections, int il) { - const int64_t n_embd_head = hparams.n_embd_head_v; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + const int64_t n_embd_head = hparams.n_embd_head_v(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); // Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention diff --git a/src/models/qwen35moe.cpp b/src/models/qwen35moe.cpp index fe081af21..fe382286e 100644 --- a/src/models/qwen35moe.cpp +++ b/src/models/qwen35moe.cpp @@ -4,9 +4,9 @@ llm_build_qwen35moe::llm_build_qwen35moe(const llama_model & model, const llm_graph_params & params) : llm_build_delta_net_base(params), model(model) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); int sections[4]; std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections); @@ -117,8 +117,8 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn( ggml_tensor * inp_pos, int * sections, int il) { - const int64_t n_embd_head = hparams.n_embd_head_v; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + const int64_t n_embd_head = hparams.n_embd_head_v(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); // Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention diff --git a/src/models/qwen3moe.cpp b/src/models/qwen3moe.cpp index 9cf1ec03c..5912a7158 100644 --- a/src/models/qwen3moe.cpp +++ b/src/models/qwen3moe.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_qwen3moe::llm_build_qwen3moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/qwen3next.cpp b/src/models/qwen3next.cpp index 9b8164ddf..30912fd5e 100644 --- a/src/models/qwen3next.cpp +++ b/src/models/qwen3next.cpp @@ -100,8 +100,8 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn( ggml_tensor * cur, ggml_tensor * inp_pos, int il) { - const int64_t n_embd_head = hparams.n_embd_head_v; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + const int64_t n_embd_head = hparams.n_embd_head_v(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); // Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention diff --git a/src/models/qwen3vl-moe.cpp b/src/models/qwen3vl-moe.cpp index f347c5d6f..195daea66 100644 --- a/src/models/qwen3vl-moe.cpp +++ b/src/models/qwen3vl-moe.cpp @@ -4,10 +4,10 @@ llm_build_qwen3vlmoe::llm_build_qwen3vlmoe(const llama_model & model, const llm_ const size_t n_deepstack_layers = hparams.n_deepstack_layers; const int64_t n_embd = hparams.n_embd; - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/qwen3vl.cpp b/src/models/qwen3vl.cpp index 0f8315b32..bbd5f42ba 100644 --- a/src/models/qwen3vl.cpp +++ b/src/models/qwen3vl.cpp @@ -4,10 +4,10 @@ llm_build_qwen3vl::llm_build_qwen3vl(const llama_model & model, const llm_graph_ const size_t n_deepstack_layers = hparams.n_deepstack_layers; const int64_t n_embd = hparams.n_embd; - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/refact.cpp b/src/models/refact.cpp index ff5eb2841..140700d9e 100644 --- a/src/models/refact.cpp +++ b/src/models/refact.cpp @@ -1,9 +1,9 @@ #include "models.h" llm_build_refact::llm_build_refact(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/rnd1.cpp b/src/models/rnd1.cpp index de9ab1c65..c8e1f4340 100644 --- a/src/models/rnd1.cpp +++ b/src/models/rnd1.cpp @@ -2,10 +2,10 @@ // RND1 is a Qwen3Moe AR model converted to diffusion model. llm_build_rnd1::llm_build_rnd1(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/seed-oss.cpp b/src/models/seed-oss.cpp index 0dc33c50b..a4d0b75d8 100644 --- a/src/models/seed-oss.cpp +++ b/src/models/seed-oss.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_seed_oss::llm_build_seed_oss(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/smallthinker.cpp b/src/models/smallthinker.cpp index 8723905e8..e2155aace 100644 --- a/src/models/smallthinker.cpp +++ b/src/models/smallthinker.cpp @@ -2,10 +2,10 @@ template llm_build_smallthinker::llm_build_smallthinker(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params){ - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/smollm3.cpp b/src/models/smollm3.cpp index 97c30deed..e267fd8f3 100644 --- a/src/models/smollm3.cpp +++ b/src/models/smollm3.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_smollm3::llm_build_smollm3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/stablelm.cpp b/src/models/stablelm.cpp index bed1915c0..ff5aced93 100644 --- a/src/models/stablelm.cpp +++ b/src/models/stablelm.cpp @@ -1,9 +1,9 @@ #include "models.h" llm_build_stablelm::llm_build_stablelm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/starcoder.cpp b/src/models/starcoder.cpp index e197af4a8..941cee982 100644 --- a/src/models/starcoder.cpp +++ b/src/models/starcoder.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_starcoder::llm_build_starcoder(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/starcoder2.cpp b/src/models/starcoder2.cpp index e40ef2cb7..a5965aceb 100644 --- a/src/models/starcoder2.cpp +++ b/src/models/starcoder2.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_starcoder2::llm_build_starcoder2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/step35-iswa.cpp b/src/models/step35-iswa.cpp index aa8e98b73..176209cd9 100644 --- a/src/models/step35-iswa.cpp +++ b/src/models/step35-iswa.cpp @@ -52,7 +52,7 @@ llm_build_step35_iswa::llm_build_step35_iswa(const llama_model & model, const ll // RoPE (partial rotary factors per layer) const bool is_swa = hparams.is_swa(il); ggml_tensor * rope_factors = is_swa ? nullptr : model.get_rope_factors(cparams, il); - const int64_t n_rot_l = is_swa ? hparams.n_rot : (hparams.n_rot / 2); + const int64_t n_rot_l = hparams.n_rot(il); Qcur = ggml_rope_ext( ctx0, Qcur, inp_pos, rope_factors, n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l, diff --git a/src/models/t5-dec.cpp b/src/models/t5-dec.cpp index 297e450de..8ca8372bd 100644 --- a/src/models/t5-dec.cpp +++ b/src/models/t5-dec.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_t5_dec::llm_build_t5_dec(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); //const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/t5-enc.cpp b/src/models/t5-enc.cpp index 70e1d80dc..395dfb510 100644 --- a/src/models/t5-enc.cpp +++ b/src/models/t5-enc.cpp @@ -1,9 +1,9 @@ #include "models.h" llm_build_t5_enc::llm_build_t5_enc(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); ggml_tensor * cur; ggml_tensor * inpL; diff --git a/src/models/xverse.cpp b/src/models/xverse.cpp index 364797dd3..3a8dfafcc 100644 --- a/src/models/xverse.cpp +++ b/src/models/xverse.cpp @@ -1,10 +1,10 @@ #include "models.h" llm_build_xverse::llm_build_xverse(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_head = hparams.n_embd_head_v(); - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + GGML_ASSERT(n_embd_head == n_rot); ggml_tensor * cur; ggml_tensor * inpL; From 0842b9b465f73a133062e80b74201c86e73a67c8 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 9 Mar 2026 23:42:24 +0100 Subject: [PATCH 21/22] model: fix step3.5 n_rot (#20318) --- src/llama-model.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 349f70a61..0fa47e1b4 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -7348,7 +7348,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { // ("rope_freqs.weight") and ggml uses only the first (n_rot_l/2) entries per layer. uint32_t n_rot_max = 0; for (int i = 0; i < n_layer; ++i) { - n_rot_max = std::max(n_rot_max, hparams.n_rot()); + n_rot_max = std::max(n_rot_max, hparams.n_rot(i)); } if (n_rot_max == 0) { n_rot_max = n_rot; From c96f608d9861f7e8466bc1b6ac2ff4e3c6f96641 Mon Sep 17 00:00:00 2001 From: Aldehir Rojas Date: Mon, 9 Mar 2026 18:29:21 -0500 Subject: [PATCH 22/22] common: consolidate PEG string parsers (#20263) * common : consolidate PEG string parsers * cont : fix json_string_content() --- common/chat-auto-parser-generator.cpp | 2 +- common/chat-peg-parser.cpp | 16 +- common/peg-parser.cpp | 248 +++++++++---------- common/peg-parser.h | 21 +- tests/peg-parser/test-python-dict-parser.cpp | 2 +- tests/peg-parser/test-unicode.cpp | 8 +- tests/test-chat-peg-parser.cpp | 4 +- 7 files changed, 142 insertions(+), 159 deletions(-) diff --git a/common/chat-auto-parser-generator.cpp b/common/chat-auto-parser-generator.cpp index 03dfb8f10..1c74ad30d 100644 --- a/common/chat-auto-parser-generator.cpp +++ b/common/chat-auto-parser-generator.cpp @@ -90,7 +90,7 @@ common_peg_arena autoparser::build_parser(const templates_params & inputs) const // pre-register a json-string rule that accepts both quote styles. This must happen // before any call to p.json() so that all JSON parsing inherits the flexible rule. if (tools.format.uses_python_dicts) { - p.rule("json-string", [&]() { return p.choice({ p.double_quoted_string(), p.single_quoted_string() }); }); + p.rule("json-string", p.quoted_string()); } parser_build_context ctx(p, inputs); diff --git a/common/chat-peg-parser.cpp b/common/chat-peg-parser.cpp index e055447e0..cbdf202f0 100644 --- a/common/chat-peg-parser.cpp +++ b/common/chat-peg-parser.cpp @@ -507,8 +507,8 @@ common_peg_parser common_chat_peg_builder::python_style_tool_calls( common_peg_parser arg_value_parser = eps(); auto string_value_parser = choice({ - literal("\"") + tool_arg_string_value(json_string_content()) + literal("\""), - literal("'") + tool_arg_string_value(json_string_content()) + literal("'") + literal("\"") + tool_arg_string_value(string_content('"')) + literal("\""), + literal("'") + tool_arg_string_value(string_content('\'')) + literal("'") }); if (is_string_type) { @@ -577,7 +577,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_function_is_key( if (!call_id_key.empty()) { auto id_parser = atomic( literal("\"" + call_id_key + "\"") + space() + literal(":") + space() + - literal("\"") + tool_id(json_string_content()) + literal("\"") + literal("\"") + tool_id(string_content('"')) + literal("\"") ); inner_fields.push_back(optional(id_parser + space() + optional(literal(",") + space()))); } @@ -586,7 +586,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_function_is_key( auto gen_id_parser = atomic( literal("\"" + gen_call_id_key + "\"") + space() + literal(":") + space() + choice({ - literal("\"") + tool_id(json_string_content()) + literal("\""), + literal("\"") + tool_id(string_content('"')) + literal("\""), tool_id(json_number()) }) ); @@ -675,7 +675,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_nested_keys( if (id_spec.first.empty()) { auto id_parser = atomic( literal("\"" + call_id_key + "\"") + space() + literal(":") + space() + - literal("\"") + tool_id(json_string_content()) + literal("\"") + literal("\"") + tool_id(string_content('"')) + literal("\"") ); tool_parser_body = tool_parser_body + optional(id_parser + space() + literal(",") + space()); } @@ -687,7 +687,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_nested_keys( auto gen_id_parser = atomic( literal("\"" + gen_call_id_key + "\"") + space() + literal(":") + space() + choice({ - literal("\"") + tool_id(json_string_content()) + literal("\""), + literal("\"") + tool_id(string_content('"')) + literal("\""), tool_id(json_number()) }) ); @@ -736,7 +736,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_flat_keys( id_parser = atomic( literal("\"" + call_id_key + "\"") + space() + literal(":") + space() + choice({ - literal("\"") + tool_id(json_string_content()) + literal("\""), + literal("\"") + tool_id(string_content('"')) + literal("\""), tool_id(json_number()) }) ); @@ -747,7 +747,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_flat_keys( gen_id_parser = atomic( literal("\"" + gen_call_id_key + "\"") + space() + literal(":") + space() + choice({ - literal("\"") + tool_id(json_string_content()) + literal("\""), + literal("\"") + tool_id(string_content('"')) + literal("\""), tool_id(json_number()) }) ); diff --git a/common/peg-parser.cpp b/common/peg-parser.cpp index 81630b68a..a6d9a4c27 100644 --- a/common/peg-parser.cpp +++ b/common/peg-parser.cpp @@ -658,7 +658,7 @@ struct parser_executor { return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos); } - static common_peg_parse_result handle_escape_sequence(common_peg_parse_context & ctx, size_t start, size_t & pos) { + static common_peg_parse_result handle_escape_sequence(common_peg_parse_context & ctx, size_t start, size_t & pos, const char delimiter) { ++pos; // consume '\' if (pos >= ctx.input.size()) { if (!ctx.is_lenient()) { @@ -667,23 +667,14 @@ struct parser_executor { return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start, pos); } - switch (ctx.input[pos]) { - case '"': - case '\'': - case '\\': - case '/': - case 'b': - case 'f': - case 'n': - case 'r': - case 't': - ++pos; - return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start, pos); - case 'u': - return handle_unicode_escape(ctx, start, pos); - default: - // Invalid escape sequence - return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start); + char c = ctx.input[pos]; + if (c == delimiter || c == '\\' || c == '/' || c == 'b' || c == 'f' || c == 'n' || c == 'r' || c == 't') { + ++pos; + return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start, pos); + } else if (c == 'u') { + return handle_unicode_escape(ctx, start, pos); + } else { + return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start); } } @@ -704,62 +695,20 @@ struct parser_executor { return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start, pos); } - common_peg_parse_result operator()(const common_peg_json_string_parser & /* p */) { + common_peg_parse_result operator()(const common_peg_string_parser & p) { auto pos = start_pos; // Parse string content (without quotes) while (pos < ctx.input.size()) { char c = ctx.input[pos]; - if (c == '"') { - // Found closing quote - success (don't consume it) + if (c == p.delimiter) { + // Found closing delimiter - success (don't consume it) return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos); } if (c == '\\') { - auto result = handle_escape_sequence(ctx, start_pos, pos); - if (!result.success()) { - return result; - } - } else { - auto utf8_result = common_parse_utf8_codepoint(ctx.input, pos); - - if (utf8_result.status == utf8_parse_result::INCOMPLETE) { - if (!ctx.is_lenient()) { - return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos); - } - return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, pos); - } - - if (utf8_result.status == utf8_parse_result::INVALID) { - return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos); - } - - pos += utf8_result.bytes_consumed; - } - } - - // Reached end without finding closing quote - if (!ctx.is_lenient()) { - return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos, pos); - } - return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, pos); - } - - common_peg_parse_result operator()(const common_peg_python_dict_string_parser & /* p */) { - auto pos = start_pos; - - // Parse string content (without quotes) - while (pos < ctx.input.size()) { - char c = ctx.input[pos]; - - if (c == '\'') { - // Found closing quote - success (don't consume it) - return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos); - } - - if (c == '\\') { - auto result = handle_escape_sequence(ctx, start_pos, pos); + auto result = handle_escape_sequence(ctx, start_pos, pos, p.delimiter); if (!result.success()) { return result; } @@ -988,8 +937,7 @@ void common_peg_arena::resolve_refs() { std::is_same_v || std::is_same_v || std::is_same_v || - std::is_same_v || - std::is_same_v || + std::is_same_v || std::is_same_v || std::is_same_v || std::is_same_v) { @@ -1065,10 +1013,8 @@ std::string common_peg_arena::dump_impl(common_peg_parser_id return "CharRepeat(" + p.pattern + ", " + std::to_string(p.min_count) + ", unbounded)"; } return "CharRepeat(" + p.pattern + ", " + std::to_string(p.min_count) + ", " + std::to_string(p.max_count) + ")"; - } else if constexpr (std::is_same_v) { - return "JsonString()"; - } else if constexpr (std::is_same_v) { - return "PythonDictString()"; + } else if constexpr (std::is_same_v) { + return "String(" + std::string(1, p.delimiter) + ")"; } else if constexpr (std::is_same_v) { return "Until(" + string_join(p.delimiters, " | ") + ")"; } else if constexpr (std::is_same_v) { @@ -1281,47 +1227,25 @@ common_peg_arena common_peg_parser_builder::build() { // String primitives -common_peg_parser common_peg_parser_builder::json_string_content() { - return wrap(arena_.add_parser(common_peg_json_string_parser{})); -} - -common_peg_parser common_peg_parser_builder::single_quoted_string_content() { - return wrap(arena_.add_parser(common_peg_python_dict_string_parser{})); +common_peg_parser common_peg_parser_builder::string_content(char delimiter) { + return wrap(arena_.add_parser(common_peg_string_parser{delimiter})); } common_peg_parser common_peg_parser_builder::double_quoted_string() { - return rule("dq-string", - [this]() { return sequence({ literal("\""), json_string_content(), literal("\""), space() }); }); -} - -common_peg_parser common_peg_parser_builder::single_quoted_string() { - return rule("sq-string", - [this]() { return sequence({ literal("'"), single_quoted_string_content(), literal("'"), space() }); }); -} - -common_peg_parser common_peg_parser_builder::flexible_string() { - return rule("flexible-string", [this]() { return choice({ double_quoted_string(), single_quoted_string() }); }); -} - -// Generic helpers for object/array structure - -common_peg_parser common_peg_parser_builder::generic_object(const std::string & name, - const common_peg_parser & string_parser, - const common_peg_parser & value_parser) { - return rule(name, [this, string_parser, value_parser]() { - auto ws = space(); - auto member = sequence({ string_parser, ws, literal(":"), ws, value_parser }); - auto members = sequence({ member, zero_or_more(sequence({ ws, literal(","), ws, member })) }); - return sequence({ literal("{"), ws, choice({ literal("}"), sequence({ members, ws, literal("}") }) }) }); + return rule("double-quoted-string", [this]() { + return sequence({literal("\""), string_content('"'), literal("\""), space()}); }); } -common_peg_parser common_peg_parser_builder::generic_array(const std::string & name, - const common_peg_parser & value_parser) { - return rule(name, [this, value_parser]() { - auto ws = space(); - auto elements = sequence({ value_parser, zero_or_more(sequence({ literal(","), ws, value_parser })) }); - return sequence({ literal("["), ws, choice({ literal("]"), sequence({ elements, ws, literal("]") }) }) }); +common_peg_parser common_peg_parser_builder::single_quoted_string() { + return rule("single-quoted-string", [this]() { + return sequence({literal("'"), string_content('\''), literal("'"), space()}); + }); +} + +common_peg_parser common_peg_parser_builder::quoted_string() { + return rule("quoted-string", [this]() { + return choice({double_quoted_string(), single_quoted_string()}); }); } @@ -1344,7 +1268,7 @@ common_peg_parser common_peg_parser_builder::json_number() { common_peg_parser common_peg_parser_builder::json_string() { return rule("json-string", [this]() { - return sequence({literal("\""), json_string_content(), literal("\""), space()}); + return sequence({literal("\""), string_content('"'), literal("\""), space()}); }); } @@ -1361,11 +1285,36 @@ common_peg_parser common_peg_parser_builder::json_null() { } common_peg_parser common_peg_parser_builder::json_object() { - return generic_object("json-object", json_string(), json()); + return rule("json-object", [this]() { + auto ws = space(); + auto member = sequence({json_string(), ws, literal(":"), ws, json()}); + auto members = sequence({member, zero_or_more(sequence({ws, literal(","), ws, member}))}); + return sequence({ + literal("{"), + ws, + choice({ + literal("}"), + sequence({members, ws, literal("}")}) + }), + ws + }); + }); } common_peg_parser common_peg_parser_builder::json_array() { - return generic_array("json-array", json()); + return rule("json-array", [this]() { + auto ws = space(); + auto elements = sequence({json(), zero_or_more(sequence({literal(","), ws, json()}))}); + return sequence({ + literal("["), + ws, + choice({ + literal("]"), + sequence({elements, ws, literal("]")}) + }), + ws + }); + }); } common_peg_parser common_peg_parser_builder::json() { @@ -1382,7 +1331,9 @@ common_peg_parser common_peg_parser_builder::json() { } common_peg_parser common_peg_parser_builder::python_string() { - return rule("python-string", [this]() { return choice({ double_quoted_string(), single_quoted_string() }); }); + return rule("python-string", [this]() { + return choice({double_quoted_string(), single_quoted_string()}); + }); } common_peg_parser common_peg_parser_builder::python_number() { @@ -1390,24 +1341,63 @@ common_peg_parser common_peg_parser_builder::python_number() { } common_peg_parser common_peg_parser_builder::python_bool() { - return rule("python-bool", [this]() { return sequence({ choice({ literal("True"), literal("False") }), space() }); }); + return rule("python-bool", [this]() { + return sequence({ + choice({literal("True"), literal("False")}), + space() + }); + }); } common_peg_parser common_peg_parser_builder::python_null() { - return rule("python-none", [this]() { return sequence({ literal("None"), space() }); }); + return rule("python-none", [this]() { + return sequence({literal("None"), space()}); + }); } common_peg_parser common_peg_parser_builder::python_dict() { - return generic_object("python-dict", python_string(), python_value()); + return rule("python-dict", [this]() { + auto ws = space(); + auto member = sequence({python_string(), ws, literal(":"), ws, python_value()}); + auto members = sequence({member, zero_or_more(sequence({ws, literal(","), ws, member}))}); + return sequence({ + literal("{"), + ws, + choice({ + literal("}"), + sequence({members, ws, literal("}")}) + }), + ws + }); + }); } common_peg_parser common_peg_parser_builder::python_array() { - return generic_array("python-array", python_value()); + return rule("python-array", [this]() { + auto ws = space(); + auto elements = sequence({python_value(), zero_or_more(sequence({literal(","), ws, python_value()}))}); + return sequence({ + literal("["), + ws, + choice({ + literal("]"), + sequence({elements, ws, literal("]")}) + }), + ws + }); + }); } common_peg_parser common_peg_parser_builder::python_value() { return rule("python-value", [this]() { - return choice({ python_dict(), python_array(), python_string(), python_number(), python_bool(), python_null() }); + return choice({ + python_dict(), + python_array(), + python_string(), + python_number(), + python_bool(), + python_null() + }); }); } @@ -1528,8 +1518,7 @@ static std::unordered_set collect_reachable_rules( std::is_same_v || std::is_same_v || std::is_same_v || - std::is_same_v || - std::is_same_v) { + std::is_same_v) { // These parsers do not have any children } else if constexpr (std::is_same_v) { for (auto child : p.children) { @@ -1665,10 +1654,9 @@ void common_peg_arena::build_grammar(const common_grammar_builder & builder, boo return result + "{" + std::to_string(p.min_count) + "}"; } return result + "{" + std::to_string(p.min_count) + "," + std::to_string(p.max_count) + "}"; - } else if constexpr (std::is_same_v) { - return R"(( [^"\\] | "\\" ( ["\\/ bfnrt] | "u" [0-9a-fA-F]{4} ) )*)"; - } else if constexpr (std::is_same_v) { - return R"(( [^"\\] | "\\" ( ["\\/ bfnrt] | "u" [0-9a-fA-F]{4} ) )*)"; + } else if constexpr (std::is_same_v) { + const std::string delim(1, p.delimiter); + return R"(( [^)" + delim + R"(\\] | "\\" ( [)" + delim + R"(\\/ bfnrt] | "u" [0-9a-fA-F]{4} ) )*)"; } else if constexpr (std::is_same_v) { if (p.delimiters.empty()) { return ".*"; @@ -1798,10 +1786,8 @@ static nlohmann::json serialize_parser_variant(const common_peg_parser_variant & {"min_count", p.min_count}, {"max_count", p.max_count} }; - } else if constexpr (std::is_same_v) { - return json{{"type", "json_string"}}; - } else if constexpr (std::is_same_v) { - return json{{ "type", "python_dict_string" }}; + } else if constexpr (std::is_same_v) { + return json{{"type", "string"}, {"delimiter", std::string(1, p.delimiter)}}; } else if constexpr (std::is_same_v) { return json{{"type", "until"}, {"delimiters", p.delimiters}}; } else if constexpr (std::is_same_v) { @@ -1928,11 +1914,15 @@ static common_peg_parser_variant deserialize_parser_variant(const nlohmann::json } return parser; } - if (type == "json_string") { - return common_peg_json_string_parser{}; - } - if (type == "python_dict_string") { - return common_peg_python_dict_string_parser{}; + if (type == "string") { + if (!j.contains("delimiter")) { + throw std::runtime_error("string parser missing delimiter field."); + } + std::string delimiter = j["delimiter"]; + if (delimiter.empty()) { + throw std::runtime_error("string parser delimiter is empty."); + } + return common_peg_string_parser{delimiter[0]}; } if (type == "until") { if (!j.contains("delimiters") || !j["delimiters"].is_array()) { diff --git a/common/peg-parser.h b/common/peg-parser.h index 9f81df2e9..31cdf9ec2 100644 --- a/common/peg-parser.h +++ b/common/peg-parser.h @@ -231,8 +231,9 @@ struct common_peg_chars_parser { int max_count; // -1 for unbounded }; -struct common_peg_json_string_parser {}; -struct common_peg_python_dict_string_parser {}; +struct common_peg_string_parser { + char delimiter; +}; struct common_peg_until_parser { std::vector delimiters; @@ -280,8 +281,7 @@ using common_peg_parser_variant = std::variant< common_peg_any_parser, common_peg_space_parser, common_peg_chars_parser, - common_peg_json_string_parser, - common_peg_python_dict_string_parser, + common_peg_string_parser, common_peg_until_parser, common_peg_schema_parser, common_peg_rule_parser, @@ -340,10 +340,6 @@ class common_peg_parser_builder { common_peg_parser wrap(common_peg_parser_id id) { return common_peg_parser(id, *this); } common_peg_parser add(const common_peg_parser_variant & p) { return wrap(arena_.add_parser(p)); } - // Generic helpers for building object/array structures with configurable string/value parsers. - common_peg_parser generic_object(const std::string & name, const common_peg_parser & string_parser, const common_peg_parser & value_parser); - common_peg_parser generic_array(const std::string & name, const common_peg_parser & value_parser); - public: common_peg_parser_builder(); @@ -444,13 +440,10 @@ class common_peg_parser_builder { common_peg_parser single_quoted_string(); // Matches a string that accepts both double-quoted and single-quoted styles. - common_peg_parser flexible_string(); + common_peg_parser quoted_string(); - // Matches double-quoted string content without the surrounding quotes. - common_peg_parser json_string_content(); - - // Matches single-quoted string content without the surrounding quotes. - common_peg_parser single_quoted_string_content(); + // Matches string content without the surrounding delimiter. + common_peg_parser string_content(char delimiter); // Creates a complete JSON parser supporting objects, arrays, strings, numbers, booleans, and null. // value -> object | array | string | number | true | false | null diff --git a/tests/peg-parser/test-python-dict-parser.cpp b/tests/peg-parser/test-python-dict-parser.cpp index 18e7d901b..1a549106b 100644 --- a/tests/peg-parser/test-python-dict-parser.cpp +++ b/tests/peg-parser/test-python-dict-parser.cpp @@ -197,7 +197,7 @@ void test_python_dict_parser(testing &t) { // Test single-quoted string content parser directly t.test("single-quoted string content parser", [](testing &t) { auto parser = build_peg_parser([](common_peg_parser_builder & p) { - return p.sequence({ p.literal("'"), p.single_quoted_string_content(), p.literal("'"), p.space() }); + return p.sequence({ p.literal("'"), p.string_content('\''), p.literal("'"), p.space() }); }); t.test("simple string", [&](testing &t) { diff --git a/tests/peg-parser/test-unicode.cpp b/tests/peg-parser/test-unicode.cpp index 9cbdb0d38..24663d701 100644 --- a/tests/peg-parser/test-unicode.cpp +++ b/tests/peg-parser/test-unicode.cpp @@ -327,7 +327,7 @@ void test_unicode(testing &t) { t.test(test_name, [&](testing &t) { auto parser = build_peg_parser([](common_peg_parser_builder& p) { - return p.sequence({p.json_string_content(), p.literal("\"")}); + return p.sequence({p.string_content('"'), p.literal("\"")}); }); common_peg_parse_context ctx(tc.input); @@ -364,7 +364,7 @@ void test_unicode(testing &t) { t.test(test_name, [&](testing &t) { auto parser = build_peg_parser([](common_peg_parser_builder& p) { - return p.json_string_content(); + return p.string_content('"'); }); common_peg_parse_context ctx(tc.input, COMMON_PEG_PARSE_FLAG_LENIENT); @@ -398,7 +398,7 @@ void test_unicode(testing &t) { t.test(test_name, [&](testing &t) { auto parser = build_peg_parser([](common_peg_parser_builder& p) { - return p.json_string_content(); + return p.string_content('"'); }); common_peg_parse_context ctx(tc.input); @@ -427,7 +427,7 @@ void test_unicode(testing &t) { t.test(test_name, [&](testing &t) { auto parser = build_peg_parser([](common_peg_parser_builder& p) { - return p.sequence({p.json_string_content(), p.literal("\"")}); + return p.sequence({p.string_content('"'), p.literal("\"")}); }); common_peg_parse_context ctx(tc.input); diff --git a/tests/test-chat-peg-parser.cpp b/tests/test-chat-peg-parser.cpp index 112de1d55..dc8724be3 100644 --- a/tests/test-chat-peg-parser.cpp +++ b/tests/test-chat-peg-parser.cpp @@ -597,9 +597,9 @@ void test_command7_parser_compare(testing & t) { auto response = "<|START_RESPONSE|>" << p.content(p.until("<|END_RESPONSE|>")) << "<|END_RESPONSE|>"; - auto tool_call_id = p.atomic("\"tool_call_id\"" << (":" << ("\"" + p.tool_id(p.json_string_content()) + "\""))); + auto tool_call_id = p.atomic("\"tool_call_id\"" << (":" << ("\"" + p.tool_id(p.string_content('"')) + "\""))); auto tool_call_name = - p.atomic("\"tool_name\"" << (":" << ("\"" + p.tool_name(p.json_string_content()) + "\""))); + p.atomic("\"tool_name\"" << (":" << ("\"" + p.tool_name(p.string_content('"')) + "\""))); auto tool_call_args = "\"parameters\"" << (":" << p.tool_args(p.json())); auto tool_call_fields = p.rule("tool-call-fields", tool_call_id | tool_call_name | tool_call_args);