Files
llama.cpp/src/models/models.h
T
dfc959b886 model : Granite Embedding support (#15641)
ModernBERT but without `head.norm` so will currently fail to convert and run any other ModernBERT models, PRs with `head.norm` support welcome!

* constants and tensor mappings for modern bert support, model not supported yet but working on getting conversion to work for encoder only

* conversion now working, hf -> gguf

* working on support, now working on building graph

* some cleanup

* cleanup

* continuing

* correct tensor shape for qkv

* fixed tensor mappings and working on buildin graph

* tensor debugging now works -> (llama-eval-callback), instead of simulated gate split with views, GEGLU is now used which does exactly this

* cleanup

* cleanup

* cleanup

* more cleanup

* ubatch issues, the assert for checking equal seqs in llama-graph.cpp when building attention  keeps failing, setting ubatch size to 1 when running llama-embedding with --ubatch-size 1 makes it work, but needs to be looked into more

* added cls token per previous modern bert attempt, still working on checking out the rest

* fixed pre tokenizer and still working through previous pr

* working through previous attemp, implimented more accurate conversion per previous attempt, added local sliding window attention that alternates every third layer

* fixed pre tokenizer

* working on swa with local and global alternating attention

* some cleanup and now fails on build attn

* starting to work, and some cleanup, currently failing on last layer construction in graph build

* alternating rope implemented and modern bert graph build succeeds

* fixed asser for equal ubatch seq

* cleanup

* added mask check in vocab

* fixed alternating rope, the hparams.rope_freq_base_train and hparams.rope_freq_base_train_swa were the same and i set them to correct values

* reuse variable

* removed repeat

* standard swa method can be used instead of a new enum being LLAMA_SWA_TYPE_LOCAL

* correct swa layer indexing, is supposed to be 0, 3, 6 ... instead of 1, 4, 7 ...

* more modular hparam setting

* replaced attn out norm with ffn_norm and cosine similarity between hf embds and llama.cpp embds went way up, from 0.05 to 0.24, replaced the cacheless kv with swa todo per the previous conversion

* Update gguf-py/gguf/tensor_mapping.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update convert_hf_to_gguf_update.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-vocab.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/tensor_mapping.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/tensor_mapping.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/tensor_mapping.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/tensor_mapping.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/tensor_mapping.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/tensor_mapping.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/tensor_mapping.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/tensor_mapping.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/tensor_mapping.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-graph.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-arch.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* removed redundant hparam set

* enums for model sizes

* conversion for modern-bert model supported rather than just granite-small

* Update src/llama-model.cpp

Co-authored-by: Gabe Goodhart <ghart@us.ibm.com>

* Update src/llama-model.cpp

Co-authored-by: Gabe Goodhart <ghart@us.ibm.com>

* fixed ordering of enum for freq_base_swa

* fixed where I added residual, now gives much much better embeddings~

* readded cacheless logic

* removing whitespace

* conversion now working for swa pattern - dense every n layers

* modern bert put into seperate src file

* removing whitespace

* fixed whitespace and newline errors in editorconfig job

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* better naming convention, n_swa_pattern -> swa_period

* reusing sliding_window_pattern key rather than making new dense_every_n_layers key, and adding writing and reading support

* fixing pyright type-check fail

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/gguf_writer.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-hparams.h

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model-saver.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/models/modern-bert.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/models/modern-bert.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/models/modern-bert.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/gguf_writer.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/models/modern-bert.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/models/modern-bert.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model-loader.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model-loader.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model-loader.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* added descriptions in llama-model

* fixed tensor mappings for conversion

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* mapping name for size

* nits

* unused

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Gabe Goodhart <ghart@us.ibm.com>
2025-12-23 00:28:19 +01:00

550 lines
20 KiB
C++

#pragma once
#include "../llama-model.h"
#include "../llama-graph.h"
// TODO: remove in follow-up PR - move to .cpp files
#include "../llama-memory-recurrent.h"
#include <cmath>
struct llm_graph_context_mamba : public llm_graph_context {
llm_graph_context_mamba(const llm_graph_params & params);
virtual ~llm_graph_context_mamba() = default;
ggml_tensor * build_mamba_layer(llm_graph_input_rs * inp, ggml_tensor * cur, const llama_model & model, const llama_ubatch & ubatch, int il);
ggml_tensor * build_mamba2_layer(llm_graph_input_rs * inp, ggml_tensor * cur, const llama_model & model, const llama_ubatch & ubatch, int il) const;
};
// Base class for RWKV-related models
struct llm_build_rwkv6_base : public llm_graph_context {
const llama_model & model;
llm_build_rwkv6_base(const llama_model & model, const llm_graph_params & params);
virtual ~llm_build_rwkv6_base() = default;
ggml_tensor * build_rwkv6_channel_mix(const llama_layer * layer,
ggml_tensor * cur,
ggml_tensor * x_prev,
llm_arch arch) const;
ggml_tensor * build_rwkv6_time_mix(llm_graph_input_rs * inp,
ggml_tensor * cur,
ggml_tensor * x_prev,
const llama_ubatch & ubatch,
int il) const;
};
// Base class for RWKV7-related models
struct llm_build_rwkv7_base : public llm_graph_context {
const llama_model & model;
llm_build_rwkv7_base(const llama_model & model, const llm_graph_params & params);
virtual ~llm_build_rwkv7_base() = default;
// RWKV7-specific graph building methods
ggml_tensor * build_rwkv7_channel_mix(const llama_layer * layer,
ggml_tensor * cur,
ggml_tensor * x_prev,
llm_arch arch) const;
ggml_tensor * build_rwkv7_time_mix(llm_graph_input_rs * inp,
ggml_tensor * cur,
ggml_tensor * x_prev,
ggml_tensor *& first_layer_value,
const llama_ubatch & ubatch,
int il) const;
};
struct llm_build_afmoe : public llm_graph_context {
llm_build_afmoe(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_apertus : public llm_graph_context {
llm_build_apertus(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_arcee : public llm_graph_context {
llm_build_arcee(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_arctic : public llm_graph_context {
llm_build_arctic(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_arwkv7 : public llm_build_rwkv7_base {
llm_build_arwkv7(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_baichuan : public llm_graph_context {
llm_build_baichuan(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_bailingmoe2 : public llm_graph_context {
llm_build_bailingmoe2(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_bailingmoe : public llm_graph_context {
llm_build_bailingmoe(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_bert : public llm_graph_context {
llm_build_bert(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_bitnet : public llm_graph_context {
llm_build_bitnet(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_bloom : public llm_graph_context {
llm_build_bloom(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_chameleon : public llm_graph_context {
llm_build_chameleon(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_chatglm : public llm_graph_context {
llm_build_chatglm(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_codeshell : public llm_graph_context {
llm_build_codeshell(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_cogvlm : public llm_graph_context {
llm_build_cogvlm(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_cohere2_iswa : public llm_graph_context {
llm_build_cohere2_iswa(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_command_r : public llm_graph_context {
llm_build_command_r(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_dbrx : public llm_graph_context {
llm_build_dbrx(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_deci : public llm_graph_context {
llm_build_deci(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_deepseek2 : public llm_graph_context {
llm_build_deepseek2(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_deepseek : public llm_graph_context {
llm_build_deepseek(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_dots1 : public llm_graph_context {
llm_build_dots1(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_dream : public llm_graph_context {
llm_build_dream(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_ernie4_5 : public llm_graph_context {
llm_build_ernie4_5(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_ernie4_5_moe : public llm_graph_context {
llm_build_ernie4_5_moe(const llama_model & model, const llm_graph_params & params);
};
template <bool iswa>
struct llm_build_exaone4 : public llm_graph_context {
llm_build_exaone4(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_exaone : public llm_graph_context {
llm_build_exaone(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_falcon : public llm_graph_context {
llm_build_falcon(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_falcon_h1 : public llm_graph_context_mamba {
llm_build_falcon_h1(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_gemma2_iswa : public llm_graph_context {
llm_build_gemma2_iswa(const llama_model & model, const llm_graph_params & params);
};
template <bool iswa>
struct llm_build_gemma3 : public llm_graph_context {
llm_build_gemma3(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_gemma3n_iswa : public llm_graph_context {
const llama_model & model;
const int64_t n_embd_head;
const int64_t n_embd_altup;
const int64_t n_altup;
const int i_altup_act;
const int n_layer_sparsity = 10; // number of layers using activation sparsity
const float f_sparsity_std_mul = 1.6448533535003662f; // std_multiplier = normal_dist.icdf(0.95)
llm_build_gemma3n_iswa(const llama_model & model, const llm_graph_params & params);
ggml_tensor * calc_magnitude(ggml_tensor * x);
ggml_tensor * view_2d_slice(ggml_tensor * x, int idx);
ggml_tensor * get_per_layer_inputs();
ggml_tensor * project_per_layer_inputs(ggml_tensor * inputs_embeds, ggml_tensor * inp_per_layer);
ggml_tensor * gaussian_topk(ggml_tensor * x);
ggml_tensor * altup_compute_router_modalities(ggml_tensor * x, int il);
ggml_tensor * altup_predict(ggml_tensor * cur, int il);
ggml_tensor * laurel(ggml_tensor * cur, int il);
ggml_tensor * altup_correct(ggml_tensor * predictions, ggml_tensor * activated, int il);
};
struct llm_build_gemma_embedding : public llm_graph_context {
llm_build_gemma_embedding(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_gemma : public llm_graph_context {
llm_build_gemma(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_glm4 : public llm_graph_context {
llm_build_glm4(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_glm4_moe : public llm_graph_context {
llm_build_glm4_moe(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_gpt2 : public llm_graph_context {
llm_build_gpt2(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_gptneox : public llm_graph_context {
llm_build_gptneox(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_granite : public llm_graph_context {
llm_build_granite(const llama_model & model, const llm_graph_params & params);
private:
ggml_tensor * build_attention_layer(
ggml_tensor * cur,
ggml_tensor * inp_pos,
llm_graph_input_attn_kv * inp_attn,
const llama_model & model,
const int64_t n_embd_head,
const int il);
ggml_tensor * build_layer_ffn(
ggml_tensor * cur,
ggml_tensor * inpSA,
const llama_model & model,
const int il);
};
struct llm_build_granite_hybrid : public llm_graph_context_mamba {
llm_build_granite_hybrid(const llama_model & model, const llm_graph_params & params);
ggml_tensor * build_layer_ffn(ggml_tensor * cur, ggml_tensor * inpSA, const llama_model & model, const int il);
ggml_tensor * build_attention_layer(ggml_tensor * cur, ggml_tensor * inp_pos, llm_graph_input_attn_kv * inp_attn,
const llama_model & model,const int64_t n_embd_head, const int il);
};
struct llm_build_grok : public llm_graph_context {
llm_build_grok(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_grovemoe : public llm_graph_context {
llm_build_grovemoe(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_hunyuan_dense : public llm_graph_context {
llm_build_hunyuan_dense(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_hunyuan_moe : public llm_graph_context {
llm_build_hunyuan_moe(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_internlm2 : public llm_graph_context {
llm_build_internlm2(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_jais : public llm_graph_context {
llm_build_jais(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_jamba : public llm_graph_context_mamba {
llm_build_jamba(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_lfm2 : public llm_graph_context {
const llama_model & model;
llm_build_lfm2(const llama_model & model, const llm_graph_params & params);
ggml_tensor * build_moe_feed_forward(ggml_tensor * cur, int il) const;
ggml_tensor * build_dense_feed_forward(ggml_tensor * cur, int il) const;
ggml_tensor * build_attn_block(ggml_tensor * cur, ggml_tensor * inp_pos, llm_graph_input_attn_kv * inp_attn, int il) const;
ggml_tensor * build_shortconv_block(ggml_tensor * cur, llm_graph_input_rs * inp_recr, int il);
};
struct llm_build_llada : public llm_graph_context {
llm_build_llada(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_llada_moe : public llm_graph_context {
llm_build_llada_moe(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_llama : public llm_graph_context {
llm_build_llama(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_llama_iswa : public llm_graph_context {
llm_build_llama_iswa(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_mamba : public llm_graph_context_mamba {
llm_build_mamba(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_minicpm3 : public llm_graph_context {
llm_build_minicpm3(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_minimax_m2 : public llm_graph_context {
llm_build_minimax_m2(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_mistral3 : public llm_graph_context {
llm_build_mistral3(const llama_model & model, const llm_graph_params & params);
};
template <bool iswa>
struct llm_build_modern_bert : public llm_graph_context {
llm_build_modern_bert(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_mpt : public llm_graph_context {
llm_build_mpt(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_nemotron : public llm_graph_context {
llm_build_nemotron(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_nemotron_h : public llm_graph_context_mamba {
llm_build_nemotron_h(const llama_model & model, const llm_graph_params & params);
ggml_tensor * build_ffn_layer(ggml_tensor * cur, const llama_model & model, const int il);
ggml_tensor * build_attention_layer(ggml_tensor * cur, llm_graph_input_attn_kv * inp_attn,
const llama_model & model, const int64_t n_embd_head, const int il);
};
struct llm_build_neo_bert : public llm_graph_context {
llm_build_neo_bert(const llama_model & model, const llm_graph_params & params);
};
template <bool iswa>
struct llm_build_olmo2 : public llm_graph_context {
llm_build_olmo2(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_olmoe : public llm_graph_context {
llm_build_olmoe(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_olmo : public llm_graph_context {
llm_build_olmo(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_openai_moe_iswa : public llm_graph_context {
llm_build_openai_moe_iswa(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_openelm : public llm_graph_context {
llm_build_openelm(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_orion : public llm_graph_context {
llm_build_orion(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_pangu_embedded : public llm_graph_context {
llm_build_pangu_embedded(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_phi2 : public llm_graph_context {
llm_build_phi2(const llama_model & model, const llm_graph_params & params);
};
template<bool iswa>
struct llm_build_phi3 : public llm_graph_context {
llm_build_phi3(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_plamo2 : public llm_graph_context_mamba {
llm_build_plamo2(const llama_model & model, const llm_graph_params & params);
private:
ggml_tensor * build_plamo2_mamba_layer(llm_graph_input_rs * inp, ggml_tensor * cur, const llama_model & model, const llama_ubatch & ubatch, int il);
ggml_tensor * build_plamo2_attn_layer(llm_graph_input_attn_kv * inp, ggml_tensor * inp_pos, ggml_tensor * cur,
const llama_model & model, int il);
};
struct llm_build_plamo : public llm_graph_context {
llm_build_plamo(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_plm : public llm_graph_context {
llm_build_plm(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_qwen2 : public llm_graph_context {
llm_build_qwen2(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_qwen2moe : public llm_graph_context {
llm_build_qwen2moe(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_qwen2vl : public llm_graph_context {
llm_build_qwen2vl(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_qwen3 : public llm_graph_context {
llm_build_qwen3(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_qwen3moe : public llm_graph_context {
llm_build_qwen3moe(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_qwen3vl : public llm_graph_context {
llm_build_qwen3vl(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_qwen3vlmoe : public llm_graph_context {
llm_build_qwen3vlmoe(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_qwen3next : public llm_graph_context_mamba {
llm_build_qwen3next(const llama_model & model, const llm_graph_params & params);
private:
ggml_tensor * build_layer_attn(
llm_graph_input_attn_kv * inp_attn,
ggml_tensor * cur,
ggml_tensor * inp_pos,
int il);
ggml_tensor * build_layer_attn_linear(
llm_graph_input_rs * inp,
ggml_tensor * cur,
ggml_tensor * causal_mask,
ggml_tensor * identity,
ggml_tensor * diag_mask,
int il);
ggml_tensor * build_layer_ffn(
ggml_tensor * cur,
int il);
ggml_tensor * build_delta_net_chunking(
ggml_tensor * q,
ggml_tensor * k,
ggml_tensor * v,
ggml_tensor * g,
ggml_tensor * beta,
ggml_tensor * state,
ggml_tensor * causal_mask,
ggml_tensor * identity,
ggml_tensor * diag_mask,
int il);
ggml_tensor * build_delta_net_autoregressive(
ggml_tensor * q,
ggml_tensor * k,
ggml_tensor * v,
ggml_tensor * g,
ggml_tensor * beta,
ggml_tensor * state,
int il);
ggml_tensor * build_norm_gated(
ggml_tensor * input,
ggml_tensor * weights,
ggml_tensor * gate,
int layer);
const llama_model & model;
};
struct llm_build_qwen : public llm_graph_context {
llm_build_qwen(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_refact : public llm_graph_context {
llm_build_refact(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_rnd1 : public llm_graph_context {
llm_build_rnd1(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_rwkv6 : public llm_build_rwkv6_base {
llm_build_rwkv6(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_rwkv6qwen2 : public llm_build_rwkv6_base {
llm_build_rwkv6qwen2(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_rwkv7 : public llm_build_rwkv7_base {
llm_build_rwkv7(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_seed_oss : public llm_graph_context {
llm_build_seed_oss(const llama_model & model, const llm_graph_params & params);
};
template <bool iswa>
struct llm_build_smallthinker : public llm_graph_context {
llm_build_smallthinker(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_smollm3 : public llm_graph_context {
llm_build_smollm3(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_stablelm : public llm_graph_context {
llm_build_stablelm(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_starcoder2 : public llm_graph_context {
llm_build_starcoder2(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_starcoder : public llm_graph_context {
llm_build_starcoder(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_t5_dec : public llm_graph_context {
llm_build_t5_dec(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_t5_enc : public llm_graph_context {
llm_build_t5_enc(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_wavtokenizer_dec : public llm_graph_context {
llm_build_wavtokenizer_dec(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_xverse : public llm_graph_context {
llm_build_xverse(const llama_model & model, const llm_graph_params & params);
};