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llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
#pragma once
#include "ggml.h" // ggml_op
#include <string>
//
// gguf constants (sync with gguf.py)
//
enum llm_arch {
LLM_ARCH_LLAMA,
llama : Support llama 4 text-only (#12791) * llama4 conversion * initial support, no chat template * clean up a bit * fix tokenizer conversion * correct hparams * try this * fix shexp * ffn_inp_normed * chat template * clean up model conversion * add_bos * add scale_before_ffn * fix order * weight_before_ffn * llm_graph_input_attn_temp * add chunk attn mask * build_inp_attn_scale() * add comment about ggml_repeat * clarify comments * fix build
2025-04-07 23:06:44 +02:00
LLM_ARCH_LLAMA4,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_ARCH_DECI,
LLM_ARCH_FALCON,
LLM_ARCH_BAICHUAN,
LLM_ARCH_GROK,
LLM_ARCH_GPT2,
LLM_ARCH_GPTJ,
LLM_ARCH_GPTNEOX,
LLM_ARCH_MPT,
LLM_ARCH_STARCODER,
LLM_ARCH_REFACT,
LLM_ARCH_BERT,
LLM_ARCH_NOMIC_BERT,
model : Nomic Embed Text V2 with Mixture-of-Experts (MoE) architecture (#12466) * Nomic Embed Text V2 with Mixture-of-Experts (MoE) architecture - Adds MoE-based embedding model supporting multilingual embeddings. - Selects architecture variant based on hyperparameter detection (MoE layers). - Removes unnecessary subclass initialization checks for clarity. https://www.nomic.ai/blog/posts/nomic-embed-text-v2 Co-authored-by: Jared Van Bortel <jared@nomic.ai> * fix tokenizer * don't rename this tensor --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2025-04-28 15:52:15 -04:00
LLM_ARCH_NOMIC_BERT_MOE,
model : add NeoBERT (#14164) * convert neobert model to gguf * add inference graph * fix flake8 lint * followed reviewer suggestions Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * follow reviewers suggestions Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * override NeoBERT feed-forward length --------- Co-authored-by: dinhhuy <huy.dinh@brains-tech.co.jp> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2025-06-16 21:53:41 +09:00
LLM_ARCH_NEO_BERT,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_ARCH_JINA_BERT_V2,
LLM_ARCH_BLOOM,
LLM_ARCH_STABLELM,
LLM_ARCH_QWEN,
LLM_ARCH_QWEN2,
LLM_ARCH_QWEN2MOE,
LLM_ARCH_QWEN2VL,
llama : Support Qwen3 and Qwen3MoE (#12828) * add qwen3 & qwen3moe support. * fix --------- Co-authored-by: bozheng-hit <dsoul0621@gmail.com>
2025-04-09 17:47:36 +08:00
LLM_ARCH_QWEN3,
LLM_ARCH_QWEN3MOE,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_ARCH_PHI2,
LLM_ARCH_PHI3,
model: Add support for PhiMoE arch (#11003) * model: support phimoe * python linter * doc: minor Co-authored-by: ThiloteE <73715071+ThiloteE@users.noreply.github.com> * doc: minor Co-authored-by: ThiloteE <73715071+ThiloteE@users.noreply.github.com> * doc: add phimoe as supported model ggml-ci --------- Co-authored-by: ThiloteE <73715071+ThiloteE@users.noreply.github.com>
2025-01-09 11:21:41 +01:00
LLM_ARCH_PHIMOE,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_ARCH_PLAMO,
model : add PLaMo-2 support (#14560) * Add PLaMo-2 model using hybrid memory module * Fix z shape * Add cmath to include from llama-vocab.h * Explicitly dequantize normalization weights before RoPE apply * Revert unnecessary cast because the problem can be solved by excluding attn_k, attn_q when quantizing * Use ATTN_K/Q_NORM for k,q weights to prevent quantization * Remove SSM_BCDT that is not used from anywhere * Do not duplicate embedding weights for output.weight * Fix tokenizer encoding problem for multibyte strings * Apply suggestion from @CISC Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Use LLM_FFN_SWIGLU instead of splitting ffn_gate and ffn_up * Remove unnecessary part for Grouped Query Attention * Fix how to load special token id to gguf * Remove unused tensor mapping * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Remove llama_vocab_plamo2 class and replace it with llm_tokenizer_plamo2_session to follow the other tokenizer implementations * Update src/llama-vocab.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update convert_hf_to_gguf.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-model.cpp 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 convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Fix plamo2 tokenizer session to prevent multiple calls of build() --------- Co-authored-by: Francis Couture-Harpin <git@compilade.net> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2025-07-16 01:11:42 +09:00
LLM_ARCH_PLAMO2,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_ARCH_CODESHELL,
LLM_ARCH_ORION,
LLM_ARCH_INTERNLM2,
LLM_ARCH_MINICPM,
LLM_ARCH_MINICPM3,
LLM_ARCH_GEMMA,
LLM_ARCH_GEMMA2,
llama : Add Gemma 3 support (+ experimental vision capability) (#12343) * llama : Add Gemma 3 text-only support * fix python coding style * fix compile on ubuntu * python: fix style * fix ubuntu compile * fix build on ubuntu (again) * fix ubuntu build, finally * clip : Experimental support for Gemma 3 vision (#12344) * clip : Experimental support for Gemma 3 vision * fix build * PRId64
2025-03-12 09:30:24 +01:00
LLM_ARCH_GEMMA3,
model : gemma3n text-only (#14400) * gemma3n * add llm_graph_input_one
2025-06-26 19:34:02 +02:00
LLM_ARCH_GEMMA3N,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_ARCH_STARCODER2,
LLM_ARCH_MAMBA,
llama : initial Mamba-2 support (#9126) * llama : initial Mamba-2 support * ggml : SIMD ggml_ssm_scan for Mamba-2 * ggml : improve ggml_mul speed when masking recurrent states * llama : support running Mamba-Codestral-7B-v0.1 * llama : fix Mamba-2 conv state saving * ggml : make the ggml_mul fast broadcast path more consistently formatted * llama : remove unused variable * llama : add missing break * convert_hf : prefer SentencePiece tokenizer for Mamba-2 when present The tokenzier.json of Mamba-Codestral-7B-v0.1 otherwise requires workarounds to work correctly. * llama : avoid redundant state copy for Mamba 1 and 2 * metal : attempt to adapt SSM_SCAN for Mamba-2 * metal : fix SSM_SCAN pipeline scope * metal : use log and exp instead of log1pf and expf in SSM_SCAN * metal : remove unused arguments for SSM_SCAN The max index is 31, so trimming the arguments is necessary. * metal : add back n_seqs to SSM_SCAN args Whoops, this is needed for the offset in the concatenated output. * metal : fix SSM_SCAN state head offset * metal : fix wrong number of tokens per sequence in SSM_SCAN * ggml : remove unused fast broadcast path in GGML_MUL This was initially added because states were masked with ggml_mul, but this is no longer done and so this "optimisation" is no longer necessary, or at least not worth the additional code complexity. * ggml : avoid multiply by D in GGML_OP_SSM_SCAN This makes the weight buft detection in src/llama.cpp simpler. * convert : transpose Mamba-2 A, D and reshape SSM_NORM This breaks existing conversions of Mamba-2 models to avoid some reshapes. Not sure if it's a good idea, but it makes the graph slightly cleaner. * llama : more appropriate SSM_SCAN and SSM_CONV buft support checks * convert : fix flake8 lint * metal : fix confusion between ; and , * metal : add missing args for nb references in ssm_scan_f32_group * metal : single-user mamba2 inference works * kv-cache : remove const_cast when setting inputs for s_copy And also fix multi-user inference for recurrent models by using cell_id instead of i as the kv cell index when populating s_copy. * convert : avoid AutoConfig for Mamba and Mamba2 hparams * kv-cache : allow context shift for recurrent models * graph : fix recurrent state copies when avoiding copies Works, but using lambda functions might not be that clean. * ggml : fix mamba2 ssm scan when compiled with SVE * ggml-cpu : reorder SVE FMA for consistency with other SIMD arches * cuda : implement ssm scan for Mamba2 There is still room for improvement, but it works! * cuda : adapt Mamba1 ssm scan to shape changes from Mamba2 * mamba : fix mismatched new and delete size for llm_build_mamba Subclasses of llm_graph_context cannot have extra fields, because the called destructor is not the one from the subclass. This otherwise would cause problems when runnning Mamba-(1|2) inference when compiled -DGGML_SANITIZE_ADDRESS=ON * cuda : graceful fallback for Mamba-1 models with weird embd size
2025-07-02 13:10:24 -04:00
LLM_ARCH_MAMBA2,
llama : support Jamba hybrid Transformer-Mamba models (#7531) * wip: llama : separate recurrent states from the KV cache This will be necessary to support Jamba (and other recurrent models mixed with Attention). Doesn't compile yet, and finding a slot isn't yet done correctly for recurrent states. * llama : use std::find for seq_nodes in llama_rs_cache * llama : state checkpoints for recurrent models * llama : correctly handle more edge cases for the rs cache * llama : rename many llama_kv_cache_* functions * llama : remove useless return value for some llama_cache_* functions * llama : rethink recurrent state cell counts * llama : begin work on support for variable GQA This will also be useful for Jamba if we consider the Mamba layers to have 0 KV heads. * llama : gracefully fail when not finding hybrid slot * llama : support Jamba * llama : fix BERT inference without KV cache * convert-hf : check for unprocessed Jamba experts * convert-hf : support Mini-Jamba conversion * llama : fix Jamba quantization sanity checks * llama : sequence-length-aware batch splitting * llama : use equal-sequence-length sub-batches for recurrent models * ggml : simplify SSM-related operators * llama : make recurrent state slot allocation contiguous * llama : adapt internal uses of batches to llama_ubatch * llama : fix batch split output count for embeddings * llama : minimize swaps when reordering logits This reduces overhead when running hellaswag on thousands of sequences with very small 100k params Mamba models. * llama : fix edge case finding batch seq_id of split recurrent cell This otherwise was a problem when running the HellaSwag benchmark with small batch sizes, making it crash. * llama : avoid copies for simple batch splits * ggml : make ggml_ssm_scan not modify its source tensors * llama : fix shared recurrent tail cell count for small ubatch sizes Otherwise it was impossible to run the 'parallel' example with '-ub 1' with a Mamba or Jamba model. * llama : fix .base() compilation error on Windows * llama : allow doing the equivalent of SSM_CONV with SUM_ROWS and MUL * ggml : allow GGML_OP_CONCAT to work on non-contiguous tensors The implementation already supported it, and this makes Mamba's conv step slightly faster. * mamba : fix non-contiguous usage of ggml_silu * llama : session saving and reloading for hybrid models * convert_hf : fix Jamba conversion * llama : fix mixed signedness comparison * llama : use unused n_embd_k_gqa in k_shift This also slightly reduces the diff from the master branch * llama : begin renaming llama_past back to llama_kv_cache * llama : remove implicit recurrent state rollbacks * llama : partially apply clang-format style * convert : fix jamba conv1d shape squeezing * graph : add back hybrid memory graph input But this time it contains the sub-cache graph inputs. This *should* make it easier to handle updating the inputs when caching the graph (eventually). * model : add Jamba to Mamba-specific hparams printing * jamba : remove redundant nullptr initializations * model : remove unnecessary prefix for tensor loading constants Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * model : use ggml_swiglu_split for Mamba Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * model : make falcon-h1 use shared mamba2 layer builder * memory : avoid referring to KV in recurrent cache logs * gguf-py : avoid adding duplicate tensor mappings for Jamba Some of the tensor names are common with Llama4 --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
2025-07-09 14:59:57 -04:00
LLM_ARCH_JAMBA,
model : add support for Falcon-H1 family (#14534) * v1 * push more fixes * another fix * fix * more fixes * minor fix * more cleaning on python code * python fixes * changed precision for multipliers float 32->64 * fixes * another fix * fix * pre-norm -> norm * fix * Revert "fix" This reverts commit 243e4d1a50bd73467d99f6b289b9a1826f83b94b. * fix * small fix ffn_norm * try * mix instead of max * fix vocab size * conflict solve * fixed multipliers * falcon-h1 specefic vocab resolved * read arch from gguf.MODEL_ARCH * mamba_d_ssm added to d_inner find_hparam * remove unused functions from gguf_writer.py * override modify_tensors instead of get_tensors * fix conversion and d_inner * added some cb functions for debugging puposes * inp_out_ids moved outside of layers loop * mup_vec create as float64 * fix rope_theta * injected mup * clean ups * rm extra space * rm unused MAMBA_CHUNK_SIZE * rm unused key * add bos False * changed ROPE_TYPE * cleaning debugging stuff * cleaning debug quant * fix comment * some cleanups * some cleanups * Update src/llama-model-loader.cpp * more cleanups * moe cleanuips * d_ssm -> d_inner; * cleaning unused hparams * cleanup * more cleanups * more cleanups on python conversion; * minor cleanups * Apply suggestions from code review Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * remove todo * added falcon-h1 * tensor not required * clean * remove unneeded attributes * more cleanups and fixed conversion * remove final_norm * flake8 fixes * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * flake8 fixes * Update src/llama-hparams.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-arch.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * added hashes * Update src/llama-arch.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update src/llama-vocab.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update the update file * Revert "update the update file" This reverts commit 082ab4ad2a3927384d878666a5f8cae4eb15f577. * fix: address suggestions * fix: update convert_hf_to_gguf.py * Update gguf-py/gguf/constants.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update src/llama-model-loader.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * d_inner fixed * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * reshaping ssm_norm for 34B * removing generate_mup * remove duplicates metadata keys * rm comment * final comment * fix unused args * fix constants * fix bad merge * Update src/llama-model.cpp Co-authored-by: compilade <git@compilade.net> * falcon-h1: remove unused ssm_in_b and bad merge * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * falcon-h1: fix last comment * Update convert_hf_to_gguf.py Co-authored-by: compilade <git@compilade.net> * falcon-h1: revert add_add_bos(False) * falcon-h1: fix tied weights * falcon-h1: remove whitespace * falcon-h1: fix wrong size param * falcon-h1: fix whitespace issues --------- Co-authored-by: younesbelkada <younes.belkada@tii.ae> Co-authored-by: Younes B <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> Co-authored-by: compilade <git@compilade.net>
2025-07-09 12:03:49 +04:00
LLM_ARCH_FALCON_H1,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_ARCH_XVERSE,
LLM_ARCH_COMMAND_R,
llama : add support for the cohere2 model architecture (#10900)
2025-01-04 09:33:31 -05:00
LLM_ARCH_COHERE2,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_ARCH_DBRX,
LLM_ARCH_OLMO,
LLM_ARCH_OLMO2,
LLM_ARCH_OLMOE,
LLM_ARCH_OPENELM,
LLM_ARCH_ARCTIC,
LLM_ARCH_DEEPSEEK,
LLM_ARCH_DEEPSEEK2,
LLM_ARCH_CHATGLM,
llama-model : add Glm4Model implementation for GLM-4-0414 (#12867) * GLM-4-0414 * use original one * Using with tensor map * fix bug * change order * change order * format with flask8
2025-04-11 18:10:10 +08:00
LLM_ARCH_GLM4,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_ARCH_BITNET,
LLM_ARCH_T5,
LLM_ARCH_T5ENCODER,
LLM_ARCH_JAIS,
LLM_ARCH_NEMOTRON,
LLM_ARCH_EXAONE,
model : add EXAONE 4.0 support (#14630)
2025-07-18 17:45:49 +09:00
LLM_ARCH_EXAONE4,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_ARCH_RWKV6,
llama: add support for QRWKV6 model architecture (#11001) llama: add support for QRWKV6 model architecture (#11001) * WIP: Add support for RWKV6Qwen2 Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * RWKV: Some graph simplification Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Add support for RWKV6Qwen2 with cpu and cuda GLA Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * RWKV6[QWEN2]: Concat lerp weights together to reduce cpu overhead Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Fix some typos Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * code format changes Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Fix wkv test & add gla test Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Fix cuda warning Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Update README.md Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Update ggml/src/ggml-cuda/gla.cu Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Fix fused lerp weights loading with RWKV6 Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * better sanity check skipping for QRWKV6 in llama-quant thanks @compilade Signed-off-by: Molly Sophia <mollysophia379@gmail.com> Co-authored-by: compilade <git@compilade.net> --------- Signed-off-by: Molly Sophia <mollysophia379@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: compilade <git@compilade.net>
2025-01-10 09:58:08 +08:00
LLM_ARCH_RWKV6QWEN2,
llama: Add support for RWKV v7 architecture (#12412) * ggml: Add op l2_norm Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * ggml: Add op rwkv_wkv7 Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * llama: Add support for RWKV7 and ARWKV7 models Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * llama: fix inference with RWKV6Qwen2 Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * llama: add more (a)rwkv7 variants in size Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Apply code-format changes Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * fix MUSA build Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * llama: fix shape error with rwkv using llama-parallel Signed-off-by: Molly Sophia <mollysophia379@gmail.com> --------- Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
2025-03-18 07:27:50 +08:00
LLM_ARCH_RWKV7,
LLM_ARCH_ARWKV7,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_ARCH_GRANITE,
LLM_ARCH_GRANITE_MOE,
model : Granite Four (#13550) * wip: llama : separate recurrent states from the KV cache This will be necessary to support Jamba (and other recurrent models mixed with Attention). Doesn't compile yet, and finding a slot isn't yet done correctly for recurrent states. * llama : use std::find for seq_nodes in llama_rs_cache * llama : state checkpoints for recurrent models * llama : correctly handle more edge cases for the rs cache * llama : rename many llama_kv_cache_* functions * llama : remove useless return value for some llama_cache_* functions * llama : rethink recurrent state cell counts * llama : begin work on support for variable GQA This will also be useful for Jamba if we consider the Mamba layers to have 0 KV heads. * llama : gracefully fail when not finding hybrid slot * llama : support Jamba * llama : fix BERT inference without KV cache * convert-hf : check for unprocessed Jamba experts * convert-hf : support Mini-Jamba conversion * llama : fix Jamba quantization sanity checks * llama : sequence-length-aware batch splitting * llama : use equal-sequence-length sub-batches for recurrent models * ggml : simplify SSM-related operators * llama : make recurrent state slot allocation contiguous * llama : adapt internal uses of batches to llama_ubatch * llama : fix batch split output count for embeddings * llama : minimize swaps when reordering logits This reduces overhead when running hellaswag on thousands of sequences with very small 100k params Mamba models. * llama : fix edge case finding batch seq_id of split recurrent cell This otherwise was a problem when running the HellaSwag benchmark with small batch sizes, making it crash. * llama : avoid copies for simple batch splits * llama : use im2col and mul_mat to perform convolution for Mamba This removes the need for ggml_ssm_conv!!! But performance seems slighly worse on my system, especially for prompt processing. Maybe ggml_mul_mat isn't optimized for small row sizes? More performance testing is necessary until GGML_OP_SSM_CONV is removed. * ggml : make ggml_ssm_scan not modify its source tensors * llama : fix shared recurrent tail cell count for small ubatch sizes Otherwise it was impossible to run the 'parallel' example with '-ub 1' with a Mamba or Jamba model. * llama : fix .base() compilation error on Windows * llama : allow doing the equivalent of SSM_CONV with SUM_ROWS and MUL * ggml : allow GGML_OP_CONCAT to work on non-contiguous tensors The implementation already supported it, and this makes Mamba's conv step slightly faster. * llama : rename llama_cache to llama_past This can be changed back later if the name change is wrong. I was renaming the functions anyway to generalize kv-cache-related functions to hybrid and recurrent model architectures. I think llama_past is a better name than llama_cache for a combined kv cache and recurrent state cache, because the states it contains pretty much always come before the newly-added ones for any particular sequence. Also 'llama_past_clear' sounds more obvious in what it does than 'llama_kv_cache_clear'. The future is what the models generate. (For embeddings, the kv cache isn't really used anyway) Still, I'm open to better suggestions. * examples : replace llama_kv_cache_seq_* with llama_past_seq_* * mamba : fix non-contiguous usage of ggml_silu * llama : initial Mamba-2 support * ggml : SIMD ggml_ssm_scan for Mamba-2 * ggml : improve ggml_mul speed when masking recurrent states * llama : support running Mamba-Codestral-7B-v0.1 * llama : fix Mamba-2 conv state saving * ggml : make the ggml_mul fast broadcast path more consistently formatted * llama : remove unused variable * llama : add missing break * convert_hf : prefer SentencePiece tokenizer for Mamba-2 when present The tokenzier.json of Mamba-Codestral-7B-v0.1 otherwise requires workarounds to work correctly. * llama : session saving and reloading for hybrid models * convert_hf : fix Jamba conversion * llama : fix mixed signedness comparison * llama : use unused n_embd_k_gqa in k_shift This also slightly reduces the diff from the master branch * llama : begin renaming llama_past back to llama_kv_cache * llama : avoid redundant state copy for Mamba 1 and 2 * metal : attempt to adapt SSM_SCAN for Mamba-2 * metal : fix SSM_SCAN pipeline scope * metal : use log and exp instead of log1pf and expf in SSM_SCAN * metal : remove unused arguments for SSM_SCAN The max index is 31, so trimming the arguments is necessary. * metal : add back n_seqs to SSM_SCAN args Whoops, this is needed for the offset in the concatenated output. * metal : fix SSM_SCAN state head offset * metal : fix wrong number of tokens per sequence in SSM_SCAN * ggml : remove unused fast broadcast path in GGML_MUL This was initially added because states were masked with ggml_mul, but this is no longer done and so this "optimisation" is no longer necessary, or at least not worth the additional code complexity. * ggml : avoid multiply by D in GGML_OP_SSM_SCAN This makes the weight buft detection in src/llama.cpp simpler. * convert : transpose Mamba-2 A, D and reshape SSM_NORM This breaks existing conversions of Mamba-2 models to avoid some reshapes. Not sure if it's a good idea, but it makes the graph slightly cleaner. * llama : more appropriate SSM_SCAN and SSM_CONV buft support checks * convert : fix flake8 lint * llama : remove implicit recurrent state rollbacks * llama : partially apply clang-format style * metal : fix confusion between ; and , * metal : add missing args for nb references in ssm_scan_f32_group * metal : single-user mamba2 inference works * kv-cache : remove const_cast when setting inputs for s_copy And also fix multi-user inference for recurrent models by using cell_id instead of i as the kv cell index when populating s_copy. * convert : avoid AutoConfig for Mamba and Mamba2 hparams * kv-cache : allow context shift for recurrent models * graph : fix recurrent state copies when avoiding copies Works, but using lambda functions might not be that clean. * ggml : fix mamba2 ssm scan when compiled with SVE * ggml-cpu : reorder SVE FMA for consistency with other SIMD arches * cuda : implement ssm scan for Mamba2 There is still room for improvement, but it works! * cuda : adapt Mamba1 ssm scan to shape changes from Mamba2 * feat: Add conversion for Bamba models This is borrowed and adapted from the original implementation https://github.com/ggml-org/llama.cpp/pull/10810 Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add Granite 4 conversion This is a manual copy from my draft branch https://github.com/gabe-l-hart/llama.cpp/blob/GraniteFourDraft/convert_hf_to_gguf.py#L5076 Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Plumb bamba through llama-arch Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add bamba to llama_arch_is_hybrid_recurrent Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add optional mamba ssm_in bias tensor Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add template specialization for get_arr to load a vector<uint32_t> for layer index arr in hparams Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Use an explicit bool to determine mamaba vs mamba2 This allows other architectures like bamba and granitemoehybrid to use mamab2 without a growing architecture `if` statement inside the mamba implementation. Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Isolate mamba(2) and granite attention layer building in static methods This will allow these layer-builder methods to be used from other build structs without complex inheritance. Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer sizes in granite build_attention_layer Also no need to pass in kv cache since it's already in the inp_attn Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: First (broken) pass at end-to-end Bamba implementation It generates (garbage) tokens! Still lots of debugging to do. Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Only do Granite multipliers if set Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Pull granite ffn portion into a static function and reuse in hybrid Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat(py): Allow gguf duplicate keys if they match by value and type This is helpful for hybrid models that want to do gguf param setting by calling multiple parent classes without needing to make those parent classes try/except on every attempt to set a gguf value. Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor(py): Simplify granitemoehybrid conversion to use parents better Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add GRANITE_MOE_HYBRID through llama-arch Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Support GRANITE_MOE_HYBRID in llama-model This re-uses the Bamba code paths heavily and simply adds the missing parts for loading MoE and the shared expert. Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix flake8 errors Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix recurrent cache get after rebase Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix hybrid granite implementation for signature changes in build_mamba*_layer Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Refactor relationship between non-hybrid classes and hybrid impl to use mixins The challenge here is to give both the non-hybrid classes (llm_build_mamba and llm_build_granite) AND the hybrid class (llm_build_hybrid_mamba) access to the same intermediate "base class" functionality (build_mamba*_layer, build_granite_attention_layer) without running into trouble with diamond inheritance of llm_graph_context. Due to the non-trivial initialization that happens in llm_graph_context, diamond inheritance results in multiple initializations of the common base which cause problems around the unique ptrs. I wanted to get away from `self->` everywhere, but this is still a bit cleaner than making those methods static I think. Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Implement the full copy-paste version to duplicate the layer builders This follows the pattern where the type of input is pinned to the type of memory and that is used to dispatch to the correct version of `build_rs` / `build_attn`. There's a lot of code duplication that can hopefully be pulled into common functions in the graph later. Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Rename llm_build_hybrid_mamba -> llm_build_granite_hybrid I've got back-and-forth a lot about how/if to try to implement reuse of the "child model" layer types for hybrid models. At the end of the day, I think hybrid models are their own beast and even if their layers are inspired by other models, they should maintain control of their own layer building (in other words, the copy-paste method). Given that, the name should reflect that this is not a generic hybrid model builder, but rather a granite- specific hybrid model builder that can do MoE (granite 4) or dense (bamba). As part if this, I also cleaned up dangling comments from previous attempts at using static methods for reusability. Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * mamba : fix mismatched new and delete size for llm_build_mamba Subclasses of llm_graph_context cannot have extra fields, because the called destructor is not the one from the subclass. This otherwise would cause problems when runnning Mamba-(1|2) inference when compiled -DGGML_SANITIZE_ADDRESS=ON * memory : correctly handle failure in apply() ggml-ci * style: Remove TODO for adding first hybrid models to the switch Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix bad merge in tensor_mapping.py w/ SSM_NORM Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix bad merge resolution with variable renames/moves in llm_build_mamba Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * docs: Fix comment about duplicate key check Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Conform to standard way of initializing inp_out_ids Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * convert : fix jamba conv1d shape squeezing * fix: Fix input initialization in granite_hybrid after removal of hybrid inputs Branch: GraniteFourWithJamba Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use llm_graph_context_mamba in llm_build_granite_hybrid Branch: GraniteFourWithJamba Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Refactor mamba2/granite/jamba/granite_hybrid relationships as mixins The key is for the mixin classes (llm_graph_context_mamba, llm_graph_context_granite) to use virtual inheritance from llm_graph_context. This allows the common members to exist only once in the class hierarchy. The downside is that llm_graph_context will be re-initialized once for each parent (ie 2x for single mixin, 3x for two mixins, etc...). Branch: GraniteFourWithJamba Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * graph : add back hybrid memory graph input But this time it contains the sub-cache graph inputs. This *should* make it easier to handle updating the inputs when caching the graph (eventually). * model : add Jamba to Mamba-specific hparams printing * fix: Fix input setup after upstream merge Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * jamba : remove redundant nullptr initializations * model : remove unnecessary prefix for tensor loading constants Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * model : use ggml_swiglu_split for Mamba Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * feat: Add support for dense FFN in GraniteMoeHybrid This was already partially supported via reusing the granite ffn builder, and there may be models that leverage this architecture going forward. The naming is a bit odd, but in the transformers version, it reuses the same model class and simply has zero regular experts and a single shared expert (which is the same as a single dense FFN). Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add support for dense FFN tensor names on c++ side Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use child inputs for Falcon H1 after merge resolution Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove unnecessary prefix on tensor constants Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * model : make falcon-h1 use shared mamba2 layer builder * memory : avoid referring to KV in recurrent cache logs * fix: Revert order changes for Falcon H1 to stay consistent with upstream Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * gguf-py : avoid adding duplicate tensor mappings for Jamba Some of the tensor names are common with Llama4 * refactor: Collapse Bamba and GraniteMoeHybrid into GraniteHybrid The only key difference is the use of rope which is now set via rope_finetuned in the hparams Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove use of diamond inheritance Per PR discussion, it's simpler to keep this with basic inheritance and not introduce the complexity of virtual inheritance and multiple inheritance https://github.com/ggml-org/llama.cpp/pull/13550#issuecomment-3053787556 Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Log mamba params for Granite Hybrid Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove unused ssm_in_b Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove ATTENTION_LAYER_INDICES hparam in favor of n_head_kv This matches how recurrent vs attention heads are identified for Jamba Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove unused template expansion for get_arr Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Review cleanup in convert_hf_to_gguf The gist is to be explicit about which base class is being used with the multiple inheritance setup Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Undo hidden warnings about duplicate identical keys in add_key_value After further discussion, this encourages sloppy overwriting in the model converters Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: If not using ROPE, context is "infinite" Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * doc: Add a comment outlining expected duplicate key warnings Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove unnecessary duplicate keys in converter Co-authored-by: Francis Couture-Harpin <git@compilade.net> (thanks for the sharp eyes and patience!) Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Francis Couture-Harpin <git@compilade.net> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
2025-07-10 18:20:13 -06:00
LLM_ARCH_GRANITE_HYBRID,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_ARCH_CHAMELEON,
LLM_ARCH_WAVTOKENIZER_DEC,
llama : add PLM GGUF Conversion & Inference Support (#12457) * add edgellm model arch[conversation feature doesn't work] * remove output.weight layer for edgellm arch * [Model] update the name of the model * update the name of model arch in convert gguf * [Model] Refarctor the model arch into llama-model * [Bug] Fix the bug in create attn kv * [Code] Fix editorconfig erros * [Code] Remove Trailing whitespace * [Code] Remove Trailing whitespace * [Code] Change the order of model arch in list * [Code] Fix flake8 Lint errors * Remove trailing white space * [Code] Remove call in model arch
2025-03-27 10:49:15 +00:00
LLM_ARCH_PLM,
llama : support BailingMoE (Ling) (#12634)
2025-03-30 22:21:03 +02:00
LLM_ARCH_BAILINGMOE,
model : add dots.llm1 architecture support (#14044) (#14118) Adds: * Dots1Model to convert_hf_to_gguf.py * Computation graph code to llama-model.cpp * Chat template to llama-chat.cpp to detect this model's template. --- The model is called "dots.llm1" (I decided to shorten it to dots1 or DOTS1 in the code generally) architecture. The only models that exist as of writing of this commit that follow this architecture are "dots.llm1.inst" and "dots.llm1.base" from here: * https://huggingface.co/rednote-hilab/dots.llm1.inst * https://huggingface.co/rednote-hilab/dots.llm1.base The model architecture is a combination of Qwen and Deepseek parts, as seen here: https://github.com/huggingface/transformers/blob/ffe12627b4e84489d2ab91dd0ec00614855edc79/src/transformers/models/dots1/modular_dots1.py
2025-06-15 00:52:06 -07:00
LLM_ARCH_DOTS1,
model : Add support for Arcee AI's upcoming AFM model (#14185) * Add Arcee AFM support * Add draft update code * Fix linter and update URL, may still not be final * Update src/llama-model.cpp Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com> * Remote accidental blank line --------- Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
2025-06-16 00:04:06 +01:00
LLM_ARCH_ARCEE,
model : add support for ERNIE 4.5 0.3B model (#14408) Add Day-0 support for Baidu ERNIE 4.5 0.3B model. Signed-off-by: Weizhao Ouyang <weizhao.ouyang@arm.com>
2025-06-28 22:08:21 +08:00
LLM_ARCH_ERNIE4_5,
model: add Ernie 4.5 MoE support (#14658) * Add Ernie4.5 MoE * Fix Flake errors. * Properly encode/decode MoE layer step * Correct tensor mappings (.weight) * Pass and read n_ff_exp * n_ff_shexp calculation and further minor changes * Rope fixes. * .gitignore fix * Add unit32 cast for Linux builds * Apply suggestions from code review Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Further fixes from code review * Fix trailing whitespace * Reenable missing experts error * Code style from code review Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Fix non-MoE regression Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
2025-07-17 23:15:32 +02:00
LLM_ARCH_ERNIE4_5_MOE,
model : add hunyuan moe (#14425) * model : add hunyuan moe * tokenizer ok * fix tensor name * cgraph init * chat template * wip * almost working * skip embed, fix bos * cleanup * yarn scaling * cleanup * correct rope type * failed token fix * ntk alpha freq_base * tokenization working * cleanup and pr changes * vocab_size sanity check * ntk alpha generic * Update convert_hf_to_gguf.py * Apply suggestions from code review * fix regression * fix style --------- Co-authored-by: kooshi <1934337+kooshi@users.noreply.github.com>
2025-07-08 10:24:06 +02:00
LLM_ARCH_HUNYUAN_MOE,
model : add SmolLM3 (#14581) * Init - first pass. * Model -> ModelBase. * fix errors in conversion. * Update the graph. * up. * up. * wip * cgraph ok * rm redundant code --------- Co-authored-by: Vaibhavs10 <vaibhavs10@gmail.com>
2025-07-08 18:07:01 +02:00
LLM_ARCH_SMOLLM3,
model : support LiquidAI LFM2 hybrid family (#14620) **Important** LFM2 was [merged ](https://github.com/huggingface/transformers/pull/39340)into transformers, but has not yet been released. To convert into gguf, install transformers from source ```shell pip install "transformers @ git+https://github.com/huggingface/transformers.git@main" ```
2025-07-11 20:27:01 +02:00
LLM_ARCH_LFM2,
Support diffusion models: Add Dream 7B (#14644) * Support diffusion models: Add Dream 7B * Move diffusion to examples * Move stuff to examples. Add patch to not use kv-cache * Address review comments * Make sampling fast * llama: remove diffusion functions * Add basic timings + cleanup * More cleanup * Review comments: better formating, use LOG instead std::cerr, re-use batch, use ubatch instead of max_length * fixup! * Review: move everything to diffusion-cli for now
2025-07-16 20:03:51 +08:00
LLM_ARCH_DREAM,
model : add support for SmallThinker series (#14898) * support smallthinker * support 20b softmax, 4b no sliding window * new build_moe_ffn_from_probs, and can run 4b * fix 4b rope bug * fix python type check * remove is_moe judge * remove set_dense_start_swa_pattern function and modify set_swa_pattern function * trim trailing whitespace * remove get_vocab_base of SmallThinkerModel in convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * better whitespace Apply suggestions from code review Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * use GGML_ASSERT for expert count validation Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Improve null pointer check for probs Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * use template parameter for SWA attention logic * better whitespace Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * move the creation of inp_out_ids before the layer loop * remove redundant judge for probs --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2025-07-28 19:47:00 +08:00
LLM_ARCH_SMALLTHINKER,
Add LLaDA 8b Diffusion model (#14771) * Add support for Llada-8b: diffusion model * Add README * Fix README and convert_hf_to_gguf * convert_hf_to_gguf.py: address review comments * Make everything in a single example * Remove model-specific sampling * Remove unused argmax * Remove braced initializers, improve README.md a bit * Add diffusion specific gguf params in set_vocab, remove setting rope_theta and rms_norm_eps * Remove adding the mask token * Move add_add_bos_token to set_vocab * use add_bool in gguf_writer.py
2025-07-31 19:49:09 +08:00
LLM_ARCH_LLADA,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_ARCH_UNKNOWN,
};
enum llm_kv {
LLM_KV_GENERAL_TYPE,
LLM_KV_GENERAL_ARCHITECTURE,
LLM_KV_GENERAL_QUANTIZATION_VERSION,
LLM_KV_GENERAL_ALIGNMENT,
llama : use LLM_KV_GENERAL_FILE_TYPE instead of gguf_find_key (#12672)
2025-04-01 20:54:28 +08:00
LLM_KV_GENERAL_FILE_TYPE,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_KV_GENERAL_NAME,
LLM_KV_GENERAL_AUTHOR,
LLM_KV_GENERAL_VERSION,
LLM_KV_GENERAL_URL,
LLM_KV_GENERAL_DESCRIPTION,
LLM_KV_GENERAL_LICENSE,
LLM_KV_GENERAL_SOURCE_URL,
LLM_KV_GENERAL_SOURCE_HF_REPO,
LLM_KV_VOCAB_SIZE,
LLM_KV_CONTEXT_LENGTH,
LLM_KV_EMBEDDING_LENGTH,
LLM_KV_FEATURES_LENGTH,
LLM_KV_BLOCK_COUNT,
LLM_KV_LEADING_DENSE_BLOCK_COUNT,
LLM_KV_FEED_FORWARD_LENGTH,
LLM_KV_EXPERT_FEED_FORWARD_LENGTH,
LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH,
LLM_KV_USE_PARALLEL_RESIDUAL,
LLM_KV_TENSOR_DATA_LAYOUT,
LLM_KV_EXPERT_COUNT,
LLM_KV_EXPERT_USED_COUNT,
LLM_KV_EXPERT_SHARED_COUNT,
LLM_KV_EXPERT_WEIGHTS_SCALE,
llama : Add support for DeepSeek V3 (#11049) * convert : extend DEEPSEEK2 model architecture to support DeepseekV3ForCausalLM by adding EXPERT_WEIGHTS_NORM and EXPERT_GATING_FUNC model parameters and FFN_EXP_PROBS_B tensor type * vocab : add DeepSeek V3 pre-tokenizer regexes * unicode : handle ACCENT_MARK and SYMBOL categories in regex * llama : add DeepSeek V3 chat template, handle new model parameters and tensor types --------- Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
2025-01-04 21:06:11 +01:00
LLM_KV_EXPERT_WEIGHTS_NORM,
LLM_KV_EXPERT_GATING_FUNC,
model : Nomic Embed Text V2 with Mixture-of-Experts (MoE) architecture (#12466) * Nomic Embed Text V2 with Mixture-of-Experts (MoE) architecture - Adds MoE-based embedding model supporting multilingual embeddings. - Selects architecture variant based on hyperparameter detection (MoE layers). - Removes unnecessary subclass initialization checks for clarity. https://www.nomic.ai/blog/posts/nomic-embed-text-v2 Co-authored-by: Jared Van Bortel <jared@nomic.ai> * fix tokenizer * don't rename this tensor --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2025-04-28 15:52:15 -04:00
LLM_KV_MOE_EVERY_N_LAYERS,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_KV_POOLING_TYPE,
LLM_KV_LOGIT_SCALE,
LLM_KV_DECODER_START_TOKEN_ID,
LLM_KV_ATTN_LOGIT_SOFTCAPPING,
LLM_KV_FINAL_LOGIT_SOFTCAPPING,
LLM_KV_SWIN_NORM,
LLM_KV_RESCALE_EVERY_N_LAYERS,
LLM_KV_TIME_MIX_EXTRA_DIM,
LLM_KV_TIME_DECAY_EXTRA_DIM,
LLM_KV_RESIDUAL_SCALE,
LLM_KV_EMBEDDING_SCALE,
llama: add support for QRWKV6 model architecture (#11001) llama: add support for QRWKV6 model architecture (#11001) * WIP: Add support for RWKV6Qwen2 Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * RWKV: Some graph simplification Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Add support for RWKV6Qwen2 with cpu and cuda GLA Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * RWKV6[QWEN2]: Concat lerp weights together to reduce cpu overhead Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Fix some typos Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * code format changes Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Fix wkv test & add gla test Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Fix cuda warning Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Update README.md Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Update ggml/src/ggml-cuda/gla.cu Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Fix fused lerp weights loading with RWKV6 Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * better sanity check skipping for QRWKV6 in llama-quant thanks @compilade Signed-off-by: Molly Sophia <mollysophia379@gmail.com> Co-authored-by: compilade <git@compilade.net> --------- Signed-off-by: Molly Sophia <mollysophia379@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: compilade <git@compilade.net>
2025-01-10 09:58:08 +08:00
LLM_KV_TOKEN_SHIFT_COUNT,
llama : Support llama 4 text-only (#12791) * llama4 conversion * initial support, no chat template * clean up a bit * fix tokenizer conversion * correct hparams * try this * fix shexp * ffn_inp_normed * chat template * clean up model conversion * add_bos * add scale_before_ffn * fix order * weight_before_ffn * llm_graph_input_attn_temp * add chunk attn mask * build_inp_attn_scale() * add comment about ggml_repeat * clarify comments * fix build
2025-04-07 23:06:44 +02:00
LLM_KV_INTERLEAVE_MOE_LAYER_STEP,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_KV_ATTENTION_HEAD_COUNT,
LLM_KV_ATTENTION_HEAD_COUNT_KV,
LLM_KV_ATTENTION_MAX_ALIBI_BIAS,
LLM_KV_ATTENTION_CLAMP_KQV,
LLM_KV_ATTENTION_KEY_LENGTH,
LLM_KV_ATTENTION_VALUE_LENGTH,
LLM_KV_ATTENTION_LAYERNORM_EPS,
LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,
LLM_KV_ATTENTION_GROUPNORM_EPS,
LLM_KV_ATTENTION_GROUPNORM_GROUPS,
LLM_KV_ATTENTION_CAUSAL,
LLM_KV_ATTENTION_Q_LORA_RANK,
LLM_KV_ATTENTION_KV_LORA_RANK,
llama: Add support for RWKV v7 architecture (#12412) * ggml: Add op l2_norm Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * ggml: Add op rwkv_wkv7 Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * llama: Add support for RWKV7 and ARWKV7 models Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * llama: fix inference with RWKV6Qwen2 Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * llama: add more (a)rwkv7 variants in size Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Apply code-format changes Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * fix MUSA build Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * llama: fix shape error with rwkv using llama-parallel Signed-off-by: Molly Sophia <mollysophia379@gmail.com> --------- Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
2025-03-18 07:27:50 +08:00
LLM_KV_ATTENTION_DECAY_LORA_RANK,
LLM_KV_ATTENTION_ICLR_LORA_RANK,
LLM_KV_ATTENTION_VALUE_RESIDUAL_MIX_LORA_RANK,
LLM_KV_ATTENTION_GATE_LORA_RANK,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,
LLM_KV_ATTENTION_SLIDING_WINDOW,
LLM_KV_ATTENTION_SCALE,
llama : DeepSeek V2/V3 MLA implementation (#12801) * Merged using squash to remove all noise commit messages * Force flash attention off for `LLM_ARCH_DEEPSEEK2` - embedding too large * Removed 3 conts (2x RoPE and 1x RMS-norm) * Changed to use `<cmath>` instead of `<math.h>` * Reverted removal of the 3 conts * Used `reshape` in `llm_graph_context::build_attn_mha()` * Use `k_pe = ggml_reshape` * Removed the 3 conts again * Removed the 3D views of `wk_b` and `wv_b`, and just save and 3D in GGUF * Removed MQA optimisation from `build_attn_mha()` as no gains now * Simplified `is_mla` branch in `llm_build_deepseek2()` * Removed `build_attn_mla` and added `nullptr` to all `build_atnn` calls * Fixed call to `build_attn` in `llm_build_t5_enc`
2025-04-15 07:49:57 +01:00
LLM_KV_ATTENTION_KEY_LENGTH_MLA,
LLM_KV_ATTENTION_VALUE_LENGTH_MLA,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_KV_ROPE_DIMENSION_COUNT,
LLM_KV_ROPE_DIMENSION_SECTIONS,
LLM_KV_ROPE_FREQ_BASE,
LLM_KV_ROPE_SCALE_LINEAR,
LLM_KV_ROPE_SCALING_TYPE,
LLM_KV_ROPE_SCALING_FACTOR,
LLM_KV_ROPE_SCALING_ATTN_FACTOR,
LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,
LLM_KV_ROPE_SCALING_FINETUNED,
LLM_KV_ROPE_SCALING_YARN_LOG_MUL,
LLM_KV_SPLIT_NO,
LLM_KV_SPLIT_COUNT,
LLM_KV_SPLIT_TENSORS_COUNT,
LLM_KV_SSM_INNER_SIZE,
LLM_KV_SSM_CONV_KERNEL,
LLM_KV_SSM_STATE_SIZE,
LLM_KV_SSM_TIME_STEP_RANK,
llama : initial Mamba-2 support (#9126) * llama : initial Mamba-2 support * ggml : SIMD ggml_ssm_scan for Mamba-2 * ggml : improve ggml_mul speed when masking recurrent states * llama : support running Mamba-Codestral-7B-v0.1 * llama : fix Mamba-2 conv state saving * ggml : make the ggml_mul fast broadcast path more consistently formatted * llama : remove unused variable * llama : add missing break * convert_hf : prefer SentencePiece tokenizer for Mamba-2 when present The tokenzier.json of Mamba-Codestral-7B-v0.1 otherwise requires workarounds to work correctly. * llama : avoid redundant state copy for Mamba 1 and 2 * metal : attempt to adapt SSM_SCAN for Mamba-2 * metal : fix SSM_SCAN pipeline scope * metal : use log and exp instead of log1pf and expf in SSM_SCAN * metal : remove unused arguments for SSM_SCAN The max index is 31, so trimming the arguments is necessary. * metal : add back n_seqs to SSM_SCAN args Whoops, this is needed for the offset in the concatenated output. * metal : fix SSM_SCAN state head offset * metal : fix wrong number of tokens per sequence in SSM_SCAN * ggml : remove unused fast broadcast path in GGML_MUL This was initially added because states were masked with ggml_mul, but this is no longer done and so this "optimisation" is no longer necessary, or at least not worth the additional code complexity. * ggml : avoid multiply by D in GGML_OP_SSM_SCAN This makes the weight buft detection in src/llama.cpp simpler. * convert : transpose Mamba-2 A, D and reshape SSM_NORM This breaks existing conversions of Mamba-2 models to avoid some reshapes. Not sure if it's a good idea, but it makes the graph slightly cleaner. * llama : more appropriate SSM_SCAN and SSM_CONV buft support checks * convert : fix flake8 lint * metal : fix confusion between ; and , * metal : add missing args for nb references in ssm_scan_f32_group * metal : single-user mamba2 inference works * kv-cache : remove const_cast when setting inputs for s_copy And also fix multi-user inference for recurrent models by using cell_id instead of i as the kv cell index when populating s_copy. * convert : avoid AutoConfig for Mamba and Mamba2 hparams * kv-cache : allow context shift for recurrent models * graph : fix recurrent state copies when avoiding copies Works, but using lambda functions might not be that clean. * ggml : fix mamba2 ssm scan when compiled with SVE * ggml-cpu : reorder SVE FMA for consistency with other SIMD arches * cuda : implement ssm scan for Mamba2 There is still room for improvement, but it works! * cuda : adapt Mamba1 ssm scan to shape changes from Mamba2 * mamba : fix mismatched new and delete size for llm_build_mamba Subclasses of llm_graph_context cannot have extra fields, because the called destructor is not the one from the subclass. This otherwise would cause problems when runnning Mamba-(1|2) inference when compiled -DGGML_SANITIZE_ADDRESS=ON * cuda : graceful fallback for Mamba-1 models with weird embd size
2025-07-02 13:10:24 -04:00
LLM_KV_SSM_GROUP_COUNT,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_KV_SSM_DT_B_C_RMS,
LLM_KV_WKV_HEAD_SIZE,
LLM_KV_TOKENIZER_MODEL,
LLM_KV_TOKENIZER_PRE,
LLM_KV_TOKENIZER_LIST,
LLM_KV_TOKENIZER_TOKEN_TYPE,
LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,
LLM_KV_TOKENIZER_SCORES,
LLM_KV_TOKENIZER_MERGES,
LLM_KV_TOKENIZER_BOS_ID,
LLM_KV_TOKENIZER_EOS_ID,
LLM_KV_TOKENIZER_EOT_ID,
LLM_KV_TOKENIZER_EOM_ID,
LLM_KV_TOKENIZER_UNK_ID,
LLM_KV_TOKENIZER_SEP_ID,
LLM_KV_TOKENIZER_PAD_ID,
LLM_KV_TOKENIZER_CLS_ID,
LLM_KV_TOKENIZER_MASK_ID,
LLM_KV_TOKENIZER_ADD_BOS,
LLM_KV_TOKENIZER_ADD_EOS,
llama : improve sep token handling (#14272)
2025-06-20 14:04:09 +02:00
LLM_KV_TOKENIZER_ADD_SEP,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_KV_TOKENIZER_ADD_PREFIX,
LLM_KV_TOKENIZER_REMOVE_EXTRA_WS,
LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP,
LLM_KV_TOKENIZER_HF_JSON,
LLM_KV_TOKENIZER_RWKV,
llama : add `llama_vocab`, functions -> methods, naming (#11110) * llama : functions -> methods (#11110) * llama : add struct llama_vocab to the API (#11156) ggml-ci * hparams : move vocab params to llama_vocab (#11159) ggml-ci * vocab : more pimpl (#11165) ggml-ci * vocab : minor tokenization optimizations (#11160) ggml-ci Co-authored-by: Diego Devesa <slarengh@gmail.com> * lora : update API names (#11167) ggml-ci * llama : update API names to use correct prefix (#11174) * llama : update API names to use correct prefix ggml-ci * cont ggml-ci * cont ggml-ci * minor [no ci] * vocab : llama_vocab_add_[be]os -> llama_vocab_get_add_[be]os (#11174) ggml-ci * vocab : llama_vocab_n_vocab -> llama_vocab_n_tokens (#11174) ggml-ci --------- Co-authored-by: Diego Devesa <slarengh@gmail.com>
2025-01-12 11:32:42 +02:00
LLM_KV_TOKENIZER_CHAT_TEMPLATE,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_KV_TOKENIZER_FIM_PRE_ID,
LLM_KV_TOKENIZER_FIM_SUF_ID,
LLM_KV_TOKENIZER_FIM_MID_ID,
LLM_KV_TOKENIZER_FIM_PAD_ID,
LLM_KV_TOKENIZER_FIM_REP_ID,
LLM_KV_TOKENIZER_FIM_SEP_ID,
LLM_KV_ADAPTER_TYPE,
LLM_KV_ADAPTER_LORA_ALPHA,
LLM_KV_POSNET_EMBEDDING_LENGTH,
LLM_KV_POSNET_BLOCK_COUNT,
LLM_KV_CONVNEXT_EMBEDDING_LENGTH,
LLM_KV_CONVNEXT_BLOCK_COUNT,
llama : add RobertaForSequenceClassification reranker support (#13875)
2025-05-29 08:15:01 +02:00
LLM_KV_CLASSIFIER_OUTPUT_LABELS,
model : support LiquidAI LFM2 hybrid family (#14620) **Important** LFM2 was [merged ](https://github.com/huggingface/transformers/pull/39340)into transformers, but has not yet been released. To convert into gguf, install transformers from source ```shell pip install "transformers @ git+https://github.com/huggingface/transformers.git@main" ```
2025-07-11 20:27:01 +02:00
LLM_KV_SHORTCONV_L_CACHE,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
// deprecated:
LLM_KV_TOKENIZER_PREFIX_ID,
LLM_KV_TOKENIZER_SUFFIX_ID,
LLM_KV_TOKENIZER_MIDDLE_ID,
};
enum llm_tensor {
LLM_TENSOR_TOKEN_EMBD,
LLM_TENSOR_TOKEN_EMBD_NORM,
LLM_TENSOR_TOKEN_TYPES,
LLM_TENSOR_POS_EMBD,
LLM_TENSOR_OUTPUT,
LLM_TENSOR_OUTPUT_NORM,
LLM_TENSOR_ROPE_FREQS,
LLM_TENSOR_ROPE_FACTORS_LONG,
LLM_TENSOR_ROPE_FACTORS_SHORT,
LLM_TENSOR_ATTN_Q,
LLM_TENSOR_ATTN_K,
LLM_TENSOR_ATTN_V,
LLM_TENSOR_ATTN_QKV,
LLM_TENSOR_ATTN_OUT,
LLM_TENSOR_ATTN_NORM,
LLM_TENSOR_ATTN_NORM_2,
LLM_TENSOR_ATTN_OUT_NORM,
LLM_TENSOR_ATTN_POST_NORM,
LLM_TENSOR_ATTN_ROT_EMBD,
LLM_TENSOR_FFN_GATE_INP,
LLM_TENSOR_FFN_GATE_INP_SHEXP,
LLM_TENSOR_FFN_NORM,
LLM_TENSOR_FFN_POST_NORM,
LLM_TENSOR_FFN_GATE,
LLM_TENSOR_FFN_DOWN,
LLM_TENSOR_FFN_UP,
LLM_TENSOR_FFN_ACT,
LLM_TENSOR_FFN_DOWN_EXP, // split experts for backward compatibility
LLM_TENSOR_FFN_GATE_EXP,
LLM_TENSOR_FFN_UP_EXP,
LLM_TENSOR_FFN_NORM_EXPS,
LLM_TENSOR_FFN_DOWN_EXPS, // merged experts
LLM_TENSOR_FFN_GATE_EXPS,
LLM_TENSOR_FFN_UP_EXPS,
LLM_TENSOR_FFN_DOWN_SHEXP,
LLM_TENSOR_FFN_GATE_SHEXP,
LLM_TENSOR_FFN_UP_SHEXP,
llama : Add support for DeepSeek V3 (#11049) * convert : extend DEEPSEEK2 model architecture to support DeepseekV3ForCausalLM by adding EXPERT_WEIGHTS_NORM and EXPERT_GATING_FUNC model parameters and FFN_EXP_PROBS_B tensor type * vocab : add DeepSeek V3 pre-tokenizer regexes * unicode : handle ACCENT_MARK and SYMBOL categories in regex * llama : add DeepSeek V3 chat template, handle new model parameters and tensor types --------- Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
2025-01-04 21:06:11 +01:00
LLM_TENSOR_FFN_EXP_PROBS_B,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_TENSOR_ATTN_Q_NORM,
LLM_TENSOR_ATTN_K_NORM,
LLM_TENSOR_LAYER_OUT_NORM,
llama-model : add Glm4Model implementation for GLM-4-0414 (#12867) * GLM-4-0414 * use original one * Using with tensor map * fix bug * change order * change order * format with flask8
2025-04-11 18:10:10 +08:00
LLM_TENSOR_POST_ATTN_NORM,
LLM_TENSOR_POST_MLP_NORM,
model : gemma3n text-only (#14400) * gemma3n * add llm_graph_input_one
2025-06-26 19:34:02 +02:00
LLM_TENSOR_PER_LAYER_TOKEN_EMBD, // gemma3n
LLM_TENSOR_PER_LAYER_MODEL_PROJ, // gemma3n
LLM_TENSOR_PER_LAYER_INP_GATE, // gemma3n
LLM_TENSOR_PER_LAYER_PROJ, // gemma3n
LLM_TENSOR_PER_LAYER_PROJ_NORM, // gemma3n
LLM_TENSOR_PER_LAYER_POST_NORM, // gemma3n
LLM_TENSOR_ALTUP_PROJ, // gemma3n
LLM_TENSOR_ALTUP_UNEMBD_PROJ, // gemma3n
LLM_TENSOR_ALTUP_CORRECT_COEF, // gemma3n
LLM_TENSOR_ALTUP_CORRECT_SCALE, // gemma3n
LLM_TENSOR_ALTUP_PREDICT_COEF, // gemma3n
LLM_TENSOR_ALTUP_ROUTER, // gemma3n
LLM_TENSOR_ALTUP_ROUTER_NORM, // gemma3n
LLM_TENSOR_LAUREL_L, // gemma3n
LLM_TENSOR_LAUREL_R, // gemma3n
LLM_TENSOR_LAUREL_POST_NORM, // gemma3n
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_TENSOR_SSM_IN,
LLM_TENSOR_SSM_CONV1D,
LLM_TENSOR_SSM_X,
LLM_TENSOR_SSM_DT,
llama : support Jamba hybrid Transformer-Mamba models (#7531) * wip: llama : separate recurrent states from the KV cache This will be necessary to support Jamba (and other recurrent models mixed with Attention). Doesn't compile yet, and finding a slot isn't yet done correctly for recurrent states. * llama : use std::find for seq_nodes in llama_rs_cache * llama : state checkpoints for recurrent models * llama : correctly handle more edge cases for the rs cache * llama : rename many llama_kv_cache_* functions * llama : remove useless return value for some llama_cache_* functions * llama : rethink recurrent state cell counts * llama : begin work on support for variable GQA This will also be useful for Jamba if we consider the Mamba layers to have 0 KV heads. * llama : gracefully fail when not finding hybrid slot * llama : support Jamba * llama : fix BERT inference without KV cache * convert-hf : check for unprocessed Jamba experts * convert-hf : support Mini-Jamba conversion * llama : fix Jamba quantization sanity checks * llama : sequence-length-aware batch splitting * llama : use equal-sequence-length sub-batches for recurrent models * ggml : simplify SSM-related operators * llama : make recurrent state slot allocation contiguous * llama : adapt internal uses of batches to llama_ubatch * llama : fix batch split output count for embeddings * llama : minimize swaps when reordering logits This reduces overhead when running hellaswag on thousands of sequences with very small 100k params Mamba models. * llama : fix edge case finding batch seq_id of split recurrent cell This otherwise was a problem when running the HellaSwag benchmark with small batch sizes, making it crash. * llama : avoid copies for simple batch splits * ggml : make ggml_ssm_scan not modify its source tensors * llama : fix shared recurrent tail cell count for small ubatch sizes Otherwise it was impossible to run the 'parallel' example with '-ub 1' with a Mamba or Jamba model. * llama : fix .base() compilation error on Windows * llama : allow doing the equivalent of SSM_CONV with SUM_ROWS and MUL * ggml : allow GGML_OP_CONCAT to work on non-contiguous tensors The implementation already supported it, and this makes Mamba's conv step slightly faster. * mamba : fix non-contiguous usage of ggml_silu * llama : session saving and reloading for hybrid models * convert_hf : fix Jamba conversion * llama : fix mixed signedness comparison * llama : use unused n_embd_k_gqa in k_shift This also slightly reduces the diff from the master branch * llama : begin renaming llama_past back to llama_kv_cache * llama : remove implicit recurrent state rollbacks * llama : partially apply clang-format style * convert : fix jamba conv1d shape squeezing * graph : add back hybrid memory graph input But this time it contains the sub-cache graph inputs. This *should* make it easier to handle updating the inputs when caching the graph (eventually). * model : add Jamba to Mamba-specific hparams printing * jamba : remove redundant nullptr initializations * model : remove unnecessary prefix for tensor loading constants Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * model : use ggml_swiglu_split for Mamba Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * model : make falcon-h1 use shared mamba2 layer builder * memory : avoid referring to KV in recurrent cache logs * gguf-py : avoid adding duplicate tensor mappings for Jamba Some of the tensor names are common with Llama4 --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
2025-07-09 14:59:57 -04:00
LLM_TENSOR_SSM_DT_NORM,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_TENSOR_SSM_A,
llama : support Jamba hybrid Transformer-Mamba models (#7531) * wip: llama : separate recurrent states from the KV cache This will be necessary to support Jamba (and other recurrent models mixed with Attention). Doesn't compile yet, and finding a slot isn't yet done correctly for recurrent states. * llama : use std::find for seq_nodes in llama_rs_cache * llama : state checkpoints for recurrent models * llama : correctly handle more edge cases for the rs cache * llama : rename many llama_kv_cache_* functions * llama : remove useless return value for some llama_cache_* functions * llama : rethink recurrent state cell counts * llama : begin work on support for variable GQA This will also be useful for Jamba if we consider the Mamba layers to have 0 KV heads. * llama : gracefully fail when not finding hybrid slot * llama : support Jamba * llama : fix BERT inference without KV cache * convert-hf : check for unprocessed Jamba experts * convert-hf : support Mini-Jamba conversion * llama : fix Jamba quantization sanity checks * llama : sequence-length-aware batch splitting * llama : use equal-sequence-length sub-batches for recurrent models * ggml : simplify SSM-related operators * llama : make recurrent state slot allocation contiguous * llama : adapt internal uses of batches to llama_ubatch * llama : fix batch split output count for embeddings * llama : minimize swaps when reordering logits This reduces overhead when running hellaswag on thousands of sequences with very small 100k params Mamba models. * llama : fix edge case finding batch seq_id of split recurrent cell This otherwise was a problem when running the HellaSwag benchmark with small batch sizes, making it crash. * llama : avoid copies for simple batch splits * ggml : make ggml_ssm_scan not modify its source tensors * llama : fix shared recurrent tail cell count for small ubatch sizes Otherwise it was impossible to run the 'parallel' example with '-ub 1' with a Mamba or Jamba model. * llama : fix .base() compilation error on Windows * llama : allow doing the equivalent of SSM_CONV with SUM_ROWS and MUL * ggml : allow GGML_OP_CONCAT to work on non-contiguous tensors The implementation already supported it, and this makes Mamba's conv step slightly faster. * mamba : fix non-contiguous usage of ggml_silu * llama : session saving and reloading for hybrid models * convert_hf : fix Jamba conversion * llama : fix mixed signedness comparison * llama : use unused n_embd_k_gqa in k_shift This also slightly reduces the diff from the master branch * llama : begin renaming llama_past back to llama_kv_cache * llama : remove implicit recurrent state rollbacks * llama : partially apply clang-format style * convert : fix jamba conv1d shape squeezing * graph : add back hybrid memory graph input But this time it contains the sub-cache graph inputs. This *should* make it easier to handle updating the inputs when caching the graph (eventually). * model : add Jamba to Mamba-specific hparams printing * jamba : remove redundant nullptr initializations * model : remove unnecessary prefix for tensor loading constants Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * model : use ggml_swiglu_split for Mamba Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * model : make falcon-h1 use shared mamba2 layer builder * memory : avoid referring to KV in recurrent cache logs * gguf-py : avoid adding duplicate tensor mappings for Jamba Some of the tensor names are common with Llama4 --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
2025-07-09 14:59:57 -04:00
LLM_TENSOR_SSM_B_NORM,
LLM_TENSOR_SSM_C_NORM,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_TENSOR_SSM_D,
llama : initial Mamba-2 support (#9126) * llama : initial Mamba-2 support * ggml : SIMD ggml_ssm_scan for Mamba-2 * ggml : improve ggml_mul speed when masking recurrent states * llama : support running Mamba-Codestral-7B-v0.1 * llama : fix Mamba-2 conv state saving * ggml : make the ggml_mul fast broadcast path more consistently formatted * llama : remove unused variable * llama : add missing break * convert_hf : prefer SentencePiece tokenizer for Mamba-2 when present The tokenzier.json of Mamba-Codestral-7B-v0.1 otherwise requires workarounds to work correctly. * llama : avoid redundant state copy for Mamba 1 and 2 * metal : attempt to adapt SSM_SCAN for Mamba-2 * metal : fix SSM_SCAN pipeline scope * metal : use log and exp instead of log1pf and expf in SSM_SCAN * metal : remove unused arguments for SSM_SCAN The max index is 31, so trimming the arguments is necessary. * metal : add back n_seqs to SSM_SCAN args Whoops, this is needed for the offset in the concatenated output. * metal : fix SSM_SCAN state head offset * metal : fix wrong number of tokens per sequence in SSM_SCAN * ggml : remove unused fast broadcast path in GGML_MUL This was initially added because states were masked with ggml_mul, but this is no longer done and so this "optimisation" is no longer necessary, or at least not worth the additional code complexity. * ggml : avoid multiply by D in GGML_OP_SSM_SCAN This makes the weight buft detection in src/llama.cpp simpler. * convert : transpose Mamba-2 A, D and reshape SSM_NORM This breaks existing conversions of Mamba-2 models to avoid some reshapes. Not sure if it's a good idea, but it makes the graph slightly cleaner. * llama : more appropriate SSM_SCAN and SSM_CONV buft support checks * convert : fix flake8 lint * metal : fix confusion between ; and , * metal : add missing args for nb references in ssm_scan_f32_group * metal : single-user mamba2 inference works * kv-cache : remove const_cast when setting inputs for s_copy And also fix multi-user inference for recurrent models by using cell_id instead of i as the kv cell index when populating s_copy. * convert : avoid AutoConfig for Mamba and Mamba2 hparams * kv-cache : allow context shift for recurrent models * graph : fix recurrent state copies when avoiding copies Works, but using lambda functions might not be that clean. * ggml : fix mamba2 ssm scan when compiled with SVE * ggml-cpu : reorder SVE FMA for consistency with other SIMD arches * cuda : implement ssm scan for Mamba2 There is still room for improvement, but it works! * cuda : adapt Mamba1 ssm scan to shape changes from Mamba2 * mamba : fix mismatched new and delete size for llm_build_mamba Subclasses of llm_graph_context cannot have extra fields, because the called destructor is not the one from the subclass. This otherwise would cause problems when runnning Mamba-(1|2) inference when compiled -DGGML_SANITIZE_ADDRESS=ON * cuda : graceful fallback for Mamba-1 models with weird embd size
2025-07-02 13:10:24 -04:00
LLM_TENSOR_SSM_NORM,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_TENSOR_SSM_OUT,
llama: Add support for RWKV v7 architecture (#12412) * ggml: Add op l2_norm Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * ggml: Add op rwkv_wkv7 Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * llama: Add support for RWKV7 and ARWKV7 models Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * llama: fix inference with RWKV6Qwen2 Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * llama: add more (a)rwkv7 variants in size Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Apply code-format changes Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * fix MUSA build Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * llama: fix shape error with rwkv using llama-parallel Signed-off-by: Molly Sophia <mollysophia379@gmail.com> --------- Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
2025-03-18 07:27:50 +08:00
LLM_TENSOR_TIME_MIX_W0,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_TENSOR_TIME_MIX_W1,
LLM_TENSOR_TIME_MIX_W2,
llama: Add support for RWKV v7 architecture (#12412) * ggml: Add op l2_norm Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * ggml: Add op rwkv_wkv7 Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * llama: Add support for RWKV7 and ARWKV7 models Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * llama: fix inference with RWKV6Qwen2 Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * llama: add more (a)rwkv7 variants in size Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Apply code-format changes Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * fix MUSA build Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * llama: fix shape error with rwkv using llama-parallel Signed-off-by: Molly Sophia <mollysophia379@gmail.com> --------- Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
2025-03-18 07:27:50 +08:00
LLM_TENSOR_TIME_MIX_A0,
LLM_TENSOR_TIME_MIX_A1,
LLM_TENSOR_TIME_MIX_A2,
LLM_TENSOR_TIME_MIX_V0,
LLM_TENSOR_TIME_MIX_V1,
LLM_TENSOR_TIME_MIX_V2,
LLM_TENSOR_TIME_MIX_G1,
LLM_TENSOR_TIME_MIX_G2,
LLM_TENSOR_TIME_MIX_K_K,
LLM_TENSOR_TIME_MIX_K_A,
LLM_TENSOR_TIME_MIX_R_K,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_TENSOR_TIME_MIX_LERP_X,
LLM_TENSOR_TIME_MIX_LERP_W,
LLM_TENSOR_TIME_MIX_LERP_K,
LLM_TENSOR_TIME_MIX_LERP_V,
LLM_TENSOR_TIME_MIX_LERP_R,
LLM_TENSOR_TIME_MIX_LERP_G,
llama: add support for QRWKV6 model architecture (#11001) llama: add support for QRWKV6 model architecture (#11001) * WIP: Add support for RWKV6Qwen2 Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * RWKV: Some graph simplification Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Add support for RWKV6Qwen2 with cpu and cuda GLA Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * RWKV6[QWEN2]: Concat lerp weights together to reduce cpu overhead Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Fix some typos Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * code format changes Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Fix wkv test & add gla test Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Fix cuda warning Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Update README.md Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * Update ggml/src/ggml-cuda/gla.cu Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Fix fused lerp weights loading with RWKV6 Signed-off-by: Molly Sophia <mollysophia379@gmail.com> * better sanity check skipping for QRWKV6 in llama-quant thanks @compilade Signed-off-by: Molly Sophia <mollysophia379@gmail.com> Co-authored-by: compilade <git@compilade.net> --------- Signed-off-by: Molly Sophia <mollysophia379@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: compilade <git@compilade.net>
2025-01-10 09:58:08 +08:00
LLM_TENSOR_TIME_MIX_LERP_FUSED,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_TENSOR_TIME_MIX_FIRST,
LLM_TENSOR_TIME_MIX_DECAY,
LLM_TENSOR_TIME_MIX_DECAY_W1,
LLM_TENSOR_TIME_MIX_DECAY_W2,
LLM_TENSOR_TIME_MIX_KEY,
LLM_TENSOR_TIME_MIX_VALUE,
LLM_TENSOR_TIME_MIX_RECEPTANCE,
LLM_TENSOR_TIME_MIX_GATE,
LLM_TENSOR_TIME_MIX_LN,
LLM_TENSOR_TIME_MIX_OUTPUT,
LLM_TENSOR_CHANNEL_MIX_LERP_K,
LLM_TENSOR_CHANNEL_MIX_LERP_R,
LLM_TENSOR_CHANNEL_MIX_KEY,
LLM_TENSOR_CHANNEL_MIX_RECEPTANCE,
LLM_TENSOR_CHANNEL_MIX_VALUE,
LLM_TENSOR_ATTN_Q_A,
LLM_TENSOR_ATTN_Q_B,
LLM_TENSOR_ATTN_KV_A_MQA,
LLM_TENSOR_ATTN_KV_B,
llama : DeepSeek V2/V3 MLA implementation (#12801) * Merged using squash to remove all noise commit messages * Force flash attention off for `LLM_ARCH_DEEPSEEK2` - embedding too large * Removed 3 conts (2x RoPE and 1x RMS-norm) * Changed to use `<cmath>` instead of `<math.h>` * Reverted removal of the 3 conts * Used `reshape` in `llm_graph_context::build_attn_mha()` * Use `k_pe = ggml_reshape` * Removed the 3 conts again * Removed the 3D views of `wk_b` and `wv_b`, and just save and 3D in GGUF * Removed MQA optimisation from `build_attn_mha()` as no gains now * Simplified `is_mla` branch in `llm_build_deepseek2()` * Removed `build_attn_mla` and added `nullptr` to all `build_atnn` calls * Fixed call to `build_attn` in `llm_build_t5_enc`
2025-04-15 07:49:57 +01:00
LLM_TENSOR_ATTN_K_B,
LLM_TENSOR_ATTN_V_B,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
LLM_TENSOR_ATTN_Q_A_NORM,
LLM_TENSOR_ATTN_KV_A_NORM,
LLM_TENSOR_ATTN_SUB_NORM,
LLM_TENSOR_FFN_SUB_NORM,
LLM_TENSOR_DEC_ATTN_NORM,
LLM_TENSOR_DEC_ATTN_Q,
LLM_TENSOR_DEC_ATTN_K,
LLM_TENSOR_DEC_ATTN_V,
LLM_TENSOR_DEC_ATTN_OUT,
LLM_TENSOR_DEC_ATTN_REL_B,
LLM_TENSOR_DEC_CROSS_ATTN_NORM,
LLM_TENSOR_DEC_CROSS_ATTN_Q,
LLM_TENSOR_DEC_CROSS_ATTN_K,
LLM_TENSOR_DEC_CROSS_ATTN_V,
LLM_TENSOR_DEC_CROSS_ATTN_OUT,
LLM_TENSOR_DEC_CROSS_ATTN_REL_B,
LLM_TENSOR_DEC_FFN_NORM,
LLM_TENSOR_DEC_FFN_GATE,
LLM_TENSOR_DEC_FFN_DOWN,
LLM_TENSOR_DEC_FFN_UP,
LLM_TENSOR_DEC_OUTPUT_NORM,
LLM_TENSOR_ENC_ATTN_NORM,
LLM_TENSOR_ENC_ATTN_Q,
LLM_TENSOR_ENC_ATTN_K,
LLM_TENSOR_ENC_ATTN_V,
LLM_TENSOR_ENC_ATTN_OUT,
LLM_TENSOR_ENC_ATTN_REL_B,
LLM_TENSOR_ENC_FFN_NORM,
LLM_TENSOR_ENC_FFN_GATE,
LLM_TENSOR_ENC_FFN_DOWN,
LLM_TENSOR_ENC_FFN_UP,
LLM_TENSOR_ENC_OUTPUT_NORM,
LLM_TENSOR_CLS,
LLM_TENSOR_CLS_OUT,
LLM_TENSOR_CONV1D,
LLM_TENSOR_CONVNEXT_DW,
LLM_TENSOR_CONVNEXT_NORM,
LLM_TENSOR_CONVNEXT_PW1,
LLM_TENSOR_CONVNEXT_PW2,
LLM_TENSOR_CONVNEXT_GAMMA,
LLM_TENSOR_POS_NET_CONV1,
LLM_TENSOR_POS_NET_CONV2,
LLM_TENSOR_POS_NET_NORM,
LLM_TENSOR_POS_NET_NORM1,
LLM_TENSOR_POS_NET_NORM2,
LLM_TENSOR_POS_NET_ATTN_NORM,
LLM_TENSOR_POS_NET_ATTN_Q,
LLM_TENSOR_POS_NET_ATTN_K,
LLM_TENSOR_POS_NET_ATTN_V,
LLM_TENSOR_POS_NET_ATTN_OUT,
model : support LiquidAI LFM2 hybrid family (#14620) **Important** LFM2 was [merged ](https://github.com/huggingface/transformers/pull/39340)into transformers, but has not yet been released. To convert into gguf, install transformers from source ```shell pip install "transformers @ git+https://github.com/huggingface/transformers.git@main" ```
2025-07-11 20:27:01 +02:00
LLM_TENSOR_SHORTCONV_CONV,
LLM_TENSOR_SHORTCONV_INPROJ,
LLM_TENSOR_SHORTCONV_OUTPROJ,
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
};
enum llm_tensor_layer {
LLM_TENSOR_LAYER_INPUT,
LLM_TENSOR_LAYER_REPEATING,
LLM_TENSOR_LAYER_OUTPUT,
};
struct LLM_KV {
Add Jinja template support (#11016) * Copy minja from https://github.com/google/minja/commit/58f0ca6dd74bcbfbd4e71229736640322b31c7f9 * Add --jinja and --chat-template-file flags * Add missing <optional> include * Avoid print in get_hf_chat_template.py * No designated initializers yet * Try and work around msvc++ non-macro max resolution quirk * Update test_chat_completion.py * Wire LLM_KV_TOKENIZER_CHAT_TEMPLATE_N in llama_model_chat_template * Refactor test-chat-template * Test templates w/ minja * Fix deprecation * Add --jinja to llama-run * Update common_chat_format_example to use minja template wrapper * Test chat_template in e2e test * Update utils.py * Update test_chat_completion.py * Update run.cpp * Update arg.cpp * Refactor common_chat_* functions to accept minja template + use_jinja option * Attempt to fix linkage of LLAMA_CHATML_TEMPLATE * Revert LLAMA_CHATML_TEMPLATE refactor * Normalize newlines in test-chat-templates for windows tests * Forward decl minja::chat_template to avoid eager json dep * Flush stdout in chat template before potential crash * Fix copy elision warning * Rm unused optional include * Add missing optional include to server.cpp * Disable jinja test that has a cryptic windows failure * minja: fix vigogne (https://github.com/google/minja/pull/22) * Apply suggestions from code review Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Finish suggested renamings * Move chat_templates inside server_context + remove mutex * Update --chat-template-file w/ recent change to --chat-template * Refactor chat template validation * Guard against missing eos/bos tokens (null token otherwise throws in llama_vocab::impl::token_get_attr) * Warn against missing eos / bos tokens when jinja template references them * rename: common_chat_template[s] * reinstate assert on chat_templates.template_default * Update minja to https://github.com/google/minja/commit/b8437df626ac6cd0ce3b333b3c74ed1129c19f25 * Update minja to https://github.com/google/minja/pull/25 * Update minja from https://github.com/google/minja/pull/27 * rm unused optional header --------- Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2025-01-21 13:18:51 +00:00
LLM_KV(llm_arch arch, const char * suffix = nullptr);
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
llm_arch arch;
Add Jinja template support (#11016) * Copy minja from https://github.com/google/minja/commit/58f0ca6dd74bcbfbd4e71229736640322b31c7f9 * Add --jinja and --chat-template-file flags * Add missing <optional> include * Avoid print in get_hf_chat_template.py * No designated initializers yet * Try and work around msvc++ non-macro max resolution quirk * Update test_chat_completion.py * Wire LLM_KV_TOKENIZER_CHAT_TEMPLATE_N in llama_model_chat_template * Refactor test-chat-template * Test templates w/ minja * Fix deprecation * Add --jinja to llama-run * Update common_chat_format_example to use minja template wrapper * Test chat_template in e2e test * Update utils.py * Update test_chat_completion.py * Update run.cpp * Update arg.cpp * Refactor common_chat_* functions to accept minja template + use_jinja option * Attempt to fix linkage of LLAMA_CHATML_TEMPLATE * Revert LLAMA_CHATML_TEMPLATE refactor * Normalize newlines in test-chat-templates for windows tests * Forward decl minja::chat_template to avoid eager json dep * Flush stdout in chat template before potential crash * Fix copy elision warning * Rm unused optional include * Add missing optional include to server.cpp * Disable jinja test that has a cryptic windows failure * minja: fix vigogne (https://github.com/google/minja/pull/22) * Apply suggestions from code review Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Finish suggested renamings * Move chat_templates inside server_context + remove mutex * Update --chat-template-file w/ recent change to --chat-template * Refactor chat template validation * Guard against missing eos/bos tokens (null token otherwise throws in llama_vocab::impl::token_get_attr) * Warn against missing eos / bos tokens when jinja template references them * rename: common_chat_template[s] * reinstate assert on chat_templates.template_default * Update minja to https://github.com/google/minja/commit/b8437df626ac6cd0ce3b333b3c74ed1129c19f25 * Update minja to https://github.com/google/minja/pull/25 * Update minja from https://github.com/google/minja/pull/27 * rm unused optional header --------- Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2025-01-21 13:18:51 +00:00
const char * suffix;
llama : refactor `src/llama.cpp` (#10902) * llama : scatter llama.cpp into multiple modules (wip) * llama : control-vector -> adapter * llama : arch * llama : mmap ggml-ci * ci : remove BUILD_SHARED_LIBS=OFF ggml-ci * llama : arch (cont) ggml-ci * llama : chat ggml-ci * llama : model ggml-ci * llama : hparams ggml-ci * llama : adapter ggml-ci * examples : fix ggml-ci * rebase ggml-ci * minor * llama : kv cache ggml-ci * llama : impl ggml-ci * llama : batch ggml-ci * cont ggml-ci * llama : context ggml-ci * minor * llama : context (cont) ggml-ci * llama : model loader ggml-ci * common : update lora ggml-ci * llama : quant ggml-ci * llama : quant (cont) ggml-ci * minor [no ci]
2025-01-03 10:18:53 +02:00
std::string operator()(llm_kv kv) const;
};
// helper to handle gguf constants
// usage:
//
// const auto tn = LLM_TN(LLM_ARCH_LLAMA);
//
// std::string name = tn(LLM_TENSOR_OUTPUT); -> "output"
// std::string name = tn(LLM_TENSOR_TOKEN_EMBD, "bias"); -> "token_embd.bias"
// std::string name = tn(LLM_TENSOR_ATTN_NORM, "weight", 3); -> "blk.3.attn_norm.weight"
//
struct LLM_TN_IMPL {
const llm_arch arch;
const llm_tensor tensor;
const char * const suffix;
const int bid;
const int xid;
std::string str() const;
operator std::string() const {
return str();
}
friend bool operator==(const std::string & str, const LLM_TN_IMPL & tn) {
return str == tn.str();
}
friend bool operator!=(const std::string & str, const LLM_TN_IMPL & tn) {
return str != tn.str();
}
};
struct LLM_TN {
LLM_TN(llm_arch arch) : arch(arch) {}
llm_arch arch;
LLM_TN_IMPL operator()(llm_tensor tensor, const char * suffix, int bid = -1, int xid = -1) const {
return { arch, tensor, suffix, bid, xid };
}
LLM_TN_IMPL operator()(llm_tensor tensor, int bid = -1, int xid = -1) const {
return { arch, tensor, nullptr, bid, xid };
}
};
struct llm_tensor_info {
llm_tensor_layer layer;
ggml_op op;
};
const char * llm_arch_name(llm_arch arch);
llm_arch llm_arch_from_string(const std::string & name);
const llm_tensor_info & llm_tensor_info_for(llm_tensor tensor);
memory : Hybrid recurrent cache (#13979) * feat: Add llama_model_is_hybrid API call Also, split llama_model_is_recurrent into llm_arch_is_recurrent in llama-arch with llama_model_is_recurrent delegating to llm_arch_is_recurrent. The same split is done for hybird. This is needed because there are places where the llama_model has not yet been initialized but we need to check if the model is recurrent (specifically for the per-layer recurrent check array in hparams). Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add c++ side constants for attention layer indices hparam Branch: GraniteFour * feat: Add support for distinguishing recurrent vs non-recurrent layers in hparams Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Auto-fill hparams.recurrent_layer_arr based on whether the model is recurrent Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: rename *_is_hybrid -> *_is_hybrid_recurrent The implementation of the hybrid cache intentionally does not specify the types of the child caches, so there was a naming mismatch with these predicate functions that used "hybrid" to imply "hybrid recurrent." Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add layer filter to recurrent cache Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer sizing everywhere in kv caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: First pass at llama_kv_cache_hybrid_recurrent This follows the pattern in iswa where the two child caches are held explicitly to support the case where a model requires a single attention cache and a single recurrent cache where each layer uses exactly one of the caches. This is a rewrite of the more generic approach in the original hybrid cache PR: https://github.com/ggml-org/llama.cpp/pull/13276 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Construct hybrid recurrent cache for hybrid recurrent models This includes a refactor of the create_memory logic to avoid needing to use the arch enum explicitly unless a model needs explicit cache instantiation logic beyond the standard logic for recurrent, hybrid, unified, and iswa. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix wrong bool condition for split equal in hybrid cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix shift logic to defer to unified cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Support hybrid recurrent in llama-graph NOTE: I intentionally did not add support for s_mask since it will be going away soon Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix logic for initializing inputs and attn layers for hybrid caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update recurrent cache for changes to remove intermediate kv_cache interface Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix status for init_update sig for recurrent cache state Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Add missing padding to n_ctx for hybrid cache construction Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update clear signature for data argument after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove errant virtual destructor leftover from previous impl attempt Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer n_embd_k/v_s calls for mamba (1) layers Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_s from unified cache No longer needed now that unified isn't also supporting recurrent https://github.com/ggml-org/llama.cpp/pull/13979#discussion_r2140761069 Branch: HybridRecurrentCache * refactor: Remove layer index from n_embd_k/v_s Now that it's not used at all in the unified cache, we don't need to use the layer index to zero it out for attention layers. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_gqa from recurrent cache This is no longer needed now that there are separate implementations https://github.com/ggml-org/llama.cpp/pull/13979#discussion_r2140825128 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Allow custom layer filters for hybrid recurrent This should help support architectures like Falcon H1 where there is overlap between layers that need attention and recurrent caches. https://github.com/ggml-org/llama.cpp/pull/13979#discussion_r2140748922 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove logits_all after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove llama_model_is_hybrid_Recurrent public API https://github.com/ggml-org/llama.cpp/pull/13979#discussion_r2141728423 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use llama_memory_state_ptr for child states in hybrid memory state Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Overhaul build_recurrent_state / build_inp_s_copy to match attention pattern https://github.com/ggml-org/llama.cpp/pull/13979/files#r2141701738 This is a big overhaul to bring consistency between how inputs and per- layer components are created for attention layers and recurrent layers. The main changes are: - Rename class llm_graph_input_s_copy -> llm_graph_input_rs - Add a corresponding llm_graph_input_rs_hybrid_recurrent - Rename build_inp_s_copy -> build_rs_inp_recurrent - Add a corresponding build_rs_inp_hybrid_recurrent - Rename build_recurrent_state -> build_rs to match build_attn w/ llm_graph_input_rs android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a corresponding overload of build_rs w/ llm_graph_input_rs_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a llm_graph_input_attn_kv_hybrid_recurrent analogous to llm_graph_input_attn_kv_unified - Add a build_attn override that takes llm_graph_input_attn_kv_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input This makes the two paradigms fully consistent. The main drawback is the code duplication in the build_attn and build_rs implementations where the only difference between implementations is how they cast the memory state. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix resize vs reserve and skip null tensors in size computation https://github.com/ggml-org/llama.cpp/pull/13979/files#r2149469788 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-Authored-By: @younesbelkada * fix: Fix initialization of child states Since initially writing this PR, the logic in the child state types changed such that using the "init full" signature and keeping the ubatches on the parent struct no longer worked. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use a common build_recurrent_state method that is cache-agnostic This reduces the code duplication between the different build_rs impls and also retains a similar signature to the previous build_recurrent_state method while standardizing on the input-dispatched build_rs implementation. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * recurrent : rework graph inputs + add TODOs ggml-ci * refactor: Make status and child states const in hybrid and iswa Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Rename llama_kv_cache_[recurrent|hybrid_recurrent] to remove kv cache This removes the notion of "kv" from the interface names for these memory types. There are still many references to kv in the implementation of the recurrent memory which will need further adjustment. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor!: Rename all k/v related values for recurrent/hybrid to r/s Anywhere that "kv_<state|cell|size|etc>" is used, I've used the more generic "mem_" prefix. The specifics of "k" (key) translate to "r" (recurrent state) and "v" (value) translate to "s" (state-space embedding states). Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refacor: _recurrent -> _recr for brevity It just _happens_ to have the same number of letters as _attn! Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for ref Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: recurrent_layer() -> is_recurrent() Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for size_s_bytes declaration Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2025-06-19 00:08:14 -05:00
bool llm_arch_is_recurrent(const llm_arch & arch);
bool llm_arch_is_hybrid (const llm_arch & arch);
Support diffusion models: Add Dream 7B (#14644) * Support diffusion models: Add Dream 7B * Move diffusion to examples * Move stuff to examples. Add patch to not use kv-cache * Address review comments * Make sampling fast * llama: remove diffusion functions * Add basic timings + cleanup * More cleanup * Review comments: better formating, use LOG instead std::cerr, re-use batch, use ubatch instead of max_length * fixup! * Review: move everything to diffusion-cli for now
2025-07-16 20:03:51 +08:00
bool llm_arch_is_diffusion(const llm_arch & arch);
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