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Commits
| Author | SHA1 | Message | Date | |
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 Pepper Gray
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ca86fb222e |
llama : add missing headers (#28566)
* fix compile-error: add missing header Bug: #28557 Signed-off-by: Pepper Gray <[email protected]> * fix compile-error: add missing header Bug: #28559 Signed-off-by: Pepper Gray <[email protected]> * fix compile-error: add missing header Bug: #28560 Signed-off-by: Pepper Gray <[email protected]> * fix compile-error: add missing header Bug: #28561 Signed-off-by: Pepper Gray <[email protected]> * fix compile-error: add missing header Bug: #28562 Signed-off-by: Pepper Gray <[email protected]> * fix compile-error: add missing header Bug: #28564 Signed-off-by: Pepper Gray <[email protected]> --------- Signed-off-by: Pepper Gray <[email protected]> |
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 PikaPikachu
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465e49b9ce |
convert : add --fuse-qkv flag to fuse Q/K/V into QKV during HF-to-GGUF conversion (#22780)
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 Aman Gupta
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8e93a9773b | CUDA + ggml: add sparse-fa for DSV4/GLM (#27970) | ||
 Xuan-Son Nguyen
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5a32f7b66e |
model: add dots3-note (#27060)
* text: conversion * init impl * address review comments * fix rope * move to a new llama_kv_cache_dsa_iswa |
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ad1de39e07 |
model: add Kimi-K3 text model (#26185)
* model: add Kimi-K3 text model Hybrid KDA (linear) + MLA (full) attention as in Kimi-Linear-48B, plus five things that architecture does not have: 1. cross-layer residual attention (attn_res_block_size) 2. latent MoE (routed experts run at n_expert_latent) 3. situ activation (replaces SwiGLU everywhere) 4. MLA output gate (sigmoid gate before o_proj) 5. full-rank KDA gate (single ssm_g instead of ssm_g_a/ssm_g_b) K3's text_config reports KimiLinearForCausalLM - the older 48B architecture - so get_model_architecture routes on the top-level name instead. The KDA decay gate has two forms, selected by linear_attn_config's gate_lower_bound. It is not a clamp: when set it swaps the activation entirely (fla/ops/kda/gate.py), from -exp(A_log)*softplus(x) to lower_bound*sigmoid(exp(A_log)*x). K3 sets it to -5.0; kimi-linear leaves it unset, so that path is unchanged. Cross-layer residuals reuse ggml_dsv4_hc_pre for the weighted sum. That op is CPU + CUDA only, so Metal/Vulkan will fall back per-node until those kernels exist. The routed experts ship as compressed-tensors "mxfp4-pack-quantized". That is bit-compatible with ggml's MXFP4 - same E2M1 code assignment, same E8M0 scale byte, only the nibble positions within a block differ - so they are repacked rather than dequantized, losslessly and without a ~5.5 TB bf16 round-trip. The repack is built lazily because gguf_writer holds every added tensor until the final write. DeepSeek-V4 was already doing the identical bit-shuffling, so it now shares the helper. Verified against Moonshot's own code path (transformers + fla's Triton KDA kernels) on a tiny model exercising every K3-specific feature. Final-position logits vs the fp32 reference: 6.7e-05 rel / corr 1.00000000 for both the chunked and the recurrent delta-net path. MXFP4 blocks dequantize to the source weights with 0.0e+00 error. Assisted-By: Claude Opus 5 (1M context) <[email protected]> * model: fix ty errors in the Kimi-K3 converter - `_res_parts` buffers (kind, tensor) pairs, not bare tensors - `get_tensors` must return an Iterator, matching ModelBase - LazyBase's `func` takes one argument, so pass the expert loaders through `args` instead of the closure - borrowing KimiLinearModel.set_vocab from an unrelated TextModel is deliberate and safe, but not expressible in the signature No behaviour change: the MXFP4 repack still dequantizes to the source weights with 0.0e+00 error and end-to-end logits are unchanged (8.386e-03 rel, corr 0.99996630). Assisted-By: Claude Opus 5 (1M context) <[email protected]> * Update conversion/kimi_k3.py Co-authored-by: Boris Dvorkin <[email protected]> * Increase LLAMA_MAX_EXPERTS from 512 to 1024 * tests : support for Kimi K3 in archs test * chat : add Kimi K3 chat format (reasoning, content, typed tool calls) K3's assistant output is an XTML-ish tagged format built by the template's open_tag/close_tag macros. Two properties break generic parsing: 1. The generation prompt ends with open_tag('think'), so the completion starts inside the think section with no opening marker in the output (thinking_forced_open). 2. Only <|open|>/<|close|>/<|sep|>/<|end_of_msg|> are special tokens; tag names ("think", "response", "message") are ordinary text tokens. Adds common_chat_params_init_kimi_k3 (PEG_NATIVE) with detection on the marker trio, reasoning extraction, response unwrapping, and tool-call parsing of the tools/call/argument tag structure with argument types taken from the tool schema. Includes the K3 chat template fixture and 9 test-chat cases derived from real generations of the full 2.8T model. Verified end-to-end against Kimi-K3-Q2_K (GrEarl/Kimi-K3-GGUF) on 8x B200: content, reasoning_content, streaming deltas, and tool_calls all correct; finish_reason stop/tool_calls as appropriate. Co-Authored-By: Claude Fable 5 <[email protected]> * chat : add message_delimiters for Kimi K3 Per-role message-start markers for token-level span splitting. User and assistant messages carry only the role attribute, so their full opener (through <|sep|>) is used; system and tool messages continue with more attributes (type=/tool=/index=), so those delimiters stop after the role's closing quote. Verified against the K3 tiktoken vocabulary that the closing quote is always a standalone token across all attribute variants, so the token-level prefix match stays exact. Co-Authored-By: Claude Fable 5 <[email protected]> * fix: apply nits from @ngxson and text fixes from @danielhanchen * tests : added missing hyperparameters and tensors for Kimi K3 in test-llama-archs * chore : move overly verbose header file comments to Kimi K3 source file * tests : re-enabled KIMI_K3 in test-llama-archs for WebGPU backend * model-saver : emit kda_gate_lower_bound for Kimi K3 Quick fix. The Kimi K3 loader reads kda_gate_lower_bound and gates a graph branch on it (it scales the KDA gate when the bound is above -INFINITY), but the model saver never wrote the key, so a save->load roundtrip silently dropped it back to the -INFINITY default and changed the model's output. The real K3 config sets gate_lower_bound = -5.0. I propose to emit it from the saver, and set it to -5.0 in the test-llama-archs K3 case so the roundtrip check exercises it (the roundtrip fails without the saver line). * Refactor conditional for model architecture check * tests : re-enabled (again) KIMI_K3 and MINIMAX_M3 in test-llama-archs for WebGPU backend * fix code comments * add template on conversion * move repack_mxfp4_blocks to model base * nits * add_value_length * optimize res_stack construction * nits --------- Co-authored-by: Boris Dvorkin <[email protected]> Co-authored-by: Stanisław Szymczyk <[email protected]> Co-authored-by: Deepankar Singh <[email protected]> Co-authored-by: Claude Fable 5 <[email protected]> Co-authored-by: Caleb DeLeeuw <[email protected]> Co-authored-by: Xuan Son Nguyen <[email protected]> |
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  Gaurav GargandGeorgi Gerganov
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dd1ea52433 |
llama : support multi-output backend sampling (#25532)
* Enable backend sampling with token speculation * Clamp the mask sum before converting it into the sampled index * Add a numeric context parameter declaring the maximum outputs one sequence * More fixes * Don't reuse memory for output views. * Match dist between CPU and GPU * Fix CPU and backend sampling mismatches * Simpify some of the changes * Fix tests on Vulkan * More test fixes * Rebase changes * Rebase and address review comments * Address review comments * Address review comments * Update src/llama-sampler.cpp Co-authored-by: Georgi Gerganov <[email protected]> --------- Co-authored-by: Georgi Gerganov <[email protected]> |
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Georgi Gerganov
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c50b34a1e0 | graph : fix unused input tensors in minimax m3 graph (#26519) | ||
 timkhronosandGeorgi Gerganov
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67d5978bb1 |
model: M3: Move MSA into a new memory implementation (#26338)
* Move MSA logic from llama-kv-cache into llama-kv-cache-msa * cont : minor * cont : ws fix --------- Co-authored-by: Georgi Gerganov <[email protected]> |
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Aman Gupta
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596a5795bd | DeepseekV4 MTP + DSpark (#25784) | ||
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Aman Gupta
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0dc74e332e |
DeepseekV4: Add fused hyper-connection ops (#25585)
* dsv4 hc-ops * add missing files; * add cparams * update rpc version * address review comments * address review comments |
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 fairydreamingandStanisław Szymczyk
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00f5442cc4 |
ggml : add GGML_OP_LIGHTNING_INDEXER that implements DeepSeek V3.2/V4 lightning indexer (#24231)
* ggml : add GGML_OP_LIGHTNING_INDEXER that implements DeepSeek V3.2/V4 lightning indexer * ggml : remove scale parameters from lightning indexer OP, add f16 mask parameter * tests : add GGML_OP_LIGHTNING_INDEXER tests * ggml : bump RPC version * chore : check if lightning indexer input tensors are not transposed * tests : count flops instead of bandwidth in lightning indexer test * chore : add missing const * chore : whitespace * ggml : renamed variables in CPU lightning indexer implementation * ggml : fix lightning indexer mask broadcasting * tests : tests for lightning indexer mask broadcasting * chore : whitespace * llama : use GGML_OP_LIGHTNING_INDEXER in DeepSeek V3.2 and DeepSeek V4 models --------- Co-authored-by: Stanisław Szymczyk <[email protected]> |
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Aman Gupta
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90e0f5cfcb | llama: refactor fused ops (#24646) | ||
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8c146a8366 |
DeepSeek V4 (#24162)
* convert: add dsv4 conversion * add basic setup * add llm_graph_input_dsv4 * add save-load state * add sinkhorn eps - correction by @fairydreaming * add rope fix * cleanup dead code * fix bugs * support pro model: added by @fairydreaming * remove redundant V cache * Chat template * remove debugging leftovers * Add mechanism for inlining templates based on architecture * s/deepseek-v4-flash/deepseek4/g * s/deepseek-v4-flash/deepseek4/g continued * enable graph reuse * enable FA * fix test llama archs * rename * compatibility with antirez ds4 GGUFs * simplified set_gguf_parameters() by calling super class method, replaced moe.score_func with expert_gating_func. * reserve worst-case kv-cache * revert max split inputs * address review comments * add padding to enable FA * pad only the final value of plan.n_kv to 256 * remove built-in cpp chat template * cont: remove cpp built-in template * rm outdated test * replace ggml_view_3d() with ggml_reshape_3d() Co-authored-by: Georgi Gerganov <[email protected]> * only support n_seq=1 for now * remove unused var * cont: remove unused var * use scale bias * use correct ptr for can_reuse * remove gen-chat-inline-templates.py * simplify graph reuse * cont: cleanup * remove unused inputs * enable partial checkpointing * add correct shape for kq_mask + set llama_model_n_swa to 0 for dsv4 * precompute source_idx + add comment about dummy write * support multi-seq * remove restored_trim_pos * use split_equal when possible * fix indent * address review comments * use LLM_KV * fix ci --------- Co-authored-by: Piotr Wilkin <[email protected]> Co-authored-by: Stanisław Szymczyk <[email protected]> Co-authored-by: Xuan Son Nguyen <[email protected]> Co-authored-by: fairydreaming <[email protected]> Co-authored-by: Georgi Gerganov <[email protected]> |
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 YiChen LvandAman Gupta
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d789527482 |
spec : Support Step3.5/3.7 flash mtp3 (#24340)
* add mtp_layer_offset + include nextn flags in graph reuse * add llama_set_mtp_layer_offset + llama_model_n_nextn_layer API * offset head select + require all MTP blocks * speculative multi-head process() * speculative multi-head draft() * gather outputs via inp_out_ids * cleanup * fix core * minor cleanup * merged draft_multi_head into draft() * mtp rename nextn * Apply suggestions from code review Co-authored-by: Aman Gupta <[email protected]> * clean-up comments * fix for multi seq * apply suggestions && chain-heads comment * add a reference for chain_heads discussion --------- Co-authored-by: Aman Gupta <[email protected]> |
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 Oliver Simons
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02810c7aa8 |
Fix and restrict NVFP4 edge-cases in llama-graph (#24331)
* Move post-GEMM MUL required for dequant b4 lora and bias add see https://github.com/ggml-org/llama.cpp/pull/23484 : 1. For lora, I would presume we want fully dequantized values before doing the residuals, but this depends on how the LORAs were generated. Literature tells me LORA happens post-mul but pre-bias add https://github.com/ggml-org/llama.cpp/pull/8332 2. For ModelOPT, bias-add should happen on [fully-dequantized values](https://github.com/NVIDIA/Model-Optimizer/blob/b49f9b9e2d747af992d78a3aa7f10efe5a8847e1/modelopt/torch/quantization/backends/nvfp4_gemm.py#L59-L64) * Restrict build_ffn for NVFP4 to supported combinations |
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88a39274ec |
spec: add EAGLE3 speculative decoding support (#18039)
* llama : enable layer input extraction * spec: support eagle3 * eagle3: fix params bug * eagle3: support Gemma4 eagle3 from RedHatAI * eagle3: set sync when get features from target Co-authored-by: tnhnyzc <[email protected]> * eagle3 : fix ubatch handling in embd_layer_inp extraction and encoder Co-authored-by: Doğaç Eldenk <[email protected]> * eagle3: adapt to upstream changes * eagle3: fix rebase issues and adapt to upstream changes * eagle3:exclude the eagle3 arch from test-llama-archs * eagle3: fix editorconfig check failures * eagle3: fix multi-seq issue in d2t vocab mapping * cont : minor style / clean-up * spec : remove `common_speculative_setup_draft_model()` * llama : clean-up unused API * eagle3: set d2t vocab mapping in decode graph * cont : assert layer inputs are configured * hparams : use n_embd_inp instead of n_embd_target_features * eagle3: make output.weight optional and inherit from target model when needed * haparams : generic norm-before-residual param * llama-ext : consistent names * cont : fix * hparams : remove target_hidden_size * cparams : rename output_layer_inp -> embeddings_layer_inp * arch : reuse ATTN_NORM_2 instead of adding new hidden norm * llama : clean-up names * cont : add assert + comment * Update conversion/llama.py Co-authored-by: Sigbjørn Skjæret <[email protected]> --------- Co-authored-by: Georgi Gerganov <[email protected]> Co-authored-by: tnhnyzc <[email protected]> Co-authored-by: Doğaç Eldenk <[email protected]> Co-authored-by: Sigbjørn Skjæret <[email protected]> |
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Aman Gupta
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04eb4c446d | llama : add Gemma4 MTP (#23398) | ||
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Aman Gupta
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166fe29492 |
qwen35: use post-norm hidden state for MTP (#24025)
* qwen35: use post-norm hidden state for MTP * rename pre_norm to nextn * fix step35 |
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 Hans Florian
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bfb4308b05 |
model : support granite multilingual embeddings R2 (ibm-granite/granite-embedding-{97,311}m-multilingual-r2) (#22716)
* Add support for the ibm-granite/granite-embedding-{97m,311m}-multilingual-r2 embedding models:
* Added a version of the gpt4o tokenizer that has a fixed regex (better handling of marks), and different token merging setting for the 97m model
* Reused gemma4 tokenizer for the 311m model
* granite-embedding-*-multilingual-r2 : add support SwiGLU FFN for Granite Embedding Multilingual R2
* added new GGUF key <arch>.hidden_activation (LLM_KV_HIDDEN_ACT) + writer
* added a forward declaration of llm_ffn_op_type to llama-hparams.h
* added llm_ffn_op in hparams
* added LLM_FFN_NONE = 0 sentinel to llm_ffn_op_type (value-initialization), modern-bert: explicitly assigns LLM_FFN_GEGLU before reading GGUF (unchanged).
* centralized hidden_act mapping in llama-model.cpp, added llm_ffn_op_type_from_string() helper, mirroring rope_scaling_type/llama_rope_scaling_type_from_string()
* modern-bert reads the GGUF key (when present) and uses the resulting op in its FFN graph
* Added granite-embedding-{97m,311m}-multilingual-r2 to the converter code
* Added the hashes for the granite embedding multilingual R2 models
* Set the hidden_activation in the GGUF if the field is present in config.json (such as for the granite embedding models)
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Sigbjørn Skjæret
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764f1e64a1 | graph : ensure DS32 kq_mask_lid is F32 (#23864) | ||
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1f0aa2a696 |
model : support for DeepseekV32ForCausalLM with generic DeepSeek Sparse Attention (DSA) implementation (#23346)
* llama : support DeepSeek V3.2 model family (with DSA lightning indexer) * convert : handle DeepseekV32ForCausalLM architecture * ggml : support for f16 GGML_OP_FILL * memory : separate hparams argument in llama_kv_cache constructor * memory : add llama_kv_cache_dsa memory (KV cache + lightning indexer cache) * llama : support for LLM_ARCH_DEEPSEEK32 * model : llama_model_deepseek32 implementation * model : merge two scale operations into one in DSA lightning indexer implementation * chore : remove unused code * model : support NVFP4 in DeepSeek V3.2 Co-authored-by: Sigbjørn Skjæret <[email protected]> * memory : refactoring TODO Co-authored-by: ggerganov <[email protected]> --------- Co-authored-by: Stanisław Szymczyk <[email protected]> Co-authored-by: Sigbjørn Skjæret <[email protected]> Co-authored-by: ggerganov <[email protected]> |
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Aman Gupta
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031ddb2e08 |
llama: use f16 mask for FA to save VRAM (#23764)
* llama: use f16 mask for FA * review: add llama_cast + formatting * simplify |
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Aman GuptaandGeorgi Gerganov
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eef59a7642 |
llama: add llm_graph_input_mtp (#23643)
* llama: add llm_graph_input_mtp * rename input_mtp -> input_token_embd * add TODO about mtmd embedding * cont : clean-up --------- Co-authored-by: Georgi Gerganov <[email protected]> |
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Georgi Gerganov
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d14ce3dab4 |
llama : MTP clean-up (#23269)
* llama : disable equal splits for recurrent memory with partial rollback * spec : re-enable p-min with MTP drafts * spec : re-enable ngram spec in combination with RS rollback * spec : fix ngram-map-* params * spec : fix acceptance logic in combined ngram + draft configs * graph : fix reuse for combined `token` + `embd` batches * spec : log parameters for each speculative implementation - add LOG_INF in each constructor with implementation type and parameters - extract device string logic into common_speculative_get_devices_str() - move 'adding speculative implementation' log from init into constructors Assisted-by: llama.cpp:local pi * spec : extend --spec-default with ngram-map-k4v Assisted-by: llama.cpp:local pi * minor : fix n_embd log * args : update draft.n_max == 3 + regen docs * spec : relax ngram-mod rejection thold to 0.25 @ 5 low * logs : improve * docs : update speculative decoding CLI argument documentation - Add missing draft model CPU scheduling and tensor override parameters - Update --spec-type to include all available types (excluding draft-eagle3 WIP) - Fix default values to match implementation (n_max=3, n_min=0, p_min=0.0) - Remove deprecated options (spec-draft-ctx-size, spec-draft-replace) - Add environment variables for new parameters Assisted-by: llama.cpp:local pi * arg : step-back on adding k4v to the default spec config * cont : fix name |
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Aman GuptaandGeorgi Gerganov
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255582687b |
llama + spec: MTP Support (#22673)
* spec: support MTP * fix batch size * rename files * cont : simplify (#7) * MTP: clean-up (#9) * MTP: clean-up * review: use llama_context_type instead of llama_graph_type * review: remove llama_model_has_mtp * review: fix convert issues * convert: fix pycheck * review: formatting * use `mtp-` for identifying mtp models * convert: fix mtp conversion * mtp -> draft-mtp * remove unused llama_arch * add need_embd in speculative * llama: allow partial seq_rm for GDN models for speculative decoding Currently speculative checkpoint needs to restart from a checkpoint after some draft tokens are not accepted, this leads to some wastage in running the target again. This PR adds the ability to rollback upto `draft_max` by storing the GDN intermediates. * fix pending state * vulkan: add GDN partial rollback * meta: extend check to axis 1 * metal: add GDN partial rollback Extend the gated delta net kernel to store intermediate states for partial rollback support on the Metal backend. - Add K (snapshot slot count) as a function constant - Read input state from slot 0 of the 3D state tensor - Write intermediate states to different slots during token loop - For K=1, maintain backward-compatible single-slot behavior Ref: https://github.com/ggml-org/llama.cpp/commit/8c05923630110223669f069af2000e9cf10c02bc Assisted-by: llama.cpp:local pi * delta_net_base: use ggml_pad instead of new_tensor * review: add need_rs_seq * review: rename part_bounded to n_rs * review: deslop comments * review: rename, add asserts * server : adjust checkpoint logic (#11) * server : adjust checkpoint logic * cont : rm asserts * server-context: fix early exit * spec : fix compatibility with n-gram and add TODOs (#13) * metal : cleanup * llama : fix faulty bitwise check in recurrent memory * server : disable RS-based MTP in combination with other spec types * spec : add TODOs * cont : fix comment * cont : update comment * common : fix logic for ngram + mtp compat * llama-memory: enable checkpointing with partial rollback * cont: add test-case for loading into a dirty ctx * llama-memory-recurrent: clear rs_idx in clear * download: fix mtp path * llama-arch: fix enorm op * docs: update docs * conversion: fix type annotations --------- Co-authored-by: Georgi Gerganov <[email protected]> |
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PikaPikachu
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9db77a020c |
model : refactor QKV into common build_qkv and create_tensor_qkv helpers (#21245)
* model : refactor QKV into common build_qkv and create_tensor_qkv helpers * model : extend build_qkv to bert/mpt/dbrx/olmo/lfm2/nemotron-h/granite-hybrid/gemma3n-iswa/t5-dec and fix wqkv_s |
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Sigbjørn Skjæret
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f772f6e434 |
model : support NVFP4 tensors for Gemma4 (#21971)
* support nvfp4 tensors for Gemma4 * add wo_s to build_attn * add wo_s to build_attn * fix glm4 |
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Georgi Gerganov
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4eb19514dd |
kv-cache : support attention rotation for heterogeneous iSWA (#21513)
* kv-cache : support attention rotation for heterogeneous iSWA * cont : remove assert |
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Georgi Gerganov
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744c0c7310 |
llama : rotate activations for better quantization (#21038)
* llama : rotate activations for better quantization * cont : rotate V more + refactor * cont : rotate caches separately + support non-power-of-2 head sizes * cont : simplify * cont : add reference for V rotation * cont : refactor * cont : support context shift * cont : consolidate * cont : dedup + allow different types for the rotation matrix * cont : add env variable to disable rotation * cont : simplify attn rot kv cache logic + rename env * cont : pre-compute the Hadamard matrices |
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 Richard Davison
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1eea6a2968 | graph : add optional scale parameter to build_lora_mm [no ci] (#20427) | ||
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Richard DavisonandSigbjørn Skjæret
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5eae9cb1d9 |
ggml : add NVFP4 quantization type support (#19769)
* WIP: add NVFP4 quantization support * tests * improve NVFP4 dot product implementation performance and fix bad super call * typo * Use nvfp4 kvalues * vulkan : fix NVFP4 shader compilation by including kvalues_mxfp4 lookup table * vulcal and perf fixes * wip * Fix metal * fix vulcan * Rename threshold & fix wrong scale * Fix MOE * Shelf backend implementations (CUDA, Metal, Vulkan, arch-specific SIMD) Remove NVFP4 support from GPU backends and architecture-specific optimized dot products. These should be added in separate PRs so backend specialists can review them independently. Reverted files: - ggml-cuda: common.cuh, convert.cu, mmq.cu/cuh, mmvq.cu, vecdotq.cuh, quantize.cu/cuh, mma.cuh, ggml-cuda.cu, fattn-tile.cuh - ggml-metal: ggml-metal.metal, ggml-metal-device.cpp, ggml-metal-impl.h, ggml-metal-ops.cpp - ggml-vulkan: ggml-vulkan.cpp, all vulkan-shaders/* - ggml-cpu arch: arm/quants.c, x86/quants.c, powerpc/quants.c, s390/quants.c Core NVFP4 support (type definition, CPU fallback dot product, quantization, dequantization, conversion) is retained. * Fix arch-fallback.h: add NVFP4 generic fallback for all platforms After shelving backend-specific SIMD implementations, the generic CPU dot product needs to be aliased on ARM, x86, PowerPC, and s390 platforms that previously relied on arch-specific versions. * quantize: add NVFP4 as a quantization type option * Fix ggml_fp32_to_ue4m3: handle subnormal values Previously, values with ue4m3_exp <= 0 were clamped to 0, causing all small scales to underflow. This made NVFP4 quantization via llama-quantize produce garbage (PPL = 5.8M) since typical transformer weights have amax/6.0 in the range 0.001-0.01, which falls in the UE4M3 subnormal range. Now subnormals are properly encoded as man * 2^-9 (exp=0, man=1..7), matching the decode path in ggml_ue4m3_to_fp32. Result: NVFP4 requantization now produces PPL = 15.25 (vs F16 = 14.33), comparable to Q4_1 (PPL = 15.81) at slightly lower BPW (4.70 vs 5.15). * Restore ARM NEON NVFP4 dot product implementation Restores the optimized ggml_vec_dot_nvfp4_q8_0 for ARM NEON using vqtbl1q_s8 lookup and ggml_vdotq_s32 dot products. tg128 performance: 4.37 t/s (generic) -> 13.66 t/s (NEON) = 3.1x speedup * Optimize ARM NEON NVFP4 dot product: LUT + vpaddq + vfmaq - Add ue4m3_scale_lut[128] to ggml-common.h replacing branch-heavy ggml_ue4m3_to_fp32() in the hot loop - Use vpaddq_s32 for pairwise int32 reduction instead of vaddvq_s32 - Accumulate with vfmaq_f32 into float32x4_t vector accumulators tg128: 8.1 -> 31.0 t/s (3.8x speedup, 77% of Q4_1 speed) * ARM NEON NVFP4: rearrange q8 to match nibble layout Alternative approach: rearrange q8 data to match the NVFP4 lo/hi nibble layout instead of rearranging the looked-up NVFP4 values. Eliminates vcombine_s8(vget_low, vget_low) shuffles. Performance is equivalent (~18.5 t/s) - the bottleneck is the 2x block overhead from QK=16 vs QK=32, not the shuffle instructions. * CPU only backend 64 super-block layout * cleanup * Remove unused LUT * int * exclude NVFP4 from unsupported ops in metal build * remove quantization for now * store scales as native UE4M3, preserve original model bits when possible * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <[email protected]> * correct comment * format * reduce duplication and cleanup * Address comments * move detection to prepare_tensors * Use math instead of const * Move * fix comment * Shelf quantize tests * Rebase and move check * cleanup * lint * Update gguf-py/gguf/scripts/gguf_convert_endian.py Co-authored-by: Sigbjørn Skjæret <[email protected]> * Use fallback quant config * Simplify Co-authored-by: Sigbjørn Skjæret <[email protected]> * organize * Refactor * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <[email protected]> * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <[email protected]> * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <[email protected]> * add quantize_nvfp4 (required for test_quants.py) * add quantize_nvfp4 (required for test_quants.py) * add quantize_nvfp4 (required for test_quants.py) * fix return type --------- Co-authored-by: Sigbjørn Skjæret <[email protected]> |
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Sigbjørn Skjæret
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35bee031e1 | graph : remove redundant scale_w parameter (#20235) | ||
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Aman GuptaandSigbjørn Skjæret
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b68d75165a |
llama: Add option to merge gate and exp weights (#19139)
* llama: Add option to merge gate and exp weights * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <[email protected]> * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <[email protected]> * update constants.py * add gate_up for the all MoE models * convert: simplify merge tensor condition * update constants.py * reduce number of models, add create_tensor_gate_up helper --------- Co-authored-by: Sigbjørn Skjæret <[email protected]> |
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 Tarek DakhranandSigbjørn Skjæret
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8004f3a8d1 |
model : add tokenizer from LFM2.5-Audio-1.5B (#19687)
* model : Add tokenizer from LFM2.5-Audio-1.5B [LFM2.5-Audio-1.5B](https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B) introduced lightweight audio tokenizer. Tokenizer based on LFM2 architecture and acts as "embedding" model with different input `n_embd` and output `n_embd_out`. To be used in https://github.com/ggml-org/llama.cpp/pull/18641. To convert use ```shell python3 convert_hf_to_gguf.py /path/to/LFM2.5-Audio-1.5B/audio_detokenizer ``` * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <[email protected]> * Formatting * Rework check for attention layers * Add LFM2 SWA model support * Address PR feedback * Set vocab to none * Move helper function definitions to cpp file --------- Co-authored-by: Sigbjørn Skjæret <[email protected]> |
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 Ryan MangenoandSigbjørn Skjæret
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c0d0430340 |
model : full modern bert support (#18330)
* full modern bert support * added gelu op in rank pooling for modern bert * still working on stuff, added mean calculation before classifier head * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <[email protected]> * first layer is dense, as per modern bert research paper * Update src/llama-graph.cpp Co-authored-by: Sigbjørn Skjæret <[email protected]> * fixed set input for mean pooling to check if pooling type is ranking since modern bert does mean & rank * Update src/llama-graph.cpp Co-authored-by: Sigbjørn Skjæret <[email protected]> * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <[email protected]> --------- Co-authored-by: Sigbjørn Skjæret <[email protected]> |
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 ymckiandPiotr Wilkin
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3688c4f504 |
Kimi-Linear support (backend agnostic + MLA KV cache) (#18755)
* kimi linear model implementation * kimi linear convert_hf_to_gguf * kimi linear constants.py tensor_mapping.py * Kimi Linear ggml.h * kimi linear ggml-cpu * Kimi Linear ggml-cuda * Kimi Linear ggml.c * kimi linear src/llama * remove "const int64_t n_seq_tokens = q->ne[2];" to get rid of unused variable warning * remove type mismatch warning * read MoE params * removed some hard coded code * removed all hard code * use DeepseekV2 tokenizer * removed unnecessary internal methods called by the old set_vocab of KimiLinear * rewrite get_vocab for KimiLinear. Removed all kda_scan code * removed all traces of kda_scan * reduce OP count by 1 due to removal of kda_scan * Move KIMI_LINEAR to llm_arch_is_hybrid to enable KV cache * set n_embd_head_k/v to ensure kv cache works * don't quantize conv1d of Kimi Linear * Kimi Linear backend agnostic * removed LOG_INFO * naive chunking form implemented * fixed some comments * add Kimi-K2 specific tokens to be recognized as EOG * build_kda_autoregressive is implemented to replace build_kda_recurrent for faster inference. sync'd to b7682 * replaced Akk and Aqk with mul_mat and clamp * no clamp version * Moved Aqk computation out of the loop * fixed typo and split wkv_b into wk_b and wv_b * MLA KV cache support * fix trailing spaces * moved const llama_model & model; around to follow qwen3next format and see if it cna pass the -Wunused-private-field error * fix trailing whitespace * removed traling whitespaces in empty line + make sure indentation is multiple of 4 * try to make lint happy * remove blank lines to make lint happy * removed at least blank line containing white space * fixed flake8 complaints locally * return ggml_tensor * pair in kda_autoregressive and kda_chunking as in ngxson's Qwen3Next improvement * removed Kimi-Linear specific change that causes failure at server-windows * removed private: from kimi_linear to make build checks happy * removed unnecessary ggml_cont before ggml_reshape * created static function causal_conv1d to abtract similar code for q/k/v * merged dt_bias to SSM_DT. Do -exp(log_A) in convert_hf_to_gguf.py. * reverted to original * fixed find_hparam calls. Fixed e_score_correction_bias to use bias instead of weight. Removed all ssm_conv bias terms. * remove DT_B from constants.py. remove one comment line in llama-model.cpp * new class llm_graph_input_mem_hybrid_k to get around the new MLA change. switch the concat order of ggml_concat calls in kimi-linear.cpp to accommodate MLA changes. Removed support for exp_probs_b.weight * remove ssm_o_norm_b * remove ssm_o_norm_b * changed hparams.kda_head_dim to hparams.n_embd_head_kda. added TODO comment for class llama_graph_mem_hybrid_k * removed all ggml_cont b4 ggml_reshape_4d * Whitespace * replaced all hparams.get with find_hparams * added new names for n_experts, n_experts_used and score_func in TextModel and removed their code in KimiLinear in convert_hf_to_gguf.py. Removed unnecessary ggml_cont and GGML_ASSERT in kimi-linear.cpp * use is_mla to switch between different mem_hybrid types * fixed logical errors in convert_hf_to_gguf.py pointed out by CISC * removed if else for required parameters kv_lora_rank and qk_rope_head_dim * add back ggml_cont for Vcur * minor changes * removed extra line in llama-vocab.cpp. Added back the comment in llama-graph.cpp * f16 gguf cannot run without context length * made a mistake of adding back n_ctx parsing --------- Co-authored-by: Piotr Wilkin (ilintar) <[email protected]> |
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Georgi Gerganov
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d9c6ce46f7 |
kv-cache : support V-less cache (#19067)
* kv-cache : support V-less cache * cuda : better check for V_is_K_view * cuda : improve V_is_K_view check * graph : add comments * hparams : refactor |
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Georgi Gerganov
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557515be1e |
graph : utilize ggml_build_forward_select() to avoid reallocations (#18898)
* graph : avoid branches between embedding and token inputs * models : make deepstack graphs (e.g. Qwen3 VL) have constant topology * ci : enable -DGGML_SCHED_NO_REALLOC=ON for server CI * cont : pad token embeddings to n_embd_inp |
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 Tarek DakhranandGeorgi Gerganov
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ad8d85bd94 |
memory : add llama_memory_hybrid_iswa (#18601)
* memory : add llama_memory_hybrid_iswa * Update src/llama-memory-hybrid-iswa.cpp Co-authored-by: Georgi Gerganov <[email protected]> --------- Co-authored-by: Georgi Gerganov <[email protected]> |
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d3dce4e0a5 |
sampling : add support for backend sampling (#17004)
* sampling : add support for backend sampling This commit adds support for performing sampling operations on the backend (e.g. GPU) as part of the model computation graph. The motivation for this feature is to enable sampling to be performed directly on the backend as part of the computation graph being executed, allowing for some or all of the sampling to be done on the backend. For example, the backend sampler chain might select/sample a token directly in which case only the sampled token needs to be transferred from device memory to host memory. It is also possible for the backend samplers to perform filtering of the logits, or compute and filter the probability distribution, in which case only the filtered logits or probabilites need to be transferred back to system memory for further processing by CPU samplers. Currently the backend sampling works in a similar manner to how pooling works, it is a function that is called by build_graph and the sampler operations become part of the models computation graph. * llama-cli : add backend sampler configuration * server : add backend sampling options/configuration * webui : add backend sampling options * ggml : add initial cumsum implementation for CUDA * sampling : enable all backend sampler tests This commit enables all exisiting backend sampler tests in the test-backend-sampler. Previously, some tests were disabled because there were missing ggml operation implementations. * graph : do not include llama-model.h * sampling : always expose sampled_ids This commit precomputes and caches the full-vocab token id list in llama_context's constructor, so llama_get_backend_sampled_token_ids_ith always returns a valid pointer. The motivation for this is that this enables both common/sampling.cpp and src/llama-sampling.cpp can simplify their logic. Not all backends samplers that process logits need to set the sampled_tokens_id as they may not change the order of the logits, for example the temperature sampler only scales the logits but does not change their order. Simliar the logit bias sampler only adds bias to specific token ids but does not change the order of the logits. In these cases there will not be a device to host copy of the sampled token ids, and this is the use case where having this precomputed list is useful. * sampling : ensure at most one output token per seq This commit adds a check in the batch allocator to ensure that when backend sampling is enabled, at most one output token is specified per sequence. * CUDA: Optimize argsort for gpu-based token sampling Argsort is used for top-k currently. WE optimize argsort by 2 things: 1. Use `DeviceRadixSort` for single-row/sequence to parallelize it across our SMs 2. Use `DeviceSegmentedSort` for multi-row/sequence as this is the correct entrypoint (the function chooses different execution paths, it contains `DeviceSegmentedRadixSort` as one of the paths and will choose the best one according to heuristics. https://nvidia.github.io/cccl/cub/api/structcub_1_1DeviceSegmentedSort.html#overview Some perf numbers for a RTX PRO 6000: On the kernel level, tested with `GGML_CUDA_DISABLE_GRAPHS=1 ./test-backend-ops -o ARGSORT perf` Before: ``` ARGSORT(type=f32,ne=[65000,16,1,1],order=0): 4130 runs - 359.24 us/run ARGSORT(type=f32,ne=[200000,1,1,1],order=0): 8192 runs - 861.34 us/run ARGSORT(type=f32,ne=[200000,16,1,1],order=0): 1343 runs - 1020.01 us/run ``` After: ``` ARGSORT(type=f32,ne=[65000,16,1,1],order=0): 4130 runs - 312.41 us/run ARGSORT(type=f32,ne=[200000,1,1,1],order=0): 16384 runs - 63.48 us/run ARGSORT(type=f32,ne=[200000,16,1,1],order=0): 1343 runs - 874.36 us/run ``` --- On the model level, tested with `llama-cli -m gpt-oss-20b-mxfp4.gguf -n 200 -p "What is the Capital of Sweden?" -no-cnv -fa 1 --backend-sampling` Before: ``` llama_perf_sampler_print: sampling time = 0.25 ms / 207 runs ( 0.00 ms per token, 824701.20 tokens per second) llama_perf_context_print: load time = 18215.58 ms llama_perf_context_print: prompt eval time = 28.20 ms / 7 tokens ( 4.03 ms per token, 248.19 tokens per second) llama_perf_context_print: eval time = 714.79 ms / 199 runs ( 3.59 ms per token, 278.40 tokens per second) llama_perf_context_print: total time = 857.62 ms / 206 tokens ``` After ``` llama_perf_sampler_print: sampling time = 0.25 ms / 207 runs ( 0.00 ms per token, 828000.00 tokens per second) llama_perf_context_print: load time = 18366.92 ms llama_perf_context_print: prompt eval time = 35.92 ms / 7 tokens ( 5.13 ms per token, 194.87 tokens per second) llama_perf_context_print: eval time = 532.79 ms / 199 runs ( 2.68 ms per token, 373.50 tokens per second) llama_perf_context_print: total time = 683.65 ms / 206 tokens ``` * sampling : remove version from sampler chain This commit removes the version field from the sampler chain and instead used the sampler pointer itself for change detection. * sampling : always populate logits for sampled probs This commit updates common/sampler.cpp set_logits and src/llama-sampling.cpp llama_sampler_sample to always populate the logits field when backend sampled probabilities are available. The motivation for this is that this ensure that CPU sampler always have access to the logits values even when probabilites have been produced by backend samplers. * sampling : simplify backend sampling logic decode This commit tries to simplify the backend sampling logic in llama_context::decode. * squash! sampling : simplify backend sampling logic decode Fix condition to check if backend actually sampled tokens, not just that backend samplers are available. * common : fix regression caused by extra memory allocations during sampling * squash! sampling : simplify backend sampling logic decode The commit fixes a variable shadowing issue in the `llama_context::decode` function which was introduced in a previous refactoring. * squash! common : fix regression caused by extra memory allocations during sampling Apply the same changes to llama-sampling.cpp, llama_sampler_sample as were applied in commit |
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Georgi Gerganov
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c560316440 |
graph : reuse SSM graphs (#16490)
* graph : reuse hybrid graphs * graph : reuse recurrent graphs * graph : fix reuse check for recurrent inputs * memory : move the recurrent state into the memory context * Revert "memory : move the recurrent state into the memory context" This reverts commit 00f115fe810815d4a22a6dee0acc346131e970e1. * cont : fix build |
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Xuan-Son Nguyen
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0759b09c90 | graph: add f_attn_temp_offset (#18025) | ||
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Georgi Gerganov
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e38b7c6e9e |
graph : support cacheless embeddings with FA and iSWA (#16528)
* graph : support cacheless embeddings with FA and iSWA * cont : deduplicate mask creation * cont : fix name |
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e08db42595 |
model: EmbeddingGemma Adding Support for SentenceTransformers Dense Modules (#16367)
* model: EmbeddingGemma sentence-transformers dense linear projections support * model: add support for EmbeddingGemma SentenceTransformers dense linear projections Adding support for the Dense modules used in EmbeddingGemma models. EmbeddingGemma is a SentenceTransformers model with additional modules beyond the base Transformer backbone. See: https://developers.googleblog.com/en/gemma-explained-embeddinggemma-architecture-and-recipe/ * model: add support for EmbeddingGemma SentenceTransformers dense linear projections - converting model with dense-layers is optional - introduced dense config params * Update convert_hf_to_gguf.py Co-authored-by: Daniel Bevenius <[email protected]> * fixed formatting issues * Update src/llama-graph.cpp Co-authored-by: Georgi Gerganov <[email protected]> * - removed pooling_type_opt, always allow overriding pooling_type - asserts checking dense features dims * fix python lint * fix ubuntu gcc build warning * - fixed thread-safety test - moved asserts to load_hparams * - tidying up code - simplifying graph-context expecting both dense weights * minor : add TODO --------- Co-authored-by: Daniel Bevenius <[email protected]> Co-authored-by: Georgi Gerganov <[email protected]> |
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 Douglas Hanley
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b5bd037832 | llama : add support for qwen3 reranker (#15824) | ||
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Daniel Bevenius
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fb15d649ed |
llama : add support for EmbeddingGemma 300m (#15798)
This commit add support for the EmbeddingGemma 300m. This model supports sliding window attention (SWA) and a new swq_type is introduced to support symmetric SWA masking. This commit also extracts the code from the function llama_is_masked_swa in llama-impl.h, so that the logic can be shared by both llm_graph_input_attn_no_cache::set_input and llama_kv_cache::set_input_kq_mask. With this commit the EmbeddingGemma 300m model can be converted to to GGUF and used with llama.cpp. Once the model has been uploaded to HuggingFace it can be used like this: ```console ./build/bin/llama-cli -hf ggml-org/embeddinggemma-300m-GGUF:Q8_0 ``` |
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Johannes Gäßler
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e81b8e4b7f |
llama: use FA + max. GPU layers by default (#15434)
* llama: use max. GPU layers by default, auto -fa * ggml-backend: abort instead of segfault |
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Georgi Gerganov
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3f196be84b |
graph : remove build_attn_with_sinks overload (#15469)
ggml-ci |
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Georgi Gerganov
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715a6db02c |
kv-cache : drop the "unified" prefix (#15467)
* kv-cache : drop the "unified" prefix ggml-ci * cont : fix comment [no ci] |
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fd1234cb46 |
llama : add gpt-oss (#15091)
* oai moe * compat with new checkpoint * add attn sink impl * add rope scaling yarn * logits match with latest transformers code * wip chat template * rm trailing space * use ggml_scale_bias * rm redundant is_swa_all * convert interleaved gate_up * graph : fix activation function to match reference (#7) * vocab : handle o200k_harmony special tokens * ggml : add attention sinks support (#1) * llama : add attn sinks * ggml : add attn sinks * cuda : add attn sinks * vulkan : add support for sinks in softmax remove unnecessary return * ggml : add fused swiglu_oai op (#11) * ggml : add fused swiglu_oai op * Update ggml/src/ggml-cpu/ops.cpp Co-authored-by: Georgi Gerganov <[email protected]> * update CUDA impl * cont : metal impl * add vulkan impl * test-backend-ops : more test cases, clean up * llama : remove unfused impl * remove extra lines --------- Co-authored-by: Georgi Gerganov <[email protected]> --------- Co-authored-by: slaren <[email protected]> * repack mxfp4 upon conversion * clean up a bit * enable thinking * add quick hack to render only some special tokens * fix bf16 conversion * remove vocab hack * webui ok * support chat parsing for gpt-oss * fix webui * direct mapping mxfp4, FINALLY * force using mxfp4 * properly use lazy tensor * ggml : add mxfp4 ggml : use e8m0 conversion instead of powf Co-authored-by: Diego Devesa <[email protected]> change kvalues_mxfp4 table to match e2m1 (#6) metal : remove quantization for now (not used) cuda : fix disabled CUDA graphs due to ffn moe bias vulkan : add support for mxfp4 cont : add cm2 dequant * ggml : add ggml_add_id (#13) * ggml : add ggml_add_id * add cuda impl * llama : add weight support check for add_id * perf opt * add vulkan impl * rename cuda files * add metal impl * allow in-place ggml_add_id * llama : keep biases on CPU with --cpu-moe * llama : fix compile error ggml-ci * cuda : add fallback for __nv_cvt_e8m0_to_bf16raw ggml-ci * cleanup ggml-ci * sycl : fix supports_op for MXFP4 ggml-ci * fix Unknown reasoning format * ggml-cpu : fix AVX build ggml-ci * fix hip build ggml-ci * cuda : add mxfp4 dequantization support for cuBLAS ggml-ci * ggml-cpu : fix mxfp4 fallback definitions for some architectures ggml-ci * cuda : fix version required for __nv_cvt_e8m0_to_bf16raw --------- Co-authored-by: Xuan Son Nguyen <[email protected]> Co-authored-by: slaren <[email protected]> |