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120 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
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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lainon1
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0b6ff47996 |
fix: correct misspellings in code comments (#21217)
- emdeddings → embeddings (gemma3.cpp, gemma3n-iswa.cpp, gemma-embedding.cpp) - imlpemented → implemented (llama-adapter.cpp) - interere → interfere (llama-graph.cpp) - overridde → overridden (chat.cpp) - stastistics → statistics (ngram-map.h) - layed → laid (llama-kv-cache.h) - worster → worst (llama-context.cpp) - sequantial → sequential (llama-batch.h) |
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Saba Fallah
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a970515bdb |
mtmd: Add DeepSeekOCR Support (#17400)
* mtmd: llama.cpp DeepSeekOCR support init commit * loading sam tensors * mtmd: fix vision model processing * deepseek-ocr clip-vit model impl * mtmd: add DeepSeek-OCR LM support with standard attention * mtmd: successfully runs DeepSeek-OCR LM in llama-cli * mtmd: Fix RoPE type for DeepSeek-OCR LM. * loading LM testing Vision model loading * sam warmup working * sam erroneous return corrected * clip-vit: corrected cls_embd concat * clip-vit: model convert qkv_proj split * corrected combining of image encoders' results * fix: update callback for ffn_moe_weighted and add callback for attn_out in deepseek2 model * concat image_newline and image_seperator tokens * visual_model warmup (technically) works * window partitioning using standard ggml ops * sam implementation without using CPU only ops * clip: fixed warnings * Merge branch 'sf/deepseek-ocr' of github.com:sfallah/llama.cpp into sf/deepseek-ocr * mtmd: fix get_rel_pos * mtmd: fixed the wrong scaler for get_rel_pos * image encoding technically works but the output can't be checked singe image decoding fails * mtmd: minor changed * mtmd: add native resolution support * - image encoding debugged - issues fixed mainly related wrong config like n_patches etc. - configs need to be corrected in the converter * mtmd: correct token order * - dynamic resizing - changes are concerning PR https://github.com/sfallah/llama.cpp/pull/4 * mtmd: quick fix token order * mtmd: fix danling pointer * mtmd: SAM numerically works * mtmd: debug CLIP-L (vit_pre_ln) * mtmd: debug CLIP-L & first working DeepSeek-OCR model * mtmd : add --dsocr-mode CLI argument for DeepSeek-OCR resolution control & all native resolution modes work * mtmd: simplify SAM patch embedding * mtmd: adapt Pillow image resizing function * mtmd: simplify DeepSeek-OCR dynamic resolution preprocessing * mtmd: remove --dsocr-mode argument * mtmd: refactor code & remove unused helper functions * mtmd: fix tensor names for image newlines and view separator * clean up * reverting automatically removed spaces * reverting automatically removed spaces * mtmd: fixed bad ocr check in Deepseek2 (LM) * mtmd: support combined QKV projection in buid_vit * using common build_attn in sam * corrected code-branch when flash-attn disabled enabling usage of --flash-attn option * mtmd: minor fix * minor formatting and style * fixed flake8 lint issues * minor editorconfig-check fixes * minor editorconfig-check fixes * mtmd: simplify get_rel_pos * mtmd: make sam hparams configurable * mtmd: add detailed comments for resize_bicubic_pillow * mtmd: fixed wrong input setting * mtmd: convert model in FP16 * mtmd: minor fix * mtmd: remove tweak to llama-mtmd-cli & deepseek-ocr template * fix: test-1.jpg ORC issue with small (640) resolution setting min-resolution base (1024) max large (1280) for dynamic-resolution * minor: editconfig-check fix * merge with changes from https://github.com/ggml-org/llama.cpp/pull/17909 added new opt to tests.sh to disable flash-attn * minor: editconfig-check fix * testing deepseek-ocr quick and dirty test script comparing results of Qwen2.5-VL vs DeepSeek-OCR * quick and (potential) dirty merge with https://github.com/ggml-org/llama.cpp/pull/17909 * refactoring, one single builder function and static helpers * added deepseek-ocr test to tests.sh * minor formatting fixes * check with fixed expected resutls * minor formatting * editorconfig-check fix * merge with changes from https://github.com/ggml-org/llama.cpp/pull/18042 * minor - added GLM-4.6V to big tests - added missing deps for python test * convert: minor fix * mtmd: format code * convert: quick fix * convert: quick fix * minor python formatting * fixed merge build issue * merge resolved - fixed issues in convert - tested several deepseek models * minor fix * minor * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * - removed clip_is_deepseekocr - removed redundant RESIZE_ALGO_BICUBIC_PILLOW resize-algo - simplified image-preprocessing - removed/simplified debug functions * - cleaning commented out code * fixing instabilities issues reintroducing resize_bicubic_pillow * - use f16 model for deepseek-ocr test - ignore llama-arch test for deepseek-ocr * rename fc_w --> mm_fc_w * add links to OCR discussion * cleaner loading code * add missing .weight to some tensors * add default jinja template (to be used by server) * move test model to ggml-org * rolling back upscale change * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> --------- Co-authored-by: bluebread <hotbread70127@gmail.com> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> Co-authored-by: Xuan Son Nguyen <son@huggingface.co> Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com> |
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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 Davison
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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 <sigbjorn.skjaeret@scala.com> * 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 <sigbjorn.skjaeret@scala.com> * Use fallback quant config * Simplify Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * organize * Refactor * 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> * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * 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 <sigbjorn.skjaeret@scala.com> |
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Vinicios Lugli
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4d99d45084 |
model : qwen3vl reranker text support (#20332)
* model : fix qwen3vl reranker support * Remove CLS_OUT Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> |
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Xuan-Son Nguyen
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59db9a357d |
llama: dynamic head_dim and n_rot for SWA (#20301)
* llama: dynamic head_dim and n_rot for SWA * also add gguf_writer wrappers * fix build * build_rope_shift arg reorder |
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Sigbjørn Skjæret
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35bee031e1 | graph : remove redundant scale_w parameter (#20235) | ||
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Johannes Gäßler
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a976ff081b |
llama: end-to-end tests (#19802)
* tests: add end-to-end tests per model architecture * fixup for rebase * fix use-after-free in llama-model-loader.cpp * fix CI * fix WebGPU * fix CI * disable CI for macOS-latest-cmake-arm64 * use expert_weights_scale only if != 0.0f * comments |
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Marcel Petrick
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92f7da00b4 |
chore : correct typos [no ci] (#20041)
* fix(docs): correct typos found during code review Non-functional changes only: - Fixed minor spelling mistakes in comments - Corrected typos in user-facing strings - No variables, logic, or functional code was modified. Signed-off-by: Marcel Petrick <mail@marcelpetrick.it> * Update docs/backend/CANN.md Co-authored-by: Aaron Teo <taronaeo@gmail.com> * Revert "Auxiliary commit to revert individual files from 846d1c301281178efbc6ce6060ad34c1ebe45af8" This reverts commit 02fcf0c7db661d5ff3eff96b2b2db9fdb7213256. * Update tests/test-backend-ops.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update tests/test-backend-ops.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> --------- Signed-off-by: Marcel Petrick <mail@marcelpetrick.it> Co-authored-by: Aaron Teo <taronaeo@gmail.com> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> |
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Aman Gupta
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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 <sigbjorn.skjaeret@scala.com> * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * 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 <sigbjorn.skjaeret@scala.com> |
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3 a l i
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2bf318fd2f |
model : add JAIS-2 architecture support (#19488)
* model: add JAIS-2 architecture support Add support for the JAIS-2 family of Arabic-English bilingual models from Inception AI (https://huggingface.co/inceptionai/Jais-2-8B-Chat). Architecture characteristics: - LayerNorm (not RMSNorm) with biases - ReLU² (ReLU squared) activation function - Separate Q/K/V projections with biases - Simple MLP without gate projection (up -> act -> down) - RoPE positional embeddings - GPT-2 BPE tokenizer Supported model sizes: - Jais-2-8B (32 layers, 26 heads, 3328 hidden) - Jais-2-70B (68 layers, 56 heads, 7168 hidden) Tested with quantizations: BF16, Q8_0, Q6_K, Q5_K_M, Q5_0, Q4_K_M, Q4_0, Q3_K_M, Q2_K Note: JAIS-2 requires F32 precision accumulators for numerical stability and uses standard attention (not flash attention) on CUDA backends. * fix: run convert_hf_to_gguf_update.py for jais-2 tokenizer hash * fix: use NEOX RoPE type for JAIS2 * fix: remove Q/K permutation (NEOX RoPE doesn't need it) * fix: enable flash attention for JAIS2 (fixed by #19115) * fix: add dedicated JAIS2 pre-tokenizer type and control vector support - Add LLAMA_VOCAB_PRE_TYPE_JAIS2 with cascading whitespace regex - Include original regex from tokenizer.json as comment - Add build_cvec call for control vector support * no longer necessary to override set_vocab --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> |
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Tarek Dakhran
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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 <sigbjorn.skjaeret@scala.com> * 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 <sigbjorn.skjaeret@scala.com> |
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Ryan Mangeno
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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 <sigbjorn.skjaeret@scala.com> * first layer is dense, as per modern bert research paper * Update src/llama-graph.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * 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 <sigbjorn.skjaeret@scala.com> * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> |
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Georgi Gerganov
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d5dfc33027 |
graph : fix KQ mask, lora, cvec reuse checks (#19644)
* graph : fix KQ mask reuse condition * cont : dedup KQ mask build and can_reuse * cont : fix build * graph : fix adapter check for reuse |
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forforever73
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b83111815e |
model : support Step3.5-Flash (#19283)
* Support Step3.5-Flash * fix: norm.weight + 1 (HF zero_centered=true) * step35: simplify GGUF conversion + drop redundant rope KVs * Address review feedback * rename limits -> clamp * Apply suggestions from code review Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Apply suggestion from @CISC Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * rename swiglu limits -> swiglu clamp in LLM_KV * avoid CI fail * Apply suggestions from code review * Apply suggestions from code review * disabled KV shifting for LLM_ARCH_STEP35 * Apply suggestions from code review * mistakenly removed cmath * add model size && apply missed suggestion * assert partial_rotary_factors * fix CI errors: * load freq_base_swa --------- Co-authored-by: lvyichen <lvyichen@stepfun.com> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> |
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ymcki
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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) <piotr.wilkin@syndatis.com> |
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Georgi Gerganov
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faa1bc26ee |
sampling : delegate input allocation to the scheduler (#19266)
* sampling : delegate input allocation to the scheduler * graph : compute backend samplers only if needed |
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Georgi Gerganov
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4fdbc1e4db |
cuda : fix nkvo, offload and cuda graph node properties matching (#19165)
* cuda : fix nkvo * cont : more robust cuda graph node property matching * cont : restore pre-leafs implementation * cont : comments + static_assert |
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Georgi Gerganov
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8f80d1b254 | graph : fix nkvo offload with FA (#19105) | ||
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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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Georgi Gerganov
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a5eaa1d6a3 |
mla : make the V tensor a view of K (#18986)
* mla : pass V as a view of K to the FA op * cuda : adjust mla logic to new layout * kv-cache : fix rope shift * tests : remove comment * cuda : fix reusable_cutoff Co-authored-by: Johannes Gäßler <johannesg@5d6.de> --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de> |
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Tarek Dakhran
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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 <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> |
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Gabe Goodhart
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076b0faf7d |
graph : clean up t5 input builders (#18795)
* fix: Remove unnecessary `h` loops where `h` was only ever 0 Branch: CleanUpT5InputBuilders Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove unnecessary padding loop that is never hit anymore The upper bound used to use GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), but was removed in https://github.com/ggml-org/llama.cpp/pull/17910 leaving the loop dead. Branch: CleanUpT5InputBuilders Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> |
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Tarek Dakhran
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73d284a250 |
model : add LFM2-ColBert-350M (#18607)
* model : add LFM2-ColBert-350M * llama_model_n_embd_out() - returns `hparams.n_embd_out` if set and fallbacks to `hparams.n_embd` |
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Georgi Gerganov
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2da64a2f8a |
models : fix backend assignment for Granite/Nemotron graphs (#18599)
* models : fix backend assignment for Granite/Nemotron graphs * cont : add ref * cont : move call to build_inp_embd() |
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Daniel Bevenius
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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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c69c7ebc90 |
graph : fix graph reuse logic when n_pos_per_embd > 1 (#18566)
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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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Daniel Bevenius
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2995341730 |
llama : add support for NVIDIA Nemotron 3 Nano (#18058)
* llama : add support for NVIDIA Nemotron Nano 3 This commit adds support for the NVIDIA Nemotron Nano 3 model, enabling the conversion and running of this model. Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> |
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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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609a2d0268 |
models : fix YaRN regression + consolidate logic (#18006)
* models : fix YaRN regression + consolidate logic * cont : fix the fix * cont : remove header * cont : add header |
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Georgi Gerganov
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7bed317f53 |
models : fix the attn_factor for mistral3 graphs + improve consistency (#17945)
* models : fix the attn_factor for mistral3 graphs * cont : rework attn_factor correction logic * cont : make deepseek2 consistent * cont : add TODO * cont : special-case DSv2 * cont : revert Mistral 3 Large changes * cont : fix DS2 to use the original attn_factor * cont : minor comments |
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Georgi Gerganov
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4dff236a52 |
ggml : remove GGML_KQ_MASK_PAD constant (#17910)
* ggml : remove GGML_KQ_MASK_PAD constant * cont : remove comment |
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Sigbjørn Skjæret
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c8554b66e0 |
graph : use fill instead of scale_bias in grouped expert selection (#17867)
* use fill instead of scale_bias in grouped expert selection * do not explicitly use _inplace |
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Xuan-Son Nguyen
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cd3c118908 |
model: support Ministral3 (#17644)
* conversion script * support ministral 3 * maybe this is better? * add TODO for rope_yarn_log_mul * better ppl (tested on 14B-Instruct) * Add Ministral3 support to Mistral format * improve arch handling * add sizes * Apply suggestions from code review Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * nits --------- Co-authored-by: Julien Denize <julien.denize@mistral.ai> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> |
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Aman Gupta
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6eea666912 | llama-graph: avoid expand_forward for fusion (#17633) | ||
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Georgi Gerganov
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583cb83416 |
ggml : add ggml_top_k (#17365)
* ggml : add ggml_top_k * cont : add ggml_argsort_top_k * metal : add top_k support * ggml : cleanup * tests : add virtual err() function for test_case * ggml : add comments |
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Aman Gupta
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a90eb94ca9 |
CUDA: fuse rope + set_rows (#16884)
* CUDA: add fused rope * move k forward_expand up * create helper function instead of re-using params * make assert statement more in line with comment * rope_norm: coalesced writes to global mem |
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Sigbjørn Skjæret
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9008027aa3 |
hparams : add n_embd_inp() to support extended embed (#16928)
* add n_embd_full to support extended embed * don't change output * rename to n_embd_inp * restore n_embd where applicable |
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Jan Boon
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d7395115ba | llama : use std::abs instead of abs (#16853) | ||
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Sigbjørn Skjæret
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f696428ce8 |
graph : add clamping to ffn_moe_weights_sum to avoid div-by-zero (#16655)
* add missing norm topk bias * use clamping instead, update number and add comment |
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Aman Gupta
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f77c13b91f | CUDA: General GEMV fusion (#16715) | ||
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Sigbjørn Skjæret
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84bf3c6778 |
model : add BailingMoeV2 support (#16063)
* add BailingMoeV2 support * update llm types * undo * undo * update llm types * add model collection link * update * almost working * correct group selection and rename n_group_exp * avoid large top_k and use argmax instead for now if we had something like argmax2 that would be equivalent, but this works fine until then * poke * skip group selection when there are no tokens * fix 1T conversion * hopefully fixed expert group selection third time's the charm? * make expert group selection generally available The new LLaDA2Moe model uses this method too, make it generally available regardless of architecture. * allow n_expert_groups to be 1 (Kimi K2) * address review suggestions |
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Georgi Gerganov
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e60f241eac |
metal : FA support F32 K and V and head size = 32 (#16531)
* metal : FA support F32 K and V and head size = 32 * graph : remove obsolete comment [no ci] |
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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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Saba Fallah
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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 <daniel.bevenius@gmail.com> * fixed formatting issues * Update src/llama-graph.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * - 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 <daniel.bevenius@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> |
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Sigbjørn Skjæret
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835b2b915c |
model : add GroveMoE support (#15510)
* add GroveMoE support * remove constexpr that fails on certain compilers * revert crude scalar div implementation, use cast * build_attn_inp_kv_unified -> build_attn_inp_kv * fix build_attn * re-apply ffn_exps regex changes |