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2025-04-14Add ability to hide imatrix details in llama-quantize (#329)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-13Improved IQ1_M quantization (#327)Kawrakow
* Much faster and it looks like better iq1_m quantiation * Cleanup * Minor --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-12Fix KLD precision (#325)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-11Correct L4 rms_norm (#324)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-10LlaMA-4 support (text only) (#321)Kawrakow
* llama4: WIP * llama4: this seems to be working --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-08Guard against attempts to use MLA for non-MLA models (#320)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-07Update AUTHORSKawrakow
Well, there was also the initial MLA PR, which was derived from @fairydreaming
2025-04-07Update AUTHORSKawrakow
Forgot to add @Nexesenex
2025-04-07Use links for ggml/llama.cpp authors (#318)Kawrakow
* Use links for ggml/llama.cpp authors * This file is not html * More --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-07Better iq2_xs quantization (#312)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-07Add copyright notices (#317)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-07Update LICENSEKawrakow
I did not realize until today that the [ggml authors](https://github.com/ggml-org/ggml/blob/master/AUTHORS) is not the same thing as the [llama.cpp authors](https://github.com/ggml-org/llama.cpp/blob/master/AUTHORS). This PR corrects my mistake.
2025-04-05We need to synchronize before using device to host async memcpy (#313)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-04Add -flax-vector-conversions for GCC on ARM (#311)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-03Metal: FA and FlashMLA (#310)Kawrakow
* Metal: WIP to update Metal FA implementation Dk=192, Dv=128 works, but not Dk = 576, Dv = 512 * Metal FA: go to float * WIP * Metal FA: MLA options now all work --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-03Fix GCC compilation errors on ARM (#309)Kawrakow
* Fix GCC compilation errors on ARM * One more --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-03Metal: much faster MoE prompt processing (#307)Kawrakow
* MoE improvements on Metal This version beats mainline, there are things I don't understand: * Mianline has effectively gone to GEMV for MUL_MAT_ID. We can do the same, but we are 30% slower. Why? * Using actual GEMM, we beat mainline with ubtach size of 128. But then performance degrades. Why? * Some cleanup * Much better --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-01docs: update README.md (#304)Ikko Eltociear Ashimine
2025-04-01Fix ARM_NEON build failure due to q8_2 (#303)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-01Quantization improvements (2) (#302)Kawrakow
* iq3_k: slightly better quantization Not much of a difference for most models, but this change avoids what it looks like a catastrophic failure for DeepSeek-Lite (PPL is now 7.041 vs 7.314 on main). * Small improvement for type-1 quants --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-01Additional guards for interleaved quants (#299)Kawrakow
* Make sure no interleaved quants are being used for token embeddings also with `--pure` and/or `--custom-q`. * Simplify --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-01Fix #300 (#301)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-29Quantization improvements (#295)Kawrakow
* Better make_qx_quants Tested with q4_0 and q3_K (pure, imatrix), and the improvement is quite significant. * Sae for iq4_nl, iq4_xs --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-27Make sure tensor row size is multiple of block size also when quantizing ↵Kawrakow
with --pure (#294) * WIP - not working * q8_0 without bells and wistles works * It works for q8_0 * Use bf16 instead of f16,int16 * q4_0_r8 * q5_0_r4 * q6_0_r4 * Also q4_1 and q5_1 * Add check if selected type is possible with --pure I often want to quantize with --pure to see quantization performance without quantization mixes. But for models where there qre tensors with row sizes that are not multiple of 256, this results in a crash for k- and i-quants. Hence, lets add a check if the quant selected via --pure is applicable, and change it if not. --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-27Use bf16 instead of fp16 block scales for q8_1 (#292)Kawrakow
* WIP - not working * q8_0 without bells and wistles works * It works for q8_0 * Use bf16 instead of f16,int16 * q4_0_r8 * q5_0_r4 * q6_0_r4 * Also q4_1 and q5_1 * q8_0_r8 on avx2 --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-25llama-bench: enable having different number of threads for tg and pp (#284)Kawrakow
* llama-bench: enable having different number of threads for tg and pp * Add -tgb to usage --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-25Update sweep bench (depracating .jsonl support) (#289)saood06
* Update sweep bench (depracating .jsonl support) * Fix README.md
2025-03-25CUDA: better MoE implementation (#283)Kawrakow
* Make fused MoE reproducible As a bonus, peak performance at pp2048 with u_batch = 2048 is ~8% better. * Slightly better * Also do it for non-fused mul_mat_id --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-23Improve DeepSeek batched processing speed (#282)Kawrakow
* Improve DeepSeek batched processing speed * Revert the commented out section in iqk_mul_mat.cpp It does have some benefit at long contexts. --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-23Attempt to improve FlashMLA on the CPU (#277)Kawrakow
* Fix it for nth > rk2 * Handle rk2%nth_k != 0 * Cleanup --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-23Test transparent huge pages on Linux (#278)Kawrakow
* Adding ability to use THP on Linux * Use the actual page size4 used for mmap also in munmap * Add -thp to llama-bench --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-22Native build ooption for CUDA when GGML_NATIVE is set (#280)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-22Fighting with cmake (#279)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-22Add Gemma3 support (text only) (#276)Kawrakow
* WIP Gemma3: not working * gemma3: build_gemma3 seems to be working now * Revert changes to convert_hf_to_gguf.py It wasn't working, so I guess, it is better to leave the conversion up tp upstream. --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-21Fix bug: missing parentheses in logical expression (#275)Kawrakow
This results in GGGGGGGGGGGGG when generating with mla = 3, fa = 0. Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-21Specify tensor name regex for tensors to be repacked (#274)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-21FlashMLA-3: the best of both worlds (CPU only) (#273)Kawrakow
* Repack a model with the quantize tool * WIP * Fixed various issues As we don't have a way to tell if a repacked quant has been modified, I had to remove the modification at the expense of a slight decrease in performance. This affects q8_0_r8, q8_KV_r8, q8_k_r8 on Zen4, and q4_0_r8 on ARM. * Create wk_b and wv_b as Q8_0_R8 if the wkv_b type is interleaved * Fix GCC 13.3 compilation error * Another one * Add missing include * FlashMLA-3: the best of both worlds - CPU only --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-21Convert models to row-interleaved quants using the quantize tool (#272)Kawrakow
* Repack a model with the quantize tool * WIP * Fixed various issues As we don't have a way to tell if a repacked quant has been modified, I had to remove the modification at the expense of a slight decrease in performance. This affects q8_0_r8, q8_KV_r8, q8_k_r8 on Zen4, and q4_0_r8 on ARM. * Create wk_b and wv_b as Q8_0_R8 if the wkv_b type is interleaved * Fix GCC 13.3 compilation error * Another one * Add missing include --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-19Honor mmap setting when using tensor overrides (#270)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-19Fix ggml_compute_forward_dup_q (#269)Kawrakow
I broke it with PR #265. I was testing with a model where the wk_b and wk_v tensors were present, so didn't need to be computed, so didn't notice that the change I made to ggml_compute_forward_dup_q breaks that computation. Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-19Prevent FlashMLA-1 from running on CUDA (#268)Kawrakow
as it is not supported. Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-18Allow q8_0 cache on the CPU for FlashMLA-2 (#265)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-18Make Q8_0 KV cache work with mla=2,fa on CUDA (#264)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-18Fix #261 (#262)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-18Compile time option to use bf16 for qunts without MMQ kernels (#261)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-18FlashMLA-2: reduce compute buffer size (CUDA and CPU) (#260)Kawrakow
* FlashMLA-2: eliminate intermediate f32 tensors This works on the CPU. PP performance is ~13% better for 16k tokens and compute buffer is quite a bit smaller. * FlashMLA-2: enable fast path only on the CPU for now I did implement the necessary ops on CUDA, but something is still wrong there, so for now we only use it when running CPU-only. * FlashMLA-2: slightly smaller computer buffer size * Prepare wk_b when loading DeepSeek models (if wk_b is missing) * Add some comments * Fix case where wkv_b is quantized with k- or i-quants. * Fix CUDA There is an issue with quantized GEMV on CUDA when the left operand (the matrix) is not contiguous. So, for now, we also create wv_b during model loading and use that instead of the 3D view of wkv_b. * FlashMLA-2: avoid conversions to f32 also on CUDA * Be able to compute for more than 65535 tokens On CUDA just a quick hack that allows us to cancatenate tensors with more than 65535 rows along zroth dimension as needed by FlashMLA-2. Also needed some care in the perplexity tool to avoid int overflows when evaluating the computed logits. * Reduce memory usage for FlashMLA-2 Oh, also fix int overflow in the CUDA concat implementation. It is funny how the llama.cpp 64-bit police has gone (almost) everywhere and replaced 32-bit ints with 64-bit ints, needed or not, but hasn't done it where it is actually needed. --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-17Prepare wk_b tensors of DeepSeek models on the fly (#259)Kawrakow
* FlashMLA-2: eliminate intermediate f32 tensors This works on the CPU. PP performance is ~13% better for 16k tokens and compute buffer is quite a bit smaller. * FlashMLA-2: enable fast path only on the CPU for now I did implement the necessary ops on CUDA, but something is still wrong there, so for now we only use it when running CPU-only. * FlashMLA-2: slightly smaller computer buffer size * Prepare wk_b when loading DeepSeek models (if wk_b is missing) * Add some comments * Fix case where wkv_b is quantized with k- or i-quants. * Fix CUDA There is an issue with quantized GEMV on CUDA when the left operand (the matrix) is not contiguous. So, for now, we also create wv_b during model loading and use that instead of the 3D view of wkv_b. --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-13FlashMLA-2 (CPU): faster and smaller compute buffer size (#253)Kawrakow
* FlashMLA-2: eliminate intermediate f32 tensors This works on the CPU. PP performance is ~13% better for 16k tokens and compute buffer is quite a bit smaller. * FlashMLA-2: enable fast path only on the CPU for now I did implement the necessary ops on CUDA, but something is still wrong there, so for now we only use it when running CPU-only. * FlashMLA-2: slightly smaller computer buffer size --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-12MLA-2: Allow usage of q8_0 for KV cache on CUDA (#252)Kawrakow
* FlashMLA(CUDA): WIP to allow q8_0 quantized cache * WIP * FlashMLA(CUDA) - allow q8_0 for KV cache This works, and PP is not bad, but TG is still quite a bit slower. * FlashMLA(CUDA) - allow q8_0 for KV cache This is better. ~9% slower than f16 cache for short contexts, nearly on par at 16k tokens. --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-03-10DeepSeek imatrix stuff (#250)Kawrakow
* This gives us ~20% TG speedup for DeepSeek on CUDA * Slightly better * Also do it for plain (not fused) mul_mat_id * Guard against numerical precision issues for MLA on CUDA * imatrix: wv_b <-> wkv_b --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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