summaryrefslogtreecommitdiff
path: root/ggml
AgeCommit message (Collapse)Author
2025-05-10TG improvements for MoE models (#404)Kawrakow
* cuda: Remove unnecessary device to host copy of row ids We get 3-4% TG speed improvement for DeepSeek-Lite just from that. * CPU: fix get_rows when SER is used With smart experts reduction (SER), one potentially uses fewer experts than specified by the model. This is accomplished by setting the ID of the not seected tensors to -1. Most of the necessary stuff was implemented when I added the SER option, but I forgot to update get_rows() for not quantized tensors. As a result, we get random garbage for the weights of the not-selected epxerts, which leads to garbage output. This commit fixes it on the CPU. I'm not quite sure yet why the GPU is not working. * CUDA: fix TG with SER --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-05-09Fix CUDA FlashMLA-3 with quantized KV cache (#400)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-05-07FlashMLA-3 for DeepSeek models on CUDA (#386)Kawrakow
* CUDA WIP: support for FlashMLA-3 * Much better The issue was that I did not change the number of warps used for 3D matrix multiplications (wk_b * kv_cache, MoE), so we ended up using 4 warps for TG. By going to 1 warp in these cases, we get a significant boost in TG performance (tested with DeepSeek-Lite) * Sadly, the previous commit was wrong * Finalizing * Also add these * Minor * Minor tweak --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-05-07Fix DeepSeek q8_0 cache (#391)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-05-07Fix build for Xeon Gold 6226R (#390)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-05-05Fix DeepSeek FA (#382)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-05-04CUDA: MMQ for IQ4_KS (#374)Kawrakow
* WIP * WIP: still getting illegal memory access * CUDA: MMQ for iq4_ks now works ~25% faster than dequantize+cuBLAS, ~10% slower than Q4_0 MMQ. --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-05-04CUDA: faster FA TG for GQA models (#370)Kawrakow
* cuda: WIP MMA FA * Use MMA for TG also when quantized --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-05-04Another attempt to fix #367 (#371)Kawrakow
* Another attempt to fix #367 * Yet another --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-05-03Trying to fix iq1_s_r4/iq1_m_r4 quantization failure (#368)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-05-02Fix FA bug on AVX2 (#364)Kawrakow
* Fix FA bug on AVX2 * Also this was wrong --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-30Fix IQK_FA_ALL_QUANTS on AVX2 (#360)Kawrakow
* Fix IQK_FA_ALL_QUANTS on AVX2 * Make it also work, not just compile --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-29CPU FA improvements (#351)Kawrakow
* FA: provide work buffer for K repacking * Add header to avoid comp0iler warnings * WIP * WIP * WIP * WIP * Slightly better * WIP (Zen4) * WIP * Try to improve for unusual number of heads/number of threads * Use mul_mat_qX_0_q8_2_Tx for q6_0 in FA * Use mul_mat_qX_0_q8_2_Tx for q4_0 in FA * Use Sum4q4 for q4_0 * WIP * WIP * Much better FA TG with q8_0 KV cache Just repack it even for TG. But do the repacking for k_step rows, not the whole K tensor. --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-26Fix division by zero bug (#349)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-26Add support for Cohere2 (#341)Kawrakow
* Add support for Cohere2 * Fixe IQ4_NL on AVX2 * Command-A needs fp32 precision for K*Q --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-25Fix q4_1 and q5_1 on Arm (#348)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-25Add ability to manually set arch flags (#347)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-25Fix FA on ARM (#346)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-24cuda: use switch in constexpr funcs (#343)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-21Fix termux/android build (#336)saood06
* Attempt fix * Attempt fix 2 * Attempt fix 3 * Attempt fix 4 * Attempt fix 5 * Attempt fix 6 * Attempt fix 7 * Attempt fix 8 * Attempt fix 9 * Attempt fix 10 * Attempt fix 11 * Attempt fix 12 * Attempt fix 13
2025-04-17Better TG performance for GQA models (CPU) (#332)Kawrakow
* Slightly better CPU TG performance for GQA * Better CPU FA implementation for TG when GQA * Minor --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-15Better gemm/gemv on AVX2 fr q4_0_r8 (#331)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-04-15Allow q8_0 KV cache for head size 256 (#330)Kawrakow
* Allow q8_0 KV cache for head size 256 * We need also these --------- 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>
2025-04-07Better 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>
2025-04-05We 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>
2025-04-01Fix 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>
2025-04-01Fix #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>
2025-03-27Use 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>
2025-03-25CUDA: 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>
2025-03-22Native 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>
2025-03-21Convert 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>
2025-03-19Fix 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>
2025-03-13FlashMLA-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>
generated by cgit v1.2.3 (git 2.25.1) at 2025-10-07 05:46:22 +0000