aboutsummaryrefslogtreecommitdiff
path: root/Makefile
AgeCommit message (Collapse)Author
2023-07-04Allow old Make to build server. (#2098)Henri Vasserman
Also make server build by default. Tested with Make 3.82
2023-07-04Update Makefile: clean simple (#2097)ZhouYuChen
2023-06-28llama : support input embeddings directly (#1910)ningshanwutuobang
* add interface for float input * fixed inpL shape and type * add examples of input floats * add test example for embd input * fixed sampling * add free for context * fixed add end condition for generating * add examples for llava.py * add READMD for llava.py * add READMD for llava.py * add example of PandaGPT * refactor the interface and fixed the styles * add cmake build for embd-input * add cmake build for embd-input * Add MiniGPT-4 example * change the order of the args of llama_eval_internal * fix ci error
2023-06-26k-quants : support for super-block size of 64 (#2001)Kawrakow
* k_quants: WIP super-blocks with 64 weights * k_quants: WIP super-blocks with 64 weights Q6_K scalar and AVX2 works * k_quants: WIP super-blocks with 64 weights Q4_K scalar and AVX2 works * k_quants: WIP super-blocks with 64 weights Q2_K scalar and AVX2 works. Q2_K is way too slow (it is actually slower than the scalar implementation) * k_quants: WIP super-blocks with 64 weights Q3_K scalar and AVX2 works. * k_quants: WIP super-blocks with 64 weights Q5_K scalar and AVX2 works, and with that all k_quants are done on AVX2 and scalar * k_quants: WIP super-blocks with 64 weights Q6_K working on CUDA. Cannot make it run quite as gast as with super-blocks with 256 weigths: 8% slower on 4080, 20% slower on the 1660 (but there we fit 1 less layer on the GPU because pf the larger model size), so some fraction of these 20% is due to that, * k_quants: WIP super-blocks with 64 weights Q4_K working on CUDA. ~10% slower on GTX-1660, 16% slower on 4080. * k_quants: WIP super-blocks with 64 weights Q2_K working on CUDA. ~3% slower on GTX-1660, 10% slower on 4080. * k_quants: WIP super-blocks with 64 weights Q3_K working on CUDA. * k_quants: WIP super-blocks with 64 weights Q5_K working on CUDA, and with this CUDA is done. * k_quants: WIP super-blocks with 64 weights Q6_K working on ARM_NEON * k_quants: WIP super-blocks with 64 weights Q4_K working on ARM_NEON, but quite a bit slower than 256 weights * k_quants: WIP super-blocks with 64 weights Q2_K working on ARM_NEON, but quite a bit slower than 256 weights * k_quants: WIP super-blocks with 64 weights Q3_K working on ARM_NEON, but quite a bit slower than 256 weights. * k_quants: WIP super-blocks with 64 weights Q5_K working on ARM_NEON, but quite a bit slower than 256 weights. With that, we have full support for ARM_NEON, although performance is not quite there. * k_quants: WIP super-blocks with 64 weights Slightly more efficient Q3_K and Q5_K * k_quants: WIP super-blocks with 64 weights Another small improvement for Q3_K and Q5_K on ARM_NEON * k_quants: WIP super-blocks with 64 weights Yet another speedup for Q5_K on ARM_NEON. We are now within 10% of the QK_K = 256 version. * k_quants: WIP super-blocks with 64 weights * We are able to pass preprocessor macros to the Metal compiler * Q6_K works and is actually slightly more efficient than the QK_K = 256 version (25.2 ms vs 25.8 ms) * k_quants: WIP super-blocks with 64 weights Q4_K works on Metal and is actually slightly faster than QK_K = 256 (21.95 ms vs 24.0 ms). * k_quants: WIP super-blocks with 64 weights Q2_K works on Metal and is very slightly faster than QK_K = 256 (23.8 ms vs 24.2 ms). * k_quants: WIP super-blocks with 64 weights Q3_K works on Metal and is slightly faster than QK_K = 256 (26.6 ms vs 28.3 ms). * k_quants: WIP super-blocks with 64 weights Q5_K works on Metal and is slightly faster than QK_K = 256 (23.7 ms vs 26.3 ms). * k_quants: call them _K, not _k, also on Metal * k_quants: correctly define QK_K in llama.cpp * Fixed bug in q4_K quantization added with the 64-block addition * Simplify via lambda * k_quants: swicth Q3_K to 4-bit scales when QK_K = 64 Otherwise there isn't much benefit from this quantization type. There is some very slight loss in accuracy, but we reduce size by ~7%. E.g., for OpenLLaMA-3B, Q3_K_S perplexity is 8.6131 with 8-bit scales and 8.6352 with 4-bit, while file size decreases from 1.53G to 1.44G. * k_quants: switch Q4_K to 4-bit scales when QK_K = 64 Here the loss in accuracy is greater than for Q3_K, but the Q4_K points still move further to the left on the perplexity vs size curve. * k_quants: forgot to add the Metal changes in last commit * k_quants: change Q5_K to be type 0 when QK_K = 64 Still needs AVX2 implementation * k_quants: AVX2 implementation for new 64-weight Q5_K * k_quants: 10% faster ARM_NEON Q5_K dot product * k_quants: fixed issue caused by merging with master --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-06-19Convert vector to f16 for dequantize mul mat vec (#1913)Johannes Gäßler
* Convert vector to f16 for dmmv * compile option * Added compilation option description to README * Changed cmake CUDA_ARCHITECTURES from "OFF" to "native"
2023-06-18metal : handle buffers larger than device's maxBufferLength (#1826)Georgi Gerganov
* metal : handle buffers larger than device's maxBufferLength * metal : print more verbose device info + handle errors * metal : fix prints for overlapping views * metal : minimize view overlap to try to utilize device memory better
2023-06-17make : do not print help for simple exampleGeorgi Gerganov
2023-06-17make : update for latest Arch (#1701)DaniAndTheWeb
With the upcoming change to the openblas package in arch the Makefile workaround is no longer needed.
2023-06-17Server Example Refactor and Improvements (#1570)Randall Fitzgerald
A major rewrite for the server example. Note that if you have built something on the previous server API, it will probably be incompatible. Check out the examples for how a typical chat app could work. This took a lot of effort, there are 24 PR's closed in the submitter's repo alone, over 160 commits and a lot of comments and testing. Summary of the changes: - adds missing generation parameters: tfs_z, typical_p, repeat_last_n, repeat_penalty, presence_penalty, frequency_penalty, mirostat, penalize_nl, seed, ignore_eos - applies missing top k sampler - removes interactive mode/terminal-like behavior, removes exclude parameter - moves threads and batch size to server command-line parameters - adds LoRA loading and matches command line parameters with main example - fixes stopping on EOS token and with the specified token amount with n_predict - adds server timeouts, host, and port settings - adds expanded generation complete response; adds generation settings, stop reason, prompt truncated, model used, and final text - sets defaults for unspecified parameters between requests - removes /next-token endpoint and as_loop parameter, adds stream parameter and server-sent events for streaming - adds CORS headers to responses - adds request logging, exception printing and optional verbose logging - adds better stopping words handling when matching multiple tokens and while streaming, or when it finishes on a partial stop string - adds printing an error when it can't bind to the host/port specified - fixes multi-byte character handling and replaces invalid UTF-8 characters on responses - prints timing and build info on startup - adds logit bias to request parameters - removes embedding mode - updates documentation; adds streaming Node.js and Bash examples - fixes code formatting - sets server threads to 1 since the current global state doesn't work well with simultaneous requests - adds truncation of the input prompt and better context reset - removes token limit from the input prompt - significantly simplified the logic and removed a lot of variables --------- Co-authored-by: anon998 <131767832+anon998@users.noreply.github.com> Co-authored-by: Henri Vasserman <henv@hot.ee> Co-authored-by: Felix Hellmann <privat@cirk2.de> Co-authored-by: Johannes Gäßler <johannesg@5d6.de> Co-authored-by: Lesaun Harvey <Lesaun@gmail.com>
2023-06-16examples : add "simple" (#1840)SuperUserNameMan
* Create `simple.cpp` * minimalist example `CMakeLists.txt` * Update Makefile for minimalist example * remove 273: Trailing whitespace * removed trailing white spaces simple.cpp * typo and comments simple.cpp --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-16CUDA : faster k-quant dot kernels (#1862)Kawrakow
* cuda : faster k-quant dot kernels * Imrove Q2_K dot kernel on older GPUs We now have a K_QUANTS_PER_ITERATION macro, which should be set to 1 on older and to 2 on newer GPUs. With this, we preserve the performance of the original PR on RTX-4080, and are faster compared to master on GTX-1660. * Imrove Q6_K dot kernel on older GPUs Using the same K_QUANTS_PER_ITERATION macro as last commit, we preserve performance on RTX-4080 and speed up Q6_K on a GTX-1660. * Add LLAMA_CUDA_KQUANTS_ITER to CMakeLists.txt and Makefile Allowed values are 1 or 2. 2 gives the best performance on modern GPUs and is set as default. On older GPUs 1 may work better. * PR comments --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-06-15make : add train-text-from-scratch (#1850)daboe01
* make finetuning example accessible * fixed: targed was in wrong line * fixed: name of executable was wrong * fixed: naming of binary * fixed: model path was wrong * fixed clean target * Update examples/train-text-from-scratch/README.md --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-15make : clean *.so files (#1857)sandyiscool
2023-06-13Allow "quantizing" to f16 and f32 (#1787)Kerfuffle
* Allow "quantizing" to f16 and f32 Fix an issue where quantizing didn't respect LLAMA_NO_K_QUANTS Add brief help to the list of quantization types in the quantize tool Ignore case for quantization type arguments in the quantize tool
2023-06-10make : add SSSE3 compilation use case (#1659)rankaiyx
2023-06-07k-quants : allow to optionally disable at compile time (#1734)Georgi Gerganov
* k-quants : put behind optional compile flag LLAMA_K_QUANTS * build : enable k-quants by default
2023-06-06ggml : fix builds, add ggml-quants-k.o (close #1712, close #1710)Georgi Gerganov
2023-06-05ggml : add SOTA 2,3,4,5,6 bit k-quantizations (#1684)Kawrakow
* Starting to add k-quantization to ggml I think it is better to have quantization separate from ggml. For now just adding the k-quants there, but it would be better to also factor out the existing ggml quantizations. * Adding Q3_K and Q8_K (de)-quantization * Q3_K now working on CUDA and AVX2/scalar CUDA is not ideal - ~50% slower than Q4_0 for single token prediction, about the same in batch mode (perplexity). CPU single token is ~55 ms (on Ryzen 7950X). * Some improvement for Q3_K on CUDA It is now ~22.5 ms/token on my GPU, so ~30% slower than Q4_0. * Some more CUDA optimizations for Q3_K Single token is now 20.5 ms/token (~20% slower than Q4_0). Perplexity is on par with Q4_0. * Adding Q4_K - scalar, AVX2, CUDA Performance is the same or perhaps very slightly better than Q4_0 on the CPU. On the GPU, single token prediction is ~10% better than Q4_0, batch mode (perplexity is about the same). * Adding Q6_K - scalar, AVX2, CUDA Performance is ~40% lower compared to Q4_K on the CPU. This is to be expected, considering that we are memory bound on the CPU and the 6-bit model is ~44% larger than the 4-bit. On the GPU, single token prediction is ~6% lower than Q4_0, batch mode (perplexity) is even closer (but still slower). * Adding Q5_K - scalar, AVX2, CUDA Performance is ~20% lower compared to Q4_K on the CPU. This is to be expected, considering that we are memory bound on the CPU and the 5-bit model is ~22% larger than the 4-bit. On the GPU, single token prediction is about the same as Q4_0 for both, single token and batch prediction. * Per convention, all QX_K quantizations use Q5_K for output.weight * Adding quantization mixes * Quantization mixes: didn't quite get what I wanted in the last commit * Q4_K dot product for ARM_NEON * Q6_K dot product for ARM_NEON * Q5_K dot product for ARM_NEON * Adding Q3_K dot for ARM_NEON It is 22% slower than Q4_K, despite the smaller model size. On x86_64, where we are memory bound, the Q3_K model is quite a bit faster than Q4_K. * A very slightly faster ARM_NEON Q3_K dot * Adding Q2_K - just CUDA for now Token prediction is pretty good - about 15.5 ms on a RTX 4080. Perplexity is about the same as Q4_K. * Adding scalar and AVX2 Q2_K dot * Adding ARM_NEON Q2_K dot About the same performance as Q4_K. * A slightly faster ARM_NEON Q2_K dot Single token prediction is now ~36 ms on M2 Max. The code is much simpler too. * Fixed bug in Q2_K CUDA dot product kernel Stranegly enough, for the few prompts I tried with the 7B model the responses looked perfectly reasonable. Only realized something is not quite right when I tried the larger models and started getting nonse back. In any case, Q2_K single token evaluation time on an RTX 4080 in a Ryzen7950X box iusing CUDA and model fully loaded on the GPU are ~15.5 ms for 7B, ~25.4 ms for 13B, and ~55.8 ms for 30B. The max number of layers that fit in VRAM for The 65B is 32. With that, we get ~330 ms per token, which is not that much faster than just running on the CPU (~470 ms per token). * Don't print zeros/NaNs when no count histogram has been collected * A 10% faster CUDA vector dot kernel for Q3_K Q3_K is now running at ~18.5 ms / token on CUDA, so the gap to Q4_0 is only 10%. It seems memory acccess pattern is more important for performance than the amount of computation the kernel does. * A slightly daster Q4_K AVX2 dot product For perplexity, where we are less memory bound, time per pass drops by ~5%. Barely measurable difference for single token prediction. * A slightly faster ARM_NEON A4_K dot product * Minor * Fix quantization error test We cannot possibly be expecting rmse < 0.002 for 2- and 3-bit quantization variants. * Fix docker build I have been sloppy with vector reinterpret casts on ARM_NEON. It seems clang is very forgiving in that regard. * Added forgotten ggml.o dependence on k_quants.h to the Makefile * Had unintentionally committed the Makefile with -Ofast enabled * ggml : rename k_quants -> ggml-quants-k, use lowercase in code --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-04llama : Metal inference (#1642)Georgi Gerganov
* mtl : export the LLaMA computation graph * ci : disable temporary * mtl : adapt the MNIST example as starter * mtl : no need for mtl-export tool, add cli arg for main instead * mtl : export just a small part of the graph for now to make it easier * mtl : move MSL code into separate file for easy editing * mtl : initial get_rows_q4_0 kernel * mtl : confirmed get_rows_q4_0 is working correctly * mtl : add rms_norm kernel + confirm working * mtl : add mul kernel + confirm working * mtl : initial mul_mat Q4 kernel (wrong results) * mtl : mul_mat fixes (still wrong) * mtl : another mul_mat Q4 (still does not work) * mtl : working mul_mat q4 * ggml : fix handling of "view" ops in ggml_graph_import() * mtl : add rope kernel * mtl : add reshape and transpose handling * ggml : store offset as opt arg for ggml_view_xd() operators * mtl : add cpy kernel + handle view ops * mtl : confirm f16 x f32 attention mul mat * mtl : add scale kernel * mtl : add diag_mask_inf kernel * mtl : fix soft_max kernel * ggml : update ggml_nbytes() to handle non-contiguous tensors * mtl : verify V tensor contents * mtl : add f32 -> f32 cpy kernel * mtl : add silu kernel * mtl : add non-broadcast mul kernel * mtl : full GPU inference of the computation graph * mtl : optimize rms_norm and soft_max kernels * mtl : add f16 mat x f32 vec multiplication kernel * mtl : fix bug in f16 x f32 mul mat + speed-up computation * mtl : faster mul_mat_q4_0_f32 kernel * mtl : fix kernel signature + roll inner loop * mtl : more threads for rms_norm + better timing * mtl : remove printfs from inner loop * mtl : simplify implementation * mtl : add save/load vocab to ggml file * mtl : plug Metal inference into llama.cpp (very quick-n-dirty) * mtl : make it work with main example Lots of hacks but at least now it generates text * mtl : preparing for merge * mtl : clean-up ggml mtl interface + suport scratch / inplace * mtl : remove temp / debug code * metal : final refactoring and simplification * Revert "ci : disable temporary" This reverts commit 98c267fc77fe811082f672538fc91bcfc9072d63. * metal : add comments * metal : clean-up stuff, fix typos * readme : add Metal instructions * readme : add example for main
2023-05-28LLAMA_DEBUG adds debug symbols (#1617)Johannes Gäßler
2023-05-27Include server in releases + other build system cleanups (#1610)Kerfuffle
Set `LLAMA_BUILD_SERVER` in workflow so the `server` example gets build. This currently only applies to Windows builds because it seems like only Windows binary artifacts are included in releases. Add `server` example target to `Makefile` (still uses `LLAMA_BUILD_SERVER` define and does not build by default) Fix issue where `vdot` binary wasn't removed when running `make clean`. Fix compile warnings in `server` example. Add `.hpp` files to trigger workflow (the server example has one).
2023-05-26cuda : performance optimizations (#1530)Johannes Gäßler
* xor hack * block y dim * loop unrolling * Fixed cmake LLAMA_CUDA_BY option * Removed hipblas compatibility code * Define GGML_CUDA_DMMV_BLOCK_Y if not defined * Fewer iters, more ops per iter * Renamed DMMV X/Y compilation options
2023-05-23OpenCL Token Generation Acceleration (#1459)0cc4m
* Move back to C++ for OpenCL * Refactor OpenCL code to work more like the CUDA code, add missing functions * Deduplicate dequant kernels * Add OpenCL compile options * Use compile args for preprocessing constants * Restore default platform + device selection by id behavior --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de> Co-authored-by: Henri Vasserman <henv@hot.ee>
2023-05-21make : .PHONY clean (#1553)Stefan Sydow
2023-05-20feature : support blis and other blas implementation (#1536)Zenix
* feature: add blis support * feature: allow all BLA_VENDOR to be assigned in cmake arguments. align with whisper.cpp pr 927 * fix: version detection for BLA_SIZEOF_INTEGER, recover min version of cmake * Fix typo in INTEGER Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Fix: blas changes on ci --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-05-20Revert "feature : add blis and other BLAS implementation support (#1502)"Georgi Gerganov
This reverts commit 07e9ace0f9da424d82e75df969642522880feb92.
2023-05-20feature : add blis and other BLAS implementation support (#1502)Zenix
* feature: add blis support * feature: allow all BLA_VENDOR to be assigned in cmake arguments. align with whisper.cpp pr 927 * fix: version detection for BLA_SIZEOF_INTEGER, recover min version of cmake * Fix typo in INTEGER Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-05-16Add alternate include path for openblas (#1476)sandyiscool
In some linux distributions (fedora, for example), the include path for openblas is located at '/usr/local/include'
2023-05-13make : fix PERF build with cuBLASGeorgi Gerganov
2023-05-05makefile: automatic Arch Linux detection (#1332)DaniAndTheWeb
This commit is a port of a detection method used in koboldcpp's Makefile in order to automatically set the -lcblas option on Arch Linux
2023-05-05Fix for OpenCL / clbast builds on macOS. (#1329)Ionoclast Laboratories
2023-05-02Call sh on build-info.sh (#1294)DannyDaemonic
2023-05-01Add git-based build information for better issue tracking (#1232)DannyDaemonic
* Add git-based build information for better issue tracking * macOS fix * "build (hash)" and "CMAKE_SOURCE_DIR" changes * Redo "CMAKE_CURRENT_SOURCE_DIR" and clearer build messages * Fix conditional dependency on missing target * Broke out build-info.cmake, added find_package fallback, and added build into to all examples, added dependencies to Makefile * 4 space indenting for cmake, attempt to clean up my mess in Makefile * Short hash, less fancy Makefile, and don't modify build-info.h if it wouldn't change it
2023-04-30build: add armv{6,7,8} support to cmake (#1251)Pavol Rusnak
- flags copied from Makefile - updated comments in both CMakeLists.txt and Makefile to match reality
2023-04-30Various fixes to mat_mul benchmark (#1253)Stephan Walter
2023-04-29ggml : adjust mul_mat_f16 work memory (#1226)Georgi Gerganov
* llama : minor - remove explicity int64_t cast * ggml : reduce memory buffer for F16 mul_mat when not using cuBLAS * ggml : add asserts to guard for incorrect wsize
2023-04-29build : fix reference to old llama_util.hGeorgi Gerganov
2023-04-29cuBLAS: use host pinned memory and dequantize while copying (#1207)slaren
* cuBLAS: dequantize simultaneously while copying memory * cuBLAS: use host pinned memory * cuBLAS: improve ggml_compute_forward_mul_mat_f16_f32 with pinned memory * cuBLAS: also pin kv cache * fix rebase
2023-04-28ggml : add CLBlast support (#1164)0cc4m
* Allow use of OpenCL GPU-based BLAS using ClBlast instead of OpenBLAS for context processing * Improve ClBlast implementation, avoid recreating buffers, remove redundant transfers * Finish merge of ClBlast support * Move CLBlast implementation to separate file Add buffer reuse code (adapted from slaren's cuda implementation) * Add q4_2 and q4_3 CLBlast support, improve code * Double CLBlast speed by disabling OpenBLAS thread workaround Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com> Co-authored-by: slaren <2141330+slaren@users.noreply.github.com> * Fix device selection env variable names * Fix cast in opencl kernels * Add CLBlast to CMakeLists.txt * Replace buffer pool with static buffers a, b, qb, c Fix compile warnings * Fix typos, use GGML_TYPE defines, improve code * Improve btype dequant kernel selection code, add error if type is unsupported * Improve code quality * Move internal stuff out of header * Use internal enums instead of CLBlast enums * Remove leftover C++ includes and defines * Make event use easier to read Co-authored-by: Henri Vasserman <henv@hot.ee> * Use c compiler for opencl files * Simplify code, fix include * First check error, then release event * Make globals static, fix indentation * Rename dequant kernels file to conform with other file names * Fix import cl file name --------- Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com> Co-authored-by: slaren <2141330+slaren@users.noreply.github.com> Co-authored-by: Henri Vasserman <henv@hot.ee> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-04-28Add Manjaro CUDA include and lib dirs to Makefile (#1212)Johannes Gäßler
2023-04-24Fix cuda compilation (#1128)slaren
* Fix: Issue with CUBLAS compilation error due to missing -fPIC flag --------- Co-authored-by: B1gM8c <89020353+B1gM8c@users.noreply.github.com>
2023-04-23ggml : better PERF prints + support "LLAMA_PERF=1 make"Georgi Gerganov
2023-04-22ggml : fix AVX build + update to new Q8_0 formatGeorgi Gerganov
2023-04-21Improve cuBLAS performance by using a memory pool (#1094)slaren
* Improve cuBLAS performance by using a memory pool * Move cuda specific definitions to ggml-cuda.h/cu * Add CXX flags to nvcc * Change memory pool synchronization mechanism to a spin lock General code cleanup
2023-04-20Add Q4_3 support to cuBLAS (#1086)slaren
2023-04-20fix: LLAMA_CUBLAS=1 undefined reference 'shm_open' (#1080)源文雨
2023-04-20Improve cuBLAS performance by dequantizing on the GPU (#1065)slaren
2023-04-19ggml : Q4 cleanup - remove 4-bit dot product code (#1061)Stephan Walter
* Q4 cleanup * Remove unused AVX512 Q4_0 code
2023-04-19Add NVIDIA cuBLAS support (#1044)slaren
2023-04-18Adding a simple program to measure speed of dot products (#1041)Kawrakow
On my Mac, the direct Q4_1 product is marginally slower (~69 vs ~55 us for Q4_0). The SIMD-ified ggml version is now almost 2X slower (~121 us). On a Ryzen 7950X CPU, the direct product for Q4_1 quantization is faster than the AVX2 implementation (~60 vs ~62 us). --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
generated by cgit v1.2.3 (git 2.25.1) at 2024-12-25 19:04:36 +0000