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2023-08-04Stream save llama context data to file instead of allocating entire buffer ↵l3utterfly
upfront (#2488) * added stream saving context data to file to avoid allocating unnecessary amounts of memory * generalised copying state data to file or buffer * added comments explaining how copy_state_data works * fixed trailing whitespaces * fixed save load state example * updated save load state to use public function in llama.cpp * - restored breakage of the llama_copy_state_data API - moved new logic for copying llama state data to internal function * fixed function declaration order * restored save load state example * fixed whitepace * removed unused llama-util.h include * Apply suggestions from code review Co-authored-by: slaren <slarengh@gmail.com> * Apply code review suggestions Co-authored-by: slaren <slarengh@gmail.com> --------- Co-authored-by: slaren <slarengh@gmail.com>
2023-07-31CUDA: mmq CLI option, fixed mmq build issues (#2453)Johannes Gäßler
2023-07-31Fix Metal backend broken from the allocator changes (#2455)slaren
* fix Metal backend broken from the allocator changes
2023-07-30ggml : add graph tensor allocator (#2411)slaren
* ggml : add graph tensor allocator * ggml : don't calculate data pointer of unallocated tensors when creating a view with an offset * ggml : refactor ggml_view_Nd into ggml_view_tensor_offset
2023-07-28llama : support more diverse tokenizers? (#2420)eric8607242
* supporting more diverse tokenizers * Update llama.cpp --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-28llama : use n_embd_gqa instead of n_embd to handle llama-2 70B (#2433)Rand Xie
2023-07-27metal : disable graph concurrency optimization due to bug (#2413)Georgi Gerganov
2023-07-26ggml : allocate graphs in a context (#2392)slaren
* ggml : graph allocation in contexts * allocate work buffer as a ggml_object in ggml_graph_compute_with_ctx * llama.cpp : allocate graph in the context * add GGML_PAD --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-25Add LLAMA_DEFAULT_RMS_EPS so we can change the default (#2384)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-07-25ggml : improve graph build time via hash table lookup (#2329)slaren
* improve graph build time * ggml_tensor : use 1 bit per flag * use a hash table instead
2023-07-25metal : concurrently dispatch commands (#2358)Shouzheng Liu
* metal: concurrently dispatch commands Function `ggml_metal_graph_find_concurrency` will run and write commands that can be issued concurrently to metal context `concur_list` array, when `ggml_metal_graph_compute` is called for the first time. * metal: don't call find_concurrency automatically. * metal : code style changes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-24make rms_norm_eps a parameter (#2374)slaren
* make rms_norm_eps a parameter * add rms_norm_eps to command line * fix baby llama, test-grad0 * use scientific notation for eps param in the help ggml-ci
2023-07-23llama : add grammar-based sampling (#1773)Evan Jones
* llama, main : constrain sampling to grammar * allow loading grammar from file * fix whitespace errors * handle & print parser errors * add comments to grammar syntax and allow newlines where unambiguous * add missing include * support alternates in root rule * fix bugs with empty token and EOS * adjust JSON grammar * remove swp file * rewrite ternary expressions Co-authored-by: Henri Vasserman <henv@hot.ee> * use struct for grammar elements and add Unicode support * add unicode escapes * add inverse char ranges * only sample full tokens (no peeking or truncation) * llama : minor style changes blindly applied in online editor - hopefully I didn't break something * update help text * add warning message if EOS is disabled --------- Co-authored-by: Henri Vasserman <henv@hot.ee> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-23llama : grouped-query attention + LLaMAv2 70B support (#2276)Georgi Gerganov
* CUDA: GQA implementation * llama : support for GQA and LLaMAv2 70B ggml-ci * py : fix hparams parsing (if-else blocks) ggml-ci * py : oh boy .. ggml-ci * help : fix gqa value for 70B ggml-ci --------- Co-authored-by: JohannesGaessler <johannesg@5d6.de>
2023-07-23llama : print max tensor size to stderr (#2336)Christian Demsar
2023-07-22llama : optimize memory buffers (#2325)Georgi Gerganov
2023-07-21ggml : fix rope args order + assert (#2054)Georgi Gerganov
2023-07-21llama : remove cfg smooth factor as it is only a reparameterization of the ↵Guillaume "Vermeille" Sanchez
guidance scale (#2280)
2023-07-21llama : make tensor_split ptr instead of array (#2272)Georgi Gerganov
2023-07-20llama : fix regression from #2000 - could not load no-mmap modelsGeorgi Gerganov
2023-07-19llama : extend API to get max devices at runtime (#2253)Rinne
2023-07-18ci : integrate with ggml-org/ci (#2250)Georgi Gerganov
* ci : run ctest ggml-ci * ci : add open llama 3B-v2 tests ggml-ci * ci : disable wget progress output ggml-ci * ci : add open llama 3B-v2 tg tests for q4 and q5 quantizations ggml-ci * tests : try to fix tail free sampling test ggml-ci * ci : add K-quants ggml-ci * ci : add short perplexity tests ggml-ci * ci : add README.md * ppl : add --chunks argument to limit max number of chunks ggml-ci * ci : update README
2023-07-17llama : fix t_start_sample_us initialization warning (#2238)Alex Klinkhamer
2023-07-15llama : add custom RoPE (#2054)Xiao-Yong Jin
* Implement customizable RoPE The original RoPE has pre-defined parameters theta_i = 10000^(−2(i−1)/d), for i in [1, 2, ..., d/2] Our customizable RoPE, ggml_rope_custom_inplace, uses theta_i = scale * base^(−2(i−1)/d), for i in [1, 2, ..., d/2] with the default matches the original scale = 1.0 base = 10000 The new command line arguments --rope-freq-base --rope-freq-scale set the two new RoPE parameter. Recent researches show changing these two parameters extends the context limit with minimal loss. 1. Extending Context to 8K kaiokendev https://kaiokendev.github.io/til#extending-context-to-8k 2. Extending Context Window of Large Language Models via Positional Interpolation Shouyuan Chen, Sherman Wong, Liangjian Chen, Yuandong Tian https://arxiv.org/abs/2306.15595 3. NTK-Aware Scaled RoPE allows LLaMA models to have extended (8k+) context size without any fine-tuning and minimal perplexity degradation. https://www.reddit.com/user/bloc97 https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ For the bold, try adding the following command line parameters to your favorite model: -c 16384 --rope-freq-base 80000 --rope-freq-scale 0.5 * ggml-metal: fix custom rope * common: fix argument names in help * llama: increase MEM_REQ_EVAL for MODEL_3B It avoids crashing for quantized weights on CPU. Better ways to calculate the required buffer size would be better. * llama: make MEM_REQ_EVAL depend on n_ctx * server: use proper Content-Type in curl examples Without the header Content-Type: application/json, curl will POST with Content-Type: application/x-www-form-urlencoded Though our simple server doesn't care, the httplib.h used has a limit with CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 8192 With Content-Type: application/json, we can send large json data. * style : minor fixes, mostly indentations * ggml : fix asserts --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-14llama : add functions that work directly on model (#2197)Bach Le
* Remove vocab reference from context * Add functions that works directly with model
2023-07-11llama : add classifier-free guidance (#2135)Bach Le
* Initial implementation * Remove debug print * Restore signature of llama_init_from_gpt_params * Free guidance context * Make freeing of guidance_ctx conditional * Make Classifier-Free Guidance a sampling function * Correct typo. CFG already means context-free grammar. * Record sampling time in llama_sample_classifier_free_guidance * Shift all values by the max value before applying logsoftmax * Fix styling based on review
2023-07-11Possible solution to allow K-quants on models with n_vocab!=32000 (#2148)LostRuins
* This allows LLAMA models that were previously incompatible with K quants to function mostly as normal. This happens when a model has a vocab != 32000, e.g 32001 which means it's not divisible by 256 or 64. Since the problematic dimensions only apply for `tok_embeddings.weight` and `output.weight` (dimentions 4096 x n_vocab), we can simply quantize these layers to Q8_0 whereas the majority of the hidden layers are still K-quanted since they have compatible dimensions. * Fix indentation Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * As an alternative, to avoid failing on Metal due to lack of Q8_0 support, instead quantize tok_embeddings.weight to Q4_0 and retain output.weight as F16. This results in a net gain of about 55mb for a 7B model compared to previous approach, but should minimize adverse impact to model quality. --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-10mpi : add support for distributed inference via MPI (#2099)Evan Miller
* MPI support, first cut * fix warnings, update README * fixes * wrap includes * PR comments * Update CMakeLists.txt * Add GH workflow, fix test * Add info to README * mpi : trying to move more MPI stuff into ggml-mpi (WIP) (#2099) * mpi : add names for layer inputs + prep ggml_mpi_graph_compute() * mpi : move all MPI logic into ggml-mpi Not tested yet * mpi : various fixes - communication now works but results are wrong * mpi : fix output tensor after MPI compute (still not working) * mpi : fix inference * mpi : minor * Add OpenMPI to GH action * [mpi] continue-on-error: true * mpi : fix after master merge * [mpi] Link MPI C++ libraries to fix OpenMPI * tests : fix new llama_backend API * [mpi] use MPI_INT32_T * mpi : factor out recv / send in functions and reuse * mpi : extend API to allow usage with outer backends (e.g. Metal) --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-09llama : remove "first token must be BOS" restriction (#2153)oobabooga
2023-07-07ggml : change ggml_graph_compute() API to not require context (#1999)Qingyou Meng
* ggml_graph_compute: deprecate using ggml_context, try resolve issue #287 * rewrite: no longer consider backward compitability; plan and make_plan * minor: rename ctx as plan; const * remove ggml_graph_compute from tests/test-grad0.c, but current change breaks backward * add static ggml_graph_compute_sugar() * minor: update comments * reusable buffers * ggml : more consistent naming + metal fixes * ggml : fix docs * tests : disable grad / opt + minor naming changes * ggml : add ggml_graph_compute_with_ctx() - backwards compatible API - deduplicates a lot of copy-paste * ci : enable test-grad0 * examples : factor out plan allocation into a helper function * llama : factor out plan stuff into a helper function * ci : fix env * llama : fix duplicate symbols + refactor example benchmark * ggml : remove obsolete assert + refactor n_tasks section * ggml : fix indentation in switch * llama : avoid unnecessary bool * ggml : remove comments from source file and match order in header --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-05Expose generation timings from server & update completions.js (#2116)Tobias Lütke
* use javascript generators as much cleaner API Also add ways to access completion as promise and EventSource * export llama_timings as struct and expose them in server * update readme, update baked includes * llama : uniform variable names + struct init --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-05ggml : generalize `quantize_fns` for simpler FP16 handling (#1237)Stephan Walter
* Generalize quantize_fns for simpler FP16 handling * Remove call to ggml_cuda_mul_mat_get_wsize * ci : disable FMA for mac os actions --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-05llama: Don't double count the sampling time (#2107)Howard Su
2023-07-05Fixed OpenCL offloading prints (#2082)Johannes Gäßler
2023-07-03Fix crash of test-tokenizer-0 under Debug build (#2064)Howard Su
* Fix crash of test-tokenizer-0 under Debug build * Change per comment
2023-07-03[llama] No need to check file version when loading vocab score (#2079)Howard Su
2023-07-01Test-based VRAM scratch size + context adjustment (#2056)Johannes Gäßler
2023-07-01metal : release buffers when freeing metal context (#2062)Aaron Miller
2023-07-01llama : fix return value of llama_load_session_file_internal (#2022)Georgi Gerganov
2023-07-01llama : catch llama_load_session_file_internal exceptions (#2022)Rand Xie
* convert checks in llama_load_session_file to throw and handle them * make llama_load_session_file_internal static * address feedbacks to avoid using exceptions
2023-06-29Use unsigned for random seed (#2006)Howard Su
* Use unsigned for random seed. Keep -1 as the value to use a time based seed. Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-28llama : replacing auto &kv with const auto &kv (#2041)m3ndax
* Replacing auto &kv with const auto &kv * Create codacy.yml * Delete codacy.yml
2023-06-28llama : remove shards weight file support (#2000)Howard Su
* Remove multiple shards * Remove multiple file loaders * Remove llama_load_tensor_shard class * Simplify load logic * Remove dead code guess_n_parts function * Remove vocab_only from constructor of llama_model_loader * Remove alignment_prevents_mmap which is not more needed. * Remove useless check
2023-06-28CUDA GPU acceleration for LoRAs + f16 models (#1970)Johannes Gäßler
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
2023-06-27llama : fix rope usage after ChatGLM changeGeorgi Gerganov
2023-06-26ggml : add NUMA support (#1556)zrm
* detect NUMA systems and pin work threads to nodes (linux) * disable mmap prefetch/readahead for NUMA systems * avoid sending finalize op to thread pool if it does nothing * silence robot * fix args * make --numa a param * recommendation that n_nodes evenly divide n_threads did not warrant such aggressive enforcement * lower synchronization overhead * statically allocate * move numa state to g_state * add description for --numa * ggml : minor style changes * ggml : minor style + try fix sanitizer build * llama : allow to initialize backend with NUMA support * llama : avoid ggml include in llama-util.h * ggml : style / formatting * ggml : fix handling of ops with n_threads > n_tasks > 1 * server : utilize numa parameter --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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>
2023-06-24llama : fix top-p sampling to match the canonical definition (#1953)Alex Renda
* Fix top-p sampling to match the standard definition (smallest set that has probability mass at least p, not largest set with probability mass less than p) * top-p: correct gt to gte * add test for correct top-p behavior
2023-06-24llama : make model stateless and context stateful (llama_state) (#1797)Didzis Gosko
* llama : make model stateless and context stateful * llama : minor cleanup * llama : update internal API declaration * Apply suggestions from code review fix style Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Missing model memory release * Fix style * Add deprecated warning for public API function llama_init_from_file * Update public API use cases: move away from deprecated llama_init_from_file * Deprecate public API function llama_apply_lora_from_file --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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