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2023-04-17Add LoRA support (#820)slaren
2023-04-13llama : merge llama_internal.h into llama.hGeorgi Gerganov
Hide it behind an #ifdef
2023-04-12Don't crash on ftype (formerly f16) == 4 (#917)Stephan Walter
2023-04-11Add enum llama_ftype, sync ggml_type to model files (#709)Stephan Walter
2023-04-10Rewrite loading code to try to satisfy everyone:comex
- Support all three formats (ggml, ggmf, ggjt). (However, I didn't include the hack needed to support GPT4All files without conversion. Those can still be used after converting them with convert.py from my other PR.) - Support both mmap and read (mmap is used by default, but can be disabled with `--no-mmap`, and is automatically disabled for pre-ggjt files or on platforms where mmap is not supported). - Support multi-file models like before, but automatically determine the number of parts rather than requiring `--n_parts`. - Improve validation and error checking. - Stop using the per-file type field (f16) entirely in favor of just relying on the per-tensor type/size fields. This has no immediate benefit, but makes it easier to experiment with different formats, and should make it easier to support the new GPTQ-for-LLaMa models in the future (I have some work in progress on that front). - Support VirtualLock on Windows (using the same `--mlock` option as on Unix). - Indicate loading progress when using mmap + mlock. (Which led me to the interesting observation that on my Linux machine, with a warm file cache, mlock actually takes some time, whereas mmap without mlock starts almost instantly...) - To help implement this, move mlock support from ggml to the loading code. - madvise/PrefetchVirtualMemory support (based on #740) - Switch from ifstream to the `fopen` family of functions to avoid unnecessary copying and, when mmap is enabled, allow reusing the same file descriptor for both metadata reads and mmap (whereas the existing implementation opens the file a second time to mmap). - Quantization now produces a single-file output even with multi-file inputs (not really a feature as much as 'it was easier this way'). Implementation notes: I tried to factor the code into more discrete pieces than before. Regarding code style: I tried to follow the code style, but I'm naughty and used a few advanced C++ features repeatedly: - Destructors to make it easier to ensure everything gets cleaned up. - Exceptions. I don't even usually use exceptions when writing C++, and I can remove them if desired... but here they make the loading code much more succinct while still properly handling a variety of errors, ranging from API calls failing to integer overflow and allocation failure. The exceptions are converted to error codes at the API boundary.) Co-authored-by: Pavol Rusnak <pavol@rusnak.io> (for the bit I copied from #740)
2023-04-08Add quantize-stats command for testing quantization (#728)unbounded
Command that calculates some statistics over the errors introduced by quantization, like mean square error, max error and some percentile errors for layer weights. Should be useful for testing quantization improvements. Exposes some internal state from ggml and llama for testing
2023-04-02Added api for getting/setting the kv_cache (#685)Christian Falch
The api provides access methods for retrieving the current memory buffer for the kv_cache and its token number. It also contains a method for setting the kv_cache from a memory buffer. This makes it possible to load/save history - maybe support --cache-prompt paramater as well? Co-authored-by: Pavol Rusnak <pavol@rusnak.io>
2023-03-30Make loading weights 10-100x fasterJustine Tunney
This is a breaking change that's going to give you three benefits: 1. Your inference commands should load 100x faster 2. You may be able to safely load models 2x larger 3. You can run many concurrent inference processes This was accomplished by changing the file format so we can mmap() weights directly into memory without having to read() or copy them thereby ensuring the kernel can make its file cache pages directly accessible to our inference processes; and secondly, that the file cache pages are much less likely to get evicted (which would force loads to hit disk) because they're no longer competing with memory pages that were needlessly created by gigabytes of standard i/o. The new file format supports single-file models like LLaMA 7b, and it also supports multi-file models like LLaMA 13B. Our Python tool now merges the foo.1, foo.2, etc. files back into a single file so that the C++ code which maps it doesn't need to reshape data every time. That's made llama.cpp so much simpler. Much of its load code has now been deleted. Furthermore, this change ensures that tensors are aligned properly on a 32-byte boundary. That opens the door to seeing if we can get additional performance gains on some microprocessors, by using ops that require memory alignment. Lastly note that both POSIX and the Windows platform are supported Fixes #91
2023-03-29Fix typo in llama.h (#593)anzz1
2023-03-28llama : fix linkage with mingw (#551)anzz1
* Revert 7e53955 (#542) Still needs to be fixed properly * Fix linking on mingw32
2023-03-28all : be more strict about converting float to double (#458)Stephan Walter
* Be more strict about converting float to double * Test equivalence of round, SILU implementations Test module is commented out in CMakeLists.txt because the tests may take a long time, depending on how much the compiler optimizes. * Fix softmax in perplexity.cpp * all : prefer float over double where appropriate * perplexity : add <cmath> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-03-28ggml : introduce structs for the q4 data blocks (#356)Stephan Walter
* Introduce structs for the q4 data blocks * ggml : rename quant struct variables + fix ARM_NEON --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-03-25Cleanup STL headers + fix embedding examples + minor stuffGeorgi Gerganov
2023-03-25Add support for file load progress reporting callbacks (#434)Jed Fox
* File load progress reporting * Move llama_progress_handler into llama_context_params * Renames * Use seekg to find file size instead * More correct load progress * Call progress callback more frequently * Fix typo
2023-03-25Add missing struct annotation (#483)Doomsdayrs
`llama_sample_top_p_top_k` was missing the struct annotation on line 126. This causes a compiler issue when being parsed by the Kotlin C interop generator. This commit fixes the above issue by adding the struct annotation.
2023-03-24Support calling mlock() on loaded model data on Linux and macOS (#453)comex
* Support calling mlock() on loaded model data on Linux and macOS This is enabled by a new --mlock command line option. Using mlock() disables swapping and memory compression for the model data. Doing so can be useful on systems where the model takes up a large fraction of system RAM. In my experience, macOS is quite eager to start compressing llama.cpp's memory, which then makes it halt for a few seconds while it decompresses, even with a model that uses "only" 25GB out of 32GB. Of course, this comes at the cost of forcing the system to swap or compress other processes' memory instead, so it needs to be used with care and shouldn't be enabled by default. In theory it should be possible to support this on Windows as well using VirtualLock(), but I'm not much of a Windows user. * Update llama.cpp --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-03-24Add embedding mode with arg flag. Currently working (#282)Luciano
* working but ugly * add arg flag, not working on embedding mode * typo * Working! Thanks to @nullhook * make params argument instead of hardcoded boolean. remove useless time check * start doing the instructions but not finished. This probably doesnt compile * Embeddings extraction support --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-03-22Introduce C-style API (#370)Georgi Gerganov
* Major refactoring - introduce C-style API * Clean up * Add <cassert> * Add <iterator> * Add <algorithm> .... * Fix timing reporting and accumulation * Measure eval time only for single-token calls * Change llama_tokenize return meaning