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2023-04-10ggml : add ggml_cont() + optimize ggml_cpy() for contiguous dstGeorgi Gerganov
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
2023-04-05ggml, llama : avoid heavy V transpose + improvements (#775)Georgi Gerganov
ggml : - added ggml_view_3d() - ggml_view_tensor() now inherits the stride too - reimplement ggml_cpy() to account for dst stride - no longer require tensor->data to be memory aligned llama : - compute RoPE on 32-bit tensors (should be more accurate) - store RoPE-ed K in the KV cache - store transposed V in the KV cache (significant speed-up) - avoid unnecessary Q copy
2023-04-02ggml : change ne to int64_t (#626)Marian Cepok
2023-03-30Ensure --mlock works properly with mmap() supportJustine Tunney
2023-03-30Add mmap support for model filesSlaren
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>
2023-03-24Support 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>
2023-03-22Deduplicate q4 quantization functions (#383)Stephan Walter
* Deduplicate q4 quantization functions * Use const; add basic test * Re-enable quantization test * Disable AVX2 flags in CI --------- 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
2023-03-16Add RMS norm and use it (#187)hoangmit
* add ggml_rms_norm * update op num
2023-03-10Initial releaseGeorgi Gerganov