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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).
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Hide it behind an #ifdef
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Fixes building for x86 processors missing F16C featureset
MSVC not included, as in MSVC F16C is implied with AVX2/AVX512
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- 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)
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the makefile (#839)
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* 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>
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* CMake: Add AVX512 option
* CI: Add AVX/AVX512 builds (Windows)
(AVX512 tests can only be run when the worker happens to support it, building works anyway)
* CMake: Fix sanitizer linkage ( merged #468 )
* CI: Add sanitizer builds (Ubuntu)
* CI: Fix release tagging
(change @zendesk/action-create-release to @anzz1/action-create-release until upstream PR Added commitish as input zendesk/action-create-release#32 is merged)
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- main -> examples
- utils -> examples (renamed to "common")
- quantize -> examples
- separate tools for "perplexity" and "embedding"
Hope I didn't break something !
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* Generate library with CMake
BUILD_SHARED_LIBS to allow llama library to be generated.
* Turn ON PIC when BUILD_SHARED_LIBS is ON
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* 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
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* Add test-tokenizer-0 to do a few tokenizations - feel free to expand
* Added option to convert-pth-to-ggml.py script to dump just the vocabulary
* Added ./models/ggml-vocab.bin containing just LLaMA vocab data (used for tests)
* Added utility to load vocabulary file from previous point (temporary implementation)
* Avoid using std::string_view and drop back to C++11 (hope I didn't break something)
* Rename gpt_vocab -> llama_vocab
* All CMake binaries go into ./bin/ now
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* Functionality addition CMakeLists.txt
Refactoring:
1. Simplify more options that are negation of negation.
LLAMA_NO_ACCELERATE -> LLAMA_ACCELERATE
2. Changed to an optional expression instead of forcing to enable AVX2 in MSVC.
3. Make CMAKE_CXX_STANDARD, which is different from Makefile, the same.
4. Use add_compile_options instead of adding options to CMAKE_C_FLAGS.
5. Make utils use target_link_libraries instead of directly referencing code.
Added features:
1. Added some options.
LLAMA_STATIC_LINK,LLAMA_NATIVE,LLAMA_LTO,LLAMA_GPROF,LLAMA_OPENBLAS
* Fix Accelerate link in CMake
* Windows build Fix
* C++11 to C++17
* Reflects C/C++ standard individually
* Change the version to 3.12
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* add ptread link to fix cmake build under linux
* add cmake to linux and macos platform
* separate make and cmake workflow
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Co-authored-by: Sebastián A <sebastian.aedo29@gmail.com>
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