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* Retire the ggml_mul_mat() for transposed src0
- It can always be made contiguous with ggml_cpy()
- The code is now simplified
- The results are deterministic in respect to num threads
* SIMD-ify dequantize_row_q4_0() for ARM_NEON (#502)
* Attempt to SIMD-ify dequantize_row_q4_0() for ARM_NEON
* Fix dequantization - forgot to interleave the quants
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* Reduce memory usage and allocate enough memory for large contexts
* Simpler scratch buffer usage
* Reenable BLAS for quantized mul_mat
* Fix number of layers in 30B and 65B
* Fix KV cache size for F32
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* 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
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* Deduplicate q4 quantization functions
* Use const; add basic test
* Re-enable quantization test
* Disable AVX2 flags in CI
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* fix: add POSIX functionality for Linux compilation
* fix: older standard for compatibility
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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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* Update Makefile to detect AVX512 support and add compiler flags if it's available
* Based on existing AVX2 implementation, dot product on one 32-value block of 4-bit quantized ints at a time
* Perform 8 bit -> 16 bit sign extension and multiply+add on 32 values at time instead of 16
* Use built-in AVX512 horizontal reduce add to get sum at the end
* Manual unrolling on inner dot product loop to reduce loop counter overhead
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I think this is what is used in the Python code
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The readme tells people to use the command line option "-t 8", causing 8
threads to be started. On systems with fewer than 8 cores, this causes a
significant slowdown. Remove the option from the example command lines
and use /proc/cpuinfo on Linux to determine a sensible default.
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* Add AVX2 version of ggml_vec_dot_q4_1
* Small optimisations to q4_1 dot product (@Const-me)
* Rearrange Q4_1 quantization to work for multipart models. (Fix #152)
* Fix ggml_vec_mad_q4_1 too
* Fix non-vectorised q4_1 vec mul
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* add ggml_rms_norm
* update op num
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Without "static" prefix, it fails to compile in clang
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* Don't use vdotq_s32 if it's not available
`dotprod` extensions aren't available on some ARM CPUs (e.g. Raspberry Pi 4), so check for them and only use them if they're available.
Reintroduces the code removed in 84d9015 if `__ARM_FEATURE_DOTPROD` isn't defined.
* Update ggml.c
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* 10% performance boost on ARM
* Back to original change
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This reverts commit 113a9e83ebc0f788f861394437087bf3ca0e019b.
There are some reports for illegal instruction.
Moved this stuff to vdotq_s32 branch until resolve
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* Apply fixes suggested to build on windows
Issue: https://github.com/ggerganov/llama.cpp/issues/22
* Remove unsupported VLAs
* MSVC: Remove features that are only available on MSVC C++20.
* Fix zero initialization of the other fields.
* Change the use of vector for stack allocations.
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