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In some linux distributions (fedora, for example), the include path for openblas is located at '/usr/local/include'
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This commit is a port of a detection method used in koboldcpp's Makefile in order to automatically set the -lcblas option on Arch Linux
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* Add git-based build information for better issue tracking
* macOS fix
* "build (hash)" and "CMAKE_SOURCE_DIR" changes
* Redo "CMAKE_CURRENT_SOURCE_DIR" and clearer build messages
* Fix conditional dependency on missing target
* Broke out build-info.cmake, added find_package fallback, and added build into to all examples, added dependencies to Makefile
* 4 space indenting for cmake, attempt to clean up my mess in Makefile
* Short hash, less fancy Makefile, and don't modify build-info.h if it wouldn't change it
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- flags copied from Makefile
- updated comments in both CMakeLists.txt and Makefile to match reality
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* llama : minor - remove explicity int64_t cast
* ggml : reduce memory buffer for F16 mul_mat when not using cuBLAS
* ggml : add asserts to guard for incorrect wsize
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* cuBLAS: dequantize simultaneously while copying memory
* cuBLAS: use host pinned memory
* cuBLAS: improve ggml_compute_forward_mul_mat_f16_f32 with pinned memory
* cuBLAS: also pin kv cache
* fix rebase
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* Allow use of OpenCL GPU-based BLAS using ClBlast instead of OpenBLAS for context processing
* Improve ClBlast implementation, avoid recreating buffers, remove redundant transfers
* Finish merge of ClBlast support
* Move CLBlast implementation to separate file
Add buffer reuse code (adapted from slaren's cuda implementation)
* Add q4_2 and q4_3 CLBlast support, improve code
* Double CLBlast speed by disabling OpenBLAS thread workaround
Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com>
Co-authored-by: slaren <2141330+slaren@users.noreply.github.com>
* Fix device selection env variable names
* Fix cast in opencl kernels
* Add CLBlast to CMakeLists.txt
* Replace buffer pool with static buffers a, b, qb, c
Fix compile warnings
* Fix typos, use GGML_TYPE defines, improve code
* Improve btype dequant kernel selection code, add error if type is unsupported
* Improve code quality
* Move internal stuff out of header
* Use internal enums instead of CLBlast enums
* Remove leftover C++ includes and defines
* Make event use easier to read
Co-authored-by: Henri Vasserman <henv@hot.ee>
* Use c compiler for opencl files
* Simplify code, fix include
* First check error, then release event
* Make globals static, fix indentation
* Rename dequant kernels file to conform with other file names
* Fix import cl file name
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Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com>
Co-authored-by: slaren <2141330+slaren@users.noreply.github.com>
Co-authored-by: Henri Vasserman <henv@hot.ee>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* Fix: Issue with CUBLAS compilation error due to missing -fPIC flag
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Co-authored-by: B1gM8c <89020353+B1gM8c@users.noreply.github.com>
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* Improve cuBLAS performance by using a memory pool
* Move cuda specific definitions to ggml-cuda.h/cu
* Add CXX flags to nvcc
* Change memory pool synchronization mechanism to a spin lock
General code cleanup
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* Q4 cleanup
* Remove unused AVX512 Q4_0 code
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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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* ggml : add Q8_0 quantization for intermediate results
* quantize-stats : fix test + add it to Makefile default
* Q8: use int8_t, AVX/AVX2 optimizations
* ggml : fix quantize_row_q8_0() ARM_NEON rounding
* minor : updates after rebase to latest master
* quantize-stats : delete obsolete strings
* ggml : fix q4_1 dot func
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Co-authored-by: Stephan Walter <stephan@walter.name>
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Hide it behind an #ifdef
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* Initial version of q4_0 matrix multiplication benchmark
* Bugfix: Added dependency to ggml.o to benchmark
* Reviewer requests: added parameter for threads, switched to ggml_time_us()
* Reviewer input: removed rtsc, use epsilon for check
* Review comment: Removed set_locale
* Feature: Param for numer of iterations, Bugfix for use of parameter threads
* Reviewer suggestion: Moved to examples
* Reviewer feedback: Updated clean: and benchmark: sections
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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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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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
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I was able to get llama-cpp-python working but only when I build libllama.so with make.
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...there was no check. ported upstream from https://github.com/zanussbaum/gpt4all.cpp/pull/2 (I dont see any clean path for upstream patches)
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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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- 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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* 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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* 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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* potential out of bounds read
* fix quantize
* style
* Update convert-pth-to-ggml.py
* mild cleanup
* don't need the space-prefixing here rn since main.cpp already does it
* new file magic + version header field
* readme notice
* missing newlines
Co-authored-by: slaren <2141330+slaren@users.noreply.github.com>
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