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* Added numa options to allow finer grained control as well as plumbing for a new mirror mode that will require numa.h * Reverted Makefile * Fixed include * Removed sched.h from ggml.h, moved ggml_get_numa_affinity into ggml.c, removed trailing whitespace and fixed up a few inconsistent variables * removed trailing whitespace * Added numa options to allow finer grained control as well as plumbing for a new mirror mode that will require numa.h * Reverting Makefile * Fixed a number of issues with the move from BOOL to ggml_numa_strategies. Added a note about mirror mode note being implemented yet * Removing MIRROR_MODE code for this PR * Removing last bit of MIRROR_MODE code for this PR * Removing unneeded branch in server.cpp example and moving get_numa_affinity and making it static * Fixed lingering init_llama_backend() bool calls in tests and examples * Remote enum llama_numa_strategies * Revert bad merge with dynatemp flags * add missing enum ggml_numa_strategies declaration and revert sync problem with master * add missing enum ggml_numa_strategies declaration * fixed ggml_init_numa variable * Update ggml.h Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> * Update READMEs with info about numa flags, change INTERLEAVE strategy name to DISTRIBUTE everywhere, implement the improved distribution strategy from @rankaiyx, fix a spelling mistake and un-merge some bad merges * split numa init out from llama_backend_init and created llama_numa_init. Updated all code paths and samples * Fix up some boolean vs enum comparisons * Added #ifdefs for non-Linux OS that don't have cpu_set_t datatype * Update ggml.h Align enum values Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update ggml.c Remove whitespace Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update ggml.c align paremeters Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update examples/server/server.cpp remove whitespace and align brace Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update common/common.cpp Remove whitespace and align brace Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * unified ggml_numa_strategy enum and fixed text alignment in server.cpp example * Update ggml.c simplified return for platforms without NUMA support Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> * removed redundant else from cli argument processing of --numa * whitespace --------- Co-authored-by: root <root@nenya.lothlorien.ca> Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Jared Van Bortel <jared@nomic.ai> |
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.. | ||
CMakeLists.txt | ||
imatrix.cpp | ||
README.md |
llama.cpp/examples/imatrix
Compute an importance matrix for a model and given text dataset. Can be used during quantization to enchance the quality of the quantum models. More information is available here: https://github.com/ggerganov/llama.cpp/pull/4861
Usage
./imatrix -m <some_fp_model> -f <some_training_data> [-o <output_file>] [--verbosity <verbosity_level>]
[-ofreq num_chunks] [-ow <0 or 1>] [other common params]
Here -m
with a model name and -f
with a file containing training data (such as e.g. wiki.train.raw
) are mandatory.
The parameters in square brackets are optional and have the following meaning:
-o
(or--output-file
) specifies the name of the file where the computed data will be stored. If missingimatrix.dat
is used.--verbosity
specifies the verbosity level. If set to0
, no output other than the perplexity of the processed chunks will be generated. If set to1
, each time the results are saved a message is written tostderr
. If>=2
, a message is output each time data is collected for any tensor. Default verbosity level is1
.-ofreq
(or--output-frequency
) specifies how often the so far computed result is saved to disk. Default is 10 (i.e., every 10 chunks)-ow
(or--output-weight
) specifies if data will be collected for theoutput.weight
tensor. My experience is that it is better to not utilize the importance matrix when quantizingoutput.weight
, so this is set tofalse
by default.
For faster computation, make sure to use GPU offloading via the -ngl
argument
Example
LLAMA_CUBLAS=1 make -j
# generate importance matrix (imatrix.dat)
./imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99
# use the imatrix to perform a Q4_K_M quantization
./quantize --imatrix imatrix.dat ggml-model-f16.gguf ./ggml-model-q4_k_m.gguf q4_k_m