Currently the Metal backend does not support BF16. `ggml_metal_supports_op` was returning true in these cases, leading to a crash with models converted with `--leave-output-tensor`. This commit checks if the first few sources types are BF16 and returns false if that's the case.
* un-ignore `build-info.cmake` and `build-info.sh`
I am assuming that ignoring them was unintentional. If they are ignored, some tools, like cargo, will consider the files inexistent, even if they're comitted, for the purpose of publishing. This leads to the build failing in such cases.
* un-ignore `build-info.cpp.in`
For the same reason as the previous two files.
* Reorganize `.gitignore`
* Add exceptions for files mentioned by @slaren
I did leave .clang-tidy since it was explicitly ignored before.
* Add comments for organization
* Sort some lines for pretty
* Test with `make` and `cmake` builds to ensure no build artifacts might be comitted
* Remove `.clang-tidy` from `.gitignore`
Per comment by @ggerganov
* Remove `IDEWorkspaceChecks.plist` from root-level `.gitignore`
On hosts which are not prepared/dedicated to execute code using CUDA
it is still possible to compile llama.cpp with CUDA support by just
installing the development packages. Missing are the runtime
libraries like /usr/lib64/libcuda.so* and currently the link step
will fail.
The development environment is prepared for such situations. There
are stub libraries for all the CUDA libraries available in the
$(CUDA_PATH)/lib64/stubs directory. Adding this directory to the end
of the search path will not change anything for environments which
currently work fine but will enable compiling llama.cpp also in case
the runtime code is not available.
* update: convert-hf-to-gguf.py to support Qwen2-57B-A14B
* fix: QWEN2MOE support for expert_feed_forward_length
previously, expert ff was taken from n_ff (intermediate size) but it is now properly taken from LLM_KV_EXPERT_FEED_FORWARD_LENGTH
n_ff_exp and n_ff_shared_exp are now properly calculated
* update: convert-hf-to-gguf.py cleanup for Qwen2MoeForCausalLM
* fix: QWEN2MOE support for expert_feed_forward_length
previously, expert ff was taken from n_ff (intermediate size) but it is now properly taken from LLM_KV_EXPERT_FEED_FORWARD_LENGTH
n_ff_exp and n_ff_shexp are now properly calculated
* Implement non-mapped async IO for CUDA on Windows. On a fast Gen5 NVMe drive this change improves model load time by >3x while it should be the same (or slightly faster) on any other drive.
* Free resources except for backend.
* Change assertions to exceptions in llama_file, find correct cuda backend to create CUDA resources and respect the use_mmap flag again for CUDA.
* Apply suggestions from code review
Co-authored-by: slaren <slarengh@gmail.com>
* Fix editorconfig and unused variable
* Fix issues with Windows build
---------
Co-authored-by: slaren <slarengh@gmail.com>
* cuda sqrt support
* enable cuda in pca
* fix comments in pca
* add test
* add sqrt to ggml_backend_cuda_supports_op
* fix test
* new line
* Use F32 sqrtf instead of F64 sqrt
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* cuda : fix bounds check for src0 rows in MMVQ kernel
* Update ggml-cuda/mmvq.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* fix compile issues introduced by loongarch_asx
* restore quant changes to merge
* fix compile issues introduced by loongarch_asx
* further optimize by using vec_msum & vec_sum4s on ppc64le
