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* tests : consolidate the context state tests into test-llama-context test-save-load-state, test-recurrent-state-rollback and test-state-restore-fragmented check the same object from three places: the KV cache and the recurrent state of a llama_context, saved, loaded, copied, rolled back. Each had its own main, its own loop over the models, its own copy of the token and the comparison helpers, and a run of all three loaded the same models several times. They are one binary now, and the case is the unit of the test: tests/llama-context/test-case.h the interface a case implements tests/llama-context/cases-kv.cpp the cases of the KV cache tests/llama-context/cases-rs.cpp the cases of the recurrent state tests/llama-context/test-util.cpp tokens, generation, comparison tests/llama-context/test-runner.cpp the models of a case, on threads tests/llama-context/test-log.cpp the log file of a model A run is a table: a row per model, a column per case, a cell holds the verdict of that pair. The models load on several threads (-j, --threads, min(cores, 4) by default), the table is printed when it is complete and the log of a model that failed is printed after it, in the order of the rows. A case returns a verdict and the reason for it, and the runner is the only one who reports - the test does not use tests/testing.h, a run over 130 models is a matrix of verdicts and not a tree of subtests. Staging no longer travels through the environment: llama_model_set_attn_rot replaces the LLAMA_ATTN_ROT_DISABLE variable, which a model loaded in one thread used to change for the models the other threads were loading, and llama_memory_get_buffers is added for the cases that copy the buffers of a memory. common_log_set_stdo keeps the console quiet while the table is being built. ggml_aligned_malloc allocates with posix_memalign when the thread sanitizer is on. On macOS it allocates with the Mach VM, which the sanitizer does not intercept, so it never sees a page range change hands and reports the first write into recycled memory as a race - 30 of them in this test, and every one of them false. Assisted-by: pi:llama.cpp/Qwen3.8-Flash-Next * cont : run single-threaded by default * cont : try to build the tests on all platforms * cont : trim down some body fat * cont : fix * cont : fix the fix |
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| README-MUSA.md | ||
| README.md | ||
| run.sh | ||
CI
This CI implements heavy-duty workflows that run on self-hosted runners. Typically the purpose of these workflows is to cover hardware configurations that are not available from Github-hosted runners and/or require more computational resource than normally available.
It is a good practice, before publishing changes to execute the full CI locally on your machine. For example:
mkdir tmp
# CPU-only build
bash ./ci/run.sh ./tmp/results ./tmp/mnt
# with CUDA support
GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
# with SYCL support
source /opt/intel/oneapi/setvars.sh
GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
# with MUSA support
GG_BUILD_MUSA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
# etc.
Adding self-hosted runners
- Add a self-hosted
ggml-ciworkflow to .github/workflows/build.yml with an appropriate label - Request a runner token from
ggml-org(for example, via a comment in the PR or email) - Set-up a machine using the received token (docs)
- Optionally update ci/run.sh to build and run on the target platform by gating the implementation with a
GG_BUILD_...env