Revisited Readme-sycl #1
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Alcpz
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Mar 18, 2024
Co-authored-by: Alberto Cabrera Pérez <[email protected]>
Co-authored-by: Alberto Cabrera Pérez <[email protected]>
Co-authored-by: Alberto Cabrera Pérez <[email protected]>
Co-authored-by: Alberto Cabrera Pérez <[email protected]>
Co-authored-by: Alberto Cabrera Pérez <[email protected]>
Co-authored-by: Alberto Cabrera Pérez <[email protected]>
Co-authored-by: Alberto Cabrera Pérez <[email protected]>
Co-authored-by: Alberto Cabrera Pérez <[email protected]>
Co-authored-by: AidanBeltonS <[email protected]>
Co-authored-by: AidanBeltonS <[email protected]>
Co-authored-by: AidanBeltonS <[email protected]>
Co-authored-by: AidanBeltonS <[email protected]>
Co-authored-by: Alberto Cabrera Pérez <[email protected]>
Co-authored-by: Alberto Cabrera Pérez <[email protected]>
OuadiElfarouki
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that referenced
this pull request
Aug 6, 2024
* [example] batched-bench "segmentation fault" When `llama-batched-bench` is invoked _without_ setting `-npl`, "number of parallel prompts", it segfaults. The segfault is caused by invoking `max_element()` on a zero-length vector, `n_pl` This commit addresses that by first checking to see if the number of parallel prompts is zero, and if so sets the maximum sequence size to 1; otherwise, sets it to the original, the result of `max_element()`. Fixes, when running `lldb build/bin/llama-batched-bench -- -m models/Meta-Llama-3-8B.gguf` ``` * thread #1, queue = 'com.apple.main-thread', stop reason = EXC_BAD_ACCESS (code=1, address=0x0) frame #0: 0x000000010000366c llama-batched-bench`main(argc=3, argv=0x000000016fdff268) at batched-bench.cpp:72:28 69 llama_context_params ctx_params = llama_context_params_from_gpt_params(params); 70 71 // ensure enough sequences are available -> 72 ctx_params.n_seq_max = *std::max_element(n_pl.begin(), n_pl.end()); ``` * Update examples/batched-bench/batched-bench.cpp Co-authored-by: compilade <[email protected]> --------- Co-authored-by: Georgi Gerganov <[email protected]> Co-authored-by: compilade <[email protected]>
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Updated & fixed SYCL Readme with an emphasis on the multi-targets (intel GPU & Nvidia GPU so far) capability of the SYCL backend in llama.cpp