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* convert : support Mixtral as LLAMA arch * convert : fix n_ff typo * llama : model loading * ggml : sync latest ggml_mul_mat_id * llama : update graph to support MoE * llama : fix cur -> cur_expert * llama : first working version * llama : fix expert weighting in the FFN * ggml : ggml_get_rows support 2D indexing [n_tokens, n_experts] (cpu only) * ggml : add n_as argument to ggml_mul_mat_id * ggml : fix ggml_get_rows to take into account ne02 / ne11 * metal : add more general support for ggml_get_rows + tests * llama : add basic support for offloading moe with CUDA * metal : add/mul/div use general kernel when src1 not cont * metal : reduce the kernel launches for ggml_mul_mat_id * ggml : get_rows : support non-contiguos tensors with gaps, generalize up to 3D * ggml : update get_rows f16 and q * cuda : support non-contiguous src1 in get_rows * llama : offload missing ffn_moe_silu * metal : fix ggml_get_rows to work with non-cont src1 * metal : add indirect mat-vec kernels for all quantization types * llama : do not quantize expert gating tensors * llama : add n_expert and n_expert_used to hparams + change quants * test-backend-ops : add moe test * cuda : fix get_rows when ncols is odd * convert : determine n_ctx correctly * metal : fix ggml_mul_mat_id for F32 * test-backend-ops : make experts more evenly probable (test_moe) * test-backend-ops : cleanup, add moe test for batches * test-backend-ops : add cpy from f32 -> all types test * test-backend-ops : fix dequantize block offset * llama : fix hard-coded number of experts * test-backend-ops : simplify and disable slow tests to avoid CI timeout * test-backend-ops : disable MOE test with thread sanitizer * cuda : fix mul_mat_id with multi gpu * convert : use 1e6 rope_freq_base for mixtral * convert : fix style * convert : support safetensors format * gguf-py : bump version * metal : add cpy f16 -> f32 kernel * metal : fix binary ops for ne10 % 4 != 0 * test-backend-ops : add one more sum_rows test * ggml : do not use BLAS with ggml_mul_mat_id * convert-hf : support for mixtral-instruct (#4428) * convert : typo fix, add additional hyperparameters, use LLaMA arch for Mixtral-instruct * convert : use sentencepiece tokenizer for Mixtral-instruct * convert : make flake8 happy * metal : fix soft_max kernels ref: ggerganov/ggml@1914017 * metal : limit kernels to not use more than the allowed threads --------- Co-authored-by: Georgi Gerganov <[email protected]> Co-authored-by: Radek Pilar <[email protected]>
* sync : ggml (SD ops, tests, kernels) ggml-ci * cuda : restore im2col ggml-ci * metal : fix accuracy of dequantization kernels ggml-ci * cuda : restore correct im2col ggml-ci * metal : try to fix moe test by reducing expert size ggml-ci * cuda : fix bin bcast when src1 and dst have different types ggml-ci --------- Co-authored-by: slaren <[email protected]>
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Aug 7, 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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