llama : add pipeline parallelism support (#6017)
* llama : add pipeline parallelism support for batch processing with multiple CUDA GPUs ggml-ci * server : add -ub, --ubatch-size parameter * fix server embedding test * llama : fix Mamba inference for pipeline parallelism Tested to work correctly with both `main` and `parallel` examples. * llama : limit max batch size to n_batch * add LLAMA_SCHED_MAX_COPIES to configure the number of input copies for pipeline parallelism default increase to 4 (from 2) changing this value may improve performance for some systems, but increases memory usage * fix hip build * fix sycl build (disable cpy_tensor_async) * fix hip build * llama : limit n_batch and n_ubatch to n_ctx during context creation * llama : fix norm backend * batched-bench : sync after decode * swiftui : sync after decode * ggml : allow ggml_get_rows to use multiple threads if they are available * check n_ubatch >= n_tokens with non-casual attention * llama : do not limit n_batch to n_ctx with non-casual attn * server : construct batch with size of llama_n_batch * ggml_backend_cpu_graph_compute : fix return value when alloc fails * llama : better n_batch and n_ubatch comment * fix merge * small fix * reduce default n_batch to 2048 --------- Co-authored-by: Francis Couture-Harpin <git@compilade.net> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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@ -51,7 +51,8 @@ struct gpt_params {
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int32_t n_threads_batch_draft = -1;
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int32_t n_predict = -1; // new tokens to predict
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int32_t n_ctx = 512; // context size
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int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
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int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
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int32_t n_ubatch = 512; // physical batch size for prompt processing (must be >=32 to use BLAS)
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int32_t n_keep = 0; // number of tokens to keep from initial prompt
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int32_t n_draft = 5; // number of tokens to draft during speculative decoding
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int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
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