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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25 changed files with 1467 additions and 887 deletions
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@ -483,6 +483,12 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
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break;
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}
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params.n_batch = std::stoi(argv[i]);
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} else if (arg == "-ub" || arg == "--ubatch-size") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.n_ubatch = std::stoi(argv[i]);
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} else if (arg == "--keep") {
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if (++i >= argc) {
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invalid_param = true;
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@ -977,7 +983,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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printf(" binary file containing multiple choice tasks.\n");
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printf(" -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
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printf(" -c N, --ctx-size N size of the prompt context (default: %d, 0 = loaded from model)\n", params.n_ctx);
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printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
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printf(" -b N, --batch-size N logical maximum batch size (default: %d)\n", params.n_batch);
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printf(" -ub N, --ubatch-size N\n");
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printf(" physical maximum batch size (default: %d)\n", params.n_ubatch);
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printf(" --samplers samplers that will be used for generation in the order, separated by \';\'\n");
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printf(" (default: %s)\n", sampler_type_names.c_str());
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printf(" --sampling-seq simplified sequence for samplers that will be used (default: %s)\n", sampler_type_chars.c_str());
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@ -1287,8 +1295,9 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
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auto cparams = llama_context_default_params();
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cparams.n_ctx = params.n_ctx;
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cparams.n_batch = params.n_batch;
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cparams.n_seq_max = params.n_parallel;
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cparams.n_batch = params.n_batch;
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cparams.n_ubatch = params.n_ubatch;
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cparams.n_threads = params.n_threads;
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cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
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cparams.seed = params.seed;
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@ -1379,6 +1388,7 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
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std::vector<llama_token> tmp = { llama_token_bos(model), llama_token_eos(model), };
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llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
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llama_kv_cache_clear(lctx);
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llama_synchronize(lctx);
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llama_reset_timings(lctx);
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}
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