mirror of https://github.com/ggerganov/llama.cpp
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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GitHub
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commit
f30ea47a87
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@ -118,6 +118,7 @@ option(LLAMA_SYCL "llama: use SYCL"
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option(LLAMA_SYCL_F16 "llama: use 16 bit floats for sycl calculations" OFF)
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set(LLAMA_SYCL_TARGET "INTEL" CACHE STRING "llama: sycl target device")
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option(LLAMA_CPU_HBM "llama: use memkind for CPU HBM" OFF)
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set(LLAMA_SCHED_MAX_COPIES "4" CACHE STRING "llama: max input copies for pipeline parallelism")
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option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
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option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
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@ -147,6 +148,8 @@ set(THREADS_PREFER_PTHREAD_FLAG ON)
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find_package(Threads REQUIRED)
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include(CheckCXXCompilerFlag)
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add_compile_definitions(GGML_SCHED_MAX_COPIES=${LLAMA_SCHED_MAX_COPIES})
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# enable libstdc++ assertions for debug builds
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if (CMAKE_SYSTEM_NAME MATCHES "Linux")
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add_compile_definitions($<$<CONFIG:Debug>:_GLIBCXX_ASSERTIONS>)
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4
Makefile
4
Makefile
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@ -167,6 +167,10 @@ ifeq ($(UNAME_S),OpenBSD)
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MK_CPPFLAGS += -D_BSD_SOURCE
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endif
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ifdef LLAMA_SCHED_MAX_COPIES
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MK_CPPFLAGS += -DGGML_SCHED_MAX_COPIES=$(LLAMA_SCHED_MAX_COPIES)
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endif
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ifdef LLAMA_DEBUG
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MK_CFLAGS += -O0 -g
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MK_CXXFLAGS += -O0 -g
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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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@ -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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@ -138,6 +138,8 @@ int main(int argc, char ** argv) {
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LOG_TEE("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret);
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return false;
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}
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llama_synchronize(ctx);
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}
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return true;
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@ -107,7 +107,7 @@ int main(int argc, char ** argv) {
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// max batch size
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const uint64_t n_batch = params.n_batch;
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GGML_ASSERT(params.n_batch == params.n_ctx);
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GGML_ASSERT(params.n_batch >= params.n_ctx);
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// tokenize the prompts and trim
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std::vector<std::vector<int32_t>> inputs;
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@ -164,6 +164,7 @@ struct cmd_params {
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std::vector<int> n_prompt;
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std::vector<int> n_gen;
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std::vector<int> n_batch;
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std::vector<int> n_ubatch;
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std::vector<ggml_type> type_k;
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std::vector<ggml_type> type_v;
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std::vector<int> n_threads;
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@ -183,7 +184,8 @@ static const cmd_params cmd_params_defaults = {
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/* model */ {"models/7B/ggml-model-q4_0.gguf"},
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/* n_prompt */ {512},
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/* n_gen */ {128},
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/* n_batch */ {512},
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/* n_batch */ {2048},
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/* n_ubatch */ {512},
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/* type_k */ {GGML_TYPE_F16},
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/* type_v */ {GGML_TYPE_F16},
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/* n_threads */ {get_num_physical_cores()},
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@ -208,6 +210,7 @@ static void print_usage(int /* argc */, char ** argv) {
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printf(" -p, --n-prompt <n> (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str());
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printf(" -n, --n-gen <n> (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str());
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printf(" -b, --batch-size <n> (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str());
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printf(" -ub N, --ubatch-size <n> (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str());
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printf(" -ctk <t>, --cache-type-k <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str());
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printf(" -ctv <t>, --cache-type-v <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
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printf(" -t, --threads <n> (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
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@ -217,7 +220,7 @@ static void print_usage(int /* argc */, char ** argv) {
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printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
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printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str());
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printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str());
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printf(" -ts, --tensor_split <ts0/ts1/..> (default: 0)\n");
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printf(" -ts, --tensor-split <ts0/ts1/..> (default: 0)\n");
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printf(" -r, --repetitions <n> (default: %d)\n", cmd_params_defaults.reps);
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printf(" -o, --output <csv|json|md|sql> (default: %s)\n", output_format_str(cmd_params_defaults.output_format));
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printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
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@ -297,6 +300,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
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}
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auto p = split<int>(argv[i], split_delim);
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params.n_batch.insert(params.n_batch.end(), p.begin(), p.end());
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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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auto p = split<int>(argv[i], split_delim);
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params.n_ubatch.insert(params.n_ubatch.end(), p.begin(), p.end());
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} else if (arg == "-ctk" || arg == "--cache-type-k") {
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if (++i >= argc) {
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invalid_param = true;
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@ -455,6 +465,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
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if (params.n_prompt.empty()) { params.n_prompt = cmd_params_defaults.n_prompt; }
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if (params.n_gen.empty()) { params.n_gen = cmd_params_defaults.n_gen; }
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if (params.n_batch.empty()) { params.n_batch = cmd_params_defaults.n_batch; }
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if (params.n_ubatch.empty()) { params.n_ubatch = cmd_params_defaults.n_ubatch; }
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if (params.type_k.empty()) { params.type_k = cmd_params_defaults.type_k; }
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if (params.type_v.empty()) { params.type_v = cmd_params_defaults.type_v; }
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if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; }
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@ -474,6 +485,7 @@ struct cmd_params_instance {
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int n_prompt;
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int n_gen;
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int n_batch;
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int n_ubatch;
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ggml_type type_k;
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ggml_type type_v;
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int n_threads;
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@ -511,6 +523,7 @@ struct cmd_params_instance {
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cparams.n_ctx = n_prompt + n_gen;
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cparams.n_batch = n_batch;
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cparams.n_ubatch = n_ubatch;
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cparams.type_k = type_k;
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cparams.type_v = type_v;
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cparams.offload_kqv = !no_kv_offload;
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@ -532,6 +545,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
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for (const auto & mmp : params.use_mmap)
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for (const auto & embd : params.embeddings)
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for (const auto & nb : params.n_batch)
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for (const auto & nub : params.n_ubatch)
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for (const auto & tk : params.type_k)
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for (const auto & tv : params.type_v)
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for (const auto & nkvo : params.no_kv_offload)
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@ -545,6 +559,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
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/* .n_prompt = */ n_prompt,
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/* .n_gen = */ 0,
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/* .n_batch = */ nb,
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/* .n_ubatch = */ nub,
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/* .type_k = */ tk,
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/* .type_v = */ tv,
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/* .n_threads = */ nt,
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@ -568,6 +583,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
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/* .n_prompt = */ 0,
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/* .n_gen = */ n_gen,
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/* .n_batch = */ nb,
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/* .n_ubatch = */ nub,
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/* .type_k = */ tk,
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/* .type_v = */ tv,
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/* .n_threads = */ nt,
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@ -604,6 +620,7 @@ struct test {
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uint64_t model_size;
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uint64_t model_n_params;
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int n_batch;
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int n_ubatch;
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int n_threads;
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ggml_type type_k;
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ggml_type type_v;
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@ -627,6 +644,7 @@ struct test {
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model_size = llama_model_size(lmodel);
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model_n_params = llama_model_n_params(lmodel);
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n_batch = inst.n_batch;
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n_ubatch = inst.n_ubatch;
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n_threads = inst.n_threads;
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type_k = inst.type_k;
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type_v = inst.type_v;
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@ -705,7 +723,8 @@ struct test {
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"cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas",
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"cpu_info", "gpu_info",
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"model_filename", "model_type", "model_size", "model_n_params",
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"n_batch", "n_threads", "type_k", "type_v",
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"n_batch", "n_ubatch",
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"n_threads", "type_k", "type_v",
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"n_gpu_layers", "split_mode",
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"main_gpu", "no_kv_offload",
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"tensor_split", "use_mmap", "embeddings",
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@ -719,7 +738,8 @@ struct test {
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enum field_type {STRING, BOOL, INT, FLOAT};
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static field_type get_field_type(const std::string & field) {
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if (field == "build_number" || field == "n_batch" || field == "n_threads" ||
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if (field == "build_number" || field == "n_batch" || field == "n_ubatch" ||
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field == "n_threads" ||
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field == "model_size" || field == "model_n_params" ||
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field == "n_gpu_layers" || field == "main_gpu" ||
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field == "n_prompt" || field == "n_gen" ||
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@ -759,7 +779,8 @@ struct test {
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std::to_string(metal), std::to_string(sycl), std::to_string(gpu_blas), std::to_string(blas),
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cpu_info, gpu_info,
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model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
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std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
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std::to_string(n_batch), std::to_string(n_ubatch),
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std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
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std::to_string(n_gpu_layers), split_mode_str(split_mode),
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std::to_string(main_gpu), std::to_string(no_kv_offload),
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tensor_split_str, std::to_string(use_mmap), std::to_string(embeddings),
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@ -957,6 +978,9 @@ struct markdown_printer : public printer {
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if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) {
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fields.emplace_back("n_batch");
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}
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if (params.n_ubatch.size() > 1 || params.n_ubatch != cmd_params_defaults.n_ubatch) {
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fields.emplace_back("n_ubatch");
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}
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if (params.type_k.size() > 1 || params.type_k != cmd_params_defaults.type_k) {
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fields.emplace_back("type_k");
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}
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@ -1096,25 +1120,32 @@ struct sql_printer : public printer {
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};
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static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) {
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llama_set_n_threads(ctx, n_threads, n_threads);
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//std::vector<llama_token> tokens(n_prompt, llama_token_bos(llama_get_model(ctx)));
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//llama_decode(ctx, llama_batch_get_one(tokens.data(), n_prompt, n_past, 0));
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//GGML_UNUSED(n_batch);
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std::vector<llama_token> tokens(n_batch, llama_token_bos(llama_get_model(ctx)));
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int n_processed = 0;
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llama_set_n_threads(ctx, n_threads, n_threads);
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while (n_processed < n_prompt) {
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int n_tokens = std::min(n_prompt - n_processed, n_batch);
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llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens, n_past + n_processed, 0));
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n_processed += n_tokens;
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}
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llama_synchronize(ctx);
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}
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static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) {
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llama_token token = llama_token_bos(llama_get_model(ctx));
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llama_set_n_threads(ctx, n_threads, n_threads);
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llama_token token = llama_token_bos(llama_get_model(ctx));
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for (int i = 0; i < n_gen; i++) {
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llama_decode(ctx, llama_batch_get_one(&token, 1, n_past + i, 0));
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llama_synchronize(ctx);
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}
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}
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@ -1203,7 +1234,8 @@ int main(int argc, char ** argv) {
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// warmup run
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if (t.n_prompt > 0) {
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test_prompt(ctx, std::min(2, t.n_batch), 0, t.n_batch, t.n_threads);
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//test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads);
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test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads);
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}
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if (t.n_gen > 0) {
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test_gen(ctx, 1, 0, t.n_threads);
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@ -1219,6 +1251,7 @@ int main(int argc, char ** argv) {
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if (t.n_gen > 0) {
|
||||
test_gen(ctx, t.n_gen, t.n_prompt, t.n_threads);
|
||||
}
|
||||
|
||||
uint64_t t_ns = get_time_ns() - t_start;
|
||||
t.samples_ns.push_back(t_ns);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -221,6 +221,7 @@ actor LlamaContext {
|
|||
if llama_decode(context, batch) != 0 {
|
||||
print("llama_decode() failed during prompt")
|
||||
}
|
||||
llama_synchronize(context)
|
||||
|
||||
let t_pp_end = ggml_time_us()
|
||||
|
||||
|
|
@ -240,6 +241,7 @@ actor LlamaContext {
|
|||
if llama_decode(context, batch) != 0 {
|
||||
print("llama_decode() failed during text generation")
|
||||
}
|
||||
llama_synchronize(context)
|
||||
}
|
||||
|
||||
let t_tg_end = ggml_time_us()
|
||||
|
|
|
|||
|
|
@ -589,9 +589,10 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
|
|||
}
|
||||
}
|
||||
|
||||
const auto t_end = std::chrono::high_resolution_clock::now();
|
||||
|
||||
if (i == 0) {
|
||||
llama_synchronize(ctx);
|
||||
const auto t_end = std::chrono::high_resolution_clock::now();
|
||||
const float t_total = std::chrono::duration<float>(t_end - t_start).count();
|
||||
fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total);
|
||||
int total_seconds = (int)(t_total*n_chunk/n_seq);
|
||||
|
|
|
|||
|
|
@ -147,7 +147,7 @@ struct server_slot {
|
|||
int32_t n_decoded = 0;
|
||||
int32_t n_remaining = -1;
|
||||
int32_t i_batch = -1;
|
||||
int32_t n_predict = -1;
|
||||
int32_t n_predict = -1; // TODO: disambiguate from params.n_predict
|
||||
|
||||
int32_t n_prompt_tokens = 0;
|
||||
int32_t n_prompt_tokens_processed = 0;
|
||||
|
|
@ -739,7 +739,13 @@ struct server_context {
|
|||
default_generation_settings_for_props = get_formated_generation(slots.front());
|
||||
default_generation_settings_for_props["seed"] = -1;
|
||||
|
||||
batch = llama_batch_init(n_ctx, 0, params.n_parallel);
|
||||
// the update_slots() logic will always submit a maximum of n_batch tokens
|
||||
// note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used)
|
||||
{
|
||||
const int32_t n_batch = llama_n_batch(ctx);
|
||||
|
||||
batch = llama_batch_init(n_batch, 0, params.n_parallel);
|
||||
}
|
||||
|
||||
metrics.init();
|
||||
}
|
||||
|
|
@ -1036,8 +1042,10 @@ struct server_context {
|
|||
llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
|
||||
}
|
||||
|
||||
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += params.n_batch) {
|
||||
const int32_t n_tokens = std::min(params.n_batch, (int32_t) (batch.n_tokens - i));
|
||||
const int32_t n_batch = llama_n_batch(ctx);
|
||||
|
||||
for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
|
||||
const int32_t n_tokens = std::min(params.n_batch, batch.n_tokens - i);
|
||||
llama_batch batch_view = {
|
||||
n_tokens,
|
||||
batch.token + i,
|
||||
|
|
@ -1226,7 +1234,7 @@ struct server_context {
|
|||
{"mirostat_eta", slot.sparams.mirostat_eta},
|
||||
{"penalize_nl", slot.sparams.penalize_nl},
|
||||
{"stop", slot.params.antiprompt},
|
||||
{"n_predict", slot.params.n_predict},
|
||||
{"n_predict", slot.params.n_predict}, // TODO: fix duplicate key n_predict
|
||||
{"n_keep", params.n_keep},
|
||||
{"ignore_eos", ignore_eos},
|
||||
{"stream", slot.params.stream},
|
||||
|
|
@ -1738,7 +1746,8 @@ struct server_context {
|
|||
}
|
||||
|
||||
// process in chunks of params.n_batch
|
||||
int32_t n_batch = params.n_batch;
|
||||
int32_t n_batch = llama_n_batch(ctx);
|
||||
int32_t n_ubatch = llama_n_ubatch(ctx);
|
||||
|
||||
// next, batch any pending prompts without exceeding n_batch
|
||||
if (params.cont_batching || batch.n_tokens == 0) {
|
||||
|
|
@ -1811,7 +1820,7 @@ struct server_context {
|
|||
|
||||
if (slot.embedding) {
|
||||
// this prompt is too large to process - discard it
|
||||
if (slot.n_prompt_tokens > n_batch) {
|
||||
if (slot.n_prompt_tokens > n_ubatch) {
|
||||
slot.state = SLOT_STATE_PROCESSING;
|
||||
slot.command = SLOT_COMMAND_NONE;
|
||||
slot.release();
|
||||
|
|
@ -2157,7 +2166,8 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co
|
|||
printf(" --pooling {none,mean,cls} pooling type for embeddings, use model default if unspecified\n");
|
||||
printf(" -dt N, --defrag-thold N\n");
|
||||
printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold);
|
||||
printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
|
||||
printf(" -b N, --batch-size N logical maximum batch size (default: %d)\n", params.n_batch);
|
||||
printf(" -ub N, --ubatch-size N physical maximum batch size (default: %d)\n", params.n_ubatch);
|
||||
printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
|
||||
printf(" not recommended: doubles context memory required and no measurable increase in quality\n");
|
||||
if (llama_supports_mlock()) {
|
||||
|
|
@ -2424,6 +2434,12 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
|
|||
break;
|
||||
}
|
||||
params.n_batch = std::stoi(argv[i]);
|
||||
} else if (arg == "-ub" || arg == "--ubatch-size") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
params.n_ubatch = std::stoi(argv[i]);
|
||||
} else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ Feature: llama.cpp server
|
|||
And 42 as server seed
|
||||
And 2 slots
|
||||
And 1024 as batch size
|
||||
And 1024 as ubatch size
|
||||
And 2048 KV cache size
|
||||
And embeddings extraction
|
||||
Then the server is starting
|
||||
|
|
|
|||
|
|
@ -33,6 +33,7 @@ def step_server_config(context, server_fqdn, server_port):
|
|||
|
||||
context.model_alias = None
|
||||
context.n_batch = None
|
||||
context.n_ubatch = None
|
||||
context.n_ctx = None
|
||||
context.n_ga = None
|
||||
context.n_ga_w = None
|
||||
|
|
@ -278,6 +279,11 @@ def step_n_batch(context, n_batch):
|
|||
context.n_batch = n_batch
|
||||
|
||||
|
||||
@step('{n_ubatch:d} as ubatch size')
|
||||
def step_n_ubatch(context, n_ubatch):
|
||||
context.n_ubatch = n_ubatch
|
||||
|
||||
|
||||
@step('{seed:d} as seed')
|
||||
def step_seed(context, seed):
|
||||
context.seed = seed
|
||||
|
|
@ -1029,6 +1035,8 @@ def start_server_background(context):
|
|||
]
|
||||
if context.n_batch:
|
||||
server_args.extend(['--batch-size', context.n_batch])
|
||||
if context.n_ubatch:
|
||||
server_args.extend(['--ubatch-size', context.n_ubatch])
|
||||
if context.n_gpu_layer:
|
||||
server_args.extend(['--n-gpu-layers', context.n_gpu_layer])
|
||||
if context.server_continuous_batching:
|
||||
|
|
|
|||
109
ggml-alloc.c
109
ggml-alloc.c
|
|
@ -61,7 +61,6 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
|
|||
}
|
||||
}
|
||||
|
||||
// TODO: GGML_PAD ?
|
||||
static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) {
|
||||
assert(alignment && !(alignment & (alignment - 1))); // power of 2
|
||||
size_t align = (alignment - (((uintptr_t)buffer + offset) % alignment)) % alignment;
|
||||
|
|
@ -69,25 +68,14 @@ static size_t aligned_offset(const void * buffer, size_t offset, size_t alignmen
|
|||
}
|
||||
|
||||
// tallocr
|
||||
struct ggml_tallocr {
|
||||
ggml_backend_buffer_t buffer;
|
||||
void * base;
|
||||
size_t alignment;
|
||||
size_t offset;
|
||||
};
|
||||
|
||||
ggml_tallocr_t ggml_tallocr_new(ggml_backend_buffer_t buffer) {
|
||||
ggml_tallocr_t talloc = malloc(sizeof(struct ggml_tallocr));
|
||||
if (talloc == NULL) {
|
||||
return NULL;
|
||||
}
|
||||
|
||||
struct ggml_tallocr ggml_tallocr_new(ggml_backend_buffer_t buffer) {
|
||||
void * base = ggml_backend_buffer_get_base(buffer);
|
||||
size_t align = ggml_backend_buffer_get_alignment(buffer);
|
||||
|
||||
assert(align && !(align & (align - 1))); // power of 2
|
||||
|
||||
*talloc = (struct ggml_tallocr) {
|
||||
struct ggml_tallocr talloc = (struct ggml_tallocr) {
|
||||
/*.buffer = */ buffer,
|
||||
/*.base = */ base,
|
||||
/*.alignment = */ align,
|
||||
|
|
@ -96,11 +84,7 @@ ggml_tallocr_t ggml_tallocr_new(ggml_backend_buffer_t buffer) {
|
|||
return talloc;
|
||||
}
|
||||
|
||||
void ggml_tallocr_free(ggml_tallocr_t talloc) {
|
||||
free(talloc);
|
||||
}
|
||||
|
||||
void ggml_tallocr_alloc(ggml_tallocr_t talloc, struct ggml_tensor * tensor) {
|
||||
void ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor) {
|
||||
size_t size = ggml_backend_buffer_get_alloc_size(talloc->buffer, tensor);
|
||||
size = GGML_PAD(size, talloc->alignment);
|
||||
|
||||
|
|
@ -354,12 +338,16 @@ struct hash_node {
|
|||
bool allocated;
|
||||
};
|
||||
|
||||
//
|
||||
struct tensor_alloc {
|
||||
size_t offset;
|
||||
size_t size_max; // 0 = pre-allocated, unused, or view
|
||||
};
|
||||
|
||||
struct leaf_alloc {
|
||||
int buffer_id;
|
||||
struct tensor_alloc leaf;
|
||||
};
|
||||
|
||||
struct node_alloc {
|
||||
int buffer_id;
|
||||
struct tensor_alloc dst;
|
||||
|
|
@ -378,7 +366,7 @@ struct ggml_gallocr {
|
|||
struct node_alloc * node_allocs; // [n_nodes]
|
||||
int n_nodes;
|
||||
|
||||
struct tensor_alloc * leaf_allocs; // [n_leafs]
|
||||
struct leaf_alloc * leaf_allocs; // [n_leafs]
|
||||
int n_leafs;
|
||||
};
|
||||
|
||||
|
|
@ -543,13 +531,20 @@ static int get_node_buffer_id(const int * node_buffer_ids, int i) {
|
|||
return node_buffer_ids ? node_buffer_ids[i] : 0;
|
||||
}
|
||||
|
||||
static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids) {
|
||||
static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
|
||||
// clear hash tables
|
||||
memset(galloc->hash_set.keys, 0, galloc->hash_set.size * sizeof(struct ggml_tensor *));
|
||||
memset(galloc->hash_values, 0, galloc->hash_set.size * sizeof(struct hash_node));
|
||||
|
||||
// allocate leafs
|
||||
// these may be tensors that the application is not using in the graph, but may still want to allocate for other purposes
|
||||
for (int i = 0; i < graph->n_leafs; i++) {
|
||||
struct ggml_tensor * leaf = graph->leafs[i];
|
||||
ggml_gallocr_allocate_node(galloc, leaf, get_node_buffer_id(leaf_buffer_ids, i));
|
||||
}
|
||||
|
||||
// count number of children and views
|
||||
// allocate all graph inputs and leafs first to avoid overwriting them
|
||||
// allocate other graph inputs and leafs first to avoid overwriting them
|
||||
for (int i = 0; i < graph->n_nodes; i++) {
|
||||
struct ggml_tensor * node = graph->nodes[i];
|
||||
|
||||
|
|
@ -577,19 +572,6 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr
|
|||
}
|
||||
}
|
||||
|
||||
// allocate the remaining leafs that are unused on the graph
|
||||
// these are effectively static tensors that the application is not using in the graph, but may still want to allocate for other purposes
|
||||
for (int i = 0; i < graph->n_leafs; i++) {
|
||||
struct ggml_tensor * leaf = graph->leafs[i];
|
||||
struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf);
|
||||
|
||||
if (hn->n_children == 0) {
|
||||
assert(!hn->allocated);
|
||||
// since buffer ids are only given for nodes, these leafs are always allocated in the first buffer
|
||||
ggml_gallocr_allocate_node(galloc, leaf, 0);
|
||||
}
|
||||
}
|
||||
|
||||
// allocate tensors
|
||||
for (int i = 0; i < graph->n_nodes; i++) {
|
||||
struct ggml_tensor * node = graph->nodes[i];
|
||||
|
|
@ -652,7 +634,7 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr
|
|||
}
|
||||
}
|
||||
|
||||
bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids) {
|
||||
bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
|
||||
size_t hash_size = graph->visited_hash_table.size;
|
||||
|
||||
// initialize hash table
|
||||
|
|
@ -676,7 +658,7 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
|
|||
}
|
||||
|
||||
// allocate in hash table
|
||||
ggml_gallocr_alloc_graph_impl(galloc, graph, node_buffer_ids);
|
||||
ggml_gallocr_alloc_graph_impl(galloc, graph, node_buffer_ids, leaf_buffer_ids);
|
||||
|
||||
// set the node_allocs from the hash table
|
||||
if (galloc->n_nodes < graph->n_nodes) {
|
||||
|
|
@ -711,15 +693,16 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
|
|||
}
|
||||
if (galloc->n_leafs < graph->n_leafs) {
|
||||
free(galloc->leaf_allocs);
|
||||
galloc->leaf_allocs = calloc(sizeof(struct tensor_alloc), graph->n_leafs);
|
||||
galloc->leaf_allocs = calloc(sizeof(galloc->leaf_allocs[0]), graph->n_leafs);
|
||||
GGML_ASSERT(galloc->leaf_allocs != NULL);
|
||||
}
|
||||
galloc->n_leafs = graph->n_leafs;
|
||||
for (int i = 0; i < graph->n_leafs; i++) {
|
||||
struct ggml_tensor * leaf = graph->leafs[i];
|
||||
struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf);
|
||||
galloc->leaf_allocs[i].offset = hn->offset;
|
||||
galloc->leaf_allocs[i].size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf);
|
||||
galloc->leaf_allocs[i].buffer_id = hn->buffer_id;
|
||||
galloc->leaf_allocs[i].leaf.offset = hn->offset;
|
||||
galloc->leaf_allocs[i].leaf.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf);
|
||||
}
|
||||
|
||||
// reallocate buffers if needed
|
||||
|
|
@ -727,7 +710,8 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
|
|||
size_t cur_size = galloc->buffers[i] ? ggml_backend_buffer_get_size(galloc->buffers[i]) : 0;
|
||||
size_t new_size = ggml_dyn_tallocr_max_size(galloc->buf_tallocs[i]);
|
||||
|
||||
if (new_size > cur_size) {
|
||||
// even if there are no tensors allocated in this buffer, we still need to allocate it to initialize views
|
||||
if (new_size > cur_size || galloc->buffers[i] == NULL) {
|
||||
#ifndef NDEBUG
|
||||
fprintf(stderr, "%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
|
||||
#endif
|
||||
|
|
@ -744,30 +728,30 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
|
|||
}
|
||||
|
||||
bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) {
|
||||
return ggml_gallocr_reserve_n(galloc, graph, NULL);
|
||||
return ggml_gallocr_reserve_n(galloc, graph, NULL, NULL);
|
||||
}
|
||||
|
||||
static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id, struct tensor_alloc * tensor_alloc) {
|
||||
assert(node->data || node->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], node) <= tensor_alloc->size_max);
|
||||
static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * tensor, int buffer_id, struct tensor_alloc * tensor_alloc) {
|
||||
assert(tensor->data || tensor->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max);
|
||||
|
||||
if (node->view_src != NULL) {
|
||||
if (node->buffer == NULL) {
|
||||
if (tensor->view_src != NULL) {
|
||||
if (tensor->buffer == NULL) {
|
||||
assert(tensor_alloc->offset == SIZE_MAX);
|
||||
if (node->view_src->buffer == NULL) {
|
||||
if (tensor->view_src->buffer == NULL) {
|
||||
// this tensor was allocated without ggml-backend
|
||||
return;
|
||||
}
|
||||
ggml_backend_view_init(galloc->buffers[buffer_id], node);
|
||||
ggml_backend_view_init(galloc->buffers[buffer_id], tensor);
|
||||
}
|
||||
} else {
|
||||
if (node->data == NULL) {
|
||||
if (tensor->data == NULL) {
|
||||
assert(tensor_alloc->offset != SIZE_MAX);
|
||||
assert(ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], node) <= tensor_alloc->size_max);
|
||||
assert(ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max);
|
||||
void * base = ggml_backend_buffer_get_base(galloc->buffers[buffer_id]);
|
||||
void * addr = (char *)base + tensor_alloc->offset;
|
||||
ggml_backend_tensor_alloc(galloc->buffers[buffer_id], node, addr);
|
||||
ggml_backend_tensor_alloc(galloc->buffers[buffer_id], tensor, addr);
|
||||
} else {
|
||||
if (node->buffer == NULL) {
|
||||
if (tensor->buffer == NULL) {
|
||||
// this tensor was allocated without ggml-backend
|
||||
return;
|
||||
}
|
||||
|
|
@ -843,13 +827,18 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph)
|
|||
|
||||
// reset buffers
|
||||
for (int i = 0; i < galloc->n_buffers; i++) {
|
||||
// zero size buffers are not allocated
|
||||
if (galloc->buffers[i] != NULL) {
|
||||
ggml_backend_buffer_reset(galloc->buffers[i]);
|
||||
}
|
||||
}
|
||||
|
||||
// allocate the graph tensors from the previous assignments
|
||||
// leafs
|
||||
for (int i = 0; i < graph->n_leafs; i++) {
|
||||
struct ggml_tensor * leaf = graph->leafs[i];
|
||||
struct leaf_alloc * leaf_alloc = &galloc->leaf_allocs[i];
|
||||
ggml_gallocr_init_tensor(galloc, leaf, leaf_alloc->buffer_id, &leaf_alloc->leaf);
|
||||
}
|
||||
// nodes
|
||||
for (int i = 0; i < graph->n_nodes; i++) {
|
||||
struct ggml_tensor * node = graph->nodes[i];
|
||||
|
|
@ -863,12 +852,6 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph)
|
|||
}
|
||||
ggml_gallocr_init_tensor(galloc, node, node_alloc->buffer_id, &node_alloc->dst);
|
||||
}
|
||||
// leafs
|
||||
for (int i = 0; i < graph->n_leafs; i++) {
|
||||
struct ggml_tensor * leaf = graph->leafs[i];
|
||||
struct tensor_alloc * leaf_alloc = &galloc->leaf_allocs[i];
|
||||
ggml_gallocr_init_tensor(galloc, leaf, 0, leaf_alloc);
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
|
@ -900,12 +883,12 @@ static bool alloc_tensor_range(struct ggml_context * ctx,
|
|||
return false;
|
||||
}
|
||||
|
||||
struct ggml_tallocr * tallocr = ggml_tallocr_new(buffer);
|
||||
struct ggml_tallocr tallocr = ggml_tallocr_new(buffer);
|
||||
|
||||
for (struct ggml_tensor * t = first; t != last; t = ggml_get_next_tensor(ctx, t)) {
|
||||
if (t->data == NULL) {
|
||||
if (t->view_src == NULL) {
|
||||
ggml_tallocr_alloc(tallocr, t);
|
||||
ggml_tallocr_alloc(&tallocr, t);
|
||||
} else if (t->buffer == NULL) {
|
||||
ggml_backend_view_init(buffer, t);
|
||||
}
|
||||
|
|
@ -917,8 +900,6 @@ static bool alloc_tensor_range(struct ggml_context * ctx,
|
|||
}
|
||||
}
|
||||
|
||||
ggml_tallocr_free(tallocr);
|
||||
|
||||
*buffers = realloc(*buffers, sizeof(ggml_backend_buffer_t) * (*n_buffers + 1));
|
||||
(*buffers)[(*n_buffers)++] = buffer;
|
||||
|
||||
|
|
|
|||
18
ggml-alloc.h
18
ggml-alloc.h
|
|
@ -11,11 +11,15 @@ typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
|
|||
typedef struct ggml_backend * ggml_backend_t;
|
||||
|
||||
// Tensor allocator
|
||||
typedef struct ggml_tallocr * ggml_tallocr_t;
|
||||
struct ggml_tallocr {
|
||||
ggml_backend_buffer_t buffer;
|
||||
void * base;
|
||||
size_t alignment;
|
||||
size_t offset;
|
||||
};
|
||||
|
||||
GGML_API ggml_tallocr_t ggml_tallocr_new(ggml_backend_buffer_t buffer);
|
||||
GGML_API void ggml_tallocr_free(ggml_tallocr_t talloc);
|
||||
GGML_API void ggml_tallocr_alloc(ggml_tallocr_t talloc, struct ggml_tensor * tensor);
|
||||
GGML_API struct ggml_tallocr ggml_tallocr_new(ggml_backend_buffer_t buffer);
|
||||
GGML_API void ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor);
|
||||
|
||||
// Graph allocator
|
||||
/*
|
||||
|
|
@ -50,7 +54,11 @@ GGML_API void ggml_gallocr_free(ggml_gallocr_t galloc);
|
|||
// not strictly required for single buffer usage: ggml_gallocr_alloc_graph will reallocate the buffers automatically if needed
|
||||
// returns false if the buffer allocation failed
|
||||
GGML_API bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph * graph);
|
||||
GGML_API bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids);
|
||||
GGML_API bool ggml_gallocr_reserve_n(
|
||||
ggml_gallocr_t galloc,
|
||||
struct ggml_cgraph * graph,
|
||||
const int * node_buffer_ids,
|
||||
const int * leaf_buffer_ids);
|
||||
|
||||
// automatic reallocation if the topology changes when using a single buffer
|
||||
// returns false if using multiple buffers and a re-allocation is needed (call ggml_gallocr_reserve_n first to set the node buffers)
|
||||
|
|
|
|||
|
|
@ -86,12 +86,12 @@ extern "C" {
|
|||
// (optional) asynchronous tensor data access
|
||||
void (*GGML_CALL set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
|
||||
void (*GGML_CALL get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
|
||||
bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * src, struct ggml_tensor * dst);
|
||||
bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst);
|
||||
|
||||
// (optional) complete all pending operations
|
||||
void (*GGML_CALL synchronize)(ggml_backend_t backend);
|
||||
|
||||
// create a plan for ggml_cgraph and free it
|
||||
// compute graph with a plan (not used currently)
|
||||
ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph);
|
||||
void (*GGML_CALL graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
|
||||
|
||||
|
|
@ -102,16 +102,27 @@ extern "C" {
|
|||
|
||||
// check if the backend supports an operation
|
||||
bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
|
||||
|
||||
// (optional) event synchronization
|
||||
ggml_backend_event_t (*GGML_CALL event_new) (ggml_backend_t backend);
|
||||
void (*GGML_CALL event_free) (ggml_backend_event_t event);
|
||||
void (*GGML_CALL event_record) (ggml_backend_event_t event);
|
||||
void (*GGML_CALL event_wait) (ggml_backend_t backend, ggml_backend_event_t event);
|
||||
void (*GGML_CALL event_synchronize) (ggml_backend_event_t event);
|
||||
};
|
||||
|
||||
struct ggml_backend {
|
||||
ggml_guid_t guid;
|
||||
|
||||
struct ggml_backend_i iface;
|
||||
|
||||
ggml_backend_context_t context;
|
||||
};
|
||||
|
||||
struct ggml_backend_event {
|
||||
ggml_backend_t backend;
|
||||
void * context;
|
||||
};
|
||||
|
||||
//
|
||||
// Backend registry
|
||||
//
|
||||
|
|
|
|||
517
ggml-backend.c
517
ggml-backend.c
|
|
@ -221,29 +221,29 @@ void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_ten
|
|||
GGML_CALL void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
|
||||
|
||||
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
|
||||
GGML_ASSERT(buf != NULL && "tensor buffer not set");
|
||||
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
|
||||
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
|
||||
|
||||
if (!size) {
|
||||
return;
|
||||
}
|
||||
|
||||
tensor->buffer->iface.set_tensor(buf, tensor, data, offset, size);
|
||||
buf->iface.set_tensor(buf, tensor, data, offset, size);
|
||||
}
|
||||
|
||||
GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
||||
ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
|
||||
|
||||
GGML_ASSERT(buf != NULL && "tensor buffer not set");
|
||||
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
|
||||
GGML_ASSERT(tensor->buffer != NULL && "tensor buffer not set");
|
||||
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
|
||||
|
||||
if (!size) {
|
||||
return;
|
||||
}
|
||||
|
||||
tensor->buffer->iface.get_tensor(buf, tensor, data, offset, size);
|
||||
buf->iface.get_tensor(buf, tensor, data, offset, size);
|
||||
}
|
||||
|
||||
void ggml_backend_synchronize(ggml_backend_t backend) {
|
||||
|
|
@ -255,18 +255,30 @@ void ggml_backend_synchronize(ggml_backend_t backend) {
|
|||
}
|
||||
|
||||
ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
|
||||
GGML_ASSERT(backend->iface.graph_plan_create != NULL);
|
||||
|
||||
return backend->iface.graph_plan_create(backend, cgraph);
|
||||
}
|
||||
|
||||
void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
|
||||
GGML_ASSERT(backend->iface.graph_plan_free != NULL);
|
||||
|
||||
backend->iface.graph_plan_free(backend, plan);
|
||||
}
|
||||
|
||||
enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
|
||||
GGML_ASSERT(backend->iface.graph_plan_compute != NULL);
|
||||
|
||||
return backend->iface.graph_plan_compute(backend, plan);
|
||||
}
|
||||
|
||||
enum ggml_status ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
|
||||
enum ggml_status err = ggml_backend_graph_compute_async(backend, cgraph);
|
||||
ggml_backend_synchronize(backend);
|
||||
return err;
|
||||
}
|
||||
|
||||
bool ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
|
||||
return backend->iface.graph_compute(backend, cgraph);
|
||||
}
|
||||
|
||||
|
|
@ -314,34 +326,68 @@ void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst
|
|||
}
|
||||
}
|
||||
|
||||
void ggml_backend_tensor_copy_async(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) {
|
||||
void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst) {
|
||||
GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");
|
||||
|
||||
if (src == dst) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (ggml_backend_buft_supports_backend(src->buffer->buft, backend) && ggml_backend_buft_supports_backend(dst->buffer->buft, backend)) {
|
||||
if (backend->iface.cpy_tensor_async != NULL) {
|
||||
if (backend->iface.cpy_tensor_async(backend, src, dst)) {
|
||||
return;
|
||||
}
|
||||
if (backend_dst->iface.cpy_tensor_async != NULL) {
|
||||
if (backend_dst->iface.cpy_tensor_async(backend_src, backend_dst, src, dst)) {
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
size_t nbytes = ggml_nbytes(src);
|
||||
// an async copy would normally happen after all the queued operations on both backends are completed
|
||||
// sync src, set_async dst
|
||||
if (ggml_backend_buffer_is_host(src->buffer)) {
|
||||
ggml_backend_tensor_set_async(backend, dst, src->data, 0, nbytes);
|
||||
}
|
||||
else {
|
||||
ggml_backend_synchronize(backend_src);
|
||||
ggml_backend_tensor_set_async(backend_dst, dst, src->data, 0, ggml_nbytes(src));
|
||||
} else {
|
||||
ggml_backend_synchronize(backend_src);
|
||||
ggml_backend_tensor_copy(src, dst);
|
||||
ggml_backend_synchronize(backend_dst);
|
||||
}
|
||||
}
|
||||
|
||||
// events
|
||||
|
||||
ggml_backend_event_t ggml_backend_event_new(ggml_backend_t backend) {
|
||||
if (backend->iface.event_new == NULL) {
|
||||
return NULL;
|
||||
}
|
||||
return backend->iface.event_new(backend);
|
||||
}
|
||||
|
||||
void ggml_backend_event_free(ggml_backend_event_t event) {
|
||||
if (event == NULL) {
|
||||
return;
|
||||
}
|
||||
event->backend->iface.event_free(event);
|
||||
}
|
||||
|
||||
void ggml_backend_event_record(ggml_backend_event_t event) {
|
||||
GGML_ASSERT(event->backend->iface.event_record != NULL);
|
||||
|
||||
event->backend->iface.event_record(event);
|
||||
}
|
||||
|
||||
void ggml_backend_event_synchronize(ggml_backend_event_t event) {
|
||||
GGML_ASSERT(event->backend->iface.event_synchronize != NULL);
|
||||
|
||||
event->backend->iface.event_synchronize(event);
|
||||
}
|
||||
|
||||
void ggml_backend_event_wait(ggml_backend_t backend, ggml_backend_event_t event) {
|
||||
GGML_ASSERT(backend->iface.event_wait != NULL);
|
||||
|
||||
backend->iface.event_wait(backend, event);
|
||||
}
|
||||
|
||||
// backend registry
|
||||
|
||||
#define GGML_MAX_BACKENDS_REG 16
|
||||
#define GGML_REG_MAX_BACKENDS 16
|
||||
|
||||
struct ggml_backend_reg {
|
||||
char name[128];
|
||||
|
|
@ -350,7 +396,7 @@ struct ggml_backend_reg {
|
|||
void * user_data;
|
||||
};
|
||||
|
||||
static struct ggml_backend_reg ggml_backend_registry[GGML_MAX_BACKENDS_REG];
|
||||
static struct ggml_backend_reg ggml_backend_registry[GGML_REG_MAX_BACKENDS];
|
||||
static size_t ggml_backend_registry_count = 0;
|
||||
|
||||
GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data);
|
||||
|
|
@ -395,7 +441,7 @@ GGML_CALL static void ggml_backend_registry_init(void) {
|
|||
}
|
||||
|
||||
GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) {
|
||||
GGML_ASSERT(ggml_backend_registry_count < GGML_MAX_BACKENDS_REG);
|
||||
GGML_ASSERT(ggml_backend_registry_count < GGML_REG_MAX_BACKENDS);
|
||||
|
||||
size_t id = ggml_backend_registry_count;
|
||||
|
||||
|
|
@ -746,8 +792,12 @@ GGML_CALL static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t
|
|||
struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads);
|
||||
|
||||
if (cpu_ctx->work_size < cplan.work_size) {
|
||||
// TODO: may be faster to free and use malloc to avoid the copy
|
||||
cpu_ctx->work_data = realloc(cpu_ctx->work_data, cplan.work_size);
|
||||
free(cpu_ctx->work_data);
|
||||
cpu_ctx->work_data = malloc(cplan.work_size);
|
||||
if (cpu_ctx->work_data == NULL) {
|
||||
cpu_ctx->work_size = 0;
|
||||
return GGML_STATUS_ALLOC_FAILED;
|
||||
}
|
||||
cpu_ctx->work_size = cplan.work_size;
|
||||
}
|
||||
cplan.work_data = cpu_ctx->work_data;
|
||||
|
|
@ -784,6 +834,11 @@ static struct ggml_backend_i cpu_backend_i = {
|
|||
/* .graph_plan_compute = */ ggml_backend_cpu_graph_plan_compute,
|
||||
/* .graph_compute = */ ggml_backend_cpu_graph_compute,
|
||||
/* .supports_op = */ ggml_backend_cpu_supports_op,
|
||||
/* .event_new = */ NULL,
|
||||
/* .event_free = */ NULL,
|
||||
/* .event_record = */ NULL,
|
||||
/* .event_wait = */ NULL,
|
||||
/* .event_synchronize = */ NULL,
|
||||
};
|
||||
|
||||
static ggml_guid_t ggml_backend_cpu_guid(void) {
|
||||
|
|
@ -939,15 +994,27 @@ static bool ggml_is_view_op(enum ggml_op op) {
|
|||
|
||||
// scheduler
|
||||
|
||||
#define GGML_MAX_BACKENDS 16
|
||||
#define GGML_MAX_SPLITS 256
|
||||
#define GGML_MAX_SPLIT_INPUTS 16
|
||||
#ifndef GGML_SCHED_MAX_BACKENDS
|
||||
#define GGML_SCHED_MAX_BACKENDS 16
|
||||
#endif
|
||||
|
||||
#ifndef GGML_SCHED_MAX_SPLITS
|
||||
#define GGML_SCHED_MAX_SPLITS 256
|
||||
#endif
|
||||
|
||||
#ifndef GGML_SCHED_MAX_SPLIT_INPUTS
|
||||
#define GGML_SCHED_MAX_SPLIT_INPUTS 16
|
||||
#endif
|
||||
|
||||
#ifndef GGML_SCHED_MAX_COPIES
|
||||
#define GGML_SCHED_MAX_COPIES 4
|
||||
#endif
|
||||
|
||||
struct ggml_backend_sched_split {
|
||||
int backend_id;
|
||||
int i_start;
|
||||
int i_end;
|
||||
struct ggml_tensor * inputs[GGML_MAX_SPLIT_INPUTS];
|
||||
struct ggml_tensor * inputs[GGML_SCHED_MAX_SPLIT_INPUTS];
|
||||
int n_inputs;
|
||||
// graph view of this split
|
||||
struct ggml_cgraph graph;
|
||||
|
|
@ -955,45 +1022,53 @@ struct ggml_backend_sched_split {
|
|||
|
||||
struct ggml_backend_sched {
|
||||
bool is_reset; // true if the scheduler has been reset since the last graph split
|
||||
bool is_alloc;
|
||||
|
||||
int n_backends;
|
||||
ggml_backend_t backends[GGML_MAX_BACKENDS];
|
||||
ggml_backend_buffer_type_t bufts[GGML_MAX_BACKENDS];
|
||||
|
||||
ggml_backend_t backends[GGML_SCHED_MAX_BACKENDS];
|
||||
ggml_backend_buffer_type_t bufts[GGML_SCHED_MAX_BACKENDS];
|
||||
ggml_gallocr_t galloc;
|
||||
|
||||
// hash keys of the nodes in the graph
|
||||
struct ggml_hash_set hash_set;
|
||||
// hash values
|
||||
int * tensor_backend_id;
|
||||
struct ggml_tensor * (* tensor_copies)[GGML_MAX_BACKENDS];
|
||||
struct ggml_tensor * (* tensor_copies)[GGML_SCHED_MAX_BACKENDS][GGML_SCHED_MAX_COPIES];
|
||||
|
||||
int * node_backend_ids; // [n_nodes]
|
||||
int n_nodes;
|
||||
int * node_backend_ids; // [graph_size]
|
||||
int * leaf_backend_ids; // [graph_size]
|
||||
|
||||
// copy of the graph with modified inputs
|
||||
struct ggml_cgraph * graph;
|
||||
|
||||
struct ggml_backend_sched_split splits[GGML_MAX_SPLITS];
|
||||
// graph splits
|
||||
struct ggml_backend_sched_split splits[GGML_SCHED_MAX_SPLITS];
|
||||
int n_splits;
|
||||
|
||||
// pipeline parallelism support
|
||||
int n_copies;
|
||||
int cur_copy;
|
||||
ggml_backend_event_t events[GGML_SCHED_MAX_BACKENDS][GGML_SCHED_MAX_COPIES];
|
||||
struct ggml_tensor * graph_inputs[GGML_SCHED_MAX_SPLIT_INPUTS];
|
||||
int n_graph_inputs;
|
||||
|
||||
struct ggml_context * ctx;
|
||||
|
||||
ggml_backend_sched_eval_callback callback_eval;
|
||||
void * callback_eval_user_data;
|
||||
|
||||
// align context_buffer to GGML_MEM_ALIGN
|
||||
#ifdef _MSC_VER
|
||||
#ifdef _MSC_VER
|
||||
__declspec(align(GGML_MEM_ALIGN))
|
||||
#else
|
||||
#else
|
||||
__attribute__((aligned(GGML_MEM_ALIGN)))
|
||||
#endif
|
||||
char context_buffer[GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS*2*sizeof(struct ggml_tensor) + sizeof(struct ggml_cgraph)];
|
||||
#endif
|
||||
char context_buffer[GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS*2*sizeof(struct ggml_tensor) + sizeof(struct ggml_cgraph)];
|
||||
};
|
||||
|
||||
#define hash_id(node) ggml_hash_find_or_insert(sched->hash_set, node)
|
||||
#define tensor_backend_id(node) sched->tensor_backend_id[hash_id(node)]
|
||||
#define tensor_backend(node) (tensor_backend_id(node) == -1 ? NULL : sched->backends[tensor_backend_id(node)])
|
||||
#define hash_id(tensor) ggml_hash_find_or_insert(sched->hash_set, tensor)
|
||||
#define tensor_backend_id(tensor) sched->tensor_backend_id[hash_id(tensor)]
|
||||
|
||||
// returns the priority of the backend, lower id is higher priority
|
||||
static int ggml_backend_sched_backend_id(ggml_backend_sched_t sched, ggml_backend_t backend) {
|
||||
|
|
@ -1005,7 +1080,8 @@ static int ggml_backend_sched_backend_id(ggml_backend_sched_t sched, ggml_backen
|
|||
return -1;
|
||||
}
|
||||
|
||||
static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, ggml_backend_buffer_t buffer) {
|
||||
static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, const struct ggml_tensor * tensor) {
|
||||
ggml_backend_buffer_t buffer = tensor->buffer;
|
||||
if (buffer == NULL) {
|
||||
return -1;
|
||||
}
|
||||
|
|
@ -1016,12 +1092,16 @@ static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, gg
|
|||
return i;
|
||||
}
|
||||
}
|
||||
GGML_ASSERT(false && "tensor buffer type not supported by any backend");
|
||||
return -1; // silence warning
|
||||
|
||||
fprintf(stderr, "%s: error: no backend supports buffer type %s used in tensor %s\n",
|
||||
__func__, ggml_backend_buffer_name(buffer), tensor->name);
|
||||
GGML_ASSERT(false);
|
||||
|
||||
return -1;
|
||||
}
|
||||
|
||||
#if 0
|
||||
static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS][128]; // debug only
|
||||
static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS][128]; // debug only
|
||||
#define SET_CAUSE(node, ...) sprintf(causes[hash_id(node)], __VA_ARGS__)
|
||||
#define GET_CAUSE(node) causes[hash_id(node)]
|
||||
#else
|
||||
|
|
@ -1035,19 +1115,28 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st
|
|||
|
||||
// assign pre-allocated nodes to their backend
|
||||
// dst
|
||||
int cur_backend = ggml_backend_sched_backend_from_buffer(sched, tensor->buffer);
|
||||
int cur_backend = ggml_backend_sched_backend_from_buffer(sched, tensor);
|
||||
if (cur_backend != -1) {
|
||||
SET_CAUSE(node, "1.dst");
|
||||
SET_CAUSE(tensor, "1.dst");
|
||||
return cur_backend;
|
||||
}
|
||||
|
||||
// view_src
|
||||
if (tensor->view_src != NULL) {
|
||||
cur_backend = ggml_backend_sched_backend_from_buffer(sched, tensor->view_src->buffer);
|
||||
cur_backend = ggml_backend_sched_backend_from_buffer(sched, tensor->view_src);
|
||||
if (cur_backend != -1) {
|
||||
SET_CAUSE(node, "1.vsrc");
|
||||
SET_CAUSE(tensor, "1.vsrc");
|
||||
return cur_backend;
|
||||
}
|
||||
}
|
||||
|
||||
// input
|
||||
if (tensor->flags & GGML_TENSOR_FLAG_INPUT) {
|
||||
cur_backend = sched->n_backends - 1; // last backend (assumed CPU)
|
||||
SET_CAUSE(tensor, "1.inp");
|
||||
return cur_backend;
|
||||
}
|
||||
|
||||
// assign nodes that use weights to the backend of the weights
|
||||
for (int i = 0; i < GGML_MAX_SRC; i++) {
|
||||
const struct ggml_tensor * src = tensor->src[i];
|
||||
|
|
@ -1055,9 +1144,9 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st
|
|||
continue;
|
||||
}
|
||||
if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
|
||||
int src_backend = ggml_backend_sched_backend_from_buffer(sched, src->buffer);
|
||||
int src_backend = ggml_backend_sched_backend_from_buffer(sched, src);
|
||||
// operations with weights are always run on the same backend as the weights
|
||||
SET_CAUSE(node, "1.wgt%d", i);
|
||||
SET_CAUSE(tensor, "1.wgt%d", i);
|
||||
return src_backend;
|
||||
}
|
||||
}
|
||||
|
|
@ -1093,7 +1182,7 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str
|
|||
if (ggml_is_view_op(node->op)) {
|
||||
continue;
|
||||
}
|
||||
ggml_backend_t tensor_backend = tensor_backend(node);
|
||||
ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node);
|
||||
fprintf(stderr, "node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name,
|
||||
fmt_size(ggml_nbytes(node)), tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", GET_CAUSE(node));
|
||||
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
||||
|
|
@ -1101,7 +1190,7 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str
|
|||
if (src == NULL) {
|
||||
continue;
|
||||
}
|
||||
ggml_backend_t src_backend = tensor_backend(src);
|
||||
ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src);
|
||||
fprintf(stderr, " %20.20s (%5.5s) [%5.5s %8.8s]", src->name,
|
||||
fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", GET_CAUSE(src));
|
||||
}
|
||||
|
|
@ -1118,6 +1207,7 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str
|
|||
static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
|
||||
// reset splits
|
||||
sched->n_splits = 0;
|
||||
sched->n_graph_inputs = 0;
|
||||
sched->is_reset = false;
|
||||
|
||||
struct ggml_init_params params = {
|
||||
|
|
@ -1163,7 +1253,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
|
|||
}
|
||||
}
|
||||
#ifdef DEBUG_PASS1
|
||||
fprintf(stderr, "PASS 1 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
|
||||
fprintf(stderr, "PASS 1 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph);
|
||||
#endif
|
||||
|
||||
// pass 2: expand current backend assignments
|
||||
|
|
@ -1171,28 +1261,6 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
|
|||
// expand gpu backends (i.e. non last prio) up and down, ignoring cpu (the lowest priority backend)
|
||||
// thus, cpu will never be used unless weights are on cpu, or there are no gpu ops between cpu ops
|
||||
|
||||
// pass 2.1 expand gpu up
|
||||
{
|
||||
int cur_backend_id = -1;
|
||||
for (int i = graph->n_nodes - 1; i >= 0; i--) {
|
||||
struct ggml_tensor * node = graph->nodes[i];
|
||||
if (ggml_is_view_op(node->op)) {
|
||||
continue;
|
||||
}
|
||||
int tensor_backend_id = tensor_backend_id(node);
|
||||
if (tensor_backend_id != -1) {
|
||||
if (tensor_backend_id == sched->n_backends - 1) {
|
||||
// skip cpu (lowest prio backend)
|
||||
cur_backend_id = -1;
|
||||
} else {
|
||||
cur_backend_id = tensor_backend_id;
|
||||
}
|
||||
} else {
|
||||
tensor_backend_id(node) = cur_backend_id;
|
||||
SET_CAUSE(node, "2.1");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// pass 2.2 expand gpu down
|
||||
{
|
||||
|
|
@ -1217,7 +1285,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
|
|||
}
|
||||
}
|
||||
|
||||
// pass 2.3 expand rest up
|
||||
// pass 2.1 expand gpu up
|
||||
{
|
||||
int cur_backend_id = -1;
|
||||
for (int i = graph->n_nodes - 1; i >= 0; i--) {
|
||||
|
|
@ -1227,14 +1295,20 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
|
|||
}
|
||||
int tensor_backend_id = tensor_backend_id(node);
|
||||
if (tensor_backend_id != -1) {
|
||||
cur_backend_id = tensor_backend_id;
|
||||
if (tensor_backend_id == sched->n_backends - 1) {
|
||||
// skip cpu (lowest prio backend)
|
||||
cur_backend_id = -1;
|
||||
} else {
|
||||
cur_backend_id = tensor_backend_id;
|
||||
}
|
||||
} else {
|
||||
tensor_backend_id(node) = cur_backend_id;
|
||||
SET_CAUSE(node, "2.3");
|
||||
SET_CAUSE(node, "2.1");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// pass 2.4 expand rest down
|
||||
{
|
||||
int cur_backend_id = -1;
|
||||
|
|
@ -1252,8 +1326,26 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
|
|||
}
|
||||
}
|
||||
}
|
||||
// pass 2.3 expand rest up
|
||||
{
|
||||
int cur_backend_id = -1;
|
||||
for (int i = graph->n_nodes - 1; i >= 0; i--) {
|
||||
struct ggml_tensor * node = graph->nodes[i];
|
||||
if (ggml_is_view_op(node->op)) {
|
||||
continue;
|
||||
}
|
||||
int tensor_backend_id = tensor_backend_id(node);
|
||||
if (tensor_backend_id != -1) {
|
||||
cur_backend_id = tensor_backend_id;
|
||||
} else {
|
||||
tensor_backend_id(node) = cur_backend_id;
|
||||
SET_CAUSE(node, "2.3");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#ifdef DEBUG_PASS2
|
||||
fprintf(stderr, "PASS 2 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
|
||||
fprintf(stderr, "PASS 2 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph);
|
||||
#endif
|
||||
|
||||
// pass 3: assign backends to remaining src from dst and view_src
|
||||
|
|
@ -1283,7 +1375,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
|
|||
}
|
||||
}
|
||||
#ifdef DEBUG_PASS3
|
||||
fprintf(stderr, "PASS 3 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
|
||||
fprintf(stderr, "PASS 3 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph);
|
||||
#endif
|
||||
|
||||
// pass 4: split graph, find tensors that need to be copied
|
||||
|
|
@ -1315,7 +1407,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
|
|||
if (tensor_backend_id != cur_backend_id) {
|
||||
sched->splits[cur_split].i_end = i;
|
||||
cur_split++;
|
||||
GGML_ASSERT(cur_split < GGML_MAX_SPLITS);
|
||||
GGML_ASSERT(cur_split < GGML_SCHED_MAX_SPLITS);
|
||||
sched->splits[cur_split].backend_id = tensor_backend_id;
|
||||
sched->splits[cur_split].i_start = i;
|
||||
sched->splits[cur_split].n_inputs = 0;
|
||||
|
|
@ -1328,25 +1420,57 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
|
|||
if (src == NULL) {
|
||||
continue;
|
||||
}
|
||||
|
||||
int src_backend_id = tensor_backend_id(src);
|
||||
assert(src_backend_id != -1); // all inputs should be assigned by now
|
||||
|
||||
if (src->flags & GGML_TENSOR_FLAG_INPUT) {
|
||||
size_t id = hash_id(src);
|
||||
if (sched->tensor_copies[id][src_backend_id][0] == NULL) {
|
||||
ggml_backend_t backend = sched->backends[src_backend_id];
|
||||
for (int c = 0; c < sched->n_copies; c++) {
|
||||
struct ggml_tensor * tensor_copy;
|
||||
if (c == sched->cur_copy) {
|
||||
tensor_copy = src; // use the original tensor as the current copy
|
||||
} else {
|
||||
tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
|
||||
ggml_format_name(tensor_copy, "%s#%s#%d", ggml_backend_name(backend), src->name, c);
|
||||
}
|
||||
if (sched->n_copies > 1) {
|
||||
ggml_set_input(tensor_copy);
|
||||
ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor
|
||||
}
|
||||
sched->tensor_copies[id][src_backend_id][c] = tensor_copy;
|
||||
tensor_backend_id(tensor_copy) = src_backend_id;
|
||||
SET_CAUSE(tensor_copy, "4.cpy");
|
||||
}
|
||||
int n_graph_inputs = sched->n_graph_inputs++;
|
||||
GGML_ASSERT(n_graph_inputs < GGML_SCHED_MAX_SPLIT_INPUTS);
|
||||
sched->graph_inputs[n_graph_inputs] = src;
|
||||
}
|
||||
}
|
||||
|
||||
if (src_backend_id != tensor_backend_id) {
|
||||
// create a copy of the input in the split's backend
|
||||
size_t id = hash_id(src);
|
||||
if (sched->tensor_copies[id][cur_backend_id] == NULL) {
|
||||
if (sched->tensor_copies[id][cur_backend_id][0] == NULL) {
|
||||
ggml_backend_t backend = sched->backends[cur_backend_id];
|
||||
struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
|
||||
ggml_format_name(tensor_copy, "%s#%s", ggml_backend_name(backend), src->name);
|
||||
|
||||
sched->tensor_copies[id][cur_backend_id] = tensor_copy;
|
||||
tensor_backend_id(tensor_copy) = cur_backend_id;
|
||||
SET_CAUSE(tensor_copy, "4.cpy");
|
||||
|
||||
for (int c = 0; c < sched->n_copies; c++) {
|
||||
struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
|
||||
ggml_format_name(tensor_copy, "%s#%s#%d", ggml_backend_name(backend), src->name, c);
|
||||
if (sched->n_copies > 1) {
|
||||
ggml_set_input(tensor_copy);
|
||||
ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor
|
||||
}
|
||||
sched->tensor_copies[id][cur_backend_id][c] = tensor_copy;
|
||||
tensor_backend_id(tensor_copy) = cur_backend_id;
|
||||
SET_CAUSE(tensor_copy, "4.cpy");
|
||||
}
|
||||
int n_inputs = sched->splits[cur_split].n_inputs++;
|
||||
GGML_ASSERT(n_inputs < GGML_MAX_SPLIT_INPUTS);
|
||||
GGML_ASSERT(n_inputs < GGML_SCHED_MAX_SPLIT_INPUTS);
|
||||
sched->splits[cur_split].inputs[n_inputs] = src;
|
||||
}
|
||||
node->src[j] = sched->tensor_copies[id][cur_backend_id];
|
||||
node->src[j] = sched->tensor_copies[id][cur_backend_id][sched->cur_copy];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1354,37 +1478,39 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
|
|||
sched->n_splits = cur_split + 1;
|
||||
}
|
||||
#ifdef DEBUG_PASS4
|
||||
fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
|
||||
fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph);
|
||||
#endif
|
||||
|
||||
#ifndef NDEBUG
|
||||
// sanity check: all sources should have the same backend as the node
|
||||
for (int i = 0; i < graph->n_nodes; i++) {
|
||||
struct ggml_tensor * node = graph->nodes[i];
|
||||
ggml_backend_t tensor_backend = tensor_backend(node);
|
||||
ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node);
|
||||
if (tensor_backend == NULL) {
|
||||
fprintf(stderr, "!!!!!!! %s has no backend\n", node->name);
|
||||
}
|
||||
if (node->view_src != NULL && tensor_backend != tensor_backend(node->view_src)) {
|
||||
if (node->view_src != NULL && tensor_backend != ggml_backend_sched_get_tensor_backend(sched, node->view_src)) {
|
||||
fprintf(stderr, "!!!!!!! %s has backend %s, view_src %s has backend %s\n",
|
||||
node->name, tensor_backend ? ggml_backend_name(tensor_backend) : "NULL",
|
||||
node->view_src->name, tensor_backend(node->view_src) ? ggml_backend_name(tensor_backend(node->view_src)) : "NULL");
|
||||
node->view_src->name, ggml_backend_sched_get_tensor_backend(sched, node->view_src) ?
|
||||
ggml_backend_name(ggml_backend_sched_get_tensor_backend(sched, node->view_src)) : "NULL");
|
||||
}
|
||||
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
||||
struct ggml_tensor * src = node->src[j];
|
||||
if (src == NULL) {
|
||||
continue;
|
||||
}
|
||||
ggml_backend_t src_backend = tensor_backend(src);
|
||||
ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src);
|
||||
if (src_backend != tensor_backend /* && src_backend != NULL */) {
|
||||
fprintf(stderr, "!!!! %s has backend %s, src %d (%s) has backend %s\n",
|
||||
node->name, tensor_backend ? ggml_backend_name(tensor_backend) : "NULL",
|
||||
j, src->name, src_backend ? ggml_backend_name(src_backend) : "NULL");
|
||||
}
|
||||
if (src->view_src != NULL && src_backend != tensor_backend(src->view_src)) {
|
||||
if (src->view_src != NULL && src_backend != ggml_backend_sched_get_tensor_backend(sched, src->view_src)) {
|
||||
fprintf(stderr, "!!!!!!! [src] %s has backend %s, view_src %s has backend %s\n",
|
||||
src->name, src_backend ? ggml_backend_name(src_backend) : "NULL",
|
||||
src->view_src->name, tensor_backend(src->view_src) ? ggml_backend_name(tensor_backend(src->view_src)) : "NULL");
|
||||
src->view_src->name, ggml_backend_sched_get_tensor_backend(sched, src->view_src) ?
|
||||
ggml_backend_name(ggml_backend_sched_get_tensor_backend(sched, src->view_src)) : "NULL");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1392,18 +1518,20 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
|
|||
#endif
|
||||
|
||||
// create copies of the graph for each split
|
||||
// FIXME: avoid this copy, pass split inputs to ggml_gallocr_alloc_graph_n in some other way
|
||||
struct ggml_cgraph * graph_copy = ggml_new_graph_custom(sched->ctx, graph->n_nodes + sched->n_splits*GGML_MAX_SPLIT_INPUTS, false);
|
||||
// TODO: avoid this copy
|
||||
struct ggml_cgraph * graph_copy = ggml_new_graph_custom(sched->ctx, graph->n_nodes + sched->n_splits*GGML_SCHED_MAX_SPLIT_INPUTS, false);
|
||||
for (int i = 0; i < sched->n_splits; i++) {
|
||||
struct ggml_backend_sched_split * split = &sched->splits[i];
|
||||
split->graph = ggml_graph_view(graph, split->i_start, split->i_end);
|
||||
|
||||
// add inputs to the graph copy so that they are allocated by ggml-alloc at the start of the split
|
||||
for (int j = 0; j < split->n_inputs; j++) {
|
||||
struct ggml_tensor * input = split->inputs[j];
|
||||
struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split->backend_id];
|
||||
struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split->backend_id][sched->cur_copy];
|
||||
|
||||
// add a dependency to the input source so that it is not freed before the copy is done
|
||||
struct ggml_tensor * input_dep = ggml_view_tensor(sched->ctx, input);
|
||||
input_dep->src[0] = input;
|
||||
sched->node_backend_ids[graph_copy->n_nodes] = tensor_backend_id(input);
|
||||
graph_copy->nodes[graph_copy->n_nodes++] = input_dep;
|
||||
|
||||
|
|
@ -1417,18 +1545,56 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
|
|||
graph_copy->nodes[graph_copy->n_nodes++] = graph->nodes[j];
|
||||
}
|
||||
}
|
||||
|
||||
if (sched->n_copies > 1) {
|
||||
// add input copies as leafs so that they are allocated first
|
||||
for (int i = 0; i < sched->n_graph_inputs; i++) {
|
||||
struct ggml_tensor * input = sched->graph_inputs[i];
|
||||
size_t id = hash_id(input);
|
||||
int backend_id = tensor_backend_id(input);
|
||||
for (int c = 0; c < sched->n_copies; c++) {
|
||||
struct ggml_tensor * input_cpy = sched->tensor_copies[id][backend_id][c];
|
||||
sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id;
|
||||
graph_copy->leafs[graph_copy->n_leafs++] = input_cpy;
|
||||
}
|
||||
}
|
||||
|
||||
for (int i = 0; i < sched->n_splits; i++) {
|
||||
struct ggml_backend_sched_split * split = &sched->splits[i];
|
||||
int backend_id = split->backend_id;
|
||||
for (int j = 0; j < split->n_inputs; j++) {
|
||||
struct ggml_tensor * input = split->inputs[j];
|
||||
size_t id = hash_id(input);
|
||||
for (int c = 0; c < sched->n_copies; c++) {
|
||||
struct ggml_tensor * input_cpy = sched->tensor_copies[id][backend_id][c];
|
||||
sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id;
|
||||
graph_copy->leafs[graph_copy->n_leafs++] = input_cpy;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// add leafs from the original graph
|
||||
for (int i = 0; i < graph->n_leafs; i++) {
|
||||
struct ggml_tensor * leaf = graph->leafs[i];
|
||||
sched->leaf_backend_ids[graph_copy->n_leafs] = tensor_backend_id(leaf);
|
||||
graph_copy->leafs[graph_copy->n_leafs++] = leaf;
|
||||
}
|
||||
|
||||
sched->graph = graph_copy;
|
||||
}
|
||||
|
||||
static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
|
||||
// ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids);
|
||||
// allocate graph
|
||||
if (!ggml_gallocr_alloc_graph(sched->galloc, sched->graph)) {
|
||||
// the re-allocation may cause the split inputs to be moved to a different address
|
||||
ggml_backend_sched_synchronize(sched);
|
||||
#ifndef NDEBUG
|
||||
fprintf(stderr, "ggml_backend_sched: failed to allocate graph, reserving\n");
|
||||
fprintf(stderr, "%s: failed to allocate graph, reserving\n", __func__);
|
||||
#endif
|
||||
ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids);
|
||||
ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids, sched->leaf_backend_ids);
|
||||
if (!ggml_gallocr_alloc_graph(sched->galloc, sched->graph)) {
|
||||
fprintf(stderr, "ggml_backend_sched: failed to allocate graph\n");
|
||||
fprintf(stderr, "%s: failed to allocate graph\n", __func__);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
|
@ -1437,9 +1603,6 @@ static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
|
|||
}
|
||||
|
||||
static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) {
|
||||
uint64_t copy_us[GGML_MAX_BACKENDS] = {0};
|
||||
uint64_t compute_us[GGML_MAX_BACKENDS] = {0};
|
||||
|
||||
struct ggml_backend_sched_split * splits = sched->splits;
|
||||
|
||||
for (int i = 0; i < sched->n_splits; i++) {
|
||||
|
|
@ -1448,34 +1611,36 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
|
|||
ggml_backend_t split_backend = sched->backends[split_backend_id];
|
||||
|
||||
// copy the input tensors to the split backend
|
||||
uint64_t copy_start_us = ggml_time_us();
|
||||
for (int j = 0; j < split->n_inputs; j++) {
|
||||
ggml_backend_t input_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[j]);
|
||||
struct ggml_tensor * input = split->inputs[j];
|
||||
struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split_backend_id];
|
||||
struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split_backend_id][sched->cur_copy];
|
||||
|
||||
GGML_ASSERT(input->buffer != NULL);
|
||||
GGML_ASSERT(input_cpy->buffer != NULL);
|
||||
if (input->flags & GGML_TENSOR_FLAG_INPUT) {
|
||||
// inputs from the user must be copied immediately to prevent the user overwriting the data before the copy is done
|
||||
if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
|
||||
ggml_backend_event_synchronize(sched->events[split_backend_id][sched->cur_copy]);
|
||||
} else {
|
||||
ggml_backend_synchronize(split_backend);
|
||||
}
|
||||
ggml_backend_tensor_copy(input, input_cpy);
|
||||
} else {
|
||||
if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
|
||||
ggml_backend_event_wait(split_backend, sched->events[split_backend_id][sched->cur_copy]);
|
||||
} else {
|
||||
ggml_backend_synchronize(split_backend);
|
||||
ggml_backend_synchronize(input_backend);
|
||||
}
|
||||
|
||||
ggml_backend_tensor_copy_async(split_backend, input, input_cpy);
|
||||
ggml_backend_tensor_copy_async(input_backend, split_backend, input, input_cpy);
|
||||
}
|
||||
}
|
||||
//ggml_backend_synchronize(split_backend); // necessary to measure copy time
|
||||
int64_t copy_end_us = ggml_time_us();
|
||||
copy_us[split_backend_id] += copy_end_us - copy_start_us;
|
||||
|
||||
#if 0
|
||||
char split_filename[GGML_MAX_NAME];
|
||||
snprintf(split_filename, GGML_MAX_NAME, "split_%i_%s.dot", i, ggml_backend_name(split_backend));
|
||||
ggml_graph_dump_dot(split->graph, NULL, split_filename);
|
||||
#endif
|
||||
|
||||
|
||||
uint64_t compute_start_us = ggml_time_us();
|
||||
if (!sched->callback_eval) {
|
||||
enum ggml_status ec = ggml_backend_graph_compute(split_backend, &split->graph);
|
||||
enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &split->graph);
|
||||
if (ec != GGML_STATUS_SUCCESS) {
|
||||
return ec;
|
||||
}
|
||||
//ggml_backend_synchronize(split_backend); // necessary to measure compute time
|
||||
} else {
|
||||
// similar to ggml_backend_compare_graph_backend
|
||||
for (int j0 = 0; j0 < split->graph.n_nodes; j0++) {
|
||||
|
|
@ -1494,11 +1659,14 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
|
|||
|
||||
struct ggml_cgraph gv = ggml_graph_view(&split->graph, j0, j1 + 1);
|
||||
|
||||
enum ggml_status ec = ggml_backend_graph_compute(split_backend, &gv);
|
||||
enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &gv);
|
||||
if (ec != GGML_STATUS_SUCCESS) {
|
||||
return ec;
|
||||
}
|
||||
|
||||
// TODO: pass backend to the callback, then the user can decide if they want to synchronize
|
||||
ggml_backend_synchronize(split_backend);
|
||||
|
||||
if (need && !sched->callback_eval(t, false, sched->callback_eval_user_data)) {
|
||||
break;
|
||||
}
|
||||
|
|
@ -1506,39 +1674,54 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
|
|||
j0 = j1;
|
||||
}
|
||||
}
|
||||
uint64_t compute_end_us = ggml_time_us();
|
||||
compute_us[split_backend_id] += compute_end_us - compute_start_us;
|
||||
}
|
||||
|
||||
#if 0
|
||||
// per-backend timings
|
||||
fprintf(stderr, "sched_compute_splits times (%d splits):\n", sched->n_splits);
|
||||
for (int i = 0; i < sched->n_backends; i++) {
|
||||
if (copy_us[i] > 0 || compute_us[i] > 0) {
|
||||
fprintf(stderr, "\t%5.5s: %lu us copy, %lu us compute\n", ggml_backend_name(sched->backends[i]), copy_us[i], compute_us[i]);
|
||||
// record the event of this copy
|
||||
if (split->n_inputs > 0) {
|
||||
if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
|
||||
ggml_backend_event_record(sched->events[split_backend_id][sched->cur_copy]);
|
||||
}
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
sched->cur_copy = (sched->cur_copy + 1) % sched->n_copies;
|
||||
|
||||
return GGML_STATUS_SUCCESS;
|
||||
}
|
||||
|
||||
ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size) {
|
||||
ggml_backend_sched_t ggml_backend_sched_new(
|
||||
ggml_backend_t * backends,
|
||||
ggml_backend_buffer_type_t * bufts,
|
||||
int n_backends,
|
||||
size_t graph_size,
|
||||
bool parallel) {
|
||||
GGML_ASSERT(n_backends > 0);
|
||||
GGML_ASSERT(n_backends <= GGML_MAX_BACKENDS);
|
||||
GGML_ASSERT(n_backends <= GGML_SCHED_MAX_BACKENDS);
|
||||
GGML_ASSERT(ggml_backend_is_cpu(backends[n_backends - 1])); // last backend must be CPU
|
||||
|
||||
struct ggml_backend_sched * sched = calloc(sizeof(struct ggml_backend_sched), 1);
|
||||
|
||||
// initialize hash table
|
||||
sched->hash_set = ggml_hash_set_new(graph_size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS);
|
||||
sched->hash_set = ggml_hash_set_new(graph_size + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS);
|
||||
sched->tensor_backend_id = calloc(sizeof(sched->tensor_backend_id[0]), sched->hash_set.size);
|
||||
sched->tensor_copies = calloc(sizeof(sched->tensor_copies[0]), sched->hash_set.size);
|
||||
sched->node_backend_ids = calloc(sizeof(sched->node_backend_ids[0]), graph_size);
|
||||
sched->leaf_backend_ids = calloc(sizeof(sched->leaf_backend_ids[0]), graph_size);
|
||||
|
||||
sched->n_backends = n_backends;
|
||||
for (int i = 0; i < n_backends; i++) {
|
||||
sched->backends[i] = backends[i];
|
||||
sched->bufts[i] = bufts ? bufts[i] : ggml_backend_get_default_buffer_type(backends[i]);
|
||||
|
||||
sched->n_copies = parallel ? GGML_SCHED_MAX_COPIES : 1;
|
||||
|
||||
GGML_ASSERT(sched->n_copies <= GGML_SCHED_MAX_COPIES);
|
||||
|
||||
for (int b = 0; b < n_backends; b++) {
|
||||
sched->backends[b] = backends[b];
|
||||
sched->bufts[b] = bufts ? bufts[b] : ggml_backend_get_default_buffer_type(backends[b]);
|
||||
GGML_ASSERT(ggml_backend_buft_supports_backend(sched->bufts[b], backends[b]));
|
||||
if (sched->n_copies > 1) {
|
||||
for (int c = 0; c < sched->n_copies; c++) {
|
||||
sched->events[b][c] = ggml_backend_event_new(backends[b]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
sched->galloc = ggml_gallocr_new_n(sched->bufts, n_backends);
|
||||
|
|
@ -1552,12 +1735,18 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) {
|
|||
if (sched == NULL) {
|
||||
return;
|
||||
}
|
||||
for (int b = 0; b < sched->n_backends; b++) {
|
||||
for (int c = 0; c < sched->n_copies; c++) {
|
||||
ggml_backend_event_free(sched->events[b][c]);
|
||||
}
|
||||
}
|
||||
ggml_gallocr_free(sched->galloc);
|
||||
ggml_free(sched->ctx);
|
||||
free(sched->hash_set.keys);
|
||||
free(sched->tensor_backend_id);
|
||||
free(sched->tensor_copies);
|
||||
free(sched->node_backend_ids);
|
||||
free(sched->leaf_backend_ids);
|
||||
free(sched);
|
||||
}
|
||||
|
||||
|
|
@ -1569,34 +1758,63 @@ void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
|
|||
memset(sched->tensor_copies, 0, sizeof(sched->tensor_copies[0]) * hash_size);
|
||||
|
||||
sched->is_reset = true;
|
||||
sched->is_alloc = false;
|
||||
}
|
||||
|
||||
bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) {
|
||||
ggml_backend_sched_split_graph(sched, measure_graph);
|
||||
|
||||
if (!ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids)) {
|
||||
// TODO: extract this to a separate function
|
||||
if (!ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids, sched->leaf_backend_ids)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
ggml_backend_sched_reset(sched);
|
||||
ggml_backend_sched_synchronize(sched);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
|
||||
GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS);
|
||||
|
||||
ggml_backend_sched_split_graph(sched, graph);
|
||||
|
||||
if (!ggml_backend_sched_alloc_splits(sched)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
sched->is_alloc = true;
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
|
||||
GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS);
|
||||
enum ggml_status err = ggml_backend_sched_graph_compute_async(sched, graph);
|
||||
ggml_backend_sched_synchronize(sched);
|
||||
return err;
|
||||
}
|
||||
|
||||
if (!sched->is_reset) {
|
||||
enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
|
||||
if (!sched->is_reset && !sched->is_alloc) {
|
||||
ggml_backend_sched_reset(sched);
|
||||
}
|
||||
|
||||
ggml_backend_sched_split_graph(sched, graph);
|
||||
if (!ggml_backend_sched_alloc_splits(sched)) {
|
||||
return GGML_STATUS_ALLOC_FAILED;
|
||||
if (!sched->is_alloc) {
|
||||
if (!ggml_backend_sched_alloc_graph(sched, graph)) {
|
||||
return GGML_STATUS_ALLOC_FAILED;
|
||||
}
|
||||
}
|
||||
|
||||
return ggml_backend_sched_compute_splits(sched);
|
||||
}
|
||||
|
||||
void ggml_backend_sched_synchronize(ggml_backend_sched_t sched) {
|
||||
for (int i = 0; i < sched->n_backends; i++) {
|
||||
ggml_backend_synchronize(sched->backends[i]);
|
||||
}
|
||||
}
|
||||
|
||||
void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) {
|
||||
sched->callback_eval = callback;
|
||||
sched->callback_eval_user_data = user_data;
|
||||
|
|
@ -1606,19 +1824,24 @@ int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched) {
|
|||
return sched->n_splits;
|
||||
}
|
||||
|
||||
int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched) {
|
||||
return sched->n_copies;
|
||||
}
|
||||
|
||||
size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend) {
|
||||
int backend_index = ggml_backend_sched_backend_id(sched, backend);
|
||||
GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
|
||||
|
||||
return ggml_gallocr_get_buffer_size(sched->galloc, backend_index);
|
||||
}
|
||||
|
||||
void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) {
|
||||
void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) {
|
||||
int backend_index = ggml_backend_sched_backend_id(sched, backend);
|
||||
GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
|
||||
tensor_backend_id(node) = backend_index;
|
||||
}
|
||||
|
||||
ggml_backend_t ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) {
|
||||
ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) {
|
||||
int backend_index = tensor_backend_id(node);
|
||||
if (backend_index == -1) {
|
||||
return NULL;
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ extern "C" {
|
|||
|
||||
typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t;
|
||||
typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
|
||||
typedef struct ggml_backend_event * ggml_backend_event_t;
|
||||
typedef struct ggml_backend * ggml_backend_t;
|
||||
typedef void * ggml_backend_graph_plan_t;
|
||||
|
||||
|
|
@ -72,11 +73,24 @@ extern "C" {
|
|||
GGML_API enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
|
||||
GGML_API enum ggml_status ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph);
|
||||
|
||||
GGML_API bool ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph);
|
||||
GGML_API bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op);
|
||||
|
||||
// tensor copy between different backends
|
||||
GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst);
|
||||
GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); // automatic fallback to sync copy
|
||||
|
||||
// asynchronous copy
|
||||
// the copy is performed after all the currently queued operations in backend_src
|
||||
// backend_dst will wait for the copy to complete before performing other operations
|
||||
// automatic fallback to sync copy if async is not supported
|
||||
GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst);
|
||||
|
||||
// events
|
||||
GGML_API ggml_backend_event_t ggml_backend_event_new (ggml_backend_t backend);
|
||||
GGML_API void ggml_backend_event_free (ggml_backend_event_t event);
|
||||
GGML_API void ggml_backend_event_record (ggml_backend_event_t event);
|
||||
GGML_API void ggml_backend_event_synchronize(ggml_backend_event_t event);
|
||||
GGML_API void ggml_backend_event_wait (ggml_backend_t backend, ggml_backend_event_t event); // wait async on event
|
||||
|
||||
//
|
||||
// CPU backend
|
||||
|
|
@ -123,27 +137,31 @@ extern "C" {
|
|||
/*
|
||||
Example usage:
|
||||
|
||||
sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, num_backends);
|
||||
// sched is initialized with measure allocators and cannot be used until allocated with a measure graph
|
||||
// operations that use tensors allocated in a buffer with USAGE_WEIGHTS will be asigned
|
||||
// preferrably to run on the same backend as the buffer
|
||||
ggml_backend_buffer_set_usage(buf_weights, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
|
||||
|
||||
// initialize buffers from a measure graph
|
||||
measure_graph = build_graph(sched); // use the allocr to allocate inputs as needed
|
||||
sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, NULL, num_backends, GGML_DEFAULT_GRAPH_SIZE, false);
|
||||
|
||||
// in build_graph:
|
||||
build_graph(...) {
|
||||
// manually assign nodes to a backend (optional, should not be needed in most cases)
|
||||
struct ggml_tensor * node = ggml_mul_mat(ctx, ...);
|
||||
ggml_backend_sched_set_node_backend(sched, node, backend_gpu);
|
||||
}
|
||||
// initialize buffers from a max size graph (optional)
|
||||
reserve_graph = build_graph(sched, max_batch_size);
|
||||
|
||||
// allocate backend buffers from measure graph
|
||||
ggml_backend_sched_init_measure(sched, measure_graph);
|
||||
// manually assign nodes to a backend (optional, should not be needed in most cases)
|
||||
struct ggml_tensor * node = ggml_mul_mat(ctx, ...);
|
||||
ggml_backend_sched_set_tensor_backend(sched, node, backend_gpu);
|
||||
|
||||
// the scheduler is now ready to compute graphs
|
||||
ggml_backend_sched_reserve(sched, reserve_graph);
|
||||
|
||||
// compute
|
||||
graph = build_graph(sched);
|
||||
ggml_backend_sched_graph_compute(sched, graph);
|
||||
|
||||
// if there are graph inputs:
|
||||
ggml_backend_sched_reset(sched);
|
||||
ggml_backend_sched_alloc_graph(sched, graph);
|
||||
ggml_backend_tensor_set(input_tensor, ...);
|
||||
ggml_backend_sched_graph_compute(sched, graph);
|
||||
}
|
||||
*/
|
||||
|
||||
struct ggml_backend_sched;
|
||||
|
|
@ -158,20 +176,26 @@ extern "C" {
|
|||
typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
|
||||
|
||||
// Initialize a backend scheduler
|
||||
GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size);
|
||||
GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel);
|
||||
GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched);
|
||||
|
||||
// Initialize backend buffers from a measure graph
|
||||
GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph);
|
||||
|
||||
// Get the number of splits of the last graph
|
||||
GGML_API int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched);
|
||||
GGML_API int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched);
|
||||
|
||||
GGML_API size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
|
||||
|
||||
GGML_API void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
|
||||
GGML_API ggml_backend_t ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
|
||||
GGML_API void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
|
||||
GGML_API ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
|
||||
|
||||
// Allocate and compute graph on the backend scheduler
|
||||
GGML_API bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
|
||||
GGML_API enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
|
||||
GGML_API enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
|
||||
GGML_API void ggml_backend_sched_synchronize(ggml_backend_sched_t sched);
|
||||
|
||||
// Reset all assignments and allocators - must be called before changing the node backends
|
||||
GGML_API void ggml_backend_sched_reset(ggml_backend_sched_t sched);
|
||||
|
|
|
|||
175
ggml-cuda.cu
175
ggml-cuda.cu
|
|
@ -72,6 +72,7 @@
|
|||
#define cudaEventCreateWithFlags hipEventCreateWithFlags
|
||||
#define cudaEventDisableTiming hipEventDisableTiming
|
||||
#define cudaEventRecord hipEventRecord
|
||||
#define cudaEventSynchronize hipEventSynchronize
|
||||
#define cudaEvent_t hipEvent_t
|
||||
#define cudaEventDestroy hipEventDestroy
|
||||
#define cudaFree hipFree
|
||||
|
|
@ -81,6 +82,7 @@
|
|||
#define cudaGetDeviceProperties hipGetDeviceProperties
|
||||
#define cudaGetErrorString hipGetErrorString
|
||||
#define cudaGetLastError hipGetLastError
|
||||
#define cudaLaunchHostFunc hipLaunchHostFunc
|
||||
#ifdef GGML_HIP_UMA
|
||||
#define cudaMalloc hipMallocManaged
|
||||
#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size)
|
||||
|
|
@ -104,6 +106,7 @@
|
|||
#define cudaStreamCreateWithFlags hipStreamCreateWithFlags
|
||||
#define cudaStreamFireAndForget hipStreamFireAndForget
|
||||
#define cudaStreamNonBlocking hipStreamNonBlocking
|
||||
#define cudaStreamPerThread hipStreamPerThread
|
||||
#define cudaStreamSynchronize hipStreamSynchronize
|
||||
#define cudaStreamWaitEvent(stream, event, flags) hipStreamWaitEvent(stream, event, flags)
|
||||
#define cudaStream_t hipStream_t
|
||||
|
|
@ -10641,8 +10644,20 @@ GGML_CALL void ggml_cuda_get_device_description(int device, char * description,
|
|||
#define UNUSED GGML_UNUSED
|
||||
|
||||
struct ggml_backend_cuda_context {
|
||||
explicit ggml_backend_cuda_context(int device) :
|
||||
device(device),
|
||||
name(GGML_CUDA_NAME + std::to_string(device)) {
|
||||
}
|
||||
|
||||
~ggml_backend_cuda_context() {
|
||||
if (copy_event != nullptr) {
|
||||
CUDA_CHECK(cudaEventDestroy(copy_event));
|
||||
}
|
||||
}
|
||||
|
||||
int device;
|
||||
std::string name;
|
||||
cudaEvent_t copy_event = nullptr;
|
||||
};
|
||||
|
||||
// cuda buffer
|
||||
|
|
@ -10732,9 +10747,8 @@ GGML_CALL static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t
|
|||
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
|
||||
|
||||
ggml_cuda_set_device(ctx->device);
|
||||
CUDA_CHECK(cudaDeviceSynchronize());
|
||||
CUDA_CHECK(cudaMemcpy((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice));
|
||||
CUDA_CHECK(cudaDeviceSynchronize());
|
||||
CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, cudaStreamPerThread));
|
||||
CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
|
||||
}
|
||||
|
||||
GGML_CALL static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
||||
|
|
@ -10743,26 +10757,25 @@ GGML_CALL static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t
|
|||
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
|
||||
|
||||
ggml_cuda_set_device(ctx->device);
|
||||
CUDA_CHECK(cudaDeviceSynchronize());
|
||||
CUDA_CHECK(cudaMemcpy(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost));
|
||||
CUDA_CHECK(cudaDeviceSynchronize());
|
||||
CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, cudaStreamPerThread));
|
||||
CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
|
||||
}
|
||||
|
||||
GGML_CALL static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
|
||||
if (ggml_backend_buffer_is_cuda(src->buffer)) {
|
||||
ggml_backend_cuda_buffer_context * src_ctx = (ggml_backend_cuda_buffer_context *)src->buffer->context;
|
||||
ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
|
||||
|
||||
ggml_cuda_set_device(src_ctx->device);
|
||||
CUDA_CHECK(cudaDeviceSynchronize());
|
||||
ggml_cuda_set_device(dst_ctx->device);
|
||||
CUDA_CHECK(cudaDeviceSynchronize());
|
||||
CUDA_CHECK(cudaMemcpy((char *)dst->data, (const char *)src->data, ggml_nbytes(src), cudaMemcpyDeviceToDevice));
|
||||
CUDA_CHECK(cudaDeviceSynchronize());
|
||||
|
||||
ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *)dst->buffer->context;
|
||||
if (src_ctx->device == dst_ctx->device) {
|
||||
CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(src), cudaMemcpyDeviceToDevice, cudaStreamPerThread));
|
||||
} else {
|
||||
CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, dst_ctx->device, src->data, src_ctx->device, ggml_nbytes(src), cudaStreamPerThread));
|
||||
}
|
||||
CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
|
||||
return true;
|
||||
}
|
||||
return false;
|
||||
|
||||
UNUSED(buffer);
|
||||
}
|
||||
|
||||
GGML_CALL static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
|
||||
|
|
@ -11007,7 +11020,11 @@ GGML_CALL static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buf
|
|||
}
|
||||
|
||||
const char * buf_host = (const char *)data + offset_split;
|
||||
CUDA_CHECK(cudaMemcpy(extra->data_device[id], buf_host, original_size, cudaMemcpyHostToDevice));
|
||||
CUDA_CHECK(cudaMemcpyAsync(extra->data_device[id], buf_host, original_size, cudaMemcpyHostToDevice, cudaStreamPerThread));
|
||||
}
|
||||
|
||||
for (int id = 0; id < g_device_count; ++id) {
|
||||
CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -11041,7 +11058,11 @@ GGML_CALL static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buf
|
|||
}
|
||||
|
||||
char * buf_host = (char *)data + offset_split;
|
||||
CUDA_CHECK(cudaMemcpy(buf_host, extra->data_device[id], original_size, cudaMemcpyDeviceToHost));
|
||||
CUDA_CHECK(cudaMemcpyAsync(buf_host, extra->data_device[id], original_size, cudaMemcpyDeviceToHost, cudaStreamPerThread));
|
||||
}
|
||||
|
||||
for (int id = 0; id < g_device_count; ++id) {
|
||||
CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -11220,6 +11241,10 @@ GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() {
|
|||
return &ggml_backend_cuda_buffer_type_host;
|
||||
}
|
||||
|
||||
//static bool ggml_backend_buffer_is_cuda_host(ggml_backend_buffer_t buffer) {
|
||||
// return buffer->buft->iface.get_name == ggml_backend_cuda_host_buffer_type_name;
|
||||
//}
|
||||
|
||||
// backend
|
||||
|
||||
GGML_CALL static const char * ggml_backend_cuda_name(ggml_backend_t backend) {
|
||||
|
|
@ -11243,8 +11268,9 @@ GGML_CALL static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer
|
|||
|
||||
GGML_CALL static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
|
||||
|
||||
GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
|
||||
GGML_ASSERT(buf->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
|
||||
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
|
||||
|
||||
CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, g_cudaStreams[cuda_ctx->device][0]));
|
||||
|
|
@ -11252,22 +11278,61 @@ GGML_CALL static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend,
|
|||
|
||||
GGML_CALL static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
|
||||
|
||||
GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
|
||||
GGML_ASSERT(buf->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
|
||||
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
|
||||
|
||||
CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[cuda_ctx->device][0]));
|
||||
}
|
||||
|
||||
GGML_CALL static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
GGML_CALL static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const ggml_tensor * src, ggml_tensor * dst) {
|
||||
GGML_ASSERT(ggml_backend_is_cuda(backend_src) || ggml_backend_is_cuda(backend_dst));
|
||||
|
||||
if (dst->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && ggml_backend_buffer_is_cuda(src->buffer)) {
|
||||
CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, g_cudaStreams[cuda_ctx->device][0]));
|
||||
return true;
|
||||
ggml_backend_buffer_t buf_src = src->view_src ? src->view_src->buffer : src->buffer;
|
||||
ggml_backend_buffer_t buf_dst = dst->view_src ? dst->view_src->buffer : dst->buffer;
|
||||
|
||||
if (!ggml_backend_buffer_is_cuda(src->buffer)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
return false;
|
||||
if (!ggml_backend_buffer_is_cuda(dst->buffer)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
// device -> device
|
||||
ggml_backend_cuda_context * cuda_ctx_src = (ggml_backend_cuda_context *)backend_src->context;
|
||||
ggml_backend_cuda_context * cuda_ctx_dst = (ggml_backend_cuda_context *)backend_dst->context;
|
||||
|
||||
if (backend_src != backend_dst) {
|
||||
ggml_backend_cuda_buffer_context * buf_ctx_src = (ggml_backend_cuda_buffer_context *)buf_src->context;
|
||||
ggml_backend_cuda_buffer_context * buf_ctx_dst = (ggml_backend_cuda_buffer_context *)buf_dst->context;
|
||||
|
||||
GGML_ASSERT(cuda_ctx_src->device == buf_ctx_src->device);
|
||||
GGML_ASSERT(cuda_ctx_dst->device == buf_ctx_dst->device);
|
||||
|
||||
if (!cuda_ctx_src->copy_event) {
|
||||
ggml_cuda_set_device(cuda_ctx_src->device);
|
||||
CUDA_CHECK(cudaEventCreateWithFlags(&cuda_ctx_src->copy_event, cudaEventDisableTiming));
|
||||
}
|
||||
|
||||
// copy on src stream
|
||||
if (cuda_ctx_src->device == cuda_ctx_dst->device) {
|
||||
CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, g_cudaStreams[cuda_ctx_dst->device][0]));
|
||||
} else {
|
||||
CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, cuda_ctx_dst->device, src->data, cuda_ctx_src->device, ggml_nbytes(dst), g_cudaStreams[cuda_ctx_src->device][0]));
|
||||
}
|
||||
|
||||
// record event on src stream
|
||||
CUDA_CHECK(cudaEventRecord(cuda_ctx_src->copy_event, g_cudaStreams[cuda_ctx_src->device][0]));
|
||||
|
||||
// wait on dst stream for the copy to complete
|
||||
CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams[cuda_ctx_dst->device][0], cuda_ctx_src->copy_event, 0));
|
||||
} else {
|
||||
// src and dst are on the same backend
|
||||
CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, g_cudaStreams[cuda_ctx_dst->device][0]));
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
GGML_CALL static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
|
||||
|
|
@ -11444,6 +11509,52 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
|
|||
UNUSED(backend);
|
||||
}
|
||||
|
||||
static ggml_backend_event_t ggml_backend_cuda_event_new(ggml_backend_t backend) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
|
||||
ggml_cuda_set_device(cuda_ctx->device);
|
||||
|
||||
cudaEvent_t event;
|
||||
CUDA_CHECK(cudaEventCreateWithFlags(&event, cudaEventDisableTiming));
|
||||
|
||||
return new ggml_backend_event {
|
||||
/* .backend = */ backend,
|
||||
/* .context = */ event,
|
||||
};
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_event_free(ggml_backend_event_t event) {
|
||||
CUDA_CHECK(cudaEventDestroy((cudaEvent_t)event->context));
|
||||
|
||||
delete event;
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_event_record(ggml_backend_event_t event) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)event->backend->context;
|
||||
|
||||
CUDA_CHECK(cudaEventRecord((cudaEvent_t)event->context, g_cudaStreams[cuda_ctx->device][0]));
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_event_wait(ggml_backend_t backend, ggml_backend_event_t event) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
|
||||
if (ggml_backend_is_cuda(event->backend)) {
|
||||
CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams[cuda_ctx->device][0], (cudaEvent_t)event->context, 0));
|
||||
} else {
|
||||
// untested
|
||||
auto wait_fn = [](void * user_data) {
|
||||
ggml_backend_event_t event = (ggml_backend_event_t)user_data;
|
||||
ggml_backend_event_synchronize(event);
|
||||
};
|
||||
|
||||
CUDA_CHECK(cudaLaunchHostFunc(g_cudaStreams[cuda_ctx->device][0], wait_fn, event));
|
||||
}
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_event_synchronize(ggml_backend_event_t event) {
|
||||
CUDA_CHECK(cudaEventSynchronize((cudaEvent_t)event->context));
|
||||
}
|
||||
|
||||
static ggml_backend_i ggml_backend_cuda_interface = {
|
||||
/* .get_name = */ ggml_backend_cuda_name,
|
||||
/* .free = */ ggml_backend_cuda_free,
|
||||
|
|
@ -11457,6 +11568,11 @@ static ggml_backend_i ggml_backend_cuda_interface = {
|
|||
/* .graph_plan_compute = */ NULL,
|
||||
/* .graph_compute = */ ggml_backend_cuda_graph_compute,
|
||||
/* .supports_op = */ ggml_backend_cuda_supports_op,
|
||||
/* .event_new = */ ggml_backend_cuda_event_new,
|
||||
/* .event_free = */ ggml_backend_cuda_event_free,
|
||||
/* .event_record = */ ggml_backend_cuda_event_record,
|
||||
/* .event_wait = */ ggml_backend_cuda_event_wait,
|
||||
/* .event_synchronize = */ ggml_backend_cuda_event_synchronize,
|
||||
};
|
||||
|
||||
static ggml_guid_t ggml_backend_cuda_guid() {
|
||||
|
|
@ -11475,10 +11591,11 @@ GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) {
|
|||
// not strictly necessary, but it may reduce the overhead of the first graph_compute
|
||||
ggml_cuda_set_main_device(device);
|
||||
|
||||
ggml_backend_cuda_context * ctx = new ggml_backend_cuda_context {
|
||||
/* .device = */ device,
|
||||
/* .name = */ GGML_CUDA_NAME + std::to_string(device),
|
||||
};
|
||||
ggml_backend_cuda_context * ctx = new ggml_backend_cuda_context(device);
|
||||
if (ctx == nullptr) {
|
||||
fprintf(stderr, "%s: error: failed to allocate context\n", __func__);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
ggml_backend_t cuda_backend = new ggml_backend {
|
||||
/* .guid = */ ggml_backend_cuda_guid(),
|
||||
|
|
|
|||
|
|
@ -1951,6 +1951,11 @@ static struct ggml_backend_i kompute_backend_i = {
|
|||
/* .graph_plan_compute = */ NULL,
|
||||
/* .graph_compute = */ ggml_backend_kompute_graph_compute,
|
||||
/* .supports_op = */ ggml_backend_kompute_supports_op,
|
||||
/* .event_new = */ NULL,
|
||||
/* .event_free = */ NULL,
|
||||
/* .event_record = */ NULL,
|
||||
/* .event_wait = */ NULL,
|
||||
/* .event_synchronize = */ NULL,
|
||||
};
|
||||
|
||||
static ggml_guid_t ggml_backend_kompute_guid() {
|
||||
|
|
|
|||
|
|
@ -2820,6 +2820,11 @@ static struct ggml_backend_i ggml_backend_metal_i = {
|
|||
/* .graph_plan_compute = */ NULL,
|
||||
/* .graph_compute = */ ggml_backend_metal_graph_compute,
|
||||
/* .supports_op = */ ggml_backend_metal_supports_op,
|
||||
/* .event_new = */ NULL,
|
||||
/* .event_free = */ NULL,
|
||||
/* .event_record = */ NULL,
|
||||
/* .event_wait = */ NULL,
|
||||
/* .event_synchronize = */ NULL,
|
||||
};
|
||||
|
||||
void ggml_backend_metal_log_set_callback(ggml_log_callback log_callback, void * user_data) {
|
||||
|
|
|
|||
|
|
@ -17249,13 +17249,18 @@ static ggml_backend_i ggml_backend_sycl_interface = {
|
|||
/* .get_default_buffer_type = */ ggml_backend_sycl_get_default_buffer_type,
|
||||
/* .set_tensor_async = */ ggml_backend_sycl_set_tensor_async,
|
||||
/* .get_tensor_async = */ ggml_backend_sycl_get_tensor_async,
|
||||
/* .cpy_tensor_async = */ ggml_backend_sycl_cpy_tensor_async,
|
||||
/* .cpy_tensor_async = */ NULL, //ggml_backend_sycl_cpy_tensor_async, // TODO: update for the new interface
|
||||
/* .synchronize = */ ggml_backend_sycl_synchronize,
|
||||
/* .graph_plan_create = */ NULL,
|
||||
/* .graph_plan_free = */ NULL,
|
||||
/* .graph_plan_compute = */ NULL,
|
||||
/* .graph_compute = */ ggml_backend_sycl_graph_compute,
|
||||
/* .supports_op = */ ggml_backend_sycl_supports_op,
|
||||
/* .event_new = */ NULL,
|
||||
/* .event_free = */ NULL,
|
||||
/* .event_record = */ NULL,
|
||||
/* .event_wait = */ NULL,
|
||||
/* .event_synchronize = */ NULL,
|
||||
};
|
||||
|
||||
static ggml_guid_t ggml_backend_sycl_guid() {
|
||||
|
|
|
|||
|
|
@ -5693,6 +5693,11 @@ static ggml_backend_i ggml_backend_vk_interface = {
|
|||
/* .graph_plan_compute = */ NULL,
|
||||
/* .graph_compute = */ ggml_backend_vk_graph_compute,
|
||||
/* .supports_op = */ ggml_backend_vk_supports_op,
|
||||
/* .event_new = */ NULL,
|
||||
/* .event_free = */ NULL,
|
||||
/* .event_record = */ NULL,
|
||||
/* .event_wait = */ NULL,
|
||||
/* .event_synchronize = */ NULL,
|
||||
};
|
||||
|
||||
static ggml_guid_t ggml_backend_vk_guid() {
|
||||
|
|
|
|||
113
ggml.c
113
ggml.c
|
|
@ -11560,8 +11560,6 @@ static void ggml_compute_forward_get_rows_q(
|
|||
const struct ggml_tensor * src0 = dst->src[0];
|
||||
const struct ggml_tensor * src1 = dst->src[1];
|
||||
|
||||
assert(params->ith == 0);
|
||||
|
||||
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
|
||||
return;
|
||||
}
|
||||
|
|
@ -11569,7 +11567,7 @@ static void ggml_compute_forward_get_rows_q(
|
|||
GGML_TENSOR_BINARY_OP_LOCALS
|
||||
|
||||
const int64_t nc = ne00;
|
||||
const int64_t nr = ggml_nelements(src1); GGML_UNUSED(nr);
|
||||
const int64_t nr = ggml_nelements(src1);
|
||||
|
||||
const enum ggml_type type = src0->type;
|
||||
ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;
|
||||
|
|
@ -11579,17 +11577,25 @@ static void ggml_compute_forward_get_rows_q(
|
|||
assert(nb00 == ggml_type_size(type));
|
||||
assert(ggml_nrows(dst) == nr);
|
||||
|
||||
// TODO: multi-thread
|
||||
for (int64_t i12 = 0; i12 < ne12; ++i12) {
|
||||
for (int64_t i11 = 0; i11 < ne11; ++i11) {
|
||||
for (int64_t i10 = 0; i10 < ne10; ++i10) {
|
||||
const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
|
||||
const int ith = params->ith;
|
||||
const int nth = params->nth;
|
||||
|
||||
dequantize_row_q(
|
||||
(const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
|
||||
(float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc);
|
||||
}
|
||||
}
|
||||
// rows per thread
|
||||
const int dr = (nr + nth - 1)/nth;
|
||||
|
||||
// row range for this thread
|
||||
const int ir0 = dr*ith;
|
||||
const int ir1 = MIN(ir0 + dr, nr);
|
||||
|
||||
for (int64_t i = ir0; i < ir1; ++i) {
|
||||
const int64_t i12 = i/(ne11*ne10);
|
||||
const int64_t i11 = (i - i12*ne11*ne10)/ne10;
|
||||
const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10);
|
||||
const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
|
||||
|
||||
dequantize_row_q(
|
||||
(const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
|
||||
(float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc);
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -11600,8 +11606,6 @@ static void ggml_compute_forward_get_rows_f16(
|
|||
const struct ggml_tensor * src0 = dst->src[0];
|
||||
const struct ggml_tensor * src1 = dst->src[1];
|
||||
|
||||
assert(params->ith == 0);
|
||||
|
||||
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
|
||||
return;
|
||||
}
|
||||
|
|
@ -11609,24 +11613,32 @@ static void ggml_compute_forward_get_rows_f16(
|
|||
GGML_TENSOR_BINARY_OP_LOCALS
|
||||
|
||||
const int64_t nc = ne00;
|
||||
const int64_t nr = ggml_nelements(src1); GGML_UNUSED(nr);
|
||||
const int64_t nr = ggml_nelements(src1);
|
||||
|
||||
assert(ne0 == nc);
|
||||
assert(ne02 == ne11);
|
||||
assert(nb00 == sizeof(ggml_fp16_t));
|
||||
assert(ggml_nrows(dst) == nr);
|
||||
|
||||
// TODO: multi-thread
|
||||
for (int64_t i12 = 0; i12 < ne12; ++i12) {
|
||||
for (int64_t i11 = 0; i11 < ne11; ++i11) {
|
||||
for (int64_t i10 = 0; i10 < ne10; ++i10) {
|
||||
const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
|
||||
const int ith = params->ith;
|
||||
const int nth = params->nth;
|
||||
|
||||
ggml_fp16_to_fp32_row(
|
||||
(const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
|
||||
(float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc);
|
||||
}
|
||||
}
|
||||
// rows per thread
|
||||
const int dr = (nr + nth - 1)/nth;
|
||||
|
||||
// row range for this thread
|
||||
const int ir0 = dr*ith;
|
||||
const int ir1 = MIN(ir0 + dr, nr);
|
||||
|
||||
for (int64_t i = ir0; i < ir1; ++i) {
|
||||
const int64_t i12 = i/(ne11*ne10);
|
||||
const int64_t i11 = (i - i12*ne11*ne10)/ne10;
|
||||
const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10);
|
||||
const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
|
||||
|
||||
ggml_fp16_to_fp32_row(
|
||||
(const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
|
||||
(float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc);
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -11637,8 +11649,6 @@ static void ggml_compute_forward_get_rows_f32(
|
|||
const struct ggml_tensor * src0 = dst->src[0];
|
||||
const struct ggml_tensor * src1 = dst->src[1];
|
||||
|
||||
assert(params->ith == 0);
|
||||
|
||||
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
|
||||
return;
|
||||
}
|
||||
|
|
@ -11646,24 +11656,32 @@ static void ggml_compute_forward_get_rows_f32(
|
|||
GGML_TENSOR_BINARY_OP_LOCALS
|
||||
|
||||
const int64_t nc = ne00;
|
||||
const int64_t nr = ggml_nelements(src1); GGML_UNUSED(nr);
|
||||
const int64_t nr = ggml_nelements(src1);
|
||||
|
||||
assert(ne0 == nc);
|
||||
assert(ne02 == ne11);
|
||||
assert(nb00 == sizeof(float));
|
||||
assert(ggml_nrows(dst) == nr);
|
||||
|
||||
// TODO: multi-thread
|
||||
for (int64_t i12 = 0; i12 < ne12; ++i12) {
|
||||
for (int64_t i11 = 0; i11 < ne11; ++i11) {
|
||||
for (int64_t i10 = 0; i10 < ne10; ++i10) {
|
||||
const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
|
||||
const int ith = params->ith;
|
||||
const int nth = params->nth;
|
||||
|
||||
ggml_vec_cpy_f32(nc,
|
||||
(float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3),
|
||||
(float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03));
|
||||
}
|
||||
}
|
||||
// rows per thread
|
||||
const int dr = (nr + nth - 1)/nth;
|
||||
|
||||
// row range for this thread
|
||||
const int ir0 = dr*ith;
|
||||
const int ir1 = MIN(ir0 + dr, nr);
|
||||
|
||||
for (int64_t i = ir0; i < ir1; ++i) {
|
||||
const int64_t i12 = i/(ne11*ne10);
|
||||
const int64_t i11 = (i - i12*ne11*ne10)/ne10;
|
||||
const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10);
|
||||
const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
|
||||
|
||||
ggml_vec_cpy_f32(nc,
|
||||
(float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3),
|
||||
(float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03));
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -17796,7 +17814,7 @@ static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const
|
|||
node->perf_time_us += time_us_cur;
|
||||
}
|
||||
|
||||
static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
|
||||
static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_threads) {
|
||||
int n_tasks = 0;
|
||||
|
||||
switch (node->op) {
|
||||
|
|
@ -17877,6 +17895,12 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
|
|||
{
|
||||
n_tasks = n_threads;
|
||||
} break;
|
||||
case GGML_OP_GET_ROWS:
|
||||
{
|
||||
// FIXME: the cost of launching additional threads decreases performance with GPU offloading
|
||||
//n_tasks = MIN(n_threads, ggml_nelements(node->src[1]));
|
||||
n_tasks = MIN(n_cur_threads, ggml_nelements(node->src[1]));
|
||||
} break;
|
||||
case GGML_OP_SCALE:
|
||||
case GGML_OP_SET:
|
||||
case GGML_OP_CONT:
|
||||
|
|
@ -17884,7 +17908,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
|
|||
case GGML_OP_VIEW:
|
||||
case GGML_OP_PERMUTE:
|
||||
case GGML_OP_TRANSPOSE:
|
||||
case GGML_OP_GET_ROWS:
|
||||
case GGML_OP_GET_ROWS_BACK:
|
||||
case GGML_OP_DIAG:
|
||||
{
|
||||
|
|
@ -18102,7 +18125,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
|
|||
/* FINALIZE */
|
||||
struct ggml_tensor * node = cgraph->nodes[node_n];
|
||||
if (GGML_OP_HAS_FINALIZE[node->op]) {
|
||||
params.nth = ggml_get_n_tasks(node, n_threads);
|
||||
params.nth = ggml_get_n_tasks(node, n_threads, state->shared->n_threads);
|
||||
ggml_compute_forward(¶ms, node);
|
||||
}
|
||||
ggml_graph_compute_perf_stats_node(node, state->shared);
|
||||
|
|
@ -18112,7 +18135,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
|
|||
while (++node_n < cgraph->n_nodes) {
|
||||
GGML_PRINT_DEBUG_5("%s: %d/%d\n", __func__, node_n, cgraph->n_nodes);
|
||||
struct ggml_tensor * node = cgraph->nodes[node_n];
|
||||
const int n_tasks = ggml_get_n_tasks(node, n_threads);
|
||||
const int n_tasks = ggml_get_n_tasks(node, n_threads, state->shared->n_threads);
|
||||
|
||||
state->shared->perf_node_start_cycles = ggml_perf_cycles();
|
||||
state->shared->perf_node_start_time_us = ggml_perf_time_us();
|
||||
|
|
@ -18160,7 +18183,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
|
|||
|
||||
/* INIT & COMPUTE */
|
||||
struct ggml_tensor * node = cgraph->nodes[node_n];
|
||||
const int n_tasks = ggml_get_n_tasks(node, n_threads);
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||||
const int n_tasks = ggml_get_n_tasks(node, n_threads, state->shared->n_threads);
|
||||
|
||||
struct ggml_compute_params params = {
|
||||
/*.type =*/ GGML_TASK_TYPE_INIT,
|
||||
|
|
@ -18225,7 +18248,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
|
|||
for (int i = 0; i < cgraph->n_nodes; i++) {
|
||||
struct ggml_tensor * node = cgraph->nodes[i];
|
||||
|
||||
const int n_tasks = ggml_get_n_tasks(node, n_threads);
|
||||
const int n_tasks = ggml_get_n_tasks(node, n_threads, 1);
|
||||
|
||||
max_tasks = MAX(max_tasks, n_tasks);
|
||||
|
||||
|
|
|
|||
9
llama.h
9
llama.h
|
|
@ -234,7 +234,8 @@ extern "C" {
|
|||
struct llama_context_params {
|
||||
uint32_t seed; // RNG seed, -1 for random
|
||||
uint32_t n_ctx; // text context, 0 = from model
|
||||
uint32_t n_batch; // prompt processing maximum batch size
|
||||
uint32_t n_batch; // logical maximum batch size that can be submitted to llama_decode
|
||||
uint32_t n_ubatch; // physical maximum batch size
|
||||
uint32_t n_seq_max; // max number of sequences (i.e. distinct states for recurrent models)
|
||||
uint32_t n_threads; // number of threads to use for generation
|
||||
uint32_t n_threads_batch; // number of threads to use for batch processing
|
||||
|
|
@ -377,6 +378,7 @@ extern "C" {
|
|||
|
||||
LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx);
|
||||
LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx);
|
||||
LLAMA_API uint32_t llama_n_ubatch (const struct llama_context * ctx);
|
||||
LLAMA_API uint32_t llama_n_seq_max (const struct llama_context * ctx);
|
||||
|
||||
LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
|
||||
|
|
@ -650,6 +652,11 @@ extern "C" {
|
|||
// Set abort callback
|
||||
LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data);
|
||||
|
||||
// Wait until all computations are finished
|
||||
// This is automatically done when using one of the functions below to obtain the computation results
|
||||
// and is not necessary to call it explicitly in most cases
|
||||
LLAMA_API void llama_synchronize(struct llama_context * ctx);
|
||||
|
||||
// Token logits obtained from the last call to llama_decode()
|
||||
// The logits for the last token are stored in the last row
|
||||
// Logits for which llama_batch.logits[i] == 0 are undefined
|
||||
|
|
|
|||
Loading…
Reference in New Issue