diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 5422dd81723f9..684b45df53660 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -778,6 +778,7 @@ jobs: cmake -S . -B build ${{ matrix.defines }} ` -DCURL_LIBRARY="$env:CURL_PATH/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:CURL_PATH/include" cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} + cp $env:CURL_PATH/bin/libcurl-*.dll build/bin/Release - name: Add libopenblas.dll id: add_libopenblas_dll diff --git a/ci/run.sh b/ci/run.sh index 2968a7dd48d42..94005570511b6 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -39,7 +39,7 @@ sd=`dirname $0` cd $sd/../ SRC=`pwd` -CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=OFF" +CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=ON" if [ ! -z ${GG_BUILD_METAL} ]; then CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON -DGGML_METAL_USE_BF16=ON" diff --git a/common/common.cpp b/common/common.cpp index 5b465150f0533..eb80cee0894a6 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -767,6 +767,9 @@ bool fs_validate_filename(const std::string & filename) { return true; } +#include + + // returns true if successful, false otherwise bool fs_create_directory_with_parents(const std::string & path) { #ifdef _WIN32 @@ -784,9 +787,16 @@ bool fs_create_directory_with_parents(const std::string & path) { // process path from front to back, procedurally creating directories while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) { const std::wstring subpath = wpath.substr(0, pos_slash); - const wchar_t * test = subpath.c_str(); - const bool success = CreateDirectoryW(test, NULL); + pos_slash += 1; + + // skip the drive letter, in some systems it can return an access denied error + if (subpath.length() == 2 && subpath[1] == ':') { + continue; + } + + const bool success = CreateDirectoryW(subpath.c_str(), NULL); + if (!success) { const DWORD error = GetLastError(); @@ -800,8 +810,6 @@ bool fs_create_directory_with_parents(const std::string & path) { return false; } } - - pos_slash += 1; } return true; diff --git a/ggml/src/ggml-cpu/ggml-cpu-impl.h b/ggml/src/ggml-cpu/ggml-cpu-impl.h index 9662e4d7b5a6a..ae68cd006336d 100644 --- a/ggml/src/ggml-cpu/ggml-cpu-impl.h +++ b/ggml/src/ggml-cpu/ggml-cpu-impl.h @@ -503,6 +503,9 @@ static __m256 __lasx_xvreplfr2vr_s(const float val) { // TODO: move to ggml-threading void ggml_barrier(struct ggml_threadpool * tp); +void ggml_threadpool_chunk_set(struct ggml_threadpool * tp, int value); +int ggml_threadpool_chunk_add(struct ggml_threadpool * tp, int value); + #ifdef __cplusplus } #endif diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index ff28bf98bc7df..2c12e493bc9b0 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -559,6 +559,14 @@ void ggml_barrier(struct ggml_threadpool * tp) { #endif } +void ggml_threadpool_chunk_set(struct ggml_threadpool * tp, int value) { + atomic_store_explicit(&tp->current_chunk, value, memory_order_relaxed); +} + +int ggml_threadpool_chunk_add(struct ggml_threadpool * tp, int value) { + return atomic_fetch_add_explicit(&tp->current_chunk, value, memory_order_relaxed); +} + #if defined(__gnu_linux__) static cpu_set_t ggml_get_numa_affinity(void) { cpu_set_t cpuset; diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp index 1d46158f928c4..1c545f803327b 100644 --- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp +++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp @@ -53,7 +53,6 @@ #include "ggml-cpu-impl.h" #include "ggml-quants.h" -#include #include #include @@ -394,8 +393,6 @@ class tinyBLAS { template NOINLINE void gemm(int64_t m, int64_t n, int64_t BN) { - static std::atomic current_chunk; - GGML_ASSERT(m % (RM * BM) == 0); const int64_t ytiles = m / (RM * BM); const int64_t xtiles = (n + RN -1) / RN; @@ -410,7 +407,7 @@ class tinyBLAS { if (params->ith == 0) { GGML_ASSERT( jj_BN * SIZE_BN + (NB_BN - jj_BN) * (SIZE_BN - 1) == xtiles); // Every thread starts at ith, so the first unprocessed chunk is nth. This save a bit of coordination right at the start. - std::atomic_store_explicit(¤t_chunk, (int64_t)params->nth, std::memory_order_relaxed); + ggml_threadpool_chunk_set(params->threadpool, params->nth); } ggml_barrier(params->threadpool); @@ -439,8 +436,7 @@ class tinyBLAS { GGML_ASSERT(jj == jj2); } - // next step. - job = std::atomic_fetch_add_explicit(¤t_chunk, (int64_t)1, std::memory_order_relaxed); + job = ggml_threadpool_chunk_add(params->threadpool, 1); } ggml_barrier(params->threadpool); diff --git a/src/llama.cpp b/src/llama.cpp index 2f06e0f8ce12d..34906cdb62844 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -198,14 +198,18 @@ static struct llama_model * llama_model_load_from_file_impl( // if using single GPU mode, remove all except the main GPU if (params.split_mode == LLAMA_SPLIT_MODE_NONE) { - if (params.main_gpu < 0 || params.main_gpu >= (int)model->devices.size()) { - LLAMA_LOG_ERROR("%s: invalid value for main_gpu: %d (available devices: %d)\n", __func__, params.main_gpu, (int)model->devices.size()); - llama_model_free(model); - return nullptr; + if (params.main_gpu < 0) { + model->devices.clear(); + } else { + if (params.main_gpu >= (int)model->devices.size()) { + LLAMA_LOG_ERROR("%s: invalid value for main_gpu: %d (available devices: %zu)\n", __func__, params.main_gpu, model->devices.size()); + llama_model_free(model); + return nullptr; + } + ggml_backend_dev_t main_gpu = model->devices[params.main_gpu]; + model->devices.clear(); + model->devices.push_back(main_gpu); } - ggml_backend_dev_t main_gpu = model->devices[params.main_gpu]; - model->devices.clear(); - model->devices.push_back(main_gpu); } for (auto * dev : model->devices) { diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index db4b2cf65cc43..fc1557a2d4065 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -185,6 +185,8 @@ llama_build_and_test(test-json-partial.cpp) llama_build_and_test(test-log.cpp) llama_build_and_test(test-regex-partial.cpp) +llama_build_and_test(test-thread-safety.cpp ARGS -hf ggml-org/models -hff tinyllamas/stories15M-q4_0.gguf -ngl 99 -p "The meaning of life is" -n 128 -c 256 -ub 32 -np 4) + # this fails on windows (github hosted runner) due to curl DLL not found (exit code 0xc0000135) if (NOT WIN32) llama_build_and_test(test-arg-parser.cpp) diff --git a/tests/test-thread-safety.cpp b/tests/test-thread-safety.cpp new file mode 100644 index 0000000000000..d525b7430f9d9 --- /dev/null +++ b/tests/test-thread-safety.cpp @@ -0,0 +1,152 @@ +// thread safety test +// - Loads a copy of the same model on each GPU, plus a copy on the CPU +// - Creates n_parallel (--parallel) contexts per model +// - Runs inference in parallel on each context + +#include +#include +#include +#include "llama.h" +#include "arg.h" +#include "common.h" +#include "log.h" +#include "sampling.h" + +int main(int argc, char ** argv) { + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) { + return 1; + } + + common_init(); + + llama_backend_init(); + llama_numa_init(params.numa); + + LOG_INF("%s\n", common_params_get_system_info(params).c_str()); + + //llama_log_set([](ggml_log_level level, const char * text, void * /*user_data*/) { + // if (level == GGML_LOG_LEVEL_ERROR) { + // common_log_add(common_log_main(), level, "%s", text); + // } + //}, NULL); + + auto cparams = common_context_params_to_llama(params); + + int dev_count = ggml_backend_dev_count(); + int gpu_dev_count = 0; + for (int i = 0; i < dev_count; ++i) { + auto * dev = ggml_backend_dev_get(i); + if (dev && ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_GPU) { + gpu_dev_count++; + } + } + const int num_models = gpu_dev_count + 1 + 1; // GPUs + 1 CPU model + 1 layer split + //const int num_models = std::max(1, gpu_dev_count); + const int num_contexts = std::max(1, params.n_parallel); + + std::vector models; + std::vector threads; + std::atomic failed = false; + + for (int m = 0; m < num_models; ++m) { + auto mparams = common_model_params_to_llama(params); + + if (m < gpu_dev_count) { + mparams.split_mode = LLAMA_SPLIT_MODE_NONE; + mparams.main_gpu = m; + } else if (m == gpu_dev_count) { + mparams.split_mode = LLAMA_SPLIT_MODE_NONE; + mparams.main_gpu = -1; // CPU model + } else { + mparams.split_mode = LLAMA_SPLIT_MODE_LAYER;; + } + + llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams); + if (model == NULL) { + LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.path.c_str()); + return 1; + } + + models.emplace_back(model); + } + + for (int m = 0; m < num_models; ++m) { + auto * model = models[m].get(); + for (int c = 0; c < num_contexts; ++c) { + threads.emplace_back([&, m, c, model]() { + LOG_INF("Creating context %d/%d for model %d/%d\n", c + 1, num_contexts, m + 1, num_models); + + llama_context_ptr ctx { llama_init_from_model(model, cparams) }; + if (ctx == NULL) { + LOG_ERR("failed to create context\n"); + failed.store(true); + return; + } + + std::unique_ptr sampler { common_sampler_init(model, params.sampling), common_sampler_free }; + if (sampler == NULL) { + LOG_ERR("failed to create sampler\n"); + failed.store(true); + return; + } + + llama_batch batch = {}; + { + auto prompt = common_tokenize(ctx.get(), params.prompt, true); + if (prompt.empty()) { + LOG_ERR("failed to tokenize prompt\n"); + failed.store(true); + return; + } + batch = llama_batch_get_one(prompt.data(), prompt.size()); + if (llama_decode(ctx.get(), batch)) { + LOG_ERR("failed to decode prompt\n"); + failed.store(true); + return; + } + } + + const auto * vocab = llama_model_get_vocab(model); + std::string result = params.prompt; + + for (int i = 0; i < params.n_predict; i++) { + llama_token token; + if (batch.n_tokens > 0) { + token = common_sampler_sample(sampler.get(), ctx.get(), batch.n_tokens - 1); + } else { + token = llama_vocab_bos(vocab); + } + + result += common_token_to_piece(ctx.get(), token); + + if (llama_vocab_is_eog(vocab, token)) { + break; + } + + batch = llama_batch_get_one(&token, 1); + if (llama_decode(ctx.get(), batch)) { + LOG_ERR("Model %d/%d, Context %d/%d: failed to decode\n", m + 1, num_models, c + 1, num_contexts); + failed.store(true); + return; + } + } + + LOG_INF("Model %d/%d, Context %d/%d: %s\n\n", m + 1, num_models, c + 1, num_contexts, result.c_str()); + }); + } + } + + for (auto & thread : threads) { + thread.join(); + } + + if (failed) { + LOG_ERR("One or more threads failed.\n"); + return 1; + } + + LOG_INF("All threads finished without errors.\n"); + return 0; +}