llama : add thread safety test

.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

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"

common/common.cpp

@@ -767,6 +767,9 @@ bool fs_validate_filename(const std::string & filename) {
     return true;
 }
 
+#include <iostream>
+
+
 // 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;

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

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;

ggml/src/ggml-cpu/llamafile/sgemm.cpp

@@ -53,7 +53,6 @@
 #include "ggml-cpu-impl.h"
 #include "ggml-quants.h"
 
-#include <atomic>
 #include <array>
 #include <type_traits>
 
@@ -394,8 +393,6 @@ class tinyBLAS {
 
     template <int RM, int RN, int BM>
     NOINLINE void gemm(int64_t m, int64_t n, int64_t BN) {
-        static std::atomic<int64_t> 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(&current_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(&current_chunk, (int64_t)1, std::memory_order_relaxed);
+            job = ggml_threadpool_chunk_add(params->threadpool, 1);
         }
 
         ggml_barrier(params->threadpool);

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) {

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)

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 <thread>
+#include <vector>
+#include <atomic>
+#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<llama_model_ptr> models;
+    std::vector<std::thread> threads;
+    std::atomic<bool> 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<common_sampler, decltype(&common_sampler_free)> 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;
+}