mirror of
https://github.com/ggerganov/llama.cpp.git
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ggml : synchronize threads using barriers (#7993)
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slaren
GitHub
parent
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commit
9c77ec1d74
14
.github/workflows/server.yml
vendored
14
.github/workflows/server.yml
vendored
@ -87,8 +87,22 @@ jobs:
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exit 1
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fi
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- name: Build (no OpenMP)
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id: cmake_build_no_openmp
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if: ${{ matrix.sanitizer == 'THREAD' }}
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run: |
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cmake -B build \
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-DLLAMA_NATIVE=OFF \
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-DLLAMA_BUILD_SERVER=ON \
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-DLLAMA_CURL=ON \
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-DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
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-DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
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-DLLAMA_OPENMP=OFF ;
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cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
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- name: Build
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id: cmake_build
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if: ${{ matrix.sanitizer != 'THREAD' }}
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run: |
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cmake -B build \
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-DLLAMA_NATIVE=OFF \
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221
ggml.c
221
ggml.c
@ -1753,9 +1753,8 @@ struct ggml_compute_state_shared {
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int n_threads;
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// synchronization primitives
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atomic_int n_active; // num active threads
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atomic_int node_n; // active graph node
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atomic_int node_task; // active graph node task phase
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atomic_int n_barrier;
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atomic_int n_barrier_passed;
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ggml_abort_callback abort_callback; // abort ggml_graph_compute when true
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void* abort_callback_data;
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@ -18972,47 +18971,49 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_
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return n_tasks;
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}
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static void ggml_graph_compute_thread_sync_node(int * node_n, struct ggml_compute_state * state, const bool do_yield) {
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// wait for other threads to finish
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const int last_node_n = * node_n;
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#ifdef GGML_USE_OPENMP
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static void ggml_barrier(struct ggml_compute_state * state) {
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if (state->shared->n_threads == 1) {
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return;
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}
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while (true) {
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if (do_yield) {
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#pragma omp barrier
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}
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#else
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static void ggml_barrier(struct ggml_compute_state * state) {
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if (state->shared->n_threads == 1) {
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return;
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}
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atomic_int * n_barrier = &state->shared->n_barrier;
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atomic_int * n_barrier_passed = &state->shared->n_barrier_passed;
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int n_threads = state->shared->n_threads;
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int passed_old = atomic_load(n_barrier_passed);
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if (atomic_fetch_add(n_barrier, 1) == n_threads - 1) {
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// last thread
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atomic_store(n_barrier, 0);
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atomic_fetch_add(n_barrier_passed, 1);
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} else {
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// wait for other threads
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//while (atomic_load(n_barrier_passed) == passed_old) {
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//}
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const int n_spin_before_sleep = 100000;
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while (true) {
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for (int i = 0; i < n_spin_before_sleep; i++) {
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if (atomic_load(n_barrier_passed) != passed_old) {
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return;
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}
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#if defined(__SSE3__)
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_mm_pause();
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#endif
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}
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sched_yield();
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}
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*node_n = atomic_load(&state->shared->node_n);
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if (*node_n != last_node_n) {
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break;
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}
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#if defined(__SSE3__)
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// Tell the processor we're spinning. It's a processor hint for spinlocks.
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_mm_pause();
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#endif
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}
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}
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static void ggml_graph_compute_thread_sync_task(int * task_phase, struct ggml_compute_state * state, const bool do_yield) {
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// wait for other threads to finish
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const int last_task_phase = *task_phase;
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while (true) {
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if (do_yield) {
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sched_yield();
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}
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*task_phase = atomic_load(&state->shared->node_task);
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if (*task_phase != last_task_phase) {
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break;
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}
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#if defined(__SSE3__)
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// Tell the processor we're spinning. It's a processor hint for spinlocks.
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_mm_pause();
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#endif
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}
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}
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static thread_ret_t ggml_graph_compute_thread(void * data) {
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struct ggml_compute_state * state = (struct ggml_compute_state *) data;
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@ -19020,136 +19021,54 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
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const struct ggml_cgraph * cgraph = state->shared->cgraph;
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const struct ggml_cplan * cplan = state->shared->cplan;
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const int n_threads = state->shared->n_threads;
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const int ith = state->ith;
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const int n_threads = state->shared->n_threads;
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set_numa_thread_affinity(state->ith);
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set_numa_thread_affinity(ith);
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int node_n = -1;
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int task_phase = GGML_TASK_TYPE_FINALIZE;
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struct ggml_compute_params params = {
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/*.type =*/ GGML_TASK_TYPE_INIT,
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/*.ith =*/ ith,
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/*.nth =*/ state->shared->n_threads,
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/*.wsize =*/ cplan->work_size,
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/*.wdata =*/ cplan->work_data,
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};
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while (true) {
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for (int node_n = 0; node_n < cgraph->n_nodes; node_n++) {
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if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) {
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state->shared->node_n += 1;
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state->ec = GGML_STATUS_ABORTED;
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return 0;
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}
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if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
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// all other threads are finished and spinning
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// do finalize and init here so we don't have synchronize again
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struct ggml_compute_params params = {
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/*.type =*/ GGML_TASK_TYPE_FINALIZE,
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/*.ith =*/ 0,
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/*.nth =*/ 0,
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/*.wsize =*/ cplan->work_size,
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/*.wdata =*/ cplan->work_data,
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};
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if (node_n != -1) {
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/* FINALIZE */
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struct ggml_tensor * node = cgraph->nodes[node_n];
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if (GGML_OP_HAS_FINALIZE[node->op]) {
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params.nth = ggml_get_n_tasks(node, n_threads, state->shared->n_threads);
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ggml_compute_forward(¶ms, node, state);
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}
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ggml_graph_compute_perf_stats_node(node, state->shared);
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}
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// distribute new work or execute it direct if 1T
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while (++node_n < cgraph->n_nodes) {
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GGML_PRINT_DEBUG_5("%s: %d/%d\n", __func__, node_n, cgraph->n_nodes);
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struct ggml_tensor * node = cgraph->nodes[node_n];
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const int n_tasks = ggml_get_n_tasks(node, n_threads, state->shared->n_threads);
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state->shared->perf_node_start_cycles = ggml_perf_cycles();
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state->shared->perf_node_start_time_us = ggml_perf_time_us();
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params.nth = n_tasks;
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if (n_tasks == 1) {
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/* INIT */
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if (GGML_OP_HAS_INIT[node->op]) {
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params.type = GGML_TASK_TYPE_INIT;
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ggml_compute_forward(¶ms, node, state);
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}
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// TODO: maybe push node_n to the atomic but if other threads see n_tasks is 1,
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// they do something more efficient than spinning (?)
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params.type = GGML_TASK_TYPE_COMPUTE;
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ggml_compute_forward(¶ms, node, state);
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if (GGML_OP_HAS_FINALIZE[node->op]) {
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params.type = GGML_TASK_TYPE_FINALIZE;
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ggml_compute_forward(¶ms, node, state);
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}
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ggml_graph_compute_perf_stats_node(node, state->shared);
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} else {
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break;
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}
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if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) {
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break;
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}
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}
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task_phase = GGML_TASK_TYPE_INIT;
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atomic_store(&state->shared->n_active, n_threads);
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atomic_store(&state->shared->node_n, node_n);
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atomic_store(&state->shared->node_task, task_phase);
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} else {
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ggml_graph_compute_thread_sync_node(&node_n, state, false);
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ggml_graph_compute_thread_sync_task(&task_phase, state, false);
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}
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// check if we should stop
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if (node_n >= cgraph->n_nodes) break;
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/* INIT & COMPUTE */
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struct ggml_tensor * node = cgraph->nodes[node_n];
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const int n_tasks = ggml_get_n_tasks(node, n_threads, state->shared->n_threads);
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struct ggml_compute_params params = {
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/*.type =*/ GGML_TASK_TYPE_INIT,
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/*.ith =*/ state->ith,
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/*.nth =*/ n_tasks,
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/*.wsize =*/ cplan->work_size,
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/*.wdata =*/ cplan->work_data,
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};
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params.nth = n_tasks;
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if (state->ith < n_tasks) {
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if (GGML_OP_HAS_INIT[node->op]) {
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/* INIT */
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if (GGML_OP_HAS_INIT[node->op]) {
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if (ith < n_tasks) {
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params.type = GGML_TASK_TYPE_INIT;
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ggml_compute_forward(¶ms, node, state);
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}
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ggml_barrier(state);
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}
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if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
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task_phase = GGML_TASK_TYPE_COMPUTE;
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atomic_store(&state->shared->n_active, n_threads);
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atomic_store(&state->shared->node_task, task_phase);
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}
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else {
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// TODO: this sched_yield can have significant impact on the performance - either positive or negative
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// depending on the workload and the operating system.
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// since it is not clear what is the best approach, it should potentially become user-configurable
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// ref: https://github.com/ggerganov/ggml/issues/291
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// UPD: adding the do_yield flag seems to resolve the issue universally
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const bool do_yield = node_n < 0 || cgraph->nodes[node_n]->op == GGML_OP_MUL_MAT;
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ggml_graph_compute_thread_sync_task(&task_phase, state, do_yield);
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}
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if (state->ith < n_tasks) {
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/* COMPUTE */
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if (ith < n_tasks) {
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params.type = GGML_TASK_TYPE_COMPUTE;
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ggml_compute_forward(¶ms, node, state);
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}
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if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
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task_phase = GGML_TASK_TYPE_FINALIZE;
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atomic_store(&state->shared->n_active, n_threads);
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atomic_store(&state->shared->node_task, task_phase);
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}
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else {
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ggml_graph_compute_thread_sync_task(&task_phase, state, false);
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ggml_barrier(state);
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/* FINALIZE */
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if (GGML_OP_HAS_FINALIZE[node->op]) {
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if (params.ith == 0) {
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params.type = GGML_TASK_TYPE_FINALIZE;
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ggml_compute_forward(¶ms, node, state);
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}
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ggml_barrier(state);
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}
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}
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@ -19336,7 +19255,6 @@ static enum ggml_status ggml_graph_compute_parallel(struct ggml_compute_state *
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// update the number of threads from the actual number of threads that we got from OpenMP
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n_threads = omp_get_num_threads();
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workers[0].shared->n_threads = n_threads;
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workers[0].shared->n_active = n_threads;
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}
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ggml_graph_compute_thread(&workers[omp_get_thread_num()]);
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}
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@ -19399,9 +19317,8 @@ enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cpl
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/*.perf_node_start_cycles =*/ 0,
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/*.perf_node_start_time_us =*/ 0,
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/*.n_threads =*/ n_threads,
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/*.n_active =*/ n_threads,
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/*.node_n =*/ -1,
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/*.node_task =*/ GGML_TASK_TYPE_FINALIZE,
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/*.n_barrier =*/ 0,
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/*.n_barrier_passed =*/ 0,
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/*.abort_callback =*/ NULL,
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/*.abort_callback_data =*/ NULL,
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/*.current_chunk; =*/ 0,
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