mirror of
https://github.com/ggerganov/llama.cpp.git
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cuda : CUDA Graph Compute Function Refactor (precursor for performance improvements) (#11042)
* Refactor: Moves cuda graph executable update step to separate function. * Refactor: Moves cuda graph update check to separate function. * Refactor: Moves cuda graph maintenance (update or adjusting copy parameters) to separate function for improved readability. * Fix: Adds missing reference to maintain_cuda_graph() definition. * Refactor: Improves structure and abstractions by moving CUDA graph evaluation and capture to its own function. * Refactor: Moves node graph checks and copy ops into individual function for improved readability. * Refactor: Removes code permanently excluded from compilation to increase readability. * Style: Adds missing newline * Style: Consolidates several neighboring '#ifdef USE_CUDA_GRAPH' into a single one * Refactor: Makes 'cuda_graph_update_required' a local variable * remove double lines between functions --------- Co-authored-by: slaren <slarengh@gmail.com>
This commit is contained in:
Andreas Kieslinger
GitHub
parent
a29f0870d4
commit
39509fb082
@ -2289,119 +2289,8 @@ static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
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}
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}
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#ifdef USE_CUDA_GRAPH
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#ifdef USE_CUDA_GRAPH
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static void set_ggml_graph_node_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
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static bool check_node_graph_compatibility_and_refresh_copy_ops(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph,
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graph_node_properties->node_address = node->data;
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std::vector<void *> & ggml_cuda_cpy_fn_ptrs, bool use_cuda_graph) {
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graph_node_properties->node_op = node->op;
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for (int i = 0; i < GGML_MAX_DIMS; i++) {
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graph_node_properties->ne[i] = node->ne[i];
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graph_node_properties->nb[i] = node->nb[i];
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}
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for (int i = 0; i < GGML_MAX_SRC; i++) {
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graph_node_properties->src_address[i] = node->src[i] ? node->src[i]->data : nullptr;
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}
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memcpy(graph_node_properties->op_params, node->op_params, GGML_MAX_OP_PARAMS);
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}
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static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
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if (node->data != graph_node_properties->node_address &&
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node->op != GGML_OP_CPY &&
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node->op != GGML_OP_VIEW) {
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return false;
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}
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if (node->op != graph_node_properties->node_op) {
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return false;
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}
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for (int i = 0; i < GGML_MAX_DIMS; i++) {
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if (node->ne[i] != graph_node_properties->ne[i]) {
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return false;
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}
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if (node->nb[i] != graph_node_properties->nb[i]) {
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return false;
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}
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}
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for (int i = 0; i < GGML_MAX_SRC; i++) {
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if (node->src[i] &&
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node->src[i]->data != graph_node_properties->src_address[i] &&
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node->op != GGML_OP_CPY &&
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node->op != GGML_OP_VIEW
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) {
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return false;
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}
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}
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if (node->op == GGML_OP_SCALE &&
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memcmp(graph_node_properties->op_params, node->op_params, GGML_MAX_OP_PARAMS) != 0) {
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return false;
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}
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return true;
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}
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#endif
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static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
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ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
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ggml_cuda_set_device(cuda_ctx->device);
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#ifdef USE_CUDA_GRAPH
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static const bool disable_cuda_graphs_due_to_env = (getenv("GGML_CUDA_DISABLE_GRAPHS") != nullptr);
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// Objects required for CUDA Graph
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if (cuda_ctx->cuda_graph == nullptr) {
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cuda_ctx->cuda_graph.reset(new ggml_cuda_graph());
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}
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bool use_cuda_graph = true;
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bool cuda_graph_update_required = false;
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// vector of pointers to CUDA cpy kernels, which are required to identify
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// kernel parameters which need updated in the graph for each token
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std::vector<void *> ggml_cuda_cpy_fn_ptrs;
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if (cuda_ctx->cuda_graph->graph == nullptr) {
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if (ggml_cuda_info().devices[cuda_ctx->device].cc < GGML_CUDA_CC_AMPERE) {
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cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true;
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#ifndef NDEBUG
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GGML_LOG_DEBUG("%s: disabling CUDA graphs due to GPU architecture\n", __func__);
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#endif
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}
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}
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// Disable CUDA graphs in presence of env var, old GPU, use-case which is changing too rapidly,
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// or previous graph capture failure.
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// Also disable for multi-gpu for now. TO DO investigate
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if (disable_cuda_graphs_due_to_env
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|| cuda_ctx->cuda_graph->disable_due_to_gpu_arch
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|| cuda_ctx->cuda_graph->disable_due_to_too_many_updates
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|| cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture) {
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use_cuda_graph = false;
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}
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if (use_cuda_graph) {
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if (cuda_ctx->cuda_graph->instance == nullptr) {
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cuda_graph_update_required = true;
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}
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// Check if the graph size has changed
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if (cuda_ctx->cuda_graph->ggml_graph_properties.size() != (size_t)cgraph->n_nodes) {
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cuda_graph_update_required = true;
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cuda_ctx->cuda_graph->ggml_graph_properties.resize(cgraph->n_nodes);
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}
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// Loop over nodes in GGML graph to determine if CUDA graph update is required
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// and store properties to allow this comparison for the next token
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for (int i = 0; i < cgraph->n_nodes; i++) {
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bool has_matching_properties = true;
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if (!cuda_graph_update_required) {
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has_matching_properties = ggml_graph_node_has_matching_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]);
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}
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if (!has_matching_properties) {
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cuda_graph_update_required = true;
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}
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set_ggml_graph_node_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]);
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}
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// Loop over nodes in GGML graph to obtain info needed for CUDA graph
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// Loop over nodes in GGML graph to obtain info needed for CUDA graph
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cuda_ctx->cuda_graph->updated_kernel_arg.clear();
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cuda_ctx->cuda_graph->updated_kernel_arg.clear();
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@ -2457,31 +2346,158 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
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}
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}
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}
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}
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// Disable CUDA graphs (from the next token) if the use-case is demanding too many consecutive graph updates.
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return use_cuda_graph;
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if (use_cuda_graph && cuda_graph_update_required) {
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}
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cuda_ctx->cuda_graph->number_consecutive_updates++;
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static void set_ggml_graph_node_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
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graph_node_properties->node_address = node->data;
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graph_node_properties->node_op = node->op;
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for (int i = 0; i < GGML_MAX_DIMS; i++) {
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graph_node_properties->ne[i] = node->ne[i];
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graph_node_properties->nb[i] = node->nb[i];
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}
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for (int i = 0; i < GGML_MAX_SRC; i++) {
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graph_node_properties->src_address[i] = node->src[i] ? node->src[i]->data : nullptr;
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}
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memcpy(graph_node_properties->op_params, node->op_params, GGML_MAX_OP_PARAMS);
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}
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static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
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if (node->data != graph_node_properties->node_address &&
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node->op != GGML_OP_CPY &&
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node->op != GGML_OP_VIEW) {
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return false;
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}
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if (node->op != graph_node_properties->node_op) {
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return false;
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}
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for (int i = 0; i < GGML_MAX_DIMS; i++) {
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if (node->ne[i] != graph_node_properties->ne[i]) {
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return false;
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}
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if (node->nb[i] != graph_node_properties->nb[i]) {
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return false;
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}
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}
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for (int i = 0; i < GGML_MAX_SRC; i++) {
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if (node->src[i] &&
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node->src[i]->data != graph_node_properties->src_address[i] &&
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node->op != GGML_OP_CPY &&
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node->op != GGML_OP_VIEW
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) {
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return false;
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}
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}
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if (node->op == GGML_OP_SCALE &&
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memcmp(graph_node_properties->op_params, node->op_params, GGML_MAX_OP_PARAMS) != 0) {
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return false;
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}
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return true;
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}
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static void maintain_cuda_graph(ggml_backend_cuda_context * cuda_ctx, std::vector<void *> & ggml_cuda_cpy_fn_ptrs, bool cuda_graph_update_required) {
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if (cuda_graph_update_required) {
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// Extract nodes from graph
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// First call with null argument gets number of nodes in graph
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CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, nullptr, &cuda_ctx->cuda_graph->num_nodes));
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// Subsequent call with non-null argument gets nodes
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cuda_ctx->cuda_graph->nodes.clear();
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cuda_ctx->cuda_graph->nodes.resize(cuda_ctx->cuda_graph->num_nodes);
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cuda_ctx->cuda_graph->params.clear();
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cuda_ctx->cuda_graph->params.resize(cuda_ctx->cuda_graph->num_nodes);
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if (cuda_ctx->cuda_graph->num_nodes > 0) {
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CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, cuda_ctx->cuda_graph->nodes.data(), &cuda_ctx->cuda_graph->num_nodes));
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// Loop over nodes, and extract kernel parameters from each node
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for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
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cudaGraphNodeType node_type;
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CUDA_CHECK(cudaGraphNodeGetType(cuda_ctx->cuda_graph->nodes[i], &node_type));
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if (node_type == cudaGraphNodeTypeKernel) {
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cudaError_t stat = cudaGraphKernelNodeGetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]); // Get params using runtime
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if (stat == cudaErrorInvalidDeviceFunction) {
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// Fails due to incorrect handling by CUDA runtime of CUDA BLAS node.
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// We don't need to update blas nodes, so clear error and move on.
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cudaGetLastError();
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} else {
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} else {
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cuda_ctx->cuda_graph->number_consecutive_updates = 0;
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GGML_ASSERT(stat == cudaSuccess);
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}
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}
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}
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}
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} else {
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// One of the arguments to the copy kernel is updated for each token, hence we need to
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// replace that argument with the updated value in the CUDA graph
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// on update steps, the live parameters will already be captured
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int k = 0;
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for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
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if(count(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), cuda_ctx->cuda_graph->params[i].func) > 0) {
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char ** updated_kernel_arg_ptr = cuda_ctx->cuda_graph->updated_kernel_arg.at(k++);
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cuda_ctx->cuda_graph->params[i].kernelParams[1] = updated_kernel_arg_ptr;
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CUDA_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]));
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}
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}
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}
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if (cuda_ctx->cuda_graph->number_consecutive_updates >= 4) {
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cuda_ctx->cuda_graph->disable_due_to_too_many_updates = true;
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#ifndef NDEBUG
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GGML_LOG_DEBUG("%s: disabling CUDA graphs due to too many consecutive updates\n", __func__);
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#endif
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}
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}
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}
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}
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if (use_cuda_graph && cuda_graph_update_required) { // Start CUDA graph capture
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static bool is_cuda_graph_update_required(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph) {
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CUDA_CHECK(cudaStreamBeginCapture(cuda_ctx->stream(), cudaStreamCaptureModeRelaxed));
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}
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#else
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bool use_cuda_graph = false;
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bool cuda_graph_update_required = false;
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bool cuda_graph_update_required = false;
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#endif // USE_CUDA_GRAPH
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bool graph_evaluated_or_captured = false;
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if (cuda_ctx->cuda_graph->instance == nullptr) {
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cuda_graph_update_required = true;
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}
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// Check if the graph size has changed
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if (cuda_ctx->cuda_graph->ggml_graph_properties.size() != (size_t)cgraph->n_nodes) {
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cuda_graph_update_required = true;
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cuda_ctx->cuda_graph->ggml_graph_properties.resize(cgraph->n_nodes);
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}
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// Loop over nodes in GGML graph to determine if CUDA graph update is required
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// and store properties to allow this comparison for the next token
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for (int i = 0; i < cgraph->n_nodes; i++) {
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bool has_matching_properties = true;
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if (!cuda_graph_update_required) {
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has_matching_properties = ggml_graph_node_has_matching_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]);
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}
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if (!has_matching_properties) {
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cuda_graph_update_required = true;
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}
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set_ggml_graph_node_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]);
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}
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return cuda_graph_update_required;
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}
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static void update_cuda_graph_executable(ggml_backend_cuda_context * cuda_ctx) {
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cudaGraphExecUpdateResultInfo result_info;
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cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info);
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if (stat == cudaErrorGraphExecUpdateFailure) {
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#ifndef NDEBUG
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GGML_LOG_DEBUG("%s: CUDA graph update failed\n", __func__);
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#endif
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// The pre-existing graph exec cannot be updated due to violated constraints
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// so instead clear error and re-instantiate
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cudaGetLastError();
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CUDA_CHECK(cudaGraphExecDestroy(cuda_ctx->cuda_graph->instance));
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cuda_ctx->cuda_graph->instance = nullptr;
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CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0));
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} else {
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GGML_ASSERT(stat == cudaSuccess);
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}
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}
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#endif
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static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph,
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[[maybe_unused]] std::vector<void *> & ggml_cuda_cpy_fn_ptrs, bool & graph_evaluated_or_captured, bool & use_cuda_graph,
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bool & cuda_graph_update_required) {
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while (!graph_evaluated_or_captured) {
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while (!graph_evaluated_or_captured) {
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// Only perform the graph execution if CUDA graphs are not enabled, or we are capturing the graph.
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// Only perform the graph execution if CUDA graphs are not enabled, or we are capturing the graph.
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@ -2519,19 +2535,8 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
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CUDA_CHECK(cudaGraphDestroy(cuda_ctx->cuda_graph->graph));
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CUDA_CHECK(cudaGraphDestroy(cuda_ctx->cuda_graph->graph));
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cuda_ctx->cuda_graph->graph = nullptr;
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cuda_ctx->cuda_graph->graph = nullptr;
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}
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}
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CUDA_CHECK(cudaStreamEndCapture(cuda_ctx->stream(), &cuda_ctx->cuda_graph->graph));
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#if 0
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CUDA_CHECK(cudaStreamEndCapture(cuda_ctx->stream(), &cuda_ctx->cuda_graph->graph));
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if (disable_cuda_graphs_due_to_failed_capture) {
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use_cuda_graph = false;
|
|
||||||
cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture = true;
|
|
||||||
#ifndef NDEBUG
|
|
||||||
GGML_LOG_DEBUG("%s: disabling CUDA graphs due to failed graph capture\n", __func__);
|
|
||||||
#endif
|
|
||||||
} else {
|
|
||||||
graph_evaluated_or_captured = true; // CUDA graph has been captured
|
|
||||||
}
|
|
||||||
#endif
|
|
||||||
graph_evaluated_or_captured = true; // CUDA graph has been captured
|
graph_evaluated_or_captured = true; // CUDA graph has been captured
|
||||||
} else {
|
} else {
|
||||||
graph_evaluated_or_captured = true; // ggml graph has been directly evaluated
|
graph_evaluated_or_captured = true; // ggml graph has been directly evaluated
|
||||||
@ -2544,72 +2549,91 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
|
|||||||
}
|
}
|
||||||
|
|
||||||
// Perform update to graph (if required for this token), and change copy parameter (required for every token)
|
// Perform update to graph (if required for this token), and change copy parameter (required for every token)
|
||||||
|
maintain_cuda_graph(cuda_ctx, ggml_cuda_cpy_fn_ptrs, cuda_graph_update_required);
|
||||||
if (cuda_graph_update_required) {
|
|
||||||
// Extract nodes from graph
|
|
||||||
// First call with null argument gets number of nodes in graph
|
|
||||||
CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, nullptr, &cuda_ctx->cuda_graph->num_nodes));
|
|
||||||
// Subsequent call with non-null argument gets nodes
|
|
||||||
cuda_ctx->cuda_graph->nodes.clear();
|
|
||||||
cuda_ctx->cuda_graph->nodes.resize(cuda_ctx->cuda_graph->num_nodes);
|
|
||||||
cuda_ctx->cuda_graph->params.clear();
|
|
||||||
cuda_ctx->cuda_graph->params.resize(cuda_ctx->cuda_graph->num_nodes);
|
|
||||||
if (cuda_ctx->cuda_graph->num_nodes > 0) {
|
|
||||||
CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, cuda_ctx->cuda_graph->nodes.data(), &cuda_ctx->cuda_graph->num_nodes));
|
|
||||||
|
|
||||||
// Loop over nodes, and extract kernel parameters from each node
|
|
||||||
for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
|
|
||||||
cudaGraphNodeType node_type;
|
|
||||||
CUDA_CHECK(cudaGraphNodeGetType(cuda_ctx->cuda_graph->nodes[i], &node_type));
|
|
||||||
if (node_type == cudaGraphNodeTypeKernel) {
|
|
||||||
cudaError_t stat = cudaGraphKernelNodeGetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]); // Get params using runtime
|
|
||||||
if (stat == cudaErrorInvalidDeviceFunction) {
|
|
||||||
// Fails due to incorrect handling by CUDA runtime of CUDA BLAS node.
|
|
||||||
// We don't need to update blas nodes, so clear error and move on.
|
|
||||||
cudaGetLastError();
|
|
||||||
} else {
|
|
||||||
GGML_ASSERT(stat == cudaSuccess);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// One of the arguments to the copy kernel is updated for each token, hence we need to
|
|
||||||
// replace that argument with the updated value in the CUDA graph
|
|
||||||
if (!cuda_graph_update_required) { // on update steps, the live parameters will already be captured
|
|
||||||
int k = 0;
|
|
||||||
for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
|
|
||||||
if(count(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), cuda_ctx->cuda_graph->params[i].func) > 0) {
|
|
||||||
char ** updated_kernel_arg_ptr = cuda_ctx->cuda_graph->updated_kernel_arg.at(k++);
|
|
||||||
cuda_ctx->cuda_graph->params[i].kernelParams[1] = updated_kernel_arg_ptr;
|
|
||||||
CUDA_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]));
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// Update graph executable
|
// Update graph executable
|
||||||
cudaGraphExecUpdateResultInfo result_info;
|
update_cuda_graph_executable(cuda_ctx);
|
||||||
cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info);
|
|
||||||
if (stat == cudaErrorGraphExecUpdateFailure) {
|
|
||||||
#ifndef NDEBUG
|
|
||||||
GGML_LOG_DEBUG("%s: CUDA graph update failed\n", __func__);
|
|
||||||
#endif
|
|
||||||
// The pre-existing graph exec cannot be updated due to violated constraints
|
|
||||||
// so instead clear error and re-instantiate
|
|
||||||
cudaGetLastError();
|
|
||||||
CUDA_CHECK(cudaGraphExecDestroy(cuda_ctx->cuda_graph->instance));
|
|
||||||
cuda_ctx->cuda_graph->instance = nullptr;
|
|
||||||
CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0));
|
|
||||||
} else {
|
|
||||||
GGML_ASSERT(stat == cudaSuccess);
|
|
||||||
}
|
|
||||||
// Launch graph
|
// Launch graph
|
||||||
CUDA_CHECK(cudaGraphLaunch(cuda_ctx->cuda_graph->instance, cuda_ctx->stream()));
|
CUDA_CHECK(cudaGraphLaunch(cuda_ctx->cuda_graph->instance, cuda_ctx->stream()));
|
||||||
#else
|
#else
|
||||||
graph_evaluated_or_captured = true;
|
graph_evaluated_or_captured = true;
|
||||||
#endif // USE_CUDA_GRAPH
|
#endif // USE_CUDA_GRAPH
|
||||||
}
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
|
||||||
|
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||||
|
|
||||||
|
ggml_cuda_set_device(cuda_ctx->device);
|
||||||
|
|
||||||
|
// vector of pointers to CUDA cpy kernels, which are required to identify
|
||||||
|
// kernel parameters which need updated in the graph for each token
|
||||||
|
std::vector<void *> ggml_cuda_cpy_fn_ptrs;
|
||||||
|
|
||||||
|
#ifdef USE_CUDA_GRAPH
|
||||||
|
static const bool disable_cuda_graphs_due_to_env = (getenv("GGML_CUDA_DISABLE_GRAPHS") != nullptr);
|
||||||
|
|
||||||
|
// Objects required for CUDA Graph
|
||||||
|
if (cuda_ctx->cuda_graph == nullptr) {
|
||||||
|
cuda_ctx->cuda_graph.reset(new ggml_cuda_graph());
|
||||||
|
}
|
||||||
|
|
||||||
|
bool use_cuda_graph = true;
|
||||||
|
bool cuda_graph_update_required = false;
|
||||||
|
|
||||||
|
if (cuda_ctx->cuda_graph->graph == nullptr) {
|
||||||
|
if (ggml_cuda_info().devices[cuda_ctx->device].cc < GGML_CUDA_CC_AMPERE) {
|
||||||
|
cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true;
|
||||||
|
#ifndef NDEBUG
|
||||||
|
GGML_LOG_DEBUG("%s: disabling CUDA graphs due to GPU architecture\n", __func__);
|
||||||
|
#endif
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Disable CUDA graphs in presence of env var, old GPU, use-case which is changing too rapidly,
|
||||||
|
// or previous graph capture failure.
|
||||||
|
// Also disable for multi-gpu for now. TO DO investigate
|
||||||
|
if (disable_cuda_graphs_due_to_env
|
||||||
|
|| cuda_ctx->cuda_graph->disable_due_to_gpu_arch
|
||||||
|
|| cuda_ctx->cuda_graph->disable_due_to_too_many_updates
|
||||||
|
|| cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture) {
|
||||||
|
use_cuda_graph = false;
|
||||||
|
}
|
||||||
|
|
||||||
|
if (use_cuda_graph) {
|
||||||
|
cuda_graph_update_required = is_cuda_graph_update_required(cuda_ctx, cgraph);
|
||||||
|
|
||||||
|
use_cuda_graph = check_node_graph_compatibility_and_refresh_copy_ops(cuda_ctx, cgraph,
|
||||||
|
ggml_cuda_cpy_fn_ptrs, use_cuda_graph);
|
||||||
|
|
||||||
|
// Disable CUDA graphs (from the next token) if the use-case is demanding too many consecutive graph updates.
|
||||||
|
if (use_cuda_graph && cuda_graph_update_required) {
|
||||||
|
cuda_ctx->cuda_graph->number_consecutive_updates++;
|
||||||
|
} else {
|
||||||
|
cuda_ctx->cuda_graph->number_consecutive_updates = 0;
|
||||||
|
}
|
||||||
|
|
||||||
|
if (cuda_ctx->cuda_graph->number_consecutive_updates >= 4) {
|
||||||
|
cuda_ctx->cuda_graph->disable_due_to_too_many_updates = true;
|
||||||
|
#ifndef NDEBUG
|
||||||
|
GGML_LOG_DEBUG("%s: disabling CUDA graphs due to too many consecutive updates\n", __func__);
|
||||||
|
#endif
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
if (use_cuda_graph && cuda_graph_update_required) { // Start CUDA graph capture
|
||||||
|
CUDA_CHECK(cudaStreamBeginCapture(cuda_ctx->stream(), cudaStreamCaptureModeRelaxed));
|
||||||
|
}
|
||||||
|
|
||||||
|
#else
|
||||||
|
bool use_cuda_graph = false;
|
||||||
|
bool cuda_graph_update_required = false;
|
||||||
|
#endif // USE_CUDA_GRAPH
|
||||||
|
|
||||||
|
bool graph_evaluated_or_captured = false;
|
||||||
|
|
||||||
|
evaluate_and_capture_cuda_graph(cuda_ctx, cgraph, ggml_cuda_cpy_fn_ptrs, graph_evaluated_or_captured, use_cuda_graph, cuda_graph_update_required);
|
||||||
|
|
||||||
return GGML_STATUS_SUCCESS;
|
return GGML_STATUS_SUCCESS;
|
||||||
}
|
}
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user