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https://github.com/ggerganov/whisper.cpp.git
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backend : add eval callback (llama/4935)
* backend : add eval callback ggml-ci * backend : group nodes in a single compute when user don't need them * backend : clean-up the implementation ggml-ci * simple : do not perform tensor data copy if not needed * simple : fix * simple : no need for ggml_is_contiguous + fix bool parse * llama : fix callback placement in llama_context_params * backend : avoid double-ask callback calls * simple : restore examples, imatrix will serve as a demo
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@ -802,6 +802,9 @@ struct ggml_backend_sched {
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__attribute__((aligned(GGML_MEM_ALIGN)))
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#endif
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char context_buffer[GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS*sizeof(struct ggml_tensor) + sizeof(struct ggml_cgraph)];
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ggml_backend_sched_eval_callback callback_eval;
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void * callback_eval_user_data;
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};
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#define hash_id(node) ggml_hash_find_or_insert(sched->hash_set, node)
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@ -1324,9 +1327,38 @@ static void sched_compute_splits(ggml_backend_sched_t sched) {
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ggml_graph_dump_dot(split->graph, NULL, split_filename);
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#endif
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uint64_t compute_start_us = ggml_time_us();
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ggml_backend_graph_compute(split_backend, &split->graph);
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//ggml_backend_synchronize(split_backend); // necessary to measure compute time
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if (!sched->callback_eval) {
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ggml_backend_graph_compute(split_backend, &split->graph);
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//ggml_backend_synchronize(split_backend); // necessary to measure compute time
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} else {
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// similar to ggml_backend_compare_graph_backend
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for (int j0 = 0; j0 < split->graph.n_nodes; j0++) {
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struct ggml_tensor * t = split->graph.nodes[j0];
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// check if the user needs data from this node
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bool need = sched->callback_eval(t, true, sched->callback_eval_user_data);
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int j1 = j0;
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// determine the range [j0, j1] of nodes that can be computed together
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while (!need && j1 < split->graph.n_nodes - 1) {
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t = split->graph.nodes[++j1];
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need = sched->callback_eval(t, true, sched->callback_eval_user_data);
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}
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struct ggml_cgraph gv = ggml_graph_view(&split->graph, j0, j1 + 1);
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ggml_backend_graph_compute(split_backend, &gv);
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if (need && !sched->callback_eval(t, false, sched->callback_eval_user_data)) {
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break;
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}
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j0 = j1;
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}
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}
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uint64_t compute_end_us = ggml_time_us();
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compute_us[split_backend_id] += compute_end_us - compute_start_us;
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}
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@ -1431,6 +1463,12 @@ void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
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sched_reset(sched);
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}
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void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) {
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sched->callback_eval = callback;
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sched->callback_eval_user_data = user_data;
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}
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int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched) {
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return sched->n_splits;
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}
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@ -148,6 +148,14 @@ extern "C" {
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struct ggml_backend_sched;
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typedef struct ggml_backend_sched * ggml_backend_sched_t;
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// when ask == true, the scheduler wants to know if the user wants to observe this node
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// this allows the scheduler to batch nodes together in order to evaluate them in a single call
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//
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// when ask == false, the scheduler is passing the node tensor to the user for observation
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// if the user returns false, the scheduler will cancel the graph compute
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//
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typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
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// Initialize a backend scheduler
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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);
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GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched);
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@ -168,6 +176,9 @@ extern "C" {
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// Reset all assignments and allocators - must be called before using the sched allocators to allocate inputs
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GGML_API void ggml_backend_sched_reset(ggml_backend_sched_t sched);
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// Set a callback to be called for each resulting node during graph compute
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GGML_API void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data);
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//
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// Utils
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//
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