llama: add cb_pre_alloc callback for pre-allocation backend reassignment
Add a new llama_pre_alloc_callback that fires after graph construction but before memory allocation in llama_decode/llama_encode. This allows downstream consumers to call ggml_backend_sched_set_tensor_backend() to route specific ops (e.g. attention) to a different backend without modifying llama.cpp internals. Changes: - Add llama_pre_alloc_callback typedef to llama.h - Add cb_pre_alloc + cb_pre_alloc_user_data to llama_context_params and llama_cparams - Invoke callback in process_ubatch() between build_graph and alloc_graph - Add test that verifies callback invocation and backend reassignment Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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@ -212,6 +212,12 @@ extern "C" {
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typedef bool (*llama_progress_callback)(float progress, void * user_data);
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// called after graph build but before memory allocation in llama_decode/llama_encode
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// use ggml_backend_sched_set_tensor_backend() to reassign ops to a different backend
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// NOTE: not called when a previous graph is reused; assignments from the last invocation
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// persist. set LLAMA_GRAPH_REUSE_DISABLE=1 for per-decode control.
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typedef void (*llama_pre_alloc_callback)(ggml_backend_sched_t sched, struct ggml_cgraph * gf, void * user_data);
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// Input data for llama_encode/llama_decode
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// A llama_batch object can contain input about one or many sequences
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// The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens
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@ -350,6 +356,11 @@ extern "C" {
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ggml_backend_sched_eval_callback cb_eval;
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void * cb_eval_user_data;
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// called after graph build but before memory allocation
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// allows reassigning tensor backends via ggml_backend_sched_set_tensor_backend()
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llama_pre_alloc_callback cb_pre_alloc;
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void * cb_pre_alloc_user_data;
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enum ggml_type type_k; // data type for K cache [EXPERIMENTAL]
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enum ggml_type type_v; // data type for V cache [EXPERIMENTAL]
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@ -62,6 +62,9 @@ llama_context::llama_context(
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cparams.cb_eval = params.cb_eval;
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cparams.cb_eval_user_data = params.cb_eval_user_data;
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cparams.cb_pre_alloc = params.cb_pre_alloc;
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cparams.cb_pre_alloc_user_data = params.cb_pre_alloc_user_data;
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// Initialize backend samplers here so they are part of the sampling graph
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// before the reserve passes run later in this function. This avoids a later
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// re-reserve when graph nodes change.
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@ -1147,6 +1150,10 @@ llm_graph_result * llama_context::process_ubatch(const llama_ubatch & ubatch, ll
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return nullptr;
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}
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if (cparams.cb_pre_alloc) {
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cparams.cb_pre_alloc(sched.get(), gf, cparams.cb_pre_alloc_user_data);
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}
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if (!ggml_backend_sched_alloc_graph(sched.get(), gf)) {
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LLAMA_LOG_ERROR("%s: failed to allocate graph\n", __func__);
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ret = GGML_STATUS_ALLOC_FAILED;
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@ -2833,6 +2840,8 @@ llama_context_params llama_context_default_params() {
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/*.defrag_thold =*/ -1.0f,
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/*.cb_eval =*/ nullptr,
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/*.cb_eval_user_data =*/ nullptr,
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/*.cb_pre_alloc =*/ nullptr,
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/*.cb_pre_alloc_user_data =*/ nullptr,
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/*.type_k =*/ GGML_TYPE_F16,
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/*.type_v =*/ GGML_TYPE_F16,
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/*.abort_callback =*/ nullptr,
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@ -43,4 +43,7 @@ struct llama_cparams {
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ggml_backend_sched_eval_callback cb_eval;
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void * cb_eval_user_data;
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llama_pre_alloc_callback cb_pre_alloc;
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void * cb_pre_alloc_user_data;
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};
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@ -238,6 +238,7 @@ llama_build_and_test(test-backend-ops.cpp)
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llama_build_and_test(test-model-load-cancel.cpp LABEL "model")
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llama_build_and_test(test-autorelease.cpp LABEL "model")
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llama_build_and_test(test-pre-alloc-callback.cpp LABEL "model")
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llama_build_and_test(test-backend-sampler.cpp LABEL "model")
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# Test for state restore with fragmented KV cache
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@ -0,0 +1,71 @@
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#include <cstdio>
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#include <cstring>
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#include "llama.h"
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#include "get-model.h"
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struct callback_state {
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bool called;
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bool reassign_ok;
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};
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static void pre_alloc_cb(ggml_backend_sched_t sched, struct ggml_cgraph * gf, void * user_data) {
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auto * state = static_cast<callback_state *>(user_data);
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state->called = true;
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// reassign the first node to the last backend (CPU) and verify
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int n_backends = ggml_backend_sched_get_n_backends(sched);
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if (n_backends < 1 || ggml_graph_n_nodes(gf) <= 0) {
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return;
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}
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ggml_backend_t target = ggml_backend_sched_get_backend(sched, n_backends - 1);
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struct ggml_tensor * node = ggml_graph_node(gf, 0);
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ggml_backend_sched_set_tensor_backend(sched, node, target);
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state->reassign_ok = (ggml_backend_sched_get_tensor_backend(sched, node) == target);
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}
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int main(int argc, char ** argv) {
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auto * model_path = get_model_or_exit(argc, argv);
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llama_backend_init();
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auto * model = llama_model_load_from_file(model_path, llama_model_default_params());
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if (!model) {
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fprintf(stderr, "FAIL: could not load model\n");
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return 1;
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}
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callback_state state = { false, false };
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auto params = llama_context_default_params();
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params.n_ctx = 64;
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params.n_batch = 1;
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params.cb_pre_alloc = pre_alloc_cb;
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params.cb_pre_alloc_user_data = &state;
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auto * ctx = llama_init_from_model(model, params);
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if (!ctx) {
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fprintf(stderr, "FAIL: could not create context\n");
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llama_model_free(model);
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llama_backend_free();
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return 1;
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}
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llama_token token = 0;
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if (llama_decode(ctx, llama_batch_get_one(&token, 1)) != 0) {
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fprintf(stderr, "FAIL: llama_decode failed\n");
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llama_free(ctx);
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llama_model_free(model);
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llama_backend_free();
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return 1;
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}
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fprintf(stderr, "called=%d reassign_ok=%d\n", state.called, state.reassign_ok);
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int ret = (state.called && state.reassign_ok) ? 0 : 1;
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llama_free(ctx);
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llama_model_free(model);
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llama_backend_free();
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return ret;
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}
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