ver4a/llama.cpp
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context : fix worst-case reserve outputs (#12545)

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ggml-ci
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Georgi Gerganov 2025-03-25 09:19:23 +02:00 • committed by GitHub
parent c95fa362b3
commit 2d77d88e70
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1 changed files with 21 additions and 4 deletions
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src/llama-context.cpp
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@ -294,10 +294,7 @@ llama_context::llama_context(
// TODO: something cleaner // TODO: something cleaner
const auto n_outputs_save = n_outputs; const auto n_outputs_save = n_outputs;
// max number of outputs LLAMA_LOG_DEBUG("%s: worst-case: n_tokens = %d, n_seqs = %d, n_outputs = %d\n", __func__, n_tokens, n_seqs, n_outputs);
n_outputs = n_tokens;
LLAMA_LOG_DEBUG("%s: n_tokens = %d, n_seqs = %d, n_outputs = %d\n", __func__, n_tokens, n_seqs, n_outputs);
int n_splits_pp = -1; int n_splits_pp = -1;
int n_nodes_pp = -1; int n_nodes_pp = -1;
@ -313,8 +310,15 @@ llama_context::llama_context(
// reserve pp graph first so that buffers are only allocated once // reserve pp graph first so that buffers are only allocated once
{ {
llama_ubatch ubatch_pp = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr}; llama_ubatch ubatch_pp = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
// max number of outputs
n_outputs = ubatch_pp.n_tokens;
LLAMA_LOG_DEBUG("%s: reserving graph for n_tokens = %d, n_seqs = %d\n", __func__, ubatch_pp.n_tokens, ubatch_pp.n_seqs);
auto * gf = graph_init(); auto * gf = graph_init();
graph_build(ctx_compute.get(), gf, ubatch_pp, LLM_GRAPH_TYPE_DEFAULT); graph_build(ctx_compute.get(), gf, ubatch_pp, LLM_GRAPH_TYPE_DEFAULT);
if (!ggml_backend_sched_reserve(sched.get(), gf)) { if (!ggml_backend_sched_reserve(sched.get(), gf)) {
throw std::runtime_error("failed to allocate compute pp buffers"); throw std::runtime_error("failed to allocate compute pp buffers");
} }
@ -326,11 +330,18 @@ llama_context::llama_context(
// reserve with tg graph to get the number of splits and nodes // reserve with tg graph to get the number of splits and nodes
{ {
llama_ubatch ubatch_tg = { true, 1, 1, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr}; llama_ubatch ubatch_tg = { true, 1, 1, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
n_outputs = ubatch_tg.n_tokens;
LLAMA_LOG_DEBUG("%s: reserving graph for n_tokens = %d, n_seqs = %d\n", __func__, ubatch_tg.n_tokens, ubatch_tg.n_seqs);
auto * gf = graph_init(); auto * gf = graph_init();
graph_build(ctx_compute.get(), gf, ubatch_tg, LLM_GRAPH_TYPE_DEFAULT); graph_build(ctx_compute.get(), gf, ubatch_tg, LLM_GRAPH_TYPE_DEFAULT);
if (!ggml_backend_sched_reserve(sched.get(), gf)) { if (!ggml_backend_sched_reserve(sched.get(), gf)) {
throw std::runtime_error("failed to allocate compute tg buffers"); throw std::runtime_error("failed to allocate compute tg buffers");
} }
n_splits_tg = ggml_backend_sched_get_n_splits(sched.get()); n_splits_tg = ggml_backend_sched_get_n_splits(sched.get());
n_nodes_tg = ggml_graph_n_nodes(gf); n_nodes_tg = ggml_graph_n_nodes(gf);
} }
@ -338,8 +349,14 @@ llama_context::llama_context(
// reserve again with pp graph to avoid ggml-alloc reallocations during inference // reserve again with pp graph to avoid ggml-alloc reallocations during inference
{ {
llama_ubatch ubatch_pp = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr}; llama_ubatch ubatch_pp = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
n_outputs = ubatch_pp.n_tokens;
LLAMA_LOG_DEBUG("%s: reserving graph for n_tokens = %d, n_seqs = %d\n", __func__, ubatch_pp.n_tokens, ubatch_pp.n_seqs);
auto * gf = graph_init(); auto * gf = graph_init();
graph_build(ctx_compute.get(), gf, ubatch_pp, LLM_GRAPH_TYPE_DEFAULT); graph_build(ctx_compute.get(), gf, ubatch_pp, LLM_GRAPH_TYPE_DEFAULT);
if (!ggml_backend_sched_reserve(sched.get(), gf)) { if (!ggml_backend_sched_reserve(sched.get(), gf)) {
throw std::runtime_error("failed to allocate compute pp buffers"); throw std::runtime_error("failed to allocate compute pp buffers");
} }