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llama: automatically set parameters not set by the user in such a way that maximizes GPU utilization (#16653)

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* llama: automatically fit args to free memory

llama-fit-params tool

* fix CI

* hints for bug reports, ensure no reallocation

* fix segfault with Vulkan

* add llama-fit-params to CI

* fix CI

* fix CI

* fix CI

* minor adjustments

* fix assignment of 1 dense layer

* fix logger not being reset on model load failure

* remove --n-gpu-layer hint on model load failure

* fix llama-fit-params verbosity

* fix edge case

* fix typo [no ci]
This commit is contained in:
Johannes Gäßler 2025-12-15 09:24:59 +01:00 • committed by GitHub
parent 4aced7a631
commit b1f3a6e5db
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GPG key ID: B5690EEEBB952194
26 changed files with 1075 additions and 63 deletions
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69
src/llama-context.cpp
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@ -258,6 +258,7 @@ llama_context::llama_context(
backend_buft.clear();
backend_ptrs.clear();
backend_buf_exp_size.clear();
for (auto & backend : backends) {
auto * buft = ggml_backend_get_default_buffer_type(backend.get());
@ -274,6 +275,7 @@ llama_context::llama_context(
backend_buft.push_back(buft);
backend_ptrs.push_back(backend.get());
backend_buf_exp_size.push_back(0);
}
LLAMA_LOG_DEBUG("%s: backend_ptrs.size() = %zu\n", __func__, backend_ptrs.size());
@ -389,7 +391,8 @@ llama_context::llama_context(
// reserve pp (prompt processing) graph first so that buffers are only allocated once
{
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get(),
model.hparams.no_alloc, model.hparams.no_alloc ? backend_buf_exp_size.data() : nullptr);
if (!gf) {
if (pipeline_parallel) {
LLAMA_LOG_WARN("%s: compute buffer allocation failed, retrying without pipeline parallelism\n", __func__);
@ -407,7 +410,7 @@ llama_context::llama_context(
// reserve with tg (token generation) graph to get the number of splits and nodes
{
auto * gf = graph_reserve(n_seqs, n_seqs, n_seqs, mctx.get());
auto * gf = graph_reserve(n_seqs, n_seqs, n_seqs, mctx.get(), model.hparams.no_alloc);
if (!gf) {
throw std::runtime_error("failed to allocate compute tg buffers");
}
@ -422,7 +425,7 @@ llama_context::llama_context(
//
// auto * gf = graph_reserve(n_tokens, 1, n_tokens, mctx.get());
//
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get(), model.hparams.no_alloc);
if (!gf) {
throw std::runtime_error("failed to allocate compute pp buffers");
}
@ -431,11 +434,13 @@ llama_context::llama_context(
for (size_t i = 0; i < backend_ptrs.size(); ++i) {
ggml_backend_t backend = backend_ptrs[i];
ggml_backend_buffer_type_t buft = backend_buft[i];
size_t size = ggml_backend_sched_get_buffer_size(sched.get(), backend);
if (size > 1) {
if (!model.hparams.no_alloc) {
backend_buf_exp_size[i] = ggml_backend_sched_get_buffer_size(sched.get(), backend);
}
if (backend_buf_exp_size[i] > 1) {
LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__,
ggml_backend_buft_name(buft),
size / 1024.0 / 1024.0);
backend_buf_exp_size[i] / 1024.0 / 1024.0);
}
}
@ -454,6 +459,23 @@ llama_context::llama_context(
}
llama_context::~llama_context() {
// FIXME this currently results in a use-after-free bug if the model is freed before the context
// if (!model.hparams.no_alloc) {
// for (size_t i = 0; i < backend_ptrs.size(); ++i) {
// ggml_backend_t backend = backend_ptrs[i];
// ggml_backend_buffer_type_t buft = backend_buft[i];
// const size_t size_exp = backend_buf_exp_size[i];
// const size_t size_act = ggml_backend_sched_get_buffer_size(sched.get(), backend);
// if (size_exp == size_act) {
// LLAMA_LOG_DEBUG("%s: %10s compute buffer size is %8.4f MiB, matches expectation of %8.4f MiB\n",
// __func__, ggml_backend_buft_name(buft), size_act / (1024.0*1024.0), size_exp / (1024.0*1024.0));
// } else {
// LLAMA_LOG_WARN("%s: %10s compute buffer size of %8.4f MiB, does not match expectation of %8.4f MiB\n",
// __func__, ggml_backend_buft_name(buft), size_act / (1024.0*1024.0), size_exp / (1024.0*1024.0));
// }
// }
// }
ggml_opt_free(opt_ctx);
}
@ -1428,7 +1450,8 @@ llm_graph_result * llama_context::get_gf_res_reserve() const {
return static_cast<llm_graph_result *>(gf_res_reserve.get());
}
ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only) {
ggml_cgraph * llama_context::graph_reserve(
uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only, size_t * sizes) {
LLAMA_LOG_DEBUG("%s: reserving a graph for ubatch with n_tokens = %4u, n_seqs = %2u, n_outputs = %4u\n", __func__, n_tokens, n_seqs, n_outputs);
GGML_ASSERT(n_outputs >= 1);
@ -1465,8 +1488,13 @@ ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, u
// initialize scheduler with the specified graph
if (split_only) {
ggml_backend_sched_split_graph(sched.get(), gf);
if (sizes) {
ggml_backend_sched_reserve_size(sched.get(), gf, sizes);
} else {
ggml_backend_sched_split_graph(sched.get(), gf);
}
} else if (!ggml_backend_sched_reserve(sched.get(), gf)) {
GGML_ASSERT(!sizes);
LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
return nullptr;
}
@ -2088,15 +2116,26 @@ void llama_context::perf_reset() {
std::map<ggml_backend_buffer_type_t, llama_memory_breakdown_data> llama_context::memory_breakdown() const {
std::map<ggml_backend_buffer_type_t, llama_memory_breakdown_data> ret;
for (const auto & buft_size : model.memory_breakdown()) {
ret[buft_size.first].model += buft_size.second;
for (const auto & [buft, size] : model.memory_breakdown()) {
ret[buft].model += size;
}
for (const auto & buft_size : memory->memory_breakdown()) {
ret[buft_size.first].context += buft_size.second;
if (memory) {
for (const auto & [buft, size] : memory->memory_breakdown()) {
ret[buft].context += size;
}
}
for (const auto & backend_ptr : backends) {
ggml_backend_t backend = backend_ptr.get();
ret[ggml_backend_sched_get_buffer_type(sched.get(), backend)].compute += ggml_backend_sched_get_buffer_size(sched.get(), backend);
if (model.hparams.no_alloc) {
for (size_t i = 0; i < backends.size(); ++i) {
ggml_backend_t backend = backends[i].get();
ggml_backend_buffer_type_t buft = ggml_backend_sched_get_buffer_type(sched.get(), backend);
ret[buft].compute += backend_buf_exp_size[i];
}
} else {
for (const auto & backend_ptr : backends) {
ggml_backend_t backend = backend_ptr.get();
ggml_backend_buffer_type_t buft = ggml_backend_sched_get_buffer_type(sched.get(), backend);
ret[buft].compute += ggml_backend_sched_get_buffer_size(sched.get(), backend);
}
}
return ret;
}