sleepy/llama.cpp
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graph : utilize ggml_build_forward_select() to avoid reallocations (#18898)

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* graph : avoid branches between embedding and token inputs

* models : make deepstack graphs (e.g. Qwen3 VL) have constant topology

* ci : enable -DGGML_SCHED_NO_REALLOC=ON for server CI

* cont : pad token embeddings to n_embd_inp
This commit is contained in:
Georgi Gerganov
2026-01-23 18:22:34 +02:00
committed by GitHub
parent cb6caca191
commit 557515be1e
7 changed files with 69 additions and 53 deletions
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src/llama-graph.cpp
+48 -17
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@@ -23,7 +23,8 @@ void llm_graph_input_embd::set_input(const llama_ubatch * ubatch) {
}
if (ubatch->embd) {
const int64_t n_embd = embd->ne[0];
GGML_ASSERT(n_embd == embd->ne[0]);
const int64_t n_tokens = ubatch->n_tokens;
ggml_backend_tensor_set(embd, ubatch->embd, 0, n_tokens*n_embd*ggml_element_size(embd));
@@ -33,8 +34,8 @@ void llm_graph_input_embd::set_input(const llama_ubatch * ubatch) {
bool llm_graph_input_embd::can_reuse(const llm_graph_params & params) {
bool res = true;
res &= (!tokens && !params.ubatch.token) || (tokens && tokens->ne[0] == params.ubatch.n_tokens);
res &= (!embd && !params.ubatch.embd) || (embd && embd->ne[1] == params.ubatch.n_tokens);
res &= (!params.ubatch.token) || (tokens && tokens->ne[0] == params.ubatch.n_tokens);
res &= (!params.ubatch.embd) || (embd && embd->ne[1] == params.ubatch.n_tokens);
return res;
}
@@ -634,7 +635,8 @@ int64_t llm_graph_result::get_max_nodes() const {
}
void llm_graph_result::reset() {
t_tokens = nullptr;
t_inp_tokens = nullptr;
t_inp_embd = nullptr;
t_logits = nullptr;
t_embd = nullptr;
t_embd_pooled = nullptr;
@@ -1338,17 +1340,29 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
// input embeddings with optional lora
ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const {
const int64_t n_embd = hparams.n_embd_inp();
const int64_t n_embd_inp = hparams.n_embd_inp();
const int64_t n_embd = hparams.n_embd;
auto inp = std::make_unique<llm_graph_input_embd>();
assert(n_embd_inp >= n_embd);
ggml_tensor * cur = nullptr;
auto inp = std::make_unique<llm_graph_input_embd>(n_embd_inp);
if (ubatch.token) {
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);
//cb(inp->tokens, "inp_tokens", -1);
ggml_set_input(inp->tokens);
res->t_tokens = inp->tokens;
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);
cb(inp->tokens, "inp_tokens", -1);
ggml_set_input(inp->tokens);
res->t_inp_tokens = inp->tokens;
inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd_inp, ubatch.n_tokens);
cb(inp->embd, "inp_embd", -1);
ggml_set_input(inp->embd);
// select one of the 2 inputs, based on the batch contents
// ref: https://github.com/ggml-org/llama.cpp/pull/18550
std::array<ggml_tensor *, 2> inps;
// token embeddings path (ubatch.token != nullptr)
{
auto & cur = inps[0];
cur = ggml_get_rows(ctx0, tok_embd, inp->tokens);
@@ -1369,19 +1383,36 @@ ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const {
cur = ggml_add(ctx0, cur, inpL_delta);
}
} else {
inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, ubatch.n_tokens);
ggml_set_input(inp->embd);
if (n_embd_inp != n_embd) {
cur = ggml_pad(ctx0, cur, hparams.n_embd_inp() - n_embd, 0, 0, 0);
}
}
// vector embeddings path (ubatch.embd != nullptr)
{
auto & cur = inps[1];
cur = inp->embd;
}
assert(ggml_are_same_shape (inps[0], inps[1]));
assert(ggml_are_same_stride(inps[0], inps[1]));
ggml_tensor * cur = ggml_build_forward_select(gf, inps.data(), inps.size(), ubatch.token ? 0 : 1);
if (n_embd_inp != n_embd) {
cur = ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0);
}
res->t_inp_embd = cur;
// For Granite architecture
if (hparams.f_embedding_scale != 0.0f) {
cur = ggml_scale(ctx0, cur, hparams.f_embedding_scale);
}
cb(cur, "inp_embd", -1);
cb(cur, "embd", -1);
res->add_input(std::move(inp));
@@ -1480,7 +1511,7 @@ ggml_tensor * llm_graph_context::build_inp_cross_embd() const {
//}
const auto n_embd = !cross->v_embd.empty() ? cross->n_embd : hparams.n_embd_inp();
const auto n_enc = !cross->v_embd.empty() ? cross->n_enc : hparams.n_ctx_train;
const auto n_enc = !cross->v_embd.empty() ? cross->n_enc : hparams.n_ctx_train;
cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_enc);
ggml_set_input(cur);