sdgoij/llama.cpp
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graph : constant topology for tokens/embeddings inputs

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This commit is contained in:
Georgi Gerganov
2026-01-02 15:46:45 +02:00
parent 4ed59dc2c7
commit bf3f12df4c
3 changed files with 19 additions and 15 deletions
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28
src/llama-graph.cpp
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@@ -21,7 +21,8 @@ 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));
@@ -1206,17 +1207,21 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const {
const int64_t n_embd = hparams.n_embd_inp();
auto inp = std::make_unique<llm_graph_input_embd>();
auto inp = std::make_unique<llm_graph_input_embd>(n_embd);
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->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, ubatch.n_tokens);
ggml_set_input(inp->embd);
ggml_tensor * cur = nullptr;
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;
{
cur = ggml_get_rows(ctx0, tok_embd, inp->tokens);
cur = ggml_scale(ctx0, cur, ubatch.token ? 1.0f : 0.0f);
// apply lora for embedding tokens if needed
for (const auto & lora : *loras) {
@@ -1235,13 +1240,10 @@ 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);
cur = inp->embd;
}
cur = ggml_add(ctx0, cur, ggml_scale(ctx0, inp->embd, ubatch.embd ? 1.0f : 0.0f));
// For Granite architecture
if (hparams.f_embedding_scale != 0.0f) {
cur = ggml_scale(ctx0, cur, hparams.f_embedding_scale);

4
src/llama-graph.h
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@@ -104,7 +104,7 @@ using llm_graph_input_ptr = std::unique_ptr<llm_graph_input_i>;
class llm_graph_input_embd : public llm_graph_input_i {
public:
llm_graph_input_embd() = default;
llm_graph_input_embd(int64_t n_embd) : n_embd(n_embd) {}
virtual ~llm_graph_input_embd() = default;
void set_input(const llama_ubatch * ubatch) override;
@@ -113,6 +113,8 @@ public:
ggml_tensor * tokens = nullptr; // I32 [n_batch]
ggml_tensor * embd = nullptr; // F32 [n_embd, n_batch]
const int64_t n_embd = 0;
};
class llm_graph_input_pos : public llm_graph_input_i {

2
src/models/gemma3n-iswa.cpp
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@@ -245,7 +245,7 @@ ggml_tensor * llm_build_gemma3n_iswa::view_2d_slice(ggml_tensor * x, int idx) {
// equivalent to get_per_layer_inputs() in python code
// output shape: [n_embd_altup, n_layer, n_tokens]
ggml_tensor * llm_build_gemma3n_iswa::get_per_layer_inputs() {
auto inp = std::make_unique<llm_graph_input_embd>();
auto inp = std::make_unique<llm_graph_input_embd>(n_embd);
ggml_tensor * inp_per_layer;
if (ubatch.token) {
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);