Skip to content

src : add n_expert_used_max function #28323

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
danbev merged 3 commits into from
Sep 4, 2026
+17 −9
Merged

src : add n_expert_used_max function #28323

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
7dc826e
src : add n_expert_used_max function
danbev Sep 3, 2026
eaf44a4
src : use hparams.n_expert_used_max in llama_model_base::load_hparams
danbev Sep 3, 2026
6233116
src : use 0 as initial value for n_expert_used_max
danbev Sep 3, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
9 changes: 9 additions & 0 deletions src/llama-hparams.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -87,6 +87,15 @@ uint32_t llama_hparams::n_expert_used(uint32_t il) const {
GGML_ABORT("fatal error");
}

uint32_t llama_hparams::n_expert_used_max() const {
uint32_t val = 0;
for (uint32_t il = 0; il < n_layer_all; ++il) {
val = std::max(val, n_expert_used(il));
}

return val;
}

uint32_t llama_hparams::n_gqa(uint32_t il) const {
const uint32_t n_head = this->n_head(il);
const uint32_t n_head_kv = this->n_head_kv(il);
Expand Down
3 changes: 3 additions & 0 deletions src/llama-hparams.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -392,6 +392,9 @@ struct llama_hparams {

uint32_t n_expert_used(uint32_t il = 0) const;

// return the maximum n_expert_used across all layers
uint32_t n_expert_used_max() const;

uint32_t n_gqa(uint32_t il = 0) const;

uint32_t n_rot(uint32_t il = 0) const;
Expand Down
4 changes: 2 additions & 2 deletions src/llama-model-loader.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -951,7 +951,7 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w
case GGML_OP_MUL_MAT_ID:
{
// Used for either MoE expert routing or embedded adapter routing
const int n_ids_used = hparams.router_layer >= 0 ? 1 : hparams.n_expert_used();
const int n_ids_used = hparams.router_layer >= 0 ? 1 : hparams.n_expert_used_max();
GGML_ASSERT(n_ids_used > 0);
ggml_tensor * b = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, w->ne[0], n_ids_used, 512);
ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_ids_used, 512);
Expand All @@ -964,7 +964,7 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w
} break;
case GGML_OP_ADD_ID:
{
const int n_expert_used = hparams.n_expert_used();
const int n_expert_used = hparams.n_expert_used_max();
GGML_ASSERT(n_expert_used > 0);
ggml_tensor * a = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, w->ne[0], n_expert_used, 512);
ggml_tensor * c = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_expert_used, 512);
Expand Down
10 changes: 3 additions & 7 deletions src/llama-model.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1254,10 +1254,7 @@ void llama_model_base::load_hparams(llama_model_loader & ml) {
}

// models may route a different number of experts per layer, so validate the maximum
uint32_t n_expert_used_max = 0;
for (uint32_t il = 0; il < hparams.n_layer_all; ++il) {
n_expert_used_max = std::max(n_expert_used_max, hparams.n_expert_used(il));
}
uint32_t n_expert_used_max = hparams.n_expert_used_max();

GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS);
GGML_ASSERT(n_expert_used_max <= hparams.n_expert);
Expand Down Expand Up @@ -1509,10 +1506,9 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) {
// TODO: move to a separate function
const auto tn = LLM_TN(arch);

const int64_t n_expert = hparams.n_expert;
const int64_t n_expert_used = hparams.n_expert_used();
const int64_t n_expert = hparams.n_expert;

if (n_expert > 0 && n_expert_used == 0) {
if (n_expert > 0 && hparams.n_expert_used_max() == 0) {
throw std::runtime_error("model has expert layers but no expert layers are used");
}

Expand Down
Loading