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ggml update to b7108 (#12992)

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* Revert "vulkan: temporary cary of vulkan fixes (#12971)"

This reverts commit 3a9e8e9fd4.

* ggml update to b7087

* fix argsort on metal

* update to b7108

* fix bakllava regression

This model lacks the metadata for the projector type.

* update to b7209

* fix TopK perf

* only build arm code on arm
This commit is contained in:
Daniel Hiltgen
2025-12-03 19:43:29 -08:00
committed by GitHub
parent 854d40edc5
commit 0cf7794b16
303 changed files with 32711 additions and 23435 deletions
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28
llama/llama.cpp/src/llama-graph.cpp vendored
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@@ -810,6 +810,9 @@ ggml_tensor * llm_graph_context::build_ffn(
GGML_ABORT("fatal error");
}
//expand here so that we can fuse ffn gate
ggml_build_forward_expand(gf, cur);
if (gate && type_gate == LLM_FFN_PAR) {
cur = ggml_mul(ctx0, cur, tmp);
cb(cur, "ffn_gate_par", il);
@@ -958,14 +961,14 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
// organize experts into n_expert_groups
ggml_tensor * selection_groups = ggml_reshape_3d(ctx0, selection_probs, n_exp_per_group, hparams.n_expert_groups, n_tokens); // [n_exp_per_group, n_expert_groups, n_tokens]
ggml_tensor * group_scores = ggml_top_k(ctx0, selection_groups, 2); // [2, n_expert_groups, n_tokens]
ggml_tensor * group_scores = ggml_argsort_top_k(ctx0, selection_groups, 2); // [2, n_expert_groups, n_tokens]
group_scores = ggml_get_rows(ctx0, ggml_reshape_4d(ctx0, selection_groups, 1, selection_groups->ne[0], selection_groups->ne[1], selection_groups->ne[2]), group_scores); // [1, 2, n_expert_groups, n_tokens]
// get top n_group_used expert groups
group_scores = ggml_sum_rows(ctx0, ggml_reshape_3d(ctx0, group_scores, group_scores->ne[1], group_scores->ne[2], group_scores->ne[3])); // [1, n_expert_groups, n_tokens]
group_scores = ggml_reshape_2d(ctx0, group_scores, group_scores->ne[1], group_scores->ne[2]); // [n_expert_groups, n_tokens]
ggml_tensor * expert_groups = ggml_top_k(ctx0, group_scores, hparams.n_group_used); // [n_group_used, n_tokens]
ggml_tensor * expert_groups = ggml_argsort_top_k(ctx0, group_scores, hparams.n_group_used); // [n_group_used, n_tokens]
cb(expert_groups, "ffn_moe_group_topk", il);
// mask out the other groups
@@ -976,7 +979,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
}
// select experts
ggml_tensor * selected_experts = ggml_top_k(ctx0, selection_probs, n_expert_used); // [n_expert_used, n_tokens]
ggml_tensor * selected_experts = ggml_argsort_top_k(ctx0, selection_probs, n_expert_used); // [n_expert_used, n_tokens]
cb(selected_experts->src[0], "ffn_moe_argsort", il);
cb(selected_experts, "ffn_moe_topk", il);
@@ -1006,10 +1009,9 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights); // [1, n_tokens]
cb(weights_sum, "ffn_moe_weights_sum", il);
if (arch == LLM_ARCH_BAILINGMOE2) {
weights_sum = ggml_scale_bias(ctx0, weights_sum, 1.0, 1e-20);
cb(weights_sum, "ffn_moe_weights_sum_biased", il);
}
// Avoid division by zero, clamp to smallest number representable by F16
weights_sum = ggml_clamp(ctx0, weights_sum, 6.103515625e-5, INFINITY);
cb(weights_sum, "ffn_moe_weights_sum_clamped", il);
weights = ggml_div(ctx0, weights, weights_sum); // [n_expert_used, n_tokens]
cb(weights, "ffn_moe_weights_norm", il);
@@ -1091,6 +1093,9 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
GGML_ABORT("fatal error");
}
//expand here so that we can fuse ffn gate
ggml_build_forward_expand(gf, cur);
experts = build_lora_mm_id(down_exps, cur, selected_experts); // [n_embd, n_expert_used, n_tokens]
cb(experts, "ffn_moe_down", il);
@@ -1137,7 +1142,7 @@ 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;
const int64_t n_embd = hparams.n_embd_inp();
auto inp = std::make_unique<llm_graph_input_embd>();
@@ -1274,7 +1279,7 @@ ggml_tensor * llm_graph_context::build_inp_cross_embd() const {
// return cur;
//}
const auto n_embd = !cross->v_embd.empty() ? cross->n_embd : hparams.n_embd;
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;
cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_enc);
@@ -1587,9 +1592,10 @@ ggml_tensor * llm_graph_context::build_attn(
int il) const {
// these nodes are added to the graph together so that they are not reordered
// by doing so, the number of splits in the graph is reduced
// expand k later to enable rope fusion which directly writes into k-v cache
ggml_build_forward_expand(gf, q_cur);
ggml_build_forward_expand(gf, k_cur);
ggml_build_forward_expand(gf, v_cur);
ggml_build_forward_expand(gf, k_cur);
const auto * mctx_cur = inp->mctx;
@@ -2030,7 +2036,7 @@ int32_t llama_relative_position_bucket(llama_pos x, llama_pos y, uint64_t n_buck
if (bidirectional) {
relative_bucket += (relative_position > 0) * n_buckets;
relative_position = abs(relative_position);
relative_position = std::abs(relative_position);
} else {
relative_position = -std::min<int32_t>(relative_position, 0);
}