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talk-llama : sync llama.cpp

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This commit is contained in:
Georgi Gerganov
2026-03-16 13:10:15 +02:00
parent ab1252c19e
commit 2bc630f197
131 changed files with 2374 additions and 1519 deletions
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examples/talk-llama/llama-graph.cpp
+65 -15
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@@ -250,7 +250,7 @@ void llm_graph_input_cls::set_input(const llama_ubatch * ubatch) {
const bool last = (
cparams.pooling_type == LLAMA_POOLING_TYPE_LAST ||
(cparams.pooling_type == LLAMA_POOLING_TYPE_RANK && arch == LLM_ARCH_QWEN3) // qwen3 reranking & embedding models use last token
(cparams.pooling_type == LLAMA_POOLING_TYPE_RANK && (arch == LLM_ARCH_QWEN3 || arch == LLM_ARCH_QWEN3VL)) // qwen3 reranking & embedding models use last token
);
for (int i = 0; i < n_tokens; ++i) {
@@ -509,6 +509,7 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
float * data = (float *) cross_kq_mask->data;
for (int i = 0; i < n_tokens; ++i) {
GGML_ASSERT(!cross->seq_ids_enc.empty() && "llama_encode must be called first");
for (int j = 0; j < n_enc; ++j) {
float f = -INFINITY;
@@ -848,13 +849,13 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) :
ubatch (params.ubatch),
n_embd (hparams.n_embd),
n_layer (hparams.n_layer),
n_rot (hparams.n_rot),
n_rot (hparams.n_rot()),
n_ctx (cparams.n_ctx),
n_head (hparams.n_head()),
n_head_kv (hparams.n_head_kv()),
n_embd_head_k (hparams.n_embd_head_k),
n_embd_head_k (hparams.n_embd_head_k()),
n_embd_k_gqa (hparams.n_embd_k_gqa()),
n_embd_head_v (hparams.n_embd_head_v),
n_embd_head_v (hparams.n_embd_head_v()),
n_embd_v_gqa (hparams.n_embd_v_gqa()),
n_expert (hparams.n_expert),
n_expert_used (cparams.warmup ? hparams.n_expert : hparams.n_expert_used),
@@ -899,7 +900,8 @@ ggml_tensor * llm_graph_context::build_cvec(
ggml_tensor * llm_graph_context::build_lora_mm(
ggml_tensor * w,
ggml_tensor * cur) const {
ggml_tensor * cur,
ggml_tensor * w_s) const {
ggml_tensor * res = ggml_mul_mat(ctx0, w, cur);
for (const auto & lora : *loras) {
@@ -920,6 +922,10 @@ ggml_tensor * llm_graph_context::build_lora_mm(
res = ggml_add(ctx0, res, ab_cur);
}
if (w_s) {
res = ggml_mul(ctx0, res, w_s);
}
return res;
}
@@ -1161,12 +1167,14 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
int64_t n_expert_used,
llm_ffn_op_type type_op,
bool norm_w,
bool scale_w,
float w_scale,
llama_expert_gating_func_type gating_op,
int il,
ggml_tensor * probs_in,
ggml_tensor * gate_up_exps) const {
ggml_tensor * gate_up_exps,
ggml_tensor * up_exps_s,
ggml_tensor * gate_exps_s,
ggml_tensor * down_exps_s) const {
return build_moe_ffn(
cur,
gate_inp, /* gate_inp_b */ nullptr,
@@ -1178,12 +1186,15 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
n_expert_used,
type_op,
norm_w,
scale_w,
w_scale,
gating_op,
il,
probs_in,
gate_up_exps
gate_up_exps,
/* gate_up_exps_b */ nullptr,
up_exps_s,
gate_exps_s,
down_exps_s
);
}
@@ -1202,13 +1213,15 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
int64_t n_expert_used,
llm_ffn_op_type type_op,
bool norm_w,
bool scale_w,
float w_scale,
llama_expert_gating_func_type gating_op,
int il,
ggml_tensor * probs_in,
ggml_tensor * gate_up_exps,
ggml_tensor * gate_up_exps_b) const {
ggml_tensor * gate_up_exps_b,
ggml_tensor * up_exps_s,
ggml_tensor * gate_exps_s,
ggml_tensor * down_exps_s) const {
const int64_t n_embd = cur->ne[0];
const int64_t n_tokens = cur->ne[1];
const bool weight_before_ffn = arch == LLM_ARCH_LLAMA4; // for llama4, we apply the sigmoid-ed weights before the FFN
@@ -1330,7 +1343,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
weights = ggml_reshape_3d(ctx0, weights, 1, n_expert_used, n_tokens);
}
if (scale_w) {
if (w_scale != 0.0f && w_scale != 1.0f) {
weights = ggml_scale(ctx0, weights, w_scale);
cb(weights, "ffn_moe_weights_scaled", il);
}
@@ -1360,6 +1373,15 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
cb(gate_up, "ffn_moe_gate_up_biased", il);
}
// apply per-expert scale2 to merged gate_up (use up_exps_s since gate and up are fused)
if (up_exps_s) {
ggml_tensor * s = ggml_reshape_3d(ctx0, up_exps_s, 1, n_expert, 1);
s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1);
s = ggml_get_rows(ctx0, s, selected_experts); // [1, n_expert_used, n_tokens]
gate_up = ggml_mul(ctx0, gate_up, s);
cb(gate_up, "ffn_moe_gate_up_scaled", il);
}
const int64_t n_ff = gate_up->ne[0] / 2;
cur = ggml_view_3d(ctx0, gate_up, n_ff, gate_up->ne[1], gate_up->ne[2], gate_up->nb[1], gate_up->nb[2], 0);
cb(cur, "ffn_moe_gate", il);
@@ -1375,6 +1397,15 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
cb(up, "ffn_moe_up_biased", il);
}
// apply per-expert scale2 to up
if (up_exps_s) {
ggml_tensor * s = ggml_reshape_3d(ctx0, up_exps_s, 1, n_expert, 1);
s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1);
s = ggml_get_rows(ctx0, s, selected_experts); // [1, n_expert_used, n_tokens]
up = ggml_mul(ctx0, up, s);
cb(up, "ffn_moe_up_scaled", il);
}
if (gate_exps) {
cur = build_lora_mm_id(gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
cb(cur, "ffn_moe_gate", il);
@@ -1386,6 +1417,15 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
cur = ggml_add_id(ctx0, cur, gate_exps_b, selected_experts);
cb(cur, "ffn_moe_gate_biased", il);
}
// apply per-expert scale2 to gate
if (gate_exps_s) {
ggml_tensor * s = ggml_reshape_3d(ctx0, gate_exps_s, 1, n_expert, 1);
s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1);
s = ggml_get_rows(ctx0, s, selected_experts); // [1, n_expert_used, n_tokens]
cur = ggml_mul(ctx0, cur, s);
cb(cur, "ffn_moe_gate_scaled", il);
}
}
const bool has_gate = gate_exps || gate_up_exps;
@@ -1465,6 +1505,15 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
cb(experts, "ffn_moe_down_biased", il);
}
// apply per-expert scale2 to down
if (down_exps_s) {
ggml_tensor * s = ggml_reshape_3d(ctx0, down_exps_s, 1, n_expert, 1);
s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1);
s = ggml_get_rows(ctx0, s, selected_experts); // [1, n_expert_used, n_tokens]
experts = ggml_mul(ctx0, experts, s);
cb(experts, "ffn_moe_down_scaled", il);
}
if (!weight_before_ffn) {
experts = ggml_mul(ctx0, experts, weights);
cb(cur, "ffn_moe_weighted", il);
@@ -1607,6 +1656,7 @@ ggml_tensor * llm_graph_context::build_inp_attn_scale() const {
// this need to be 1x1xN for broadcasting
cur = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 1, 1, n_tokens);
ggml_set_input(cur);
ggml_set_name(cur, "attn_scale");
res->add_input(std::move(inp));
@@ -1616,7 +1666,7 @@ ggml_tensor * llm_graph_context::build_inp_attn_scale() const {
ggml_tensor * llm_graph_context::build_inp_out_ids() const {
// note: when all tokens are output, we could skip this optimization to spare the ggml_get_rows() calls,
// but this would make the graph topology depend on the number of output tokens, which can interere with
// features that require constant topology such as pipline parallelism
// features that require constant topology such as pipeline parallelism
// ref: https://github.com/ggml-org/llama.cpp/pull/14275#issuecomment-2987424471
//if (n_outputs < n_tokens) {
// return nullptr;
@@ -1779,7 +1829,7 @@ ggml_tensor * llm_graph_context::build_attn_mha(
if (v_mla) {
#if 0
// v_mla can be applied as a matrix-vector multiplication with broadcasting across dimension 3 == n_tokens.
// However, the code is optimized for dimensions 0 and 1 being large, so this is ineffient.
// However, the code is optimized for dimensions 0 and 1 being large, so this is inefficient.
cur = ggml_reshape_4d(ctx0, cur, v_mla->ne[0], 1, n_head, n_tokens);
cur = ggml_mul_mat(ctx0, v_mla, cur);
#else
@@ -2553,7 +2603,7 @@ void llm_graph_context::build_pooling(
}
// softmax for qwen3 reranker
if (arch == LLM_ARCH_QWEN3) {
if (arch == LLM_ARCH_QWEN3 || arch == LLM_ARCH_QWEN3VL) {
cur = ggml_soft_max(ctx0, cur);
}
} break;