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talk-llama : sync llama.cpp

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This commit is contained in:
Georgi Gerganov
2026-06-08 14:36:36 +03:00
parent 4df9a57df2
commit 84bd03a438
151 changed files with 3439 additions and 871 deletions
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examples/talk-llama/models/lfm2.cpp
+12 -8
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@@ -5,10 +5,13 @@
void llama_model_lfm2::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_SHORTCONV_L_CACHE, hparams.n_shortconv_l_cache);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
for (uint32_t il = 0; il < hparams.n_layer; ++il) {
hparams.recurrent_layer_arr[il] = hparams.n_head_kv(il) == 0;
for (uint32_t il = 0; il < hparams.n_layer(); ++il) {
hparams.is_recr_impl[il] = hparams.n_head_kv(il) == 0;
}
hparams.n_layer_dense_lead = hparams.n_layer;
hparams.n_layer_dense_lead = hparams.n_layer();
switch (hparams.n_ff()) {
case 4608: type = LLM_TYPE_350M; break;
case 6912: type = LLM_TYPE_700M; break;
@@ -16,10 +19,11 @@ void llama_model_lfm2::load_arch_hparams(llama_model_loader & ml) {
case 10752: type = LLM_TYPE_2_6B; break;
default: type = LLM_TYPE_UNKNOWN;
}
if (const auto is_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false); is_swa && hparams.n_swa > 0) {
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
for (uint32_t il = 0; il < hparams.n_layer; ++il) {
hparams.swa_layers[il] = !hparams.recurrent_layer_arr[il];
for (uint32_t il = 0; il < hparams.n_layer(); ++il) {
hparams.is_swa_impl[il] = !hparams.is_recr_impl[il];
}
}
}
@@ -59,7 +63,7 @@ void llama_model_lfm2::load_arch_tensors(llama_model_loader &) {
// for operator_norm
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
if (!hparams.is_recurrent(i)) {
if (!hparams.is_recr(i)) {
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
GGML_ASSERT(n_embd_v_gqa == n_embd_k_gqa);
@@ -235,8 +239,8 @@ llama_model_lfm2::graph<iswa>::graph(const llama_model & model, const llm_graph_
cur = build_norm(cur, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "model.layers.{}.operator_norm", il);
cur = hparams.is_recurrent(il) ? build_shortconv_block(cur, inp_hybrid->get_recr(), il) :
build_attn_block(cur, inp_pos, inp_hybrid->get_attn(), il);
cur = hparams.is_recr(il) ? build_shortconv_block(cur, inp_hybrid->get_recr(), il) :
build_attn_block(cur, inp_pos, inp_hybrid->get_attn(), il);
if (il == n_layer - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);