tqcq/llama.cpp
0
0
Fork 0
mirror of https://github.com/ggml-org/llama.cpp.git synced 2025-06-28 20:25:20 +00:00
Code Issues Packages Projects Releases Wiki Activity

hparams : add SWA rope parameters (#12374)

Browse Source 
ggml-ci
This commit is contained in:
Georgi Gerganov
2025-03-14 09:03:24 +02:00
committed by GitHub
parent 84d5475541
commit 081bee8c64
5 changed files with 26 additions and 20 deletions
Download Patch File Download Diff File Expand all files Collapse all files

22
src/llama-model.cpp
View File

@ -475,6 +475,10 @@ void llama_model::load_hparams(llama_model_loader & ml) {
}
hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale;
// by default assume that the sliding-window layers use the same scaling type as the non-sliding-window layers
hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
ml.get_key(LLM_KV_ROPE_SCALING_ATTN_FACTOR, hparams.rope_attn_factor, false);
// non-transformer models do not have attention heads
@ -877,6 +881,9 @@ void llama_model::load_hparams(llama_model_loader & ml) {
{
hparams.n_swa_pattern = 6;
hparams.rope_freq_base_train_swa = 10000.0f;
hparams.rope_freq_scale_train_swa = 1.0f;
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@ -1346,13 +1353,14 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
const int i_gpu_start = std::max((int) hparams.n_layer - n_gpu_layers, (int) 0);
const int act_gpu_layers = devices.empty() ? 0 : std::min(n_gpu_layers, (int)n_layer + 1);
auto get_layer_buft_list = [&](int il) -> llama_model::impl::layer_dev {
const bool is_swa = il < (int) hparams.n_layer && hparams.is_swa(il);
if (il < i_gpu_start || (il - i_gpu_start) >= act_gpu_layers) {
LLAMA_LOG_DEBUG("load_tensors: layer %3d assigned to device %s\n", il, ggml_backend_dev_name(cpu_dev));
LLAMA_LOG_DEBUG("load_tensors: layer %3d assigned to device %s, is_swa = %d\n", il, ggml_backend_dev_name(cpu_dev), is_swa);
return {cpu_dev, &pimpl->cpu_buft_list};
}
const int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + n_devices(), float(il - i_gpu_start)/act_gpu_layers) - splits.begin();
auto * dev = devices.at(layer_gpu);
LLAMA_LOG_DEBUG("load_tensors: layer %3d assigned to device %s\n", il, ggml_backend_dev_name(dev));
LLAMA_LOG_DEBUG("load_tensors: layer %3d assigned to device %s, is_swa = %d\n", il, ggml_backend_dev_name(dev), is_swa);
return {dev, &pimpl->gpu_buft_list.at(dev)};
};
@ -7381,10 +7389,10 @@ struct llm_build_gemma3 : public llm_graph_context {
auto * inp_attn = build_attn_inp_kv_unified(true, true);
for (int il = 0; il < n_layer; ++il) {
const bool is_sliding = hparams.is_sliding(il);
const bool is_swa = hparams.is_swa(il);
const float freq_base_l = is_sliding ? 10000.0f : freq_base;
const float freq_scale_l = is_sliding ? 1.0f : freq_scale;
const float freq_base_l = is_swa ? hparams.rope_freq_base_train_swa : cparams.rope_freq_base;
const float freq_scale_l = is_swa ? hparams.rope_freq_scale_train_swa : cparams.rope_freq_scale;
// norm
cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
@ -7973,7 +7981,7 @@ struct llm_build_cohere2 : public llm_graph_context {
auto * inp_attn = build_attn_inp_kv_unified(true, true);
for (int il = 0; il < n_layer; ++il) {
const bool is_sliding = hparams.is_sliding(il);
const bool is_swa = hparams.is_swa(il);
// norm
cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM, il);
@ -8007,7 +8015,7 @@ struct llm_build_cohere2 : public llm_graph_context {
cb(Vcur, "Vcur", il);
}
if (is_sliding) {
if (is_swa) {
Qcur = ggml_rope_ext(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor,
beta_fast, beta_slow);