LLM-Inference/ik_llama.cpp
1
0
Fork 0
mirror of https://github.com/ikawrakow/ik_llama.cpp.git synced 2026-08-12 22:29:39 +04:00
Code Issues Packages Projects Releases Wiki Activity

Clean it up

Browse Source
This commit is contained in:
Kawrakow
2026-08-11 13:04:29 +00:00
parent 4c410c08d6
commit 3cb36873d8
1 changed files with 32 additions and 77 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/graphs/build_muse_glimmer.cpp
+32 -77
View File
@@ -33,8 +33,6 @@ ggml_cgraph * llm_build_context::build_muse_glimmer() {
ggml_tensor * ffn_inp = nullptr;
//post_norm_data pnd;
//pnd.f_rms_eps = post_norm_eps;
post_norm_data * pnd_ptr = nullptr;
bool add_input = model.split_mode == LLAMA_SPLIT_MODE_GRAPH ? false : true;
@@ -43,62 +41,51 @@ ggml_cgraph * llm_build_context::build_muse_glimmer() {
std::vector<ggml_tensor *> pn_tensors;
auto do_post_norm = [&] (ggml_tensor * cur, ggml_tensor * post_norm, ggml_tensor * inp, const std::string & tag, int il, bool get_rows) {
GGML_ASSERT(cur->op == GGML_OP_REDUCE);
int n = cur->op_params[1];
if ((int)pn_tensors.size() != n) pn_tensors.resize(n);
for (int id = 0; id < n; ++id) {
if (!cur->src[id]) {
pn_tensors[id] = nullptr;
continue;
}
auto pn_extra = (ggml_split_tensor_t *)post_norm->extra;
GGML_ASSERT(pn_extra && pn_extra->splits[id]);
auto normed = ggml_fused_rms_norm(ctx0, cur->src[id], pn_extra->splits[id], post_norm_eps);
cb(normed, (tag + "_pn").c_str(), 1000*(il+1) + id);
auto add = get_input_tensor_sm_graph(ctx0, inp, id);
if (get_rows && il == n_active_layer - 1 && inp_out_ids) {
add = ggml_get_rows(ctx0, add, inp_out_ids);
}
auto added = ggml_add(ctx0, normed, add);
cb(added, (tag + "_pn_add").c_str(), 1000*(il+1) + id);
pn_tensors[id] = added;
}
cur = ggml_reduce(ctx0, pn_tensors.data(), n, GGML_OP_ADD);
cb(cur, (tag + "_final").c_str(), il);
cur->op_params[3] = 1;
ggml_build_forward_expand(gf, cur);
return cur;
};
for (int il = 0; il < n_active_layer; ++il) {
bool use_rope = hparams.swa_layers[il];
auto this_KQ_mask = use_rope ? KQ_mask_swa : KQ_mask;
int this_n_swa = this_KQ_mask == KQ_mask_swa ? hparams.n_swa : 0;
auto this_KQ_mask = use_rope ? KQ_mask_swa : KQ_mask;
int this_n_swa = use_rope ? hparams.n_swa : 0;
//if (model.split_mode == LLAMA_SPLIT_MODE_GRAPH && il > 0) {
// pnd.norm = model.layers[il-1].ffn_post_norm;
// pnd.add = ffn_inp;
// pnd_ptr = &pnd;
//}
// self-attention
cur = build_std_attention(gf, model.layers[il].attn_norm, inpL,
inp_pos, il == n_active_layer - 1 ? inp_out_ids : nullptr, nullptr,
this_KQ_mask, nullptr, nullptr, kq_scale, 0.0f, this_n_swa, il, use_rope, false, add_input, false, false,
model.layers[il].attn_post_norm, -1, post_norm_eps, pnd_ptr);
if (model.split_mode == LLAMA_SPLIT_MODE_GRAPH) {
GGML_ASSERT(cur->op == GGML_OP_REDUCE);
int n = cur->op_params[1];
if ((int)pn_tensors.size() != n) pn_tensors.resize(n);
for (int id = 0; id < n; ++id) {
if (!cur->src[id]) {
pn_tensors[id] = nullptr;
continue;
}
auto pn_extra = (ggml_split_tensor_t *)model.layers[il].attn_post_norm->extra;
GGML_ASSERT(pn_extra && pn_extra->splits[id]);
auto normed = ggml_fused_rms_norm(ctx0, cur->src[id], pn_extra->splits[id], post_norm_eps);
cb(normed, "attn_pn", 1000*(il+1) + id);
auto add = get_input_tensor_sm_graph(ctx0, inpL, id);
if (il == n_active_layer - 1 && inp_out_ids) {
add = ggml_get_rows(ctx0, add, inp_out_ids);
}
auto added = ggml_add(ctx0, normed, add);
cb(added, "attn_pn_add", 1000*(il+1) + id);
pn_tensors[id] = added;
}
cur = ggml_reduce(ctx0, pn_tensors.data(), n, GGML_OP_ADD);
cb(cur, "attn_final", il);
cur->op_params[3] = 1;
ggml_build_forward_expand(gf, cur);
cur = do_post_norm(cur, model.layers[il].attn_post_norm, inpL, "attn", il, true);
}
ffn_inp = cur;
//if (model.split_mode == LLAMA_SPLIT_MODE_GRAPH) {
// pnd.norm = model.layers[il].attn_post_norm;
// if (il == n_active_layer - 1 && inp_out_ids) {
// inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
// }
// pnd.add = inpL;
// pnd_ptr = &pnd;
//}
cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, ffn_inp,
model.layers[il].ffn_up, nullptr, nullptr,
model.layers[il].ffn_gate, nullptr, nullptr,
@@ -109,27 +96,7 @@ ggml_cgraph * llm_build_context::build_muse_glimmer() {
cb(cur, "ffn_out", il);
if (model.split_mode == LLAMA_SPLIT_MODE_GRAPH) {
GGML_ASSERT(cur->op == GGML_OP_REDUCE);
int n = cur->op_params[1];
if ((int)pn_tensors.size() != n) pn_tensors.resize(n);
for (int id = 0; id < n; ++id) {
if (!cur->src[id]) {
pn_tensors[id] = nullptr;
continue;
}
auto pn_extra = (ggml_split_tensor_t *)model.layers[il].ffn_post_norm->extra;
GGML_ASSERT(pn_extra && pn_extra->splits[id]);
auto normed = ggml_fused_rms_norm(ctx0, cur->src[id], pn_extra->splits[id], post_norm_eps);
cb(normed, "ffn_pn", 1000*(il+1) + id);
auto add = get_input_tensor_sm_graph(ctx0, ffn_inp, id);
auto added = ggml_add(ctx0, normed, add);
cb(added, "ffn_pn_add", 1000*(il+1) + id);
pn_tensors[id] = added;
}
cur = ggml_reduce(ctx0, pn_tensors.data(), n, GGML_OP_ADD);
cb(cur, "ffn_final", il);
cur->op_params[3] = 1;
ggml_build_forward_expand(gf, cur);
cur = do_post_norm(cur, model.layers[il].ffn_post_norm, ffn_inp, "ffn", il, false);
}
cur = lctx.cvec.apply_to(ctx0, cur, il);
@@ -140,18 +107,6 @@ ggml_cgraph * llm_build_context::build_muse_glimmer() {
}
cur = inpL;
//if (model.split_mode == LLAMA_SPLIT_MODE_GRAPH) {
// if (inp_out_ids) {
// ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
// }
// auto pn_extra = (ggml_split_tensor_t *)model.layers[n_active_layer-1].ffn_post_norm->extra;
// GGML_ASSERT(pn_extra && pn_extra->splits[pn_extra->n_device-1]);
// cur = ggml_fused_rms_norm(ctx0, cur, pn_extra->splits[pn_extra->n_device-1], post_norm_eps);
// cb(cur, "ffn_post_norm", n_active_layer-1);
// cur = ggml_add(ctx0, cur, ffn_inp);
// cb(cur, "ffn_with_inp", n_active_layer-1);
//}
// lm_head
cur = build_output(lctx, ctx0, cur, model.output, model.output_norm, cb);
cur = ggml_scale(ctx0, cur, hparams.f_logit_scale);