Lumpiasty/llama.cpp
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model: move load_hparams and load_tensors to per-model definition (#22004)

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* git-friendly migration

* add build_graph

* nits

* exclude old code from build

* wip

* add llm_arch_model_i

* prepare downstream functions

* nits

* nits

* wip

* wip

* add back create_tensor_qkv

* fix files missing include

* enforce one llm_build per arch

* cmake: use glob

* missing model params

* nits

* wip

* wip (2)

* wip (3)

* test-llama-archs is happy

* improve switch case

* move more stuff into llm_arch_model_i

* fix downstream code

* nits

* nits (2)

* fix order

* llama_model_base

* LLAMA_LOAD_LOCALS

* small fix

* fix build errors

* auto

* rm migration script and ifdef
This commit is contained in:
Xuan-Son Nguyen
2026-05-04 12:36:59 +02:00
committed by GitHub
parent c84e6d6db5
commit 994118a183
129 changed files with 10667 additions and 8117 deletions
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src/models/smallthinker.cpp
+76 -3
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@@ -1,7 +1,80 @@
#include "models.h"
void llama_model_smallthinker::load_arch_hparams(llama_model_loader & ml) {
const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
if (found_swa && hparams.n_swa > 0) {
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
hparams.n_swa = 4096;
uint32_t swa_period = 4;
ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
hparams.set_swa_pattern(swa_period, true);
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_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
} else {
hparams.swa_type = LLAMA_SWA_TYPE_NONE;
hparams.n_no_rope_layer_step = hparams.n_layer;
}
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
switch (hparams.n_layer) {
case 32: type = LLM_TYPE_4B; break;
case 52: type = LLM_TYPE_20B; break;
default: type = LLM_TYPE_UNKNOWN;
}
}
void llama_model_smallthinker::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
// output
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
// if output is NULL, init from the input tok embed
if (output == NULL) {
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
}
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
GGML_ASSERT(n_expert > 0 && "n_expert must be > 0 for SMALLTHINKER");
GGML_ASSERT(n_expert_used > 0 && "n_expert_used must be > 0 for SMALLTHINKER");
// MoE branch
const int64_t n_ff_exp = hparams.n_ff_exp;
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, 0);
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
}
}
std::unique_ptr<llm_graph_context> llama_model_smallthinker::build_arch_graph(const llm_graph_params & params) const {
if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {
return std::make_unique<graph<true>> (*this, params);
} else {
return std::make_unique<graph<false>>(*this, params);
}
}
template <bool iswa>
llm_build_smallthinker<iswa>::llm_build_smallthinker(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params){
llama_model_smallthinker::graph<iswa>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params){
const int64_t n_embd_head = hparams.n_embd_head_v();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
@@ -113,5 +186,5 @@ llm_build_smallthinker<iswa>::llm_build_smallthinker(const llama_model & model,
}
// Explicit template instantiations
template struct llm_build_smallthinker<false>;
template struct llm_build_smallthinker<true>;
template struct llama_model_smallthinker::graph<false>;
template struct llama_model_smallthinker::graph<true>;