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llama.cpp
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more cleanup

This commit is contained in:
ryan-mangeno 2025-08-28 12:47:10 -04:00
parent 40249dd5ec
commit 853f344cfe
4 changed files with 10 additions and 19 deletions
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1
ggml/src/ggml.c
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@ -3015,7 +3015,6 @@ struct ggml_tensor * ggml_mul_mat(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b) {
GGML_ASSERT(ggml_can_mul_mat(a, b));
GGML_ASSERT(!ggml_is_transposed(a));

22
src/llama-model.cpp
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@ -451,7 +451,6 @@ void llama_model::load_arch(llama_model_loader & ml) {
}
void llama_model::load_hparams(llama_model_loader & ml) {
const gguf_context * ctx = ml.meta.get();
// get metadata as string
@ -465,7 +464,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
gguf_kv.emplace(name, value);
}
// get general kv
ml.get_key(LLM_KV_GENERAL_NAME, name, false);
@ -586,7 +584,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
}
// arch-specific KVs
LLAMA_LOG_INFO("Switching Arch\n");
switch (arch) {
case LLM_ARCH_LLAMA:
{
@ -1901,6 +1898,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
void llama_model::load_vocab(llama_model_loader & ml) {
const auto kv = LLM_KV(arch);
vocab.load(ml, kv);
}
@ -2045,7 +2043,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
auto create_tensor = [&](const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags) -> ggml_tensor * {
ggml_tensor * t_meta = ml.get_tensor_meta(tn.str().c_str());
LLAMA_LOG_INFO("Creating Tensor: %s\n", tn.str().c_str());
if (!t_meta) {
if (flags & TENSOR_NOT_REQUIRED) {
return nullptr;
@ -2120,6 +2118,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
}
ggml_backend_buffer_type_t buft = nullptr;
// check overrides
if (ml.tensor_buft_overrides) {
std::string tensor_name = tn.str();
@ -2167,6 +2166,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
first_moved_to_buft = buft;
}
}
ggml_context * ctx = ctx_for_buft(buft);
// if duplicated, check if the original tensor was allocated in the same buffer type context and avoid creating a new one
@ -2624,14 +2624,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
case LLM_ARCH_NOMIC_BERT_MOE:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, TENSOR_NOT_REQUIRED);
tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, 0);
if (arch == LLM_ARCH_BERT) {
pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}, 0);
cls = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, TENSOR_NOT_REQUIRED);
cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"), {n_embd}, TENSOR_NOT_REQUIRED);
@ -2639,11 +2636,14 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"), {hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
}
tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, 0);
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, TENSOR_NOT_REQUIRED);
layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, TENSOR_NOT_REQUIRED);
if (!layer.wqkv) {
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
@ -2658,7 +2658,6 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, TENSOR_NOT_REQUIRED);
layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}, 0);
@ -2668,7 +2667,6 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0);
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
} else {
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
@ -2683,7 +2681,6 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}, 0);
}
} break;
case LLM_ARCH_MODERN_BERT:
@ -7549,7 +7546,6 @@ struct llm_build_modern_bert : public llm_graph_context {
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
const int64_t n_tokens = ubatch.n_tokens;
const int64_t n_ff = hparams.n_ff();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

3
src/llama-vocab.cpp
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@ -1661,13 +1661,10 @@ private:
void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
struct gguf_context * ctx = ml.meta.get();
LLAMA_LOG_INFO("Determining Vocab Type\n");
// determine vocab type
{
ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_model);
ml.get_key(LLM_KV_TOKENIZER_PRE, tokenizer_pre, false);
LLAMA_LOG_INFO("pre tokenizer model: %s\n", tokenizer_pre.c_str());
LLAMA_LOG_INFO("tokenizer model: %s\n", tokenizer_model.c_str());
ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, n_token_types, false);

1
src/llama.cpp
View File

@ -126,7 +126,6 @@ static int llama_model_load(const std::string & fname, std::vector<std::string>
if (!model.load_tensors(ml)) {
return -2;
}
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: error loading model: %s\n", __func__, err.what());
return -1;