sleepy/llama.cpp
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llama: end-to-end tests (#19802)

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* tests: add end-to-end tests per model architecture

* fixup for rebase

* fix use-after-free in llama-model-loader.cpp

* fix CI

* fix WebGPU

* fix CI

* disable CI for macOS-latest-cmake-arm64

* use expert_weights_scale only if != 0.0f

* comments
This commit is contained in:
Johannes Gäßler
2026-03-08 12:30:21 +01:00
committed by GitHub
parent a95047979a
commit a976ff081b
33 changed files with 1607 additions and 633 deletions
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src/llama-model-saver.cpp
+49 -39
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@@ -7,14 +7,19 @@
#include "llama-model.h"
#include "llama-vocab.h"
#include <cstdint>
#include <string>
llama_model_saver::llama_model_saver(const struct llama_model & model) : model(model), llm_kv(model.arch) {
gguf_ctx = gguf_init_empty();
}
llama_model_saver::llama_model_saver(const struct llama_model * model) :
gguf_ctx(gguf_init_empty()), gguf_ctx_owned(true), model(model), llm_kv(model->arch) {}
llama_model_saver::llama_model_saver(enum llm_arch arch, struct gguf_context * gguf_ctx) :
gguf_ctx(gguf_ctx == nullptr ? gguf_init_empty() : gguf_ctx), gguf_ctx_owned(gguf_ctx == nullptr), model(nullptr), llm_kv(arch) {}
llama_model_saver::~llama_model_saver() {
gguf_free(gguf_ctx);
if (gguf_ctx_owned) {
gguf_free(gguf_ctx);
}
}
void llama_model_saver::add_kv(const enum llm_kv key, const uint32_t value) {
@@ -46,7 +51,8 @@ void llama_model_saver::add_kv(const enum llm_kv key, const char value) {
template <typename Container>
void llama_model_saver::add_kv(const enum llm_kv key, const Container & value, const bool per_layer) {
const size_t n_values = per_layer ? size_t(model.hparams.n_layer) : value.size();
GGML_ASSERT(model != nullptr || !per_layer);
const size_t n_values = per_layer ? size_t(model->hparams.n_layer) : value.size();
GGML_ASSERT(n_values <= value.size());
if (n_values == 0) {
@@ -83,6 +89,8 @@ void llama_model_saver::add_kv(const enum llm_kv key, const Container & value, c
GGML_ABORT("fatal error");
}
}
// instantiate for external usage:
template void llama_model_saver::add_kv<std::vector<uint32_t>>(const enum llm_kv, const std::vector<uint32_t> &, const bool);
void llama_model_saver::add_kv(const enum llm_kv key, const std::vector<std::string> & value) {
std::vector<const char *> tmp(value.size());
@@ -104,37 +112,39 @@ void llama_model_saver::add_tensor(const struct ggml_tensor * tensor) {
}
void llama_model_saver::add_kv_from_model() {
const llama_hparams & hparams = model.hparams;
const llama_vocab & vocab = model.vocab;
const llama_hparams & hparams = model->hparams;
const llama_vocab & vocab = model->vocab;
const int32_t n_vocab = vocab.n_tokens();
std::vector<std::string> tokens(n_vocab);
std::vector<float> scores(n_vocab);
std::vector<int32_t> token_types(n_vocab);
for (int32_t id = 0; id < n_vocab; ++id) {
const llama_vocab::token_data & token_data = vocab.get_token_data(id);
if (vocab.get_type() != LLAMA_VOCAB_TYPE_NONE) {
for (int32_t id = 0; id < n_vocab; ++id) {
const llama_vocab::token_data & token_data = vocab.get_token_data(id);
tokens[id] = token_data.text;
scores[id] = token_data.score;
tokens[id] = token_data.text;
scores[id] = token_data.score;
switch(token_data.attr) {
case LLAMA_TOKEN_ATTR_UNKNOWN: token_types[id] = LLAMA_TOKEN_TYPE_UNKNOWN; break;
case LLAMA_TOKEN_ATTR_UNUSED: token_types[id] = LLAMA_TOKEN_TYPE_UNUSED; break;
case LLAMA_TOKEN_ATTR_NORMAL: token_types[id] = LLAMA_TOKEN_TYPE_NORMAL; break;
case LLAMA_TOKEN_ATTR_CONTROL: token_types[id] = LLAMA_TOKEN_TYPE_CONTROL; break;
case LLAMA_TOKEN_ATTR_USER_DEFINED: token_types[id] = LLAMA_TOKEN_TYPE_USER_DEFINED; break;
case LLAMA_TOKEN_ATTR_BYTE: token_types[id] = LLAMA_TOKEN_TYPE_BYTE; break;
case LLAMA_TOKEN_ATTR_UNDEFINED:
default: token_types[id] = LLAMA_TOKEN_TYPE_UNDEFINED; break;
switch(token_data.attr) {
case LLAMA_TOKEN_ATTR_UNKNOWN: token_types[id] = LLAMA_TOKEN_TYPE_UNKNOWN; break;
case LLAMA_TOKEN_ATTR_UNUSED: token_types[id] = LLAMA_TOKEN_TYPE_UNUSED; break;
case LLAMA_TOKEN_ATTR_NORMAL: token_types[id] = LLAMA_TOKEN_TYPE_NORMAL; break;
case LLAMA_TOKEN_ATTR_CONTROL: token_types[id] = LLAMA_TOKEN_TYPE_CONTROL; break;
case LLAMA_TOKEN_ATTR_USER_DEFINED: token_types[id] = LLAMA_TOKEN_TYPE_USER_DEFINED; break;
case LLAMA_TOKEN_ATTR_BYTE: token_types[id] = LLAMA_TOKEN_TYPE_BYTE; break;
case LLAMA_TOKEN_ATTR_UNDEFINED:
default: token_types[id] = LLAMA_TOKEN_TYPE_UNDEFINED; break;
}
}
}
// add_kv(LLM_KV_GENERAL_TYPE, ???);
add_kv(LLM_KV_GENERAL_ARCHITECTURE, model.arch_name());
add_kv(LLM_KV_GENERAL_ARCHITECTURE, model->arch_name());
// add_kv(LLM_KV_GENERAL_QUANTIZATION_VERSION, ???);
// add_kv(LLM_KV_GENERAL_ALIGNMENT, ???);
add_kv(LLM_KV_GENERAL_NAME, model.name);
add_kv(LLM_KV_GENERAL_NAME, model->name);
// add_kv(LLM_KV_GENERAL_AUTHOR, ???);
// add_kv(LLM_KV_GENERAL_VERSION, ???);
// add_kv(LLM_KV_GENERAL_URL, ???);
@@ -255,25 +265,25 @@ void llama_model_saver::add_kv_from_model() {
}
void llama_model_saver::add_tensors_from_model() {
if (std::string(model.output->name) != std::string(model.tok_embd->name)) {
add_tensor(model.tok_embd); // some models use the same tensor for tok_embd and output
if (std::string(model->output->name) != std::string(model->tok_embd->name)) {
add_tensor(model->tok_embd); // some models use the same tensor for tok_embd and output
}
add_tensor(model.type_embd);
add_tensor(model.pos_embd);
add_tensor(model.tok_norm);
add_tensor(model.tok_norm_b);
add_tensor(model.output_norm);
add_tensor(model.output_norm_b);
add_tensor(model.output);
add_tensor(model.output_b);
add_tensor(model.output_norm_enc);
add_tensor(model.cls);
add_tensor(model.cls_b);
add_tensor(model.cls_out);
add_tensor(model.cls_out_b);
add_tensor(model.cls_norm);
add_tensor(model->type_embd);
add_tensor(model->pos_embd);
add_tensor(model->tok_norm);
add_tensor(model->tok_norm_b);
add_tensor(model->output_norm);
add_tensor(model->output_norm_b);
add_tensor(model->output);
add_tensor(model->output_b);
add_tensor(model->output_norm_enc);
add_tensor(model->cls);
add_tensor(model->cls_b);
add_tensor(model->cls_out);
add_tensor(model->cls_out_b);
add_tensor(model->cls_norm);
for (const struct llama_layer & layer : model.layers) {
for (const struct llama_layer & layer : model->layers) {
for (size_t i = 0; i < sizeof(layer)/sizeof(struct ggml_tensor *); ++i) {
add_tensor(reinterpret_cast<const struct ggml_tensor * const *>(&layer)[i]);
}