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llama.cpp
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Refactor variable names

This commit is contained in:
Ed Addario 2026-01-07 18:28:57 +00:00
parent 774ba01367
commit d18ddbac9c
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GPG Key ID: E7875815A3230993
1 changed files with 12 additions and 12 deletions
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24
src/llama-quant.cpp
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@ -811,8 +811,8 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
}
}
// Serializes vector<tensor_info> to disk
auto save_bpw_state = [&](const std::vector<tensor_info> & all_vec) {
// Serializes vector<tensor_info> state to disk
auto save_state = [&](const std::vector<tensor_info> & all_vec) {
const std::string tmp = checkpoint_file + ".tmp";
std::ofstream ofs(tmp, std::ios::binary | std::ios::trunc);
if (!ofs) { return; }
@ -847,11 +847,11 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
ofs.close();
std::remove(checkpoint_file.c_str());
std::rename(tmp.c_str(), checkpoint_file.c_str());
LLAMA_LOG_INFO("%s: saved progress for %lu tensors to %s\n", func, all_vec.size(), checkpoint_file.c_str());
LLAMA_LOG_INFO("%s: saved target progress for %lu tensors to %s\n", func, all_vec.size(), checkpoint_file.c_str());
};
// Deserializes vector<tensor_info> from disk
auto load_bpw_state = [&]() -> std::unordered_map<std::string, saved_info> {
// Deserializes vector<tensor_info> state from disk
auto load_state = [&]() -> std::unordered_map<std::string, saved_info> {
std::unordered_map<std::string, saved_info> out;
std::ifstream ifs(checkpoint_file, std::ios::binary);
if (!ifs) { return out; }
@ -905,16 +905,15 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
out.emplace(std::move(name), std::move(si));
}
LLAMA_LOG_INFO("%s: loaded bpw state for %lu tensors from %s\n", func, out.size(), checkpoint_file.c_str());
LLAMA_LOG_INFO("%s: loaded target state for %lu tensors from %s\n", func, out.size(), checkpoint_file.c_str());
return out;
};
// Check for user interrupt and save progress
auto check_signal_handler = [&](const std::vector<tensor_info> & all_vec) {
if (bpw_stop.load(std::memory_order_relaxed)) {
LLAMA_LOG_INFO("\n%s: saving progress for %lu tensors to %s\n", func, all_vec.size(), checkpoint_file.c_str());
save_bpw_state(all_vec);
save_state(all_vec);
throw std::runtime_error("user interrupted the process");
}
};
@ -1172,7 +1171,8 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
return lambdas;
};
const auto bpw_data = load_bpw_state();
std::unordered_map<std::string, saved_info> bpw_data;
if (params->state_file && !checkpoint_file.empty()) { bpw_data = load_state(); }
// Parallelize tensor processing (courtesy of https://github.com/ddh0)
auto process_tensor = [&](const llama_model_loader::llama_tensor_weight * tw,
@ -1530,7 +1530,7 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
}
check_signal_handler(all);
if (params->keep_bpw_state) { save_bpw_state(all); }
if (params->save_state) { save_state(all); }
if (all.empty()) { return {}; }
@ -1610,7 +1610,7 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
// Certain tensors have a higher impact on model quality, so we apply a lower penalty to them
auto is_important = [&](const std::string & tensor_name) -> bool {
bool important = tensor_name == "output.weight";
if (!important && !params->no_importance) {
if (!important && !params->ignore_tensor_importance) {
important = tensor_name.find(".attn_v.weight") != std::string::npos ||
tensor_name.find(".time_mix_value.weight") != std::string::npos ||
tensor_name.find(".ffn_down.weight") != std::string::npos ||
@ -2025,7 +2025,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
} else {
LLAMA_LOG_INFO("%s: imatrix does not have activations, process may be less accurate\n", __func__);
}
if (params->no_importance) {
if (params->ignore_tensor_importance) {
LLAMA_LOG_INFO("%s: distributing budget equitably across all tensors\n", __func__);
} else {
LLAMA_LOG_INFO("%s: assigning more budget to important tensors\n", __func__);