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llama.cpp
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Generate model ID hash

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Ed Addario 2025-10-09 11:58:01 +01:00
parent 044fa783c7
commit c11184a3c1
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GPG Key ID: E7875815A3230993
1 changed files with 38 additions and 13 deletions
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51
src/llama-quant.cpp
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@ -674,7 +674,6 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
constexpr double infinity = std::numeric_limits<double>::infinity();
constexpr uint32_t file_magic = 0x42505731; // BPW1
const char * func = __func__;
const std::string checkpoint_file = ml.arch_name + ".bpw_state";
auto tensor_bytes = [](const ggml_tensor * t, const ggml_type typ) -> size_t {
const int64_t n_per_row = t->ne[0];
@ -745,6 +744,26 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
size_t n_elements = 0;
};
auto djb2_hash = [](const uint8_t * data, size_t n) -> uint64_t {
uint64_t h = 5381;
for (size_t i = 0; i < n; ++i) {
h = (h << 5) + h + data[i];
}
return h ? h : 0xeabada55cafed00d;
};
auto metadata_id = [&](const gguf_context * ctx) -> uint64_t {
const size_t sz = gguf_get_meta_size(ctx);
std::vector<uint8_t> buf(sz);
gguf_get_meta_data(ctx, buf.data());
return djb2_hash(buf.data(), buf.size());
};
char hex[17];
const uint64_t model_id = metadata_id(ml.meta.get());
std::snprintf(hex, sizeof(hex), "%016" PRIx64, (uint64_t)model_id);
const std::string checkpoint_file = ml.arch_name + "-" + std::string(hex) + ".bpw_state";
auto save_bpw_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);
@ -752,6 +771,7 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
const float target_bpw = params->target_bpw;
const uint8_t bias_mode = params->no_bias ? 1 : 0;
ofs.write((const char *)&file_magic, sizeof(file_magic));
ofs.write((const char *)&model_id, sizeof(model_id));
ofs.write((const char *)&target_bpw, sizeof(target_bpw));
ofs.write((const char *)&bias_mode, sizeof(bias_mode));
const uint64_t n = all_vec.size();
@ -781,9 +801,9 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
}
ofs.close();
std::remove(checkpoint_file.c_str()); // TODO: handle errors
std::remove(checkpoint_file.c_str());
std::rename(tmp.c_str(), checkpoint_file.c_str());
LLAMA_LOG_INFO("%s: saved bpw progress for %lu tensors to %s\n", func, all_vec.size(), checkpoint_file.c_str());
LLAMA_LOG_INFO("%s: saved progress for %lu tensors to %s\n", func, all_vec.size(), checkpoint_file.c_str());
};
auto load_bpw_state = [&]() -> std::unordered_map<std::string, saved_info> {
@ -792,22 +812,27 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
if (!ifs) { return out; }
uint32_t magic = 0;
float target_bpw = 0.0f;
uint8_t bias_mode = 0;
uint64_t id = 0;
float bpw = 0.0f;
uint8_t bias = 0;
ifs.read((char *)&magic, sizeof(magic));
ifs.read((char *)&target_bpw, sizeof(target_bpw));
ifs.read((char *)&bias_mode, sizeof(bias_mode));
ifs.read((char *)&id, sizeof(id));
ifs.read((char *)&bpw, sizeof(bpw));
ifs.read((char *)&bias, sizeof(bias));
if (magic != file_magic) {
LLAMA_LOG_WARN("%s: invalid resume file, ignoring: %s\n", func, checkpoint_file.c_str());
return out;
}
if (target_bpw != params->target_bpw) {
LLAMA_LOG_WARN("%s: target bpw of %f does not match %f, ignoring: %s\n", func, params->target_bpw, target_bpw, checkpoint_file.c_str());
} else if (id != model_id) {
LLAMA_LOG_WARN("%s: model ID mismatch, ignoring: %s\n", func, checkpoint_file.c_str());
return out;
}
if (bias_mode != (params->no_bias ? 1 : 0)) {
} else if (bpw != params->target_bpw) {
LLAMA_LOG_WARN("%s: target bpw of %f does not match %f, ignoring: %s\n", func, params->target_bpw, bpw, checkpoint_file.c_str());
return out;
} else if (bias != (params->no_bias ? 1 : 0)) {
LLAMA_LOG_WARN("%s: bias mode does not match, ignoring: %s\n", func, checkpoint_file.c_str());
return out;
} else {
LLAMA_LOG_INFO("%s: resuming tensor quantization\n", func);
}
uint64_t n = 0;
@ -859,7 +884,7 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
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 bpw progress for %lu tensors to %s\n", func, all_vec.size(), checkpoint_file.c_str());
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);
throw std::runtime_error("user interrupted the process");
}