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llama.cpp
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Add load_bpw_state()

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Ed Addario 2025-10-05 20:18:36 +01:00
parent e48ca32f19
commit 02c3073b81
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1 changed files with 64 additions and 0 deletions
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64
src/llama-quant.cpp
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@ -672,7 +672,9 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
constexpr double epsilon = 1e-12;
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];
@ -784,6 +786,68 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
LLAMA_LOG_INFO("%s: saved bpw 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> {
std::unordered_map<std::string, saved_info> out;
std::ifstream ifs(checkpoint_file, std::ios::binary);
if (!ifs) { return out; }
uint32_t magic = 0;
float target_bpw = 0.0f;
uint8_t bias_mode = 0;
ifs.read((char *)&magic, sizeof(magic));
ifs.read((char *)&target_bpw, sizeof(target_bpw));
ifs.read((char *)&bias_mode, sizeof(bias_mode));
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());
return out;
}
if (bias_mode != (params->no_bias ? 1 : 0)) {
LLAMA_LOG_WARN("%s: bias mode does not match, ignoring: %s\n", func, checkpoint_file.c_str());
return out;
}
uint64_t n = 0;
ifs.read((char *)&n, sizeof(n));
for (uint64_t i = 0; i < n; ++i) {
uint32_t len = 0;
ifs.read((char *)&len, sizeof(len));
std::string name(len, '\0');
ifs.read(name.data(), len);
uint64_t cn = 0;
ifs.read((char *)&cn, sizeof(cn));
saved_info si;
ifs.read((char *)&si.choice, sizeof(si.choice));
ifs.read((char *)&si.min_bpw, sizeof(si.min_bpw));
ifs.read((char *)&si.max_bpw, sizeof(si.max_bpw));
uint64_t ne = 0;
ifs.read((char *)&ne, sizeof(ne));
si.n_elements = (size_t)ne;
si.candidate.resize(cn);
for (size_t j = 0; j < si.candidate.size(); ++j) {
int32_t t = 0;
uint64_t b = 0;
ifs.read((char *)&t, sizeof(t));
si.candidate[j].type = (ggml_type)t;
ifs.read((char *)&si.candidate[j].bpw, sizeof(si.candidate[j].bpw));
ifs.read((char *)&b, sizeof(b));
si.candidate[j].bytes = (size_t)b;
ifs.read((char *)&si.candidate[j].error, sizeof(si.candidate[j].error));
}
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());
return out;
};
// Estimate error for a given type using a sampled subset of rows
auto estimate_error = [&](const ggml_tensor * t,
const ggml_type quant_type,