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llama.cpp
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Improve logging and some minor code refactoring

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Ed Addario 2025-08-24 13:39:10 +01:00
parent 61c0e01f50
commit d4ac2106fb
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GPG Key ID: E7875815A3230993
2 changed files with 16 additions and 17 deletions
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20
src/llama-quant.cpp
View File

@ -132,7 +132,6 @@ static std::string remap_imatrix (const std::string & orig_name, const std::map<
for (const auto & p : mapped) {
if (p.second == blk) {
LLAMA_LOG_DEBUG("(blk.%d imatrix) ", p.first);
return new_name.replace(match.position(1), match.length(1), std::to_string(p.first));
}
}
@ -1257,7 +1256,7 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
// Build the override map
std::unordered_map<std::string, ggml_type> overrides;
LLAMA_LOG_INFO("%s: - estimated tensor quantization mix to achieve %.4f bpw at lowest ppl\n", __func__, target_bpw);
LLAMA_LOG_INFO("%s: - estimated tensor quantization mix:\n", __func__);
for (const auto & ti : all) {
LLAMA_LOG_INFO("\t%s: %45s - \t%8s, \t%1.4f bpw,\terror: %.4f\n",
__func__, ggml_get_name(ti.w->tensor), ggml_type_name(ti.candidate[ti.choice].type), ti.candidate[ti.choice].bpw, ti.candidate[ti.choice].error);
@ -1352,7 +1351,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
if (params->imatrix) {
values_data = static_cast<const std::unordered_map<std::string, std::vector<float>>*>(params->imatrix);
if (values_data) {
LLAMA_LOG_INFO("================================ Have weights data with %d entries\n",int(values_data->size()));
LLAMA_LOG_INFO("================================ Have weights data with %d entries",int(values_data->size()));
qs.has_imatrix = true;
// check imatrix for nans or infs
for (const auto & kv : *values_data) {
@ -1367,7 +1366,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
if (params->activations) {
activations_data = static_cast<const std::unordered_map<std::string, std::vector<float>>*>(params->activations);
if (activations_data) {
LLAMA_LOG_INFO("================================ Have activations data with %d entries\n",int(activations_data->size()));
LLAMA_LOG_INFO(" and %d activations",int(activations_data->size()));
qs.has_activations = true;
// check activations for nans or infs
for (const auto & kv : *activations_data) {
@ -1379,6 +1378,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
}
}
}
LLAMA_LOG_INFO("\n");
gguf_context_ptr ctx_out { gguf_init_empty() };
@ -1655,12 +1655,16 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
if (!params->pure && ggml_is_quantized(default_type)) {
int fallback = qs.n_fallback;
new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
// get bpw override
// get quantization type overrides targeting a given bits per weight budget
if (params->target_bpw != -1.0f && !bpw_overrides.empty()) {
const auto override = bpw_overrides.find(name);
if (override != bpw_overrides.end() && override->second != new_type) {
LLAMA_LOG_DEBUG("(bpw overriding %s) ", ggml_type_name(new_type));
LLAMA_LOG_DEBUG("(bpw override %s) ", ggml_type_name(new_type));
new_type = override->second;
}
}
// unless the user specifies a type, and the tensor shape will not require fallback quantisation
if (params->tensor_types && qs.n_fallback - fallback == 0) {
const std::vector<tensor_quantization> & tensor_types = *static_cast<const std::vector<tensor_quantization> *>(params->tensor_types);
@ -1668,7 +1672,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
for (const auto & [tname, qtype] : tensor_types) {
if (std::regex pattern(tname); std::regex_search(tensor_name, pattern)) {
if (qtype != new_type) {
LLAMA_LOG_DEBUG("(type overriding %s) ", ggml_type_name(new_type));
LLAMA_LOG_DEBUG("(type override %s) ", ggml_type_name(new_type));
new_type = qtype; // if two or more types are specified for the same tensor, the last match wins
}
}
@ -1699,7 +1703,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
if (values_data) {
auto it = values_data->find(remap_imatrix(tensor->name, mapped));
if (it == values_data->end()) {
LLAMA_LOG_INFO("\n====== %s: did not find weights for %s\n", __func__, tensor->name);
LLAMA_LOG_INFO("\n====== %s: did not find weights for %s, ", __func__, tensor->name);
} else {
if (it->second.size() == (size_t)tensor->ne[0]*tensor->ne[2]) {
imatrix = it->second.data();

7
tools/quantize/quantize.cpp
View File

@ -399,12 +399,7 @@ static int prepare_imatrix(const std::string & imatrix_file,
values_data = std::move(tmp_values);
activations_data = std::move(tmp_activations);
}
if (!values_data.empty()) {
printf("%s: have %d importance matrix value entries\n", __func__, int(values_data.size()));
}
if (!activations_data.empty()) {
printf("%s: have %d importance matrix activation entries\n", __func__, int(activations_data.size()));
}
return m_last_call;
}