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llama.cpp
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Finetune heuristics

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Ed Addario 2025-10-25 12:09:20 +01:00
parent 04561d5782
commit d6ccd5649a
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1 changed files with 44 additions and 39 deletions
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83
src/llama-quant.cpp
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@ -838,7 +838,7 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
LLAMA_LOG_WARN("%s: model ID mismatch, ignoring: %s\n", func, checkpoint_file.c_str());
return out;
} else {
LLAMA_LOG_INFO("%s: resuming tensor quantization\n", func);
LLAMA_LOG_INFO("%s: state file found, resuming tensor quantization\n", func);
}
uint64_t n = 0;
@ -1569,54 +1569,59 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
return emit_overrides();
}
auto tensor_importance = [&](const std::vector<tensor_info> & all_vec) -> std::unordered_map<std::string, float> {
auto tensor_depth = [&](const std::string & name) -> float {
static const std::regex layer_pattern(R"(blk\.(\d+)\.)");
std::smatch match;
// Depth component: output, embeddings & early/late layers are important
if (name == "output.weight" || name == "token_embd.weight") {
return 1.0f;
}
if (std::regex_search(name, match, layer_pattern)) {
const int layer = std::stoi(match[1]);
const float normalized_layer = (float)layer / (float)std::max(1, (int)model.hparams.n_layer - 1);
const float center_dist = std::abs(normalized_layer - 0.5f) * 2.0f;
return 0.01f + 0.9f * center_dist;
}
return 0.0f;
};
auto tensor_importance = [&](const std::vector<tensor_info> & all_tensors) -> std::unordered_map<std::string, float> {
std::unordered_map<std::string, float> scores;
for (const auto & ti : all_vec) {
const std::string name = ggml_get_name(ti.w->tensor);
for (const auto & t : all_tensors) {
const std::string name = ggml_get_name(t.w->tensor);
float total_score = 0.0f;
float depth_score = 0.0f;
float type_score = 0.0f;
// Depth component: output & early/late layers are important
if (name == "output.weight") {
depth_score = 1.0f;
} else {
static const std::regex layer_pattern(R"(blk\.(\d+)\.)");
std::smatch match;
if (std::regex_search(name, match, layer_pattern)) {
const int layer = std::stoi(match[1]);
const float normalized_layer = (float)layer / (float)std::max(1, (int)model.hparams.n_layer - 1);
const float center_dist = std::abs(normalized_layer - 0.5f) * 2.0f;
depth_score = 0.9f * center_dist;
}
}
// Type component: certain tensor types have more impact on model quality
const std::vector<std::pair<float, std::vector<const char*>>> tensor_scores = {
{0.9f, {".ffn_down.weight", ".ffn_down_exps.weight"}},
{0.89f, {".attn_output.weight", ".time_mix_output.weight", ".attn_o.weight"}},
{0.3f, {".ffn_up.weight", ".ffn_gate.weight", ".ffn_up_exps.weight", ".ffn_gate_exps.weight"}},
{0.29f, {".attn_q.weight", ".attn_k.weight", ".attn_v.weight", ".attn_qkv.weight"}},
{0.2f, {"token_embd.weight"}}
};
if (name == "output.weight") {
type_score = 1.0f;
} else if (name.find(".ffn_down.weight") != std::string::npos ||
name.find(".ffn_down_exps.weight") != std::string::npos) {
type_score = 0.9f;
} else if (name.find(".attn_output.weight") != std::string::npos ||
name.find(".time_mix_output.weight") != std::string::npos ||
name.find(".attn_o.weight") != std::string::npos) {
type_score = 0.8f;
} else if (name.find(".ffn_up.weight") != std::string::npos ||
name.find(".ffn_gate.weight") != std::string::npos ||
name.find(".ffn_up_exps.weight") != std::string::npos ||
name.find(".ffn_gate_exps.weight") != std::string::npos) {
type_score = 0.3f;
} else if (name.find(".attn_q.weight") != std::string::npos ||
name.find(".attn_k.weight") != std::string::npos ||
name.find(".attn_v.weight") != std::string::npos ||
name.find(".attn_qkv.weight") != std::string::npos) {
type_score = 0.2f;
} else if (name.find("token_embd.weight") != std::string::npos) {
type_score = 0.1f;
} else {
for (const auto& ts : tensor_scores) {
const bool found = std::any_of(ts.second.begin(), ts.second.end(), [&](const char* pattern) {
return name.find(pattern) != std::string::npos;
});
if (found) {
type_score = ts.first;
break;
}
}
}
if (type_score > 0.0f) {
depth_score = tensor_depth(name);
}
// Weighted combination
total_score = 0.8f * type_score + 0.2f * depth_score; // 80% type + 20% depth
total_score = 0.90f * type_score + 0.10f * depth_score; // 90% type + 10% depth
if (total_score != 0.0f) {
scores[name] = total_score;
LLAMA_LOG_DEBUG("\t%s: \t %45s \t depth score %.4f \t type score %.4f \t total score %.4f\n", func, name.c_str(), depth_score, type_score, total_score);
@ -1634,7 +1639,7 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
std::sort(sorted_scores.begin(), sorted_scores.end(), [](const auto & a, const auto & b) { return a.second > b.second; });
// Select top percentile
const size_t n_important = std::max<size_t>(1, std::llround((double)sorted_scores.size() * 0.25f)); // bump top 25%
const size_t n_important = std::max<size_t>(1, std::llround((double)sorted_scores.size() * 0.29f)); // 29% seems to be the pareto front
std::unordered_set<std::string> important;
for (size_t i = 0; i < std::min(n_important, sorted_scores.size()); ++i) {