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llama.cpp
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Relax lambda clamping

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Ed Addario 2025-10-05 14:41:42 +01:00
parent 66d4aed173
commit 560e8c9d70
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1 changed files with 4 additions and 4 deletions
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8
src/llama-quant.cpp
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@ -701,7 +701,7 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
};
auto make_compatible = [&](const ggml_tensor * t, const ggml_type typ) -> ggml_type {
if (is_compatible(t, typ)) return typ;
if (is_compatible(t, typ)) { return typ; }
ggml_type fb = fallback_type(typ);
return is_compatible(t, fb) ? fb : GGML_TYPE_F16;
};
@ -941,7 +941,7 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
if (s1 > 0.0) {
const auto n = (double)n_per_row;
const double c = std::max(0.0, s2 / (s1 * s1 + epsilon) - 1.0 / n);
l = (float)std::clamp(12.0 * (c / (c + 1.0)), 0.0, 12.0);
l = (float)std::clamp(12.0 * (c / (c + 1.0)), 0.0, 16.0);
}
lambdas[(size_t)s] = l;
@ -1035,7 +1035,7 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
for (int64_t r = offset; r < nrows_total && current < rows_sample_max; r += stride) {
const uint8_t * src_row = (const uint8_t *)tensor->data + slice * (src_row_sz * nrows_total) + r * src_row_sz;
if (src_type == GGML_TYPE_F32) {
auto src_f32 = (const float *)src_row;
const auto *src_f32 = (const float *)src_row;
f32_sample.insert(f32_sample.end(), src_f32, src_f32 + n_per_row);
} else {
row_to_fp32(src_row, row_buffer.data());
@ -1173,7 +1173,7 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
// Keep only the pareto‑optimal candidates and enforce convexity in (bytes, error) curve
auto pareto_convex = [](std::vector<candidate_types> & candidates) {
if (candidates.empty()) return;
if (candidates.empty()) { return; }
std::sort(candidates.begin(), candidates.end(), [](const candidate_types & a, const candidate_types & b) {
if (a.bytes != b.bytes) { return a.bytes < b.bytes; }