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llama.cpp
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Increase precision for error calculation

This commit is contained in:
Ed Addario 2025-08-19 23:31:22 +01:00
parent f22b3097eb
commit 936294f6af
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1 changed files with 9 additions and 11 deletions
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src/llama-quant.cpp
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@ -730,7 +730,7 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
std::vector<float> f32_sample(sample_rows * n_per_row); std::vector<float> f32_sample(sample_rows * n_per_row);
std::vector<float> deq(sample_rows * n_per_row); std::vector<float> deq(sample_rows * n_per_row);
float total_err = 0.0; double total_err = 0.0;
for (int64_t slice = 0; slice < ne2; ++slice) { for (int64_t slice = 0; slice < ne2; ++slice) {
const float * value = values_all ? (values_all + slice * n_per_row) : nullptr; const float * value = values_all ? (values_all + slice * n_per_row) : nullptr;
@ -754,9 +754,9 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
const float * xs = f32_sample.data() + s * n_per_row; const float * xs = f32_sample.data() + s * n_per_row;
const float * ys = deq.data() + s * n_per_row; const float * ys = deq.data() + s * n_per_row;
float mse_w = 0.0; double mse_w = 0.0;
float bias = 0.0; double bias = 0.0;
float bias_sum = 0.0; double bias_sum = 0.0;
if (value) { if (value) {
for (int64_t j = 0; j < n_per_row; ++j) { for (int64_t j = 0; j < n_per_row; ++j) {
@ -769,19 +769,17 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
} else { } else {
for (int64_t j = 0; j < n_per_row; ++j) { for (int64_t j = 0; j < n_per_row; ++j) {
const float e = ys[j] - xs[j]; const float e = ys[j] - xs[j];
mse_w += e*e; mse_w += e * e;
if (activation) { if (activation) {
bias_sum += e * activation[j]; bias_sum += e * activation[j];
} }
} }
} }
if (activation) { if (activation) { bias = std::abs(bias_sum); }
bias = std::abs(bias_sum);
}
// Normalize by n_per_row to get a per-row average scale // Normalize by n_per_row to get a per-row average scale
float row_err = mse_w / std::max<int64_t>(1, n_per_row); double row_err = mse_w / std::max<int64_t>(1, n_per_row);
if (bias_lambda != 0.0) { if (bias_lambda != 0.0) {
row_err += bias_lambda * (bias / std::max<int64_t>(1, n_per_row)); row_err += bias_lambda * (bias / std::max<int64_t>(1, n_per_row));
} }
@ -790,11 +788,11 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
} }
// Scale for the rows we didn't sample in this expert: multiply by stride-ish factor // Scale for the rows we didn't sample in this expert: multiply by stride-ish factor
const float scale_rows = (float)rows_per_expert / std::max(1.0f, (float)rs); const auto scale_rows = (double)rows_per_expert / std::max(1.0, (double)rs);
total_err *= scale_rows; total_err *= scale_rows;
} }
return total_err; return std::isfinite(total_err) ? total_err : 1e35;
}; };
std::vector<tensor_info> all; std::vector<tensor_info> all;