Skip experts with zero count (unused)
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@ -133,30 +133,40 @@ static std::vector<float> compute_tensor_averages(const Stats & tstats) {
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std::vector<float> vec;
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vec.reserve(len);
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bool has_valid = false;
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if (tstats.activations.empty()) {
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// Mean of squares
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// Mean of squares (legacy: only values are available)
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for (size_t m = 0; m < n_mat; ++m) {
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const auto c = (float)tstats.counts[m];
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const float c = (float) tstats.counts[m];
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const size_t off = m * row;
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if (c <= 0.0f) {
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vec.insert(vec.end(), row, 0.0f); // zero-fill rows for experts with zero count to preserve shape
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for (size_t j = 0; j < row; ++j) { vec.push_back(0.0f); }
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continue;
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}
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for (size_t j = 0; j < row; ++j) { vec.push_back(tstats.values[off + j] / c); }
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has_valid = true;
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for (size_t j = 0; j < row; ++j) {
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vec.push_back(tstats.values[off + j] / c);
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}
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}
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} else {
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// Mean
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// Mean (new format: activations + values)
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for (size_t m = 0; m < n_mat; ++m) {
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const auto c = (float)tstats.counts[m];
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const float c = (float) tstats.counts[m];
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const size_t off = m * row;
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if (c <= 0.0f) {
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vec.insert(vec.end(), row, 0.0f); // zero-fill rows for experts with zero count to preserve shape
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for (size_t j = 0; j < row; ++j) { vec.push_back(0.0f); }
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continue;
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}
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for (size_t j = 0; j < row; ++j) { vec.push_back(tstats.activations[off + j] / c); }
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has_valid = true;
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for (size_t j = 0; j < row; ++j) {
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vec.push_back(tstats.activations[off + j] / c);
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}
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}
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}
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if (!has_valid) { return {}; }
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return vec;
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}
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