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Load model dry-run #2

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nicoboss wants to merge 5 commits into from
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Load model dry-run #2

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b5b73d1
imatrix : Allow partial data in imatrix
nicoboss Feb 16, 2025
9640acc
imatrix : Fixed compile warnings in allow partial data in imatrix code
nicoboss Feb 16, 2025
2332601
Merge branch 'ggml-org:master' into imatrix-allow-partial-data
nicoboss Feb 28, 2025
f2db416
Merge pull request #1 from nicoboss/imatrix-allow-partial-data
nicoboss Feb 28, 2025
55e158f
Merge branch 'ggml-org:master' into mradermacher
nicoboss Feb 28, 2025
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39 changes: 37 additions & 2 deletions examples/imatrix/imatrix.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -32,6 +32,7 @@ struct Stats {
std::vector<float> values;
std::vector<int> counts;
int ncall = 0;
int n_as = 1;
};

class IMatrixCollector {
Expand Down Expand Up @@ -124,11 +125,15 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
if (e.values.empty()) {
e.values.resize(src1->ne[0]*n_as, 0);
e.counts.resize(src1->ne[0]*n_as, 0);
e.n_as = n_as;
}
else if (e.values.size() != (size_t)src1->ne[0]*n_as) {
LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.values.size(), (int)src1->ne[0]*n_as);
exit(1); //GGML_ABORT("fatal error");
}
else if (e.n_as != n_as) {
LOG_ERR("%s: inconsistent n_as for %s (%d vs %d)\n", __func__, wname.c_str(), e.n_as, n_as);
}
LOG_DBGV(2, "%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[2], (int)src1->type);
// loop over all possible experts, regardless if they are used or not in the batch
for (int ex = 0; ex < n_as; ++ex) {
Expand Down Expand Up @@ -247,8 +252,38 @@ void IMatrixCollector::save_imatrix(int ncall) const {
}

if (n_zeros > 0) {
LOG_WRN("%s: entry '%40s' has partial data (%.2f%%) - skipping\n", __func__, kv.first.c_str(), 100.0f * (n_all - n_zeros) / n_all);
continue;
LOG_WRN("%s: entry '%40s' has partial data (%.2f%%)\n", __func__, kv.first.c_str(), 100.0f * (n_all - n_zeros) / n_all);
bool store_it = false;
if (kv.second.n_as > 1) {
int n_per_expert = n_all / kv.second.n_as;
std::vector<int> bad_experts;
bad_experts.reserve(kv.second.n_as);
for (int i = 0; i < kv.second.n_as; ++i) {
auto counts = kv.second.counts.data() + i*n_per_expert;
int nz_i = 0;
for (int j = 0; j < n_per_expert; ++j) {
if (counts[j] == 0) ++nz_i;
}
if (nz_i > 0) bad_experts.push_back(i);
}
LOG_WRN("%s: %d out of %d experts are missing data\n", __func__, int(bad_experts.size()), kv.second.n_as);
if (bad_experts.size() < round(kv.second.n_as * 0.05)) {
LOG_WRN("%s: %d out of %d experts are missing data - storing but be aware\n", __func__, int(bad_experts.size()), kv.second.n_as);
store_it = true;
for (auto i : bad_experts) {
auto counts = const_cast<int*>(kv.second.counts.data()) + i * n_per_expert;
auto values = const_cast<float*>(kv.second.values.data()) + i * n_per_expert;
for (int j = 0; j < n_per_expert; ++j) {
counts[j] = 1;
values[j] = 1;
}
}
}
}
if (!store_it) {
LOG_WRN("%s: Skipping expert with missing data!\n", __func__);
continue;
}
}

n_entries++;
Expand Down