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EAddarioEAddario
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Refactor variable name
1 parent f1c2a4c commit c39c4e2

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+35
-35
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1 file changed

+35
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‎tools/imatrix/imatrix.cpp‎

Lines changed: 35 additions & 35 deletions
Original file line numberDiff line numberDiff line change
@@ -40,7 +40,7 @@ static const char * const LLM_KV_IMATRIX_CHUNK_SIZE = "imatrix.chunk_size";
4040

4141
struct Stats {
4242
std::vector<float> in_sum;
43-
std::vector<float> in_sum2;
43+
std::vector<float> values;
4444
std::vector<int64_t> counts;
4545
};
4646

@@ -130,7 +130,7 @@ static void process_tensor_name(const std::string & input, std::string & layer,
130130
static std::vector<float> compute_tensor_averages(const Stats & tstats) {
131131
if (tstats.counts.empty()) return {};
132132
const size_t n_mat = tstats.counts.size();
133-
const size_t len = !tstats.in_sum.empty() ? tstats.in_sum.size() : tstats.in_sum2.size();
133+
const size_t len = !tstats.in_sum.empty() ? tstats.in_sum.size() : tstats.values.size();
134134

135135
if (len == 0 || len % n_mat != 0) return {};
136136
const size_t row = len / n_mat;
@@ -152,7 +152,7 @@ static std::vector<float> compute_tensor_averages(const Stats & tstats) {
152152
if (c <= 0) return {};
153153
const size_t off = m * row;
154154
for (size_t j = 0; j < row; ++j) {
155-
vec.push_back(tstats.in_sum2[off + j] / c);
155+
vec.push_back(tstats.values[off + j] / c);
156156
}
157157
}
158158
}
@@ -161,8 +161,8 @@ static std::vector<float> compute_tensor_averages(const Stats & tstats) {
161161
}
162162

163163
static int compute_vector_statistics(std::vector<tensor_statistics> & tstats, const std::string & name, const Stats & e) {
164-
if (e.in_sum2.size() % e.counts.size() != 0) {
165-
LOG_ERR("%s: activation size mismatch for tensor %s (%zu vs %zu)\n", __func__, name.c_str(), e.counts.size(), e.in_sum2.size());
164+
if (e.values.size() % e.counts.size() != 0) {
165+
LOG_ERR("%s: activation size mismatch for tensor %s (%zu vs %zu)\n", __func__, name.c_str(), e.counts.size(), e.values.size());
166166
return -1;;
167167
}
168168
if (e.counts.empty()) {
@@ -171,17 +171,17 @@ static int compute_vector_statistics(std::vector<tensor_statistics> & tstats, co
171171
}
172172

173173
const int n_mat = e.counts.size();
174-
const int row_size = e.in_sum2.size() / n_mat;
174+
const int row_size = e.values.size() / n_mat;
175175
const int calc_mode = e.in_sum.empty() ? 2 : 1;
176176

177177
std::vector<float> activations;
178178

179179
if (e.in_sum.empty()) {
180-
activations.reserve(e.in_sum2.size());
180+
activations.reserve(e.values.size());
181181

182182
for (int i = 0; i < n_mat; ++i) {
183183
for (int j = 0; j < row_size; ++j) {
184-
activations.push_back(e.in_sum2[i*row_size + j] / e.counts[i]);
184+
activations.push_back(e.values[i*row_size + j] / e.counts[i]);
185185
}
186186
}
187187
} else {
@@ -420,13 +420,13 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
420420
// broadcast, when loading an old imatrix
421421
e.counts.resize(n_as, e.counts[0]);
422422
}
423-
if (e.in_sum2.empty()) {
423+
if (e.values.empty()) {
424424
e.in_sum.resize(src1->ne[0]*n_as, 0);
425-
e.in_sum2.resize(src1->ne[0]*n_as, 0);
425+
e.values.resize(src1->ne[0]*n_as, 0);
426426
e.counts.resize(n_as, 0);
427427
}
428-
else if (e.in_sum2.size() != (size_t)src1->ne[0]*n_as) {
429-
LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.in_sum2.size(), (int)(src1->ne[0]*n_as));
428+
else if (e.values.size() != (size_t)src1->ne[0]*n_as) {
429+
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));
430430
exit(1); //GGML_ABORT("fatal error");
431431
}
432432
else if (e.counts.size() != (size_t)n_as) {
@@ -454,9 +454,9 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
454454

455455
for (int64_t j = 0; j < src1->ne[0]; ++j) {
456456
e.in_sum[e_start + j] += x[j];
457-
e.in_sum2[e_start + j] += x[j] * x[j];
458-
if (!std::isfinite((float)e.in_sum2[e_start + j])) {
459-
LOG_ERR("%f detected in %s\n", (float)e.in_sum2[e_start + j], wname.c_str());
457+
e.values[e_start + j] += x[j] * x[j];
458+
if (!std::isfinite((float)e.values[e_start + j])) {
459+
LOG_ERR("%f detected in %s\n", (float)e.values[e_start + j], wname.c_str());
460460
exit(1);
461461
}
462462
}
@@ -478,13 +478,13 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
478478
auto & e = m_stats[wname];
479479
const int64_t n_mat = src1->ne[2] * src1->ne[3];
480480

481-
if (e.in_sum2.empty()) {
481+
if (e.values.empty()) {
482482
e.in_sum.resize(src1->ne[0] * n_mat, 0);
483-
e.in_sum2.resize(src1->ne[0] * n_mat, 0);
483+
e.values.resize(src1->ne[0] * n_mat, 0);
484484
e.counts.resize(n_mat, 0);
485485
}
486-
else if (e.in_sum2.size() != (size_t)(src1->ne[0] * n_mat)) {
487-
LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.in_sum2.size(), (int)(src1->ne[0] * n_mat));
486+
else if (e.values.size() != (size_t)(src1->ne[0] * n_mat)) {
487+
LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.values.size(), (int)(src1->ne[0] * n_mat));
488488
exit(1); //GGML_ABORT("fatal error");
489489
}
490490
else if (e.counts.size() != (size_t)n_mat) {
@@ -502,9 +502,9 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
502502
e.counts[mat_id]++;
503503
for (int64_t j = 0; j < src1->ne[0]; ++j) {
504504
e.in_sum[mat_start + j] += x[j];
505-
e.in_sum2[mat_start + j] += x[j] * x[j];
506-
if (!std::isfinite((float)e.in_sum2[j])) {
507-
LOG_ERR("%f detected in %s\n", (float)e.in_sum2[j], wname.c_str());
505+
e.values[mat_start + j] += x[j] * x[j];
506+
if (!std::isfinite((float)e.values[j])) {
507+
LOG_ERR("%f detected in %s\n", (float)e.values[j], wname.c_str());
508508
exit(1);
509509
}
510510
}
@@ -593,14 +593,14 @@ void IMatrixCollector::save_imatrix_legacy(int32_t ncall) const {
593593
// ceiling division to avoid accidental zeros
594594
const int32_t ncall = (*std::max_element(stat.counts.begin(), stat.counts.end()) + (chunk_size - 1)) / chunk_size;
595595
out.write((const char *) &ncall, sizeof(ncall));
596-
const int32_t nval = stat.in_sum2.size();
596+
const int32_t nval = stat.values.size();
597597
const int32_t nmat = stat.counts.size();
598598
out.write((const char *) &nval, sizeof(nval));
599599
if (nval > 0 && nmat > 0) {
600600
std::vector<float> tmp(nval);
601601
for (int32_t i = 0; i < nval; i++) {
602602
float count = static_cast<float>(stat.counts[i / (nval / nmat)]);
603-
float value = stat.in_sum2[i];
603+
float value = stat.values[i];
604604
if (count == 0.0f) {
605605
// store 1 for partial data
606606
value = 1.0f;
@@ -676,7 +676,7 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const {
676676

677677
to_store.push_back(kv.first);
678678
data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.in_sum.size(), GGML_MEM_ALIGN);
679-
data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.in_sum2.size(), GGML_MEM_ALIGN);
679+
data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.values.size(), GGML_MEM_ALIGN);
680680
data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.counts.size(), GGML_MEM_ALIGN);
681681
}
682682

@@ -711,7 +711,7 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const {
711711

712712
for (const auto & name : to_store) {
713713
const auto & stat = m_stats.at(name);
714-
const int32_t nval = (int32_t) stat.in_sum2.size();
714+
const int32_t nval = (int32_t) stat.values.size();
715715
const int32_t nmat = (int32_t) stat.counts.size();
716716
if (nval > 0 && nmat > 0) {
717717
struct ggml_tensor * in_sum2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nval / nmat, nmat);
@@ -720,7 +720,7 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const {
720720
ggml_format_name(counts, "%s.counts", name.c_str());
721721

722722
for (int32_t j = 0; j < nval; ++j) {
723-
((float *) in_sum2->data)[j] = (float) stat.in_sum2[j];
723+
((float *) in_sum2->data)[j] = (float) stat.values[j];
724724
}
725725
for (int32_t j = 0; j < nmat; ++j) {
726726
((float *) counts->data)[j] = (float) stat.counts[j];
@@ -787,8 +787,8 @@ bool IMatrixCollector::load_imatrix_legacy(const char * fname) {
787787
return false;
788788
}
789789

790-
if (e.in_sum2.empty()) {
791-
e.in_sum2.resize(nval, 0.0f);
790+
if (e.values.empty()) {
791+
e.values.resize(nval, 0.0f);
792792
e.counts.resize(1, 0);
793793
}
794794

@@ -802,7 +802,7 @@ bool IMatrixCollector::load_imatrix_legacy(const char * fname) {
802802

803803
// Recreate the state as expected by save_imatrix(), and correct for weighted sum.
804804
for (int i = 0; i < nval; i++) {
805-
e.in_sum2[i] += tmp[i] * chunk_size;
805+
e.values[i] += tmp[i] * chunk_size;
806806
}
807807
// The legacy format doesn't distinguish the counts for different experts
808808
for (size_t j = 0; j < e.counts.size(); ++j) {
@@ -922,11 +922,11 @@ bool IMatrixCollector::load_imatrix(const char * file_name) {
922922
auto & e = m_stats[name];
923923

924924
int64_t nval = ggml_nelements(in_sum2);
925-
if (e.in_sum2.empty()) {
926-
e.in_sum2.resize(nval, 0.0f);
925+
if (e.values.empty()) {
926+
e.values.resize(nval, 0.0f);
927927
e.in_sum.resize(nval, 0.0f);
928-
} else if ((size_t) nval != e.in_sum2.size()) {
929-
LOG_ERR("%s: mismatched sums size for %s: %zu != %zu\n", __func__, name.c_str(), (size_t) nval, e.in_sum2.size());
928+
} else if ((size_t) nval != e.values.size()) {
929+
LOG_ERR("%s: mismatched sums size for %s: %zu != %zu\n", __func__, name.c_str(), (size_t) nval, e.values.size());
930930
gguf_free(ctx_gguf);
931931
ggml_free(ctx);
932932
return false;
@@ -947,7 +947,7 @@ bool IMatrixCollector::load_imatrix(const char * file_name) {
947947

948948
// Recreate the state as expected by save_imatrix()
949949
for (int64_t j = 0; j < nval; j++) {
950-
e.in_sum2[j] += ((const float *) in_sum2->data)[j];
950+
e.values[j] += ((const float *) in_sum2->data)[j];
951951
}
952952
for (int64_t j = 0; j < ncounts; j++) {
953953
e.counts[j] += std::lround(((const float *) counts->data)[j]);

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