From c39c4e2a331c3386f019885865698b826dcbce00 Mon Sep 17 00:00:00 2001 From: Ed Addario Date: Mon, 4 Aug 2025 22:15:50 +0100 Subject: [PATCH] Refactor variable name --- tools/imatrix/imatrix.cpp | 70 +++++++++++++++++++-------------------- 1 file changed, 35 insertions(+), 35 deletions(-) diff --git a/tools/imatrix/imatrix.cpp b/tools/imatrix/imatrix.cpp index 6a07fdd354..a28701944d 100644 --- a/tools/imatrix/imatrix.cpp +++ b/tools/imatrix/imatrix.cpp @@ -40,7 +40,7 @@ static const char * const LLM_KV_IMATRIX_CHUNK_SIZE = "imatrix.chunk_size"; struct Stats { std::vector in_sum; - std::vector in_sum2; + std::vector values; std::vector counts; }; @@ -130,7 +130,7 @@ static void process_tensor_name(const std::string & input, std::string & layer, static std::vector compute_tensor_averages(const Stats & tstats) { if (tstats.counts.empty()) return {}; const size_t n_mat = tstats.counts.size(); - const size_t len = !tstats.in_sum.empty() ? tstats.in_sum.size() : tstats.in_sum2.size(); + const size_t len = !tstats.in_sum.empty() ? tstats.in_sum.size() : tstats.values.size(); if (len == 0 || len % n_mat != 0) return {}; const size_t row = len / n_mat; @@ -152,7 +152,7 @@ static std::vector compute_tensor_averages(const Stats & tstats) { if (c <= 0) return {}; const size_t off = m * row; for (size_t j = 0; j < row; ++j) { - vec.push_back(tstats.in_sum2[off + j] / c); + vec.push_back(tstats.values[off + j] / c); } } } @@ -161,8 +161,8 @@ static std::vector compute_tensor_averages(const Stats & tstats) { } static int compute_vector_statistics(std::vector & tstats, const std::string & name, const Stats & e) { - if (e.in_sum2.size() % e.counts.size() != 0) { - LOG_ERR("%s: activation size mismatch for tensor %s (%zu vs %zu)\n", __func__, name.c_str(), e.counts.size(), e.in_sum2.size()); + if (e.values.size() % e.counts.size() != 0) { + LOG_ERR("%s: activation size mismatch for tensor %s (%zu vs %zu)\n", __func__, name.c_str(), e.counts.size(), e.values.size()); return -1;; } if (e.counts.empty()) { @@ -171,17 +171,17 @@ static int compute_vector_statistics(std::vector & tstats, co } const int n_mat = e.counts.size(); - const int row_size = e.in_sum2.size() / n_mat; + const int row_size = e.values.size() / n_mat; const int calc_mode = e.in_sum.empty() ? 2 : 1; std::vector activations; if (e.in_sum.empty()) { - activations.reserve(e.in_sum2.size()); + activations.reserve(e.values.size()); for (int i = 0; i < n_mat; ++i) { for (int j = 0; j < row_size; ++j) { - activations.push_back(e.in_sum2[i*row_size + j] / e.counts[i]); + activations.push_back(e.values[i*row_size + j] / e.counts[i]); } } } else { @@ -420,13 +420,13 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * // broadcast, when loading an old imatrix e.counts.resize(n_as, e.counts[0]); } - if (e.in_sum2.empty()) { + if (e.values.empty()) { e.in_sum.resize(src1->ne[0]*n_as, 0); - e.in_sum2.resize(src1->ne[0]*n_as, 0); + e.values.resize(src1->ne[0]*n_as, 0); e.counts.resize(n_as, 0); } - else if (e.in_sum2.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.in_sum2.size(), (int)(src1->ne[0]*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.counts.size() != (size_t)n_as) { @@ -454,9 +454,9 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * for (int64_t j = 0; j < src1->ne[0]; ++j) { e.in_sum[e_start + j] += x[j]; - e.in_sum2[e_start + j] += x[j] * x[j]; - if (!std::isfinite((float)e.in_sum2[e_start + j])) { - LOG_ERR("%f detected in %s\n", (float)e.in_sum2[e_start + j], wname.c_str()); + e.values[e_start + j] += x[j] * x[j]; + if (!std::isfinite((float)e.values[e_start + j])) { + LOG_ERR("%f detected in %s\n", (float)e.values[e_start + j], wname.c_str()); exit(1); } } @@ -478,13 +478,13 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * auto & e = m_stats[wname]; const int64_t n_mat = src1->ne[2] * src1->ne[3]; - if (e.in_sum2.empty()) { + if (e.values.empty()) { e.in_sum.resize(src1->ne[0] * n_mat, 0); - e.in_sum2.resize(src1->ne[0] * n_mat, 0); + e.values.resize(src1->ne[0] * n_mat, 0); e.counts.resize(n_mat, 0); } - else if (e.in_sum2.size() != (size_t)(src1->ne[0] * n_mat)) { - 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)); + else if (e.values.size() != (size_t)(src1->ne[0] * n_mat)) { + 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)); exit(1); //GGML_ABORT("fatal error"); } else if (e.counts.size() != (size_t)n_mat) { @@ -502,9 +502,9 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * e.counts[mat_id]++; for (int64_t j = 0; j < src1->ne[0]; ++j) { e.in_sum[mat_start + j] += x[j]; - e.in_sum2[mat_start + j] += x[j] * x[j]; - if (!std::isfinite((float)e.in_sum2[j])) { - LOG_ERR("%f detected in %s\n", (float)e.in_sum2[j], wname.c_str()); + e.values[mat_start + j] += x[j] * x[j]; + if (!std::isfinite((float)e.values[j])) { + LOG_ERR("%f detected in %s\n", (float)e.values[j], wname.c_str()); exit(1); } } @@ -593,14 +593,14 @@ void IMatrixCollector::save_imatrix_legacy(int32_t ncall) const { // ceiling division to avoid accidental zeros const int32_t ncall = (*std::max_element(stat.counts.begin(), stat.counts.end()) + (chunk_size - 1)) / chunk_size; out.write((const char *) &ncall, sizeof(ncall)); - const int32_t nval = stat.in_sum2.size(); + const int32_t nval = stat.values.size(); const int32_t nmat = stat.counts.size(); out.write((const char *) &nval, sizeof(nval)); if (nval > 0 && nmat > 0) { std::vector tmp(nval); for (int32_t i = 0; i < nval; i++) { float count = static_cast(stat.counts[i / (nval / nmat)]); - float value = stat.in_sum2[i]; + float value = stat.values[i]; if (count == 0.0f) { // store 1 for partial data value = 1.0f; @@ -676,7 +676,7 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const { to_store.push_back(kv.first); data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.in_sum.size(), GGML_MEM_ALIGN); - data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.in_sum2.size(), GGML_MEM_ALIGN); + data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.values.size(), GGML_MEM_ALIGN); data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.counts.size(), GGML_MEM_ALIGN); } @@ -711,7 +711,7 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const { for (const auto & name : to_store) { const auto & stat = m_stats.at(name); - const int32_t nval = (int32_t) stat.in_sum2.size(); + const int32_t nval = (int32_t) stat.values.size(); const int32_t nmat = (int32_t) stat.counts.size(); if (nval > 0 && nmat > 0) { 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 { ggml_format_name(counts, "%s.counts", name.c_str()); for (int32_t j = 0; j < nval; ++j) { - ((float *) in_sum2->data)[j] = (float) stat.in_sum2[j]; + ((float *) in_sum2->data)[j] = (float) stat.values[j]; } for (int32_t j = 0; j < nmat; ++j) { ((float *) counts->data)[j] = (float) stat.counts[j]; @@ -787,8 +787,8 @@ bool IMatrixCollector::load_imatrix_legacy(const char * fname) { return false; } - if (e.in_sum2.empty()) { - e.in_sum2.resize(nval, 0.0f); + if (e.values.empty()) { + e.values.resize(nval, 0.0f); e.counts.resize(1, 0); } @@ -802,7 +802,7 @@ bool IMatrixCollector::load_imatrix_legacy(const char * fname) { // Recreate the state as expected by save_imatrix(), and correct for weighted sum. for (int i = 0; i < nval; i++) { - e.in_sum2[i] += tmp[i] * chunk_size; + e.values[i] += tmp[i] * chunk_size; } // The legacy format doesn't distinguish the counts for different experts for (size_t j = 0; j < e.counts.size(); ++j) { @@ -922,11 +922,11 @@ bool IMatrixCollector::load_imatrix(const char * file_name) { auto & e = m_stats[name]; int64_t nval = ggml_nelements(in_sum2); - if (e.in_sum2.empty()) { - e.in_sum2.resize(nval, 0.0f); + if (e.values.empty()) { + e.values.resize(nval, 0.0f); e.in_sum.resize(nval, 0.0f); - } else if ((size_t) nval != e.in_sum2.size()) { - LOG_ERR("%s: mismatched sums size for %s: %zu != %zu\n", __func__, name.c_str(), (size_t) nval, e.in_sum2.size()); + } else if ((size_t) nval != e.values.size()) { + LOG_ERR("%s: mismatched sums size for %s: %zu != %zu\n", __func__, name.c_str(), (size_t) nval, e.values.size()); gguf_free(ctx_gguf); ggml_free(ctx); return false; @@ -947,7 +947,7 @@ bool IMatrixCollector::load_imatrix(const char * file_name) { // Recreate the state as expected by save_imatrix() for (int64_t j = 0; j < nval; j++) { - e.in_sum2[j] += ((const float *) in_sum2->data)[j]; + e.values[j] += ((const float *) in_sum2->data)[j]; } for (int64_t j = 0; j < ncounts; j++) { e.counts[j] += std::lround(((const float *) counts->data)[j]);