mirror of
https://github.com/LostRuins/koboldcpp.git
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* Use activations to calculate the stats * Determine calculation mode * Compute entropy for activations * Compute cosine similarity based on activations * Compute l2 norm * Add compute_layer_statistics() function * Update aggregated statistic report layout * Fix printing l2 norm when calc_mode = 1 * Refactor variable name * Compute aggregated (per layer) l2 norm * Update aggregated sum of squared activations per layer * Make ZD Score two-tailed * Update report layout * Reverse conditional logic to match convention * Rename report heading * Add --activation-statistics parameter * Add Euclidean–Cosine Score (ECS) * Add --activation-statistics logic to avoid doubling the imatrix size by default * Update stats output sort based on imatrix type * Process external NextN draft files (-md / --model-draft) * Refactor to use new llama_batch_ext Co-authored-by: compilade <git@compilade.net> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
190 lines
6.7 KiB
C++
190 lines
6.7 KiB
C++
#include "imatrix-loader.h"
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#include "common.h"
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#include "log.h"
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#include "gguf.h"
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#include <cmath>
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#include <cstring>
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#include <fstream>
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static bool common_imatrix_load_legacy(const std::string & fname, common_imatrix & imatrix) {
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std::ifstream in(fname, std::ios::binary);
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if (!in) {
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LOG_ERR("%s: failed to open %s\n", __func__, fname.c_str());
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return false;
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}
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int n_entries;
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in.read((char *) &n_entries, sizeof(n_entries));
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if (in.fail() || n_entries < 1) {
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LOG_ERR("%s: no data in file %s\n", __func__, fname.c_str());
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return false;
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}
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for (int i = 0; i < n_entries; ++i) {
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int32_t len = 0;
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in.read((char *) &len, sizeof(len));
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std::vector<char> name_as_vec(len + 1);
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in.read((char *) name_as_vec.data(), len);
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if (in.fail()) {
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LOG_ERR("%s: failed reading name for entry %d from %s\n", __func__, i + 1, fname.c_str());
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return false;
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}
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name_as_vec[len] = 0;
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std::string name{ name_as_vec.data() };
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int32_t ncall = 0;
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in.read((char *) &ncall, sizeof(ncall));
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int32_t nval = 0;
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in.read((char *) &nval, sizeof(nval));
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if (in.fail() || nval < 1) {
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LOG_ERR("%s: failed reading number of values for entry %d\n", __func__, i);
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return false;
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}
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auto & e = imatrix.entries[std::move(name)];
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e.sums.resize(nval);
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in.read((char *) e.sums.data(), nval * sizeof(float));
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if (in.fail()) {
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LOG_ERR("%s: failed reading data for entry %d\n", __func__, i);
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return false;
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}
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e.counts.resize(1);
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e.counts[0] = ncall;
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}
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// the trailing data (chunk count + dataset name) is optional
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if (in.peek() != EOF) {
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int32_t n_calls = 0;
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in.read((char *) &n_calls, sizeof(n_calls));
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imatrix.chunk_count = n_calls;
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if (!in.fail()) {
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int32_t len = 0;
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in.read((char *) &len, sizeof(len));
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if (!in.fail() && len > 0) {
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std::vector<char> dataset(len + 1, 0);
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in.read(dataset.data(), len);
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if (!in.fail()) {
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imatrix.datasets.push_back(dataset.data());
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}
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}
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}
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}
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imatrix.chunk_size = 0;
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imatrix.is_legacy = true;
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return true;
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}
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bool common_imatrix_load(const std::string & fname, common_imatrix & imatrix) {
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struct ggml_context * ctx = nullptr;
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struct gguf_init_params meta_gguf_params = {
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/* .no_alloc = */ false,
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/* .ctx = */ &ctx,
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};
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struct gguf_context * ctx_gguf = gguf_init_from_file(fname.c_str(), meta_gguf_params);
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if (!ctx_gguf) {
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return common_imatrix_load_legacy(fname, imatrix);
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}
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const int32_t n_entries = gguf_get_n_tensors(ctx_gguf);
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if (n_entries < 1) {
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LOG_ERR("%s: no data in file %s\n", __func__, fname.c_str());
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gguf_free(ctx_gguf);
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ggml_free(ctx);
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return false;
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}
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const int64_t datasets_key = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_DATASETS);
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const int64_t chunk_count_key = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_CHUNK_COUNT);
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const int64_t chunk_size_key = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_CHUNK_SIZE);
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const int64_t nextn_key = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_N_LAYER_NEXTN);
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if (datasets_key != -1 && gguf_get_kv_type(ctx_gguf, datasets_key) == GGUF_TYPE_ARRAY &&
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gguf_get_arr_type(ctx_gguf, datasets_key) == GGUF_TYPE_STRING) {
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const int64_t n = gguf_get_arr_n(ctx_gguf, datasets_key);
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imatrix.datasets.reserve(imatrix.datasets.size() + n);
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for (int64_t i = 0; i < n; ++i) {
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imatrix.datasets.push_back(gguf_get_arr_str(ctx_gguf, datasets_key, i));
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}
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}
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imatrix.has_metadata = datasets_key != -1 && chunk_count_key != -1 && chunk_size_key != -1;
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imatrix.chunk_count = chunk_count_key != -1 ? gguf_get_val_u32(ctx_gguf, chunk_count_key) : 0;
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imatrix.chunk_size = chunk_size_key != -1 ? gguf_get_val_u32(ctx_gguf, chunk_size_key) : 0;
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imatrix.n_layer_nextn = nextn_key != -1 ? gguf_get_val_u32(ctx_gguf, nextn_key) : 0;
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const std::string in_sum_suffix{ ".in_sum" };
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const std::string in_sum2_suffix{ ".in_sum2" };
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const std::string counts_suffix{ ".counts" };
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struct sum_tensors {
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struct ggml_tensor * in_sum = nullptr;
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struct ggml_tensor * in_sum2 = nullptr;
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struct ggml_tensor * counts = nullptr;
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};
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std::map<std::string, sum_tensors> sums_counts_for;
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for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
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std::string name = cur->name;
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if (name.empty()) { continue; }
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if (string_remove_suffix(name, in_sum_suffix)) {
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sums_counts_for[std::move(name)].in_sum = cur;
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} else if (string_remove_suffix(name, in_sum2_suffix)) {
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sums_counts_for[std::move(name)].in_sum2 = cur;
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} else if (string_remove_suffix(name, counts_suffix)) {
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sums_counts_for[std::move(name)].counts = cur;
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}
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}
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for (const auto & sc : sums_counts_for) {
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const std::string & name = sc.first;
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const struct ggml_tensor * in_sum = sc.second.in_sum;
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const struct ggml_tensor * in_sum2 = sc.second.in_sum2;
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const struct ggml_tensor * counts = sc.second.counts;
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if (!in_sum2 || !counts || (in_sum != nullptr && ggml_nelements(in_sum) != ggml_nelements(in_sum2))) {
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LOG_ERR("%s: mismatched sums and counts for %s\n", __func__, name.c_str());
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gguf_free(ctx_gguf);
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ggml_free(ctx);
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return false;
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}
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if (in_sum2->type != GGML_TYPE_F32 || counts->type != GGML_TYPE_F32) {
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LOG_ERR("%s: sums and counts for %s must be F32\n", __func__, name.c_str());
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gguf_free(ctx_gguf);
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ggml_free(ctx);
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return false;
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}
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auto & e = imatrix.entries[name];
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const int64_t nval = ggml_nelements(in_sum2);
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const int64_t ncounts = ggml_nelements(counts);
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e.sums.resize(nval);
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for (int64_t j = 0; j < nval; ++j) {
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e.sums[j] = ((const float *) in_sum2->data)[j];
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}
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e.counts.resize(ncounts);
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for (int64_t j = 0; j < ncounts; ++j) {
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e.counts[j] = std::lround(((const float *) counts->data)[j]);
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}
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if (in_sum && ggml_nelements(in_sum) == nval) {
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e.activations.resize(nval);
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for (int64_t j = 0; j < nval; ++j) {
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e.activations[j] = ((const float *) in_sum->data)[j];
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}
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}
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}
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gguf_free(ctx_gguf);
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ggml_free(ctx);
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return true;
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}
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