imatrix : avoid using imatrix.dat in README

Also make the legacy format store partial data
by using neutral values for missing data.
This matches what is done at read-time for the new format,
and so should get the same quality in case the old format is still used.

common/common.cpp

@@ -448,6 +448,15 @@ void string_replace_all(std::string & s, const std::string & search, const std::
 bool string_ends_with(const std::string_view & str, const std::string_view & suffix) {
     return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;
 }
+
+bool string_remove_suffix(std::string & str, const std::string_view & suffix) {
+    bool has_suffix = string_ends_with(str, suffix);
+    if (has_suffix) {
+        str = str.substr(0, str.size() - suffix.size());
+    }
+    return has_suffix;
+}
+
 size_t string_find_partial_stop(const std::string_view & str, const std::string_view & stop) {
     if (!str.empty() && !stop.empty()) {
         const char text_last_char = str.back();

common/common.h

@@ -522,6 +522,7 @@ static bool string_starts_with(const std::string & str,
 
 // While we wait for C++20's std::string::ends_with...
 bool string_ends_with(const std::string_view & str, const std::string_view & suffix);
+bool string_remove_suffix(std::string & str, const std::string_view & suffix);
 size_t string_find_partial_stop(const std::string_view & str, const std::string_view & stop);
 
 bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);

gguf-py/gguf/constants.py

@@ -233,6 +233,11 @@ class Keys:
         TYPE       = "adapter.type"
         LORA_ALPHA = "adapter.lora.alpha"
 
+    class IMatrix:
+        CHUNK_COUNT = "imatrix.chunk_count"
+        CHUNK_SIZE  = "imatrix.chunk_size"
+        DATASETS    = "imatrix.datasets"
+
     class Clip:
         PROJECTOR_TYPE      = "clip.projector_type"
         HAS_VISION_ENCODER  = "clip.has_vision_encoder"
@@ -282,6 +287,7 @@ class Keys:
 class GGUFType:
     MODEL   = "model"
     ADAPTER = "adapter"
+    IMATRIX = "imatrix"
     MMPROJ  = "mmproj" # dummy, unused for now
 
 

tools/imatrix/README.md

@@ -7,14 +7,15 @@ More information is available here: https://github.com/ggml-org/llama.cpp/pull/4
 
 ```
 ./llama-imatrix \
-    -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] [--verbosity 1] \
+    -m model.gguf -f some-text.txt [-o imatrix.gguf] [--process-output] \
     [--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \
-    [--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...]
+    [--in-file imatrix-prev-0.gguf --in-file imatrix-prev-1.gguf ...] \
+    [--parse-special]
 ```
 
 Here `-m` with a model name and `-f` with a file containing training data (such as e.g. `wiki.train.raw`) are mandatory.
 The parameters in square brackets are optional and have the following meaning:
-* `-o` (or `--output-file`) specifies the name of the file where the computed data will be stored. If missing `imatrix.dat` is used.
+* `-o` (or `--output-file`) specifies the name of the file where the computed data will be stored. If missing `imatrix.gguf` is used.
 * `--verbosity` specifies the verbosity level. If set to `0`, no output other than the perplexity of the processed chunks will be generated. If set to `1`, each time the results are saved a message is written to `stderr`. If `>=2`, a message is output each time data is collected for any tensor. Default verbosity level is `1`.
 * `--output-frequency` specifies how often the so far computed result is saved to disk. Default is 10 (i.e., every 10 chunks)
 * `--save-frequency` specifies how often to save a copy of the imatrix in a separate file. Default is 0 (i.e., never)
@@ -25,9 +26,9 @@ For faster computation, make sure to use GPU offloading via the `-ngl` argument
 ## Example
 
 ```bash
-# generate importance matrix (imatrix.dat)
+# generate importance matrix (imatrix.gguf)
 ./llama-imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99
 
 # use the imatrix to perform a Q4_K_M quantization
-./llama-quantize --imatrix imatrix.dat ggml-model-f16.gguf ./ggml-model-q4_k_m.gguf q4_k_m
+./llama-quantize --imatrix imatrix.gguf ggml-model-f16.gguf ./ggml-model-q4_k_m.gguf q4_k_m
 ```

tools/imatrix/imatrix.cpp

@@ -2,7 +2,9 @@
 #include "common.h"
 #include "log.h"
 #include "llama.h"
+#include "gguf.h"
 
+#include <algorithm>
 #include <chrono>
 #include <cmath>
 #include <cstdio>
@@ -13,7 +15,7 @@
 #include <vector>
 #include <fstream>
 #include <unordered_map>
-#include <algorithm>
+#include <map>
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
@@ -22,17 +24,20 @@
 static void print_usage(int, char ** argv) {
     LOG("\nexample usage:\n");
     LOG("\n    %s \\\n"
-            "       -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] \\\n"
+            "       -m model.gguf -f some-text.txt [-o imatrix.gguf] [--process-output] \\\n"
             "       [--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \\\n"
-            "       [--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...] \\\n"
+            "       [--in-file imatrix-prev-0.gguf --in-file imatrix-prev-1.gguf ...] \\\n"
             "       [--parse-special]\n" , argv[0]);
     LOG("\n");
 }
 
+static const char * const LLM_KV_IMATRIX_DATASETS    = "imatrix.datasets";
+static const char * const LLM_KV_IMATRIX_CHUNK_COUNT = "imatrix.chunk_count";
+static const char * const LLM_KV_IMATRIX_CHUNK_SIZE  = "imatrix.chunk_size";
+
 struct Stats {
-    std::vector<float> values;
-    std::vector<int> counts;
-    int ncall = 0;
+    std::vector<float>   values;
+    std::vector<int64_t> counts;
 };
 
 class IMatrixCollector {
@@ -40,13 +45,16 @@ public:
     IMatrixCollector() = default;
     void set_params(common_params params) { m_params = std::move(params); }
     bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
-    void save_imatrix(int ncall = -1) const;
-    bool load_imatrix(const char * fname);
+    void save_imatrix_legacy(int32_t ncall = -1) const;
+    void save_imatrix(int32_t n_chunk = -1) const;
+    bool load_imatrix_legacy(const char * fname);
+    bool load_imatrix(const char * file_name);
 private:
     std::unordered_map<std::string, Stats> m_stats;
     common_params                          m_params;
     std::mutex                             m_mutex;
-    int                                    m_last_call = 0;
+    std::vector<std::string>               m_datasets;
+    int32_t                                m_last_chunk = 0;
     std::vector<char>                      m_src1_data;
     std::vector<char>                      m_ids; // the expert ids from ggml_mul_mat_id
 };
@@ -77,6 +85,8 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
     const struct ggml_tensor * src1 = t->src[1];
     std::string wname = filter_tensor_name(src0->name);
 
+    const int32_t chunk_size = m_params.n_ctx / m_params.n_parallel;
+
     // when ask is true, the scheduler wants to know if we are interested in data from this tensor
     // if we return true, a follow-up call will be made with ask=false in which we can do the actual collection
     if (ask) {
@@ -102,14 +112,21 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
     const char * data = is_host ? (const char *) src1->data : m_src1_data.data();
     GGML_ASSERT(src1->nb[0] == ggml_element_size(src1));
 
+    // TODO: 4d? (is that even used in practice?)
+    // the extra dimension would need to be stored somewhere to be reflected in the imatrix file
+    if (ggml_nrows(src1) != src1->ne[1] * src1->ne[2]) {
+        LOG_ERR("%s: tensor has more than 3 dimensions: %s", __func__, wname.c_str());
+        GGML_ASSERT(false);
+    }
+
     // this has been adapted to the new format of storing merged experts in a single 3d tensor
     // ref: https://github.com/ggml-org/llama.cpp/pull/6387
     if (t->op == GGML_OP_MUL_MAT_ID) {
         //   ids  -> [n_experts_used, n_tokens]
         //   src1 -> [cols, n_expert_used, n_tokens]
         const ggml_tensor * ids = t->src[2];
-        const int n_as = src0->ne[2];
-        const int n_ids = ids->ne[0];
+        const int64_t n_as = src0->ne[2];
+        const int64_t n_ids = ids->ne[0];
 
         // the top-k selected expert ids are stored in the ids tensor
         // for simplicity, always copy ids to host, because it is small
@@ -122,23 +139,29 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
 
         auto & e = m_stats[wname];
 
-        ++e.ncall;
-
+        if (e.counts.size() == 1 && n_as > 1) {
+            // broadcast, when loading an old imatrix
+            e.counts.resize(n_as, e.counts[0]);
+        }
         if (e.values.empty()) {
             e.values.resize(src1->ne[0]*n_as, 0);
-            e.counts.resize(src1->ne[0]*n_as, 0);
+            e.counts.resize(n_as, 0);
         }
         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);
+            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");
         }
-        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);
+        else if (e.counts.size() != (size_t)n_as) {
+            LOG_ERR("%s: inconsistent expert count for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.counts.size(), (int)n_as);
+            exit(1); //GGML_ABORT("fatal error");
+        }
+        LOG_DBGV(2, "%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_chunk, 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) {
+        for (int64_t ex = 0; ex < n_as; ++ex) {
             size_t e_start = ex*src1->ne[0];
 
-            for (int idx = 0; idx < n_ids; ++idx) {
-                for (int row = 0; row < (int)src1->ne[2]; ++row) {
+            for (int64_t idx = 0; idx < n_ids; ++idx) {
+                for (int64_t row = 0; row < src1->ne[2]; ++row) {
                     const int excur = *(const int32_t *) (m_ids.data() + row*ids->nb[1] + idx*ids->nb[0]);
 
                     GGML_ASSERT(excur >= 0 && excur < n_as); // sanity check
@@ -149,57 +172,73 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
                     const int64_t i12 = row;
                     const float * x = (const float *)(data + i11*src1->nb[1] + i12*src1->nb[2]);
 
-                    for (int j = 0; j < (int)src1->ne[0]; ++j) {
-                        e.values[e_start + j] += x[j]*x[j];
-                        e.counts[e_start + j]++;
-                        if (!std::isfinite(e.values[e_start + j])) {
-                            LOG("\n");
-                            LOG_ERR("%f detected in %s\n", e.values[e_start + j], wname.c_str());
+                    e.counts[ex]++;
+
+                    for (int64_t j = 0; j < src1->ne[0]; ++j) {
+                        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);
                         }
                     }
                 }
             }
-            if (e.ncall > m_last_call) {
-                m_last_call = e.ncall;
-                if (m_last_call % m_params.n_out_freq == 0) {
+            const int32_t n_chunk = e.counts[ex] / chunk_size;
+            if (n_chunk > m_last_chunk) {
+                const int32_t chunk_step = n_chunk - m_last_chunk;
+                m_last_chunk = n_chunk;
+                if ((m_last_chunk % m_params.n_out_freq) / chunk_step == 0) {
                     save_imatrix();
                 }
-                if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) {
-                    save_imatrix(m_last_call);
+                if (m_params.n_save_freq > 0 && (m_last_chunk % m_params.n_save_freq) / chunk_step == 0) {
+                    save_imatrix(m_last_chunk);
                 }
             }
         }
     } else {
         auto & e = m_stats[wname];
+        const int64_t n_mat = src1->ne[2] * src1->ne[3];
+
         if (e.values.empty()) {
-            e.values.resize(src1->ne[0], 0);
-            e.counts.resize(src1->ne[0], 0);
+            e.values.resize(src1->ne[0] * n_mat, 0);
+            e.counts.resize(n_mat, 0);
         }
-        else if (e.values.size() != (size_t)src1->ne[0]) {
-            LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.values.size(), (int)src1->ne[0]);
+        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");
         }
-        ++e.ncall;
-        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[1], (int)src1->type);
-        for (int row = 0; row < (int)src1->ne[1]; ++row) {
-            const float * x = (const float *) (data + row * src1->nb[1]);
-            for (int j = 0; j < (int)src1->ne[0]; ++j) {
-                e.values[j] += x[j]*x[j];
-                e.counts[j]++;
-                if (!std::isfinite(e.values[j])) {
-                    LOG_ERR("%f detected in %s\n", e.values[j], wname.c_str());
-                    exit(1);
-                }
-            }
+        else if (e.counts.size() != (size_t)n_mat) {
+            LOG_ERR("%s: inconsistent expert count for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.counts.size(), (int)n_mat);
+            exit(1); //GGML_ABORT("fatal error");
         }
-        if (e.ncall > m_last_call) {
-            m_last_call = e.ncall;
-            if (m_last_call % m_params.n_out_freq == 0) {
-                save_imatrix();
-            }
-            if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) {
-                save_imatrix(m_last_call);
+        LOG_DBGV(2, "%s[%d]: %32s, %s, %5d x %5d x %5d, %d\n", __func__, m_last_chunk, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->ne[2], (int)src1->type);
+        for (int64_t i3 = 0; i3 < src1->ne[3]; ++i3) {
+            for (int64_t i2 = 0; i2 < src1->ne[2]; ++i2) {
+                const int64_t mat_id = i3 * src1->ne[2] + i2;
+                const int64_t mat_start = mat_id * src1->ne[0];
+
+                for (int64_t row = 0; row < src1->ne[1]; ++row) {
+                    const float * x = (const float *) (data + row * src1->nb[1] + i2 * src1->nb[2] + i3 * src1->ne[3]);
+                    e.counts[mat_id]++;
+                    for (int64_t j = 0; j < src1->ne[0]; ++j) {
+                        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);
+                        }
+                    }
+                }
+                const int32_t n_chunk = e.counts[mat_id] / chunk_size;
+                if (n_chunk > m_last_chunk) {
+                    const int32_t chunk_step = n_chunk - m_last_chunk;
+                    m_last_chunk = n_chunk;
+                    if ((m_last_chunk % m_params.n_out_freq) / chunk_step == 0) {
+                        save_imatrix();
+                    }
+                    if (m_params.n_save_freq > 0 && (m_last_chunk % m_params.n_save_freq) / chunk_step == 0) {
+                        save_imatrix(m_last_chunk);
+                    }
+                }
             }
         }
     }
@@ -207,7 +246,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
     return true;
 }
 
-void IMatrixCollector::save_imatrix(int ncall) const {
+void IMatrixCollector::save_imatrix_legacy(int32_t ncall) const {
     auto fname = m_params.out_file;
 
     if (ncall > 0) {
@@ -215,7 +254,7 @@ void IMatrixCollector::save_imatrix(int ncall) const {
         fname += std::to_string(ncall);
     }
 
-    // avoid writing imatrix entries that do not have full data
+    // warn when writing imatrix entries that do not have full data
     // this can happen with MoE models where some of the experts end up not being exercised by the provided training data
 
     int n_entries = 0;
@@ -247,8 +286,7 @@ 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);
         }
 
         n_entries++;
@@ -259,93 +297,378 @@ void IMatrixCollector::save_imatrix(int ncall) const {
         LOG_WRN("%s: storing only %zu out of %zu entries\n", __func__, to_store.size(), m_stats.size());
     }
 
+    // deterministic tensor name order
+    std::sort(to_store.begin(), to_store.end());
+
+    const int32_t chunk_size = m_params.n_ctx / m_params.n_parallel;
+
     std::ofstream out(fname, std::ios::binary);
     out.write((const char *) &n_entries, sizeof(n_entries));
     for (const auto & name : to_store) {
         const auto & stat = m_stats.at(name);
-        int len = name.size();
+        const int32_t len = name.size();
         out.write((const char *) &len, sizeof(len));
         out.write(name.c_str(), len);
-        out.write((const char *) &stat.ncall, sizeof(stat.ncall));
-        int nval = stat.values.size();
+        // 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.values.size();
+        const int32_t nmat = stat.counts.size();
         out.write((const char *) &nval, sizeof(nval));
-        if (nval > 0) {
+        if (nval > 0 && nmat > 0) {
             std::vector<float> tmp(nval);
-            for (int i = 0; i < nval; i++) {
-                tmp[i] = (stat.values[i] / static_cast<float>(stat.counts[i])) * static_cast<float>(stat.ncall);
+            for (int32_t i = 0; i < nval; i++) {
+                float count = static_cast<float>(stat.counts[i / (nval / nmat)]);
+                float value = stat.values[i];
+                if (count == 0.0f) {
+                    // store 1 for partial data
+                    value = 1.0f;
+                    count = 1.0f;
+                }
+                tmp[i] = (value / count) * static_cast<float>(ncall);
             }
-            out.write((const char*)tmp.data(), nval*sizeof(float));
+            out.write((const char *) tmp.data(), nval * sizeof(float));
         }
     }
 
     // Write the number of call the matrix was computed with
-    out.write((const char *) &m_last_call, sizeof(m_last_call));
+    out.write((const char *) &m_last_chunk, sizeof(m_last_chunk));
 
     // Write the input filename at the end of the file to later on specify it in quantize
     {
-        int len = m_params.prompt_file.size();
+        const char * dataset_file = m_params.prompt_file.c_str();
+        int32_t len = m_params.prompt_file.size();
+        // When there is no prompt but there were other imatrix files loaded, use the last dataset
+        if (m_params.prompt_file.empty() && !m_datasets.empty()) {
+            const std::string & dataset_str = m_datasets[m_datasets.size() - 1];
+            dataset_file = dataset_str.c_str();
+            len = dataset_str.size();
+        }
         out.write((const char *) &len, sizeof(len));
-        out.write(m_params.prompt_file.c_str(), len);
+        out.write(dataset_file, len);
     }
 
     LOGV(1, "\n");
-    LOG_DBGV(1, "%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname.c_str());
+    LOG_DBGV(1, "%s: stored collected data after %d chunks in %s\n", __func__, m_last_chunk, fname.c_str());
 }
 
-bool IMatrixCollector::load_imatrix(const char * fname) {
+void IMatrixCollector::save_imatrix(int32_t n_chunk) const {
+    auto fname = m_params.out_file;
+
+    // TODO: use the new format in more cases
+    if (!string_ends_with(fname, ".gguf")) {
+        LOG_WRN("\n%s: saving to legacy imatrix format because output suffix is not .gguf\n", __func__);
+        this->save_imatrix_legacy(n_chunk);
+        return;
+    }
+
+    if (n_chunk > 0) {
+        fname += ".at_";
+        fname += std::to_string(n_chunk);
+    }
+
+    // write imatrix entries even if they don't have full data. (can be corrected when reading)
+    // this can happen with MoE models where some of the experts end up not being exercised by the provided training data
+
+    std::vector<std::string> to_store;
+    size_t data_size = 0;
+
+    bool is_first = true; // for printing
+    for (const auto & kv : m_stats) {
+        const int n_all = kv.second.counts.size();
+
+        int n_zeros = 0;
+        for (const auto c : kv.second.counts) {
+            if (c == 0) {
+                n_zeros++;
+            }
+        }
+
+        if (n_zeros != 0 && is_first) {
+            LOG_INF("\n");
+            is_first = false;
+        }
+
+        if (n_zeros > 0) {
+            LOG_WRN("%s: entry '%40s' has partial data (%.2f%%)\n", __func__, kv.first.c_str(), 100.0f * (n_all - n_zeros) / n_all);
+        }
+
+        to_store.push_back(kv.first);
+        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);
+    }
+
+    // deterministic tensor name order
+    std::sort(to_store.begin(), to_store.end());
+
+    struct ggml_init_params params = {
+        /* .mem_size   = */ data_size,
+        /* .mem_buffer = */ NULL,
+        /* .no_alloc   = */ false,
+    };
+    struct ggml_context * ctx = ggml_init(params);
+    struct gguf_context * ctx_gguf = gguf_init_empty();
+
+    {
+        std::vector<const char *> datasets;
+        datasets.reserve(m_datasets.size() + 1);
+        for (size_t i = 0; i < m_datasets.size(); ++i) {
+            datasets.push_back(m_datasets[i].c_str());
+        }
+        if (!m_params.prompt_file.empty()) {
+            datasets.push_back(m_params.prompt_file.c_str());
+        }
+
+        gguf_set_val_str(ctx_gguf, "general.type", "imatrix");
+        // Write the dataset paths
+        gguf_set_arr_str(ctx_gguf, LLM_KV_IMATRIX_DATASETS, datasets.data(), datasets.size());
+        // Write the number of chunks the matrix was computed with
+        gguf_set_val_u32(ctx_gguf, LLM_KV_IMATRIX_CHUNK_COUNT, m_last_chunk);
+        gguf_set_val_u32(ctx_gguf, LLM_KV_IMATRIX_CHUNK_SIZE, m_params.n_ctx / m_params.n_parallel);
+    }
+
+    for (const auto & name : to_store) {
+        const auto & stat = m_stats.at(name);
+        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);
+            struct ggml_tensor * counts  = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, nmat);
+            ggml_format_name(in_sum2, "%s.in_sum2", name.c_str());
+            ggml_format_name(counts, "%s.counts", name.c_str());
+
+            for (int32_t j = 0; j < nval; ++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];
+            }
+
+            gguf_add_tensor(ctx_gguf, in_sum2);
+            gguf_add_tensor(ctx_gguf, counts);
+        }
+    }
+
+    gguf_write_to_file(ctx_gguf, fname.c_str(), false);
+
+    LOGV(1, "\n");
+    LOG_DBGV(1, "%s: stored collected data after %d chunks in %s\n", __func__, m_last_chunk, fname.c_str());
+
+    gguf_free(ctx_gguf);
+    ggml_free(ctx);
+}
+
+bool IMatrixCollector::load_imatrix_legacy(const char * fname) {
     std::ifstream in(fname, std::ios::binary);
     if (!in) {
-        LOG_ERR("%s: failed to open %s\n",__func__, fname);
+        LOG_ERR("%s: failed to open %s\n", __func__, fname);
         return false;
     }
     int n_entries;
-    in.read((char*)&n_entries, sizeof(n_entries));
+    in.read((char *) &n_entries, sizeof(n_entries));
     if (in.fail() || n_entries < 1) {
         LOG_ERR("%s: no data in file %s\n", __func__, fname);
         return false;
     }
+    // Guess the chunk size because it's not stored in the file
+    const int32_t chunk_size = m_params.n_ctx / m_params.n_parallel;
+
     for (int i = 0; i < n_entries; ++i) {
-        int len; in.read((char *)&len, sizeof(len));
-        std::vector<char> name_as_vec(len+1);
-        in.read((char *)name_as_vec.data(), len);
+        int32_t len = 0;
+        in.read((char *) &len, sizeof(len));
+        std::vector<char> name_as_vec(len + 1);
+        in.read((char *) name_as_vec.data(), len);
         if (in.fail()) {
-            LOG_ERR("%s: failed reading name for entry %d from %s\n",__func__,i+1, fname);
+            LOG_ERR("%s: failed reading name for entry %d from %s\n", __func__, i + 1, fname);
             return false;
         }
         name_as_vec[len] = 0;
-        std::string name{name_as_vec.data()};
+        std::string name{ name_as_vec.data() };
         auto & e = m_stats[std::move(name)];
-        int ncall;
-        in.read((char*)&ncall, sizeof(ncall));
-        int nval;
-        in.read((char *)&nval, sizeof(nval));
+        int32_t ncall = 0;
+        in.read((char *) &ncall, sizeof(ncall));
+        int32_t nval = 0;
+        in.read((char *) &nval, sizeof(nval));
         if (in.fail() || nval < 1) {
-            LOG_ERR("%s: failed reading number of values for entry %d\n",__func__,i);
+            LOG_ERR("%s: failed reading number of values for entry %d\n", __func__, i);
             m_stats = {};
             return false;
         }
 
         if (e.values.empty()) {
-            e.values.resize(nval, 0);
-            e.counts.resize(nval, 0);
+            e.values.resize(nval, 0.0f);
+            e.counts.resize(1, 0);
         }
 
         std::vector<float> tmp(nval);
-        in.read((char*)tmp.data(), nval*sizeof(float));
+        in.read((char *) tmp.data(), nval * sizeof(float));
         if (in.fail()) {
-            LOG_ERR("%s: failed reading data for entry %d\n",__func__,i);
+            LOG_ERR("%s: failed reading data for entry %d\n", __func__, i);
             m_stats = {};
             return false;
         }
 
-        // Recreate the state as expected by save_imatrix(), and corerct for weighted sum.
+        // Recreate the state as expected by save_imatrix(), and correct for weighted sum.
         for (int i = 0; i < nval; i++) {
-            e.values[i] += tmp[i];
-            e.counts[i] += ncall;
+            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) {
+            e.counts[j] += ncall * chunk_size;
         }
-        e.ncall += ncall;
-
     }
+
+    {
+        // TODO: extract into its own method; this is also used by the GGUF-based format
+        // Calculate the last chunk count
+        int64_t max_count = 0;
+        for (const auto & stats : m_stats) {
+            for (int64_t count : stats.second.counts) {
+                if (count > max_count) {
+                    max_count = count;
+                }
+            }
+        }
+        m_last_chunk = max_count / (chunk_size);
+    }
+
+    {
+        // Read the number of calls the matrix was computed with
+        int32_t n_calls;
+        in.read((char *) &n_calls, sizeof(n_calls));
+        // ignore it because it's not important
+    }
+
+    // Read the dataset path to include it when writing to GGUF
+    if (!in.fail()){
+        int32_t len = 0;
+        in.read((char *) &len, sizeof(len));
+        if (!in.fail()) {
+            std::vector<char> dataset;
+            dataset.resize(len + 1, 0);
+            in.read(dataset.data(), len);
+            if (!in.fail()) {
+                m_datasets.push_back(dataset.data());
+            }
+        }
+    }
+
+    return true;
+}
+
+// Using GGUF as the file format, for greater extensibility
+bool IMatrixCollector::load_imatrix(const char * file_name) {
+    struct ggml_context * ctx = nullptr;
+    struct gguf_init_params meta_gguf_params = {
+        /* .no_alloc = */ false, // the data is needed
+        /* .ctx      = */ &ctx,
+    };
+    struct gguf_context * ctx_gguf = gguf_init_from_file(file_name, meta_gguf_params);
+    if (!ctx_gguf) {
+        return this->load_imatrix_legacy(file_name);
+    }
+    const int32_t n_entries = gguf_get_n_tensors(ctx_gguf);
+    if (n_entries < 1) {
+        LOG_ERR("%s: no data in file %s\n", __func__, file_name);
+        gguf_free(ctx_gguf);
+        ggml_free(ctx);
+        return false;
+    }
+
+    const int64_t datasets_key = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_DATASETS);
+    if (datasets_key != -1 && gguf_get_arr_type(ctx_gguf, datasets_key) == GGUF_TYPE_STRING) {
+        const int64_t n = gguf_get_arr_n(ctx_gguf, datasets_key);
+        m_datasets.reserve(m_datasets.size() + n);
+        for (int64_t i = 0; i < n; ++i) {
+            m_datasets.push_back(gguf_get_arr_str(ctx_gguf, datasets_key, i));
+        }
+    }
+
+    const std::string in_sum2_suffix{ ".in_sum2" };
+    const std::string counts_suffix{ ".counts" };
+
+    // Could re-use m_stats instead, but this allows
+    // checking for completeness of *each* loaded imatrix file
+    // and also makes it easier to re-use a similar implementation in quantize.cpp
+    // Using an ordered map to get a deterministic iteration order.
+    std::map<std::string, std::pair<struct ggml_tensor *, struct ggml_tensor *>> sums_counts_for;
+
+    for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
+        std::string name = cur->name;
+
+        if (name.empty()) { continue; }
+
+        if (string_remove_suffix(name, in_sum2_suffix)) {
+            // in_sum2
+            sums_counts_for[std::move(name)].first = cur;
+        } else if (string_remove_suffix(name, counts_suffix)) {
+            // counts
+            sums_counts_for[std::move(name)].second = cur;
+        } else {
+            // ignore other tensors
+        }
+    }
+
+    for (const auto & sc : sums_counts_for) {
+        const std::string &        name    = sc.first;
+        const struct ggml_tensor * in_sum2 = sc.second.first;
+        const struct ggml_tensor * counts  = sc.second.second;
+
+        if (!in_sum2 || !counts) {
+            LOG_ERR("%s: mismatched sums and counts for %s\n", __func__, name.c_str());
+            gguf_free(ctx_gguf);
+            ggml_free(ctx);
+            return false;
+        }
+
+        auto & e = m_stats[name];
+
+        int64_t nval = ggml_nelements(in_sum2);
+        if (e.values.empty()) {
+            e.values.resize(nval, 0.0f);
+        } 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;
+        }
+
+        int64_t ncounts = ggml_nelements(counts);
+        if (e.counts.empty()) {
+            e.counts.resize(ncounts, 0);
+        } else if (e.counts.size() == 1 && ncounts > 1) {
+            // broadcast, when loading an old imatrix
+            e.counts.resize(ncounts, e.counts[0]);
+        } else if ((size_t) ncounts != e.counts.size()) {
+            LOG_ERR("%s: mismatched counts size for %s: %zu != %zu\n", __func__, name.c_str(), (size_t) ncounts, e.counts.size());
+            gguf_free(ctx_gguf);
+            ggml_free(ctx);
+            return false;
+        }
+
+        // Recreate the state as expected by save_imatrix()
+        for (int64_t j = 0; j < nval; 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]);
+        }
+    }
+
+    // TODO: extract into its own method; this is also used by the legacy format
+    // Calculate the last chunk count
+    int64_t max_count = 0;
+    for (const auto & stats : m_stats) {
+        for (int64_t count : stats.second.counts) {
+            if (count > max_count) {
+                max_count = count;
+            }
+        }
+    }
+    m_last_chunk = max_count / (m_params.n_ctx / m_params.n_parallel);
+
+    gguf_free(ctx_gguf);
+    ggml_free(ctx);
     return true;
 }
 
@@ -428,12 +751,11 @@ static void process_logits(
     }
 }
 
-static bool compute_imatrix(llama_context * ctx, const common_params & params) {
+static bool compute_imatrix(llama_context * ctx, const common_params & params, const int32_t n_ctx) {
     const llama_model * model = llama_get_model(ctx);
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
     const bool add_bos = llama_vocab_get_add_bos(vocab);
-    const int n_ctx = llama_n_ctx(ctx);
 
     GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
 
@@ -478,45 +800,61 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) {
     double nll = 0.0;
     double nll2 = 0.0;
 
-    LOG_INF("%s: computing over %d chunks with batch_size %d\n", __func__, n_chunk, n_batch);
-
-    std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
-
     const int num_batches = (n_ctx + n_batch - 1) / n_batch;
+    const int n_seq = std::max(1, n_batch / n_ctx);
+
+    GGML_ASSERT(n_batch < n_ctx || n_batch % n_ctx == 0);
+    GGML_ASSERT(params.n_ctx == n_seq * n_ctx);
+
+    llama_batch batch = llama_batch_init(std::min(n_batch, n_ctx*n_seq), 0, 1);
 
     std::vector<float> logits;
     if (params.compute_ppl && num_batches > 1) {
         logits.reserve((size_t)n_ctx * n_vocab);
     }
 
-    for (int i = 0; i < n_chunk; ++i) {
+    LOG_INF("%s: computing over %d chunks, n_ctx=%d, batch_size=%d, n_seq=%d\n", __func__, n_chunk, n_ctx, n_batch, n_seq);
+
+    std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
+
+    for (int i = 0; i < n_chunk; i += n_seq) {
         const int start =     i * n_ctx;
         const int end   = start + n_ctx;
 
-        std::vector<float> logits;
+        const int n_seq_batch = std::min(n_seq, n_chunk - i);
 
         const auto t_start = std::chrono::high_resolution_clock::now();
 
         // clear the KV cache
         llama_memory_clear(llama_get_memory(ctx), true);
 
-        llama_batch batch = llama_batch_init(n_batch, 0, 1);
-
         for (int j = 0; j < num_batches; ++j) {
             const int batch_start = start + j * n_batch;
             const int batch_size  = std::min(end - batch_start, n_batch);
 
-            // save original token and restore it after eval
-            const auto token_org = tokens[batch_start];
-
-            // add BOS token for the first batch of each chunk
-            if (add_bos && j == 0) {
-                tokens[batch_start] = llama_vocab_bos(vocab);
-            }
-
+            // clear the batch
             common_batch_clear(batch);
-            for (int i = 0; i < batch_size; i++) {
-                common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true);
+
+            for (int seq = 0; seq < n_seq_batch; seq++) {
+                int seq_start = batch_start + seq*n_ctx;
+
+                // save original token and restore it after eval
+                const auto token_org = tokens[seq_start];
+
+                // add BOS token for the first batch of each chunk
+                if (add_bos && j == 0) {
+                    tokens[seq_start] = llama_vocab_bos(vocab);
+                }
+                for (int k = 0; k < batch_size; ++k) {
+                    // NOTE: specifying all logits to get activations for the output.weight tensor
+                    //       and also for the perplexity calculation.
+                    // TODO: only get outputs when (params.process_output || params.compute_ppl)
+                    //       (not possible when this skips FFN computation of the last layer)
+                    common_batch_add(batch, tokens[seq_start + k], j*n_batch + k, { seq }, true);
+                }
+
+                // restore the original token in case it was set to BOS
+                tokens[seq_start] = token_org;
             }
 
             if (llama_decode(ctx, batch)) {
@@ -525,23 +863,19 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) {
                 return false;
             }
 
-            // restore the original token in case it was set to BOS
-            tokens[batch_start] = token_org;
-
             if (params.compute_ppl && num_batches > 1) {
                 const auto * batch_logits = llama_get_logits(ctx);
                 logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
             }
         }
 
-        llama_batch_free(batch);
-
-        const auto t_end = std::chrono::high_resolution_clock::now();
 
         if (i == 0) {
+            llama_synchronize(ctx);
+            const auto t_end = std::chrono::high_resolution_clock::now();
             const float t_total = std::chrono::duration<float>(t_end - t_start).count();
             LOG_INF("%s: %.2f seconds per pass - ETA ", __func__, t_total);
-            int total_seconds = (int)(t_total * n_chunk);
+            int total_seconds = (int)(t_total * n_chunk / n_seq);
             if (total_seconds >= 60*60) {
                 LOG("%d hours ", total_seconds / (60*60));
                 total_seconds = total_seconds % (60*60);
@@ -551,17 +885,27 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) {
 
         if (params.compute_ppl) {
             const int first = n_ctx/2;
-            const auto * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
-            process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
-                    workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);
-            count += n_ctx - first - 1;
+            for (int seq = 0; seq < n_seq_batch; seq++) {
+                const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits_ith(ctx, seq*n_ctx);
 
-            LOG("[%d]%.4lf,", i + 1, std::exp(nll / count));
+                llama_token * tokens_data = tokens.data() + start + seq*n_ctx + first;
+
+                process_logits(n_vocab, all_logits + first*n_vocab,
+                        tokens_data, n_ctx - 1 - first,
+                        workers, nll, nll2,
+                        logit_history.data() + start + seq*n_ctx + first,
+                        prob_history.data()  + start + seq*n_ctx + first);
+
+                count += n_ctx - first - 1;
+
+                LOG("[%d]%.4lf,", i + seq + 1, std::exp(nll / count));
+            }
             fflush(stdout);
 
             logits.clear();
         }
     }
+
     LOG("\n");
 
     if (params.compute_ppl) {
@@ -577,13 +921,15 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) {
         }
     }
 
+    llama_batch_free(batch);
+
     return true;
 }
 
 int main(int argc, char ** argv) {
     common_params params;
 
-    params.out_file = "imatrix.dat" ;
+    params.out_file = "imatrix.gguf";
 
     params.n_ctx = 512;
     params.escape = false;
@@ -594,7 +940,22 @@ int main(int argc, char ** argv) {
 
     common_init();
 
-    params.n_batch = std::min(params.n_batch, params.n_ctx);
+    const int32_t n_ctx = params.n_ctx;
+
+    if (n_ctx <= 0) {
+        LOG_ERR("%s: imatrix tool requires '--ctx-size' > 0\n", __func__);
+        return 1;
+    }
+
+    {
+        const int32_t n_seq = std::max(1, params.n_batch / n_ctx);
+        const int32_t n_kv = n_seq * n_ctx;
+
+        params.n_parallel = n_seq;
+        params.n_ctx      = n_kv;
+
+        params.n_batch = std::min(params.n_batch, n_kv);
+    }
 
     g_collector.set_params(params);
 
@@ -606,9 +967,23 @@ int main(int argc, char ** argv) {
         }
     }
 
-    if (params.in_files.size() > 1) {
-        LOG_INF("%s : saving combined imatrix to '%s'\n", __func__, params.out_file.c_str());
+    if (params.prompt.empty()) {
+        LOG_INF("No prompt provided; combining precomputed matrices only.\n");
+
+        if (params.in_files.empty()) {
+            LOG_ERR("Error: No prompt provided and no precomputed matrices (--in-file) to combine.\n");
+            return 1;
+        }
+
+        if (params.in_files.size() == 1) {
+            LOG_INF("%s : saving imatrix to '%s'\n", __func__, params.out_file.c_str());
+        } else if (params.in_files.size() > 1) {
+            LOG_INF("%s : saving combined imatrix to '%s'\n", __func__, params.out_file.c_str());
+        }
+
         g_collector.save_imatrix();
+
+        return 0;
     }
 
     llama_backend_init();
@@ -643,19 +1018,10 @@ int main(int argc, char ** argv) {
         LOG_INF("%s\n", common_params_get_system_info(params).c_str());
     }
 
-    if (params.prompt.empty()) {
-        if (params.in_files.empty()) {
-            LOG_ERR("Error: No prompt provided and no precomputed matrices (--in-file) to combine.\n");
-            return 1;
-        }
-        LOG_INF("No prompt provided; combining precomputed matrices only.\n");
-    } else {
-        if (!compute_imatrix(ctx, params)) {
-            return 1;
-        }
+    if (!compute_imatrix(ctx, params, n_ctx)) {
+        return 1;
     }
 
-
     g_collector.save_imatrix();
 
     LOG("\n");

tools/quantize/quantize.cpp

@@ -1,11 +1,13 @@
 #include "common.h"
 #include "llama.h"
+#include "gguf.h"
 
 #include <cstdio>
 #include <cstring>
 #include <vector>
 #include <string>
 #include <unordered_map>
+#include <map>
 #include <fstream>
 #include <cmath>
 #include <cctype>
@@ -68,6 +70,11 @@ static const char * const LLM_KV_QUANTIZE_IMATRIX_DATASET    = "quantize.imatrix
 static const char * const LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES  = "quantize.imatrix.entries_count";
 static const char * const LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS   = "quantize.imatrix.chunks_count";
 
+// TODO: share with imatrix.cpp
+static const char * const LLM_KV_IMATRIX_DATASETS    = "imatrix.datasets";
+static const char * const LLM_KV_IMATRIX_CHUNK_COUNT = "imatrix.chunk_count";
+static const char * const LLM_KV_IMATRIX_CHUNK_SIZE  = "imatrix.chunk_size";
+
 static bool striequals(const char * a, const char * b) {
     while (*a && *b) {
         if (std::tolower(*a) != std::tolower(*b)) {
@@ -84,7 +91,7 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp
     for (auto ch : ftype_str_in) {
         ftype_str.push_back(std::toupper(ch));
     }
-    for (auto & it : QUANT_OPTIONS) {
+    for (const auto & it : QUANT_OPTIONS) {
         if (striequals(it.name.c_str(), ftype_str.c_str())) {
             ftype = it.ftype;
             ftype_str_out = it.name;
@@ -93,7 +100,7 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp
     }
     try {
         int ftype_int = std::stoi(ftype_str);
-        for (auto & it : QUANT_OPTIONS) {
+        for (const auto & it : QUANT_OPTIONS) {
             if (it.ftype == ftype_int) {
                 ftype = it.ftype;
                 ftype_str_out = it.name;
@@ -129,7 +136,7 @@ static void usage(const char * executable) {
     printf("      Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n");
     printf("Note: --include-weights and --exclude-weights cannot be used together\n");
     printf("\nAllowed quantization types:\n");
-    for (auto & it : QUANT_OPTIONS) {
+    for (const auto & it : QUANT_OPTIONS) {
         if (it.name != "COPY") {
             printf("  %2d  or  ", it.ftype);
         } else {
@@ -140,7 +147,7 @@ static void usage(const char * executable) {
     exit(1);
 }
 
-static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_dataset, std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
+static int load_legacy_imatrix(const std::string & imatrix_file, std::vector<std::string> & imatrix_datasets, std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
     std::ifstream in(imatrix_file.c_str(), std::ios::binary);
     if (!in) {
         printf("%s: failed to open %s\n",__func__, imatrix_file.c_str());
@@ -180,7 +187,9 @@ static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_
             exit(1);
         }
         if (ncall > 0) {
-            for (auto& v : e) v /= ncall;
+            for (auto & v : e) {
+                v /= ncall;
+            }
         }
 
         if (getenv("LLAMA_TRACE")) {
@@ -188,7 +197,7 @@ static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_
         }
     }
 
-    // latest imatrix version contains the dataset filename at the end of the file
+    // latest legacy imatrix version contains the dataset filename at the end of the file
     int m_last_call = 0;
     if (in.peek() != EOF) {
         in.read((char *)&m_last_call, sizeof(m_last_call));
@@ -196,15 +205,130 @@ static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_
         in.read((char *)&dataset_len, sizeof(dataset_len));
         std::vector<char> dataset_as_vec(dataset_len);
         in.read(dataset_as_vec.data(), dataset_len);
-        imatrix_dataset.assign(dataset_as_vec.begin(), dataset_as_vec.end());
-        printf("%s: imatrix dataset='%s'\n", __func__, imatrix_dataset.c_str());
+        imatrix_datasets.resize(1);
+        imatrix_datasets[0].assign(dataset_as_vec.begin(), dataset_as_vec.end());
+        printf("%s: imatrix dataset='%s'\n", __func__, imatrix_datasets[0].c_str());
     }
     printf("%s: loaded %d importance matrix entries from %s computed on %d chunks\n", __func__, int(imatrix_data.size()), imatrix_file.c_str(), m_last_call);
     return m_last_call;
 }
 
+static int load_imatrix(const std::string & imatrix_file, std::vector<std::string> & imatrix_datasets, std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
+
+    struct ggml_context * ctx = nullptr;
+    struct gguf_init_params meta_gguf_params = {
+        /* .no_alloc = */ false, // the data is needed
+        /* .ctx      = */ &ctx,
+    };
+    struct gguf_context * ctx_gguf = gguf_init_from_file(imatrix_file.c_str(), meta_gguf_params);
+    if (!ctx_gguf) {
+        fprintf(stderr, "%s: imatrix file '%s' is using old format\n", __func__, imatrix_file.c_str());
+        return load_legacy_imatrix(imatrix_file, imatrix_datasets, imatrix_data);
+    }
+    const int32_t n_entries = gguf_get_n_tensors(ctx_gguf);
+    if (n_entries < 1) {
+        fprintf(stderr, "%s: no data in file %s\n", __func__, imatrix_file.c_str());
+        gguf_free(ctx_gguf);
+        ggml_free(ctx);
+        exit(1);
+    }
+
+    const int dataset_idx     = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_DATASETS);
+    const int chunk_count_idx = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_CHUNK_COUNT);
+    const int chunk_size_idx  = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_CHUNK_SIZE);
+    if (dataset_idx < 0 || chunk_count_idx < 0 || chunk_size_idx < 0) {
+        fprintf(stderr, "%s: missing imatrix metadata in file %s\n", __func__, imatrix_file.c_str());
+        gguf_free(ctx_gguf);
+        ggml_free(ctx);
+        exit(1);
+    }
+
+    const uint32_t chunk_size = gguf_get_val_u32(ctx_gguf, chunk_size_idx);
+
+    const std::string sums_suffix{ ".in_sum2" };
+    const std::string counts_suffix{ ".counts" };
+
+    // Using an ordered map to get a deterministic iteration order.
+    std::map<std::string, std::pair<struct ggml_tensor *, struct ggml_tensor *>> sums_counts_for;
+
+    for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
+        std::string name = cur->name;
+
+        if (name.empty()) { continue; }
+
+        if (string_remove_suffix(name, sums_suffix)) {
+            // in_sum2
+            sums_counts_for[std::move(name)].first = cur;
+        } else if (string_remove_suffix(name, counts_suffix)) {
+            // counts
+            sums_counts_for[std::move(name)].second = cur;
+        } else {
+            // ignore other tensors
+        }
+    }
+
+    for (const auto & sc : sums_counts_for) {
+        const        std::string & name   = sc.first;
+        const struct ggml_tensor * sums   = sc.second.first;
+        const struct ggml_tensor * counts = sc.second.second;
+
+        if (!sums || !counts) {
+            fprintf(stderr, "%s: mismatched sums and counts for %s\n", __func__, name.c_str());
+            gguf_free(ctx_gguf);
+            ggml_free(ctx);
+            exit(1);
+        }
+
+        const int64_t ne0 = sums->ne[0];
+        const int64_t ne1 = sums->ne[1];
+
+        auto & e = imatrix_data[name];
+        e.resize(ggml_nelements(sums));
+        float max_count = 0.0f;
+        for (int64_t j = 0; j < ne1; ++j) {
+            const float count = ((const float *) counts->data)[j];
+            if (count > 0.0f) {
+                for (int64_t i = 0; i < ne0; ++i) {
+                    e[j*ne0 + i] = ((const float *) sums->data)[j*ne0 + i] / count;
+                }
+            } else {
+                // Partial imatrix data, this tensor never got any input during calibration
+                for (int64_t i = 0; i < ne0; ++i) {
+                    e[j*ne0 + i] = 1;
+                }
+            }
+            if (count > max_count) {
+                max_count = count;
+            }
+        }
+        if (getenv("LLAMA_TRACE")) {
+            printf("%s: loaded data (size = %6d, n_tokens = %6d, n_chunks = %6d) for '%s'\n", __func__, int(e.size()), int(max_count), int(max_count / chunk_size), name.c_str());
+        }
+    }
+
+    int m_last_chunk = gguf_get_val_u32(ctx_gguf, chunk_count_idx);
+
+    int64_t n_datasets = gguf_get_arr_n(ctx_gguf, dataset_idx);
+    imatrix_datasets.reserve(n_datasets);
+    for (int64_t i = 0; i < n_datasets; ++i) {
+        imatrix_datasets.push_back(gguf_get_val_str(ctx_gguf, dataset_idx));
+    }
+    printf("%s: imatrix datasets=['%s'", __func__, imatrix_datasets[0].c_str());
+    for (size_t i = 1; i < imatrix_datasets.size(); ++i) {
+        printf(", '%s'", imatrix_datasets[i].c_str());
+    }
+    printf("]\n");
+
+    printf("%s: loaded %d importance matrix entries from %s computed on %d chunks\n", __func__, int(imatrix_data.size()), imatrix_file.c_str(), m_last_chunk);
+
+    gguf_free(ctx_gguf);
+    ggml_free(ctx);
+
+    return m_last_chunk;
+}
+
 static int prepare_imatrix(const std::string & imatrix_file,
-        std::string & imatrix_dataset,
+        std::vector<std::string> & imatrix_dataset,
         const std::vector<std::string> & included_weights,
         const std::vector<std::string> & excluded_weights,
         std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
@@ -216,18 +340,21 @@ static int prepare_imatrix(const std::string & imatrix_file,
         return m_last_call;
     }
     if (!excluded_weights.empty()) {
-        for (auto& name : excluded_weights) {
-            for (auto it = imatrix_data.begin(); it != imatrix_data.end(); ) {
+        for (const auto & name : excluded_weights) {
+            for (auto it = imatrix_data.begin(); it != imatrix_data.end();) {
                 auto pos = it->first.find(name);
-                if (pos != std::string::npos) it = imatrix_data.erase(it);
-                else ++it;
+                if (pos != std::string::npos) {
+                    it = imatrix_data.erase(it);
+                } else {
+                    ++it;
+                }
             }
         }
     }
     if (!included_weights.empty()) {
         std::unordered_map<std::string, std::vector<float>> tmp;
-        for (auto& name : included_weights) {
-            for (auto& e : imatrix_data) {
+        for (const auto & name : included_weights) {
+            for (auto & e : imatrix_data) {
                 auto pos = e.first.find(name);
                 if (pos != std::string::npos) {
                     tmp.emplace(std::move(e));
@@ -396,9 +523,9 @@ int main(int argc, char ** argv) {
         usage(argv[0]);
     }
 
-    std::string imatrix_dataset;
+    std::vector<std::string> imatrix_datasets;
     std::unordered_map<std::string, std::vector<float>> imatrix_data;
-    int m_last_call = prepare_imatrix(imatrix_file, imatrix_dataset, included_weights, excluded_weights, imatrix_data);
+    int m_last_call = prepare_imatrix(imatrix_file, imatrix_datasets, included_weights, excluded_weights, imatrix_data);
     if (!imatrix_data.empty()) {
         params.imatrix = &imatrix_data;
         {
@@ -409,11 +536,12 @@ int main(int argc, char ** argv) {
             kvo.val_str[127] = '\0';
             kv_overrides.emplace_back(std::move(kvo));
         }
-        if (!imatrix_dataset.empty()) {
+        if (!imatrix_datasets.empty()) {
             llama_model_kv_override kvo;
+            // TODO: list multiple datasets when there are more than one
             std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_DATASET);
             kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
-            strncpy(kvo.val_str, imatrix_dataset.c_str(), 127);
+            strncpy(kvo.val_str, imatrix_datasets[0].c_str(), 127);
             kvo.val_str[127] = '\0';
             kv_overrides.emplace_back(std::move(kvo));
         }