Merge 2d79a7077c into 8db003a19d

* Support of converting local models added to convert-hf-to-gguf-update.py

* Description fixed

* shutil added to imports

common/common.cpp

@@ -941,11 +941,37 @@ struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_p
 
 #ifdef LLAMA_USE_CURL
 
+#define CURL_MAX_RETRY 3
+#define CURL_RETRY_DELAY_SECONDS 2
+
+
 static bool starts_with(const std::string & str, const std::string & prefix) {
     // While we wait for C++20's std::string::starts_with...
     return str.rfind(prefix, 0) == 0;
 }
 
+static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_attempts, int retry_delay_seconds) {
+    int remaining_attempts = max_attempts;
+
+    while (remaining_attempts > 0) {
+        fprintf(stderr, "%s: Trying to download from %s (attempt %d of %d)...\n", __func__ , url.c_str(), max_attempts - remaining_attempts + 1, max_attempts);
+
+        CURLcode res = curl_easy_perform(curl);
+        if (res == CURLE_OK) {
+            return true;
+        }
+
+        int exponential_backoff_delay = std::pow(retry_delay_seconds, max_attempts - remaining_attempts) * 1000;
+        fprintf(stderr, "%s: curl_easy_perform() failed: %s, retrying after %d milliseconds...\n", __func__, curl_easy_strerror(res), exponential_backoff_delay);
+
+        remaining_attempts--;
+        std::this_thread::sleep_for(std::chrono::milliseconds(exponential_backoff_delay));
+    }
+
+    fprintf(stderr, "%s: curl_easy_perform() failed after %d attempts\n", __func__, max_attempts);
+    return false;
+}
+
 static bool llama_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
 
     // Initialize libcurl
@@ -1049,9 +1075,8 @@ static bool llama_download_file(const std::string & url, const std::string & pat
         curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
         curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
 
-        CURLcode res = curl_easy_perform(curl.get());
-        if (res != CURLE_OK) {
-            fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
+        bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS);
+        if (!was_perform_successful) {
             return false;
         }
 
@@ -1126,11 +1151,10 @@ static bool llama_download_file(const std::string & url, const std::string & pat
         };
 
         // start the download
-        fprintf(stderr, "%s: downloading from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
-                llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
-        auto res = curl_easy_perform(curl.get());
-        if (res != CURLE_OK) {
-            fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
+        fprintf(stderr, "%s: trying to download model from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
+            llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
+        bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS);
+        if (!was_perform_successful) {
             return false;
         }
 

convert_hf_to_gguf_update.py

@@ -31,6 +31,7 @@ import re
 import requests
 import sys
 import json
+import shutil
 
 from hashlib import sha256
 from enum import IntEnum, auto
@@ -125,12 +126,27 @@ def download_model(model):
     if tokt == TOKENIZER_TYPE.UGM:
         files.append("spiece.model")
 
-    for file in files:
-        save_path = f"models/tokenizers/{name}/{file}"
-        if os.path.isfile(save_path):
-            logger.info(f"{name}: File {save_path} already exists - skipping")
-            continue
-        download_file_with_auth(f"{repo}/resolve/main/{file}", token, save_path)
+    if os.path.isdir(repo):
+        # If repo is a path on the file system, copy the directory
+        for file in files:
+            src_path = os.path.join(repo, file)
+            dst_path = f"models/tokenizers/{name}/{file}"
+            if os.path.isfile(dst_path):
+                logger.info(f"{name}: File {dst_path} already exists - skipping")
+                continue
+            if os.path.isfile(src_path):
+                shutil.copy2(src_path, dst_path)
+                logger.info(f"{name}: Copied {src_path} to {dst_path}")
+            else:
+                logger.warning(f"{name}: Source file {src_path} does not exist")
+    else:
+        # If repo is a URL, download the files
+        for file in files:
+            save_path = f"models/tokenizers/{name}/{file}"
+            if os.path.isfile(save_path):
+                logger.info(f"{name}: File {save_path} already exists - skipping")
+                continue
+            download_file_with_auth(f"{repo}/resolve/main/{file}", token, save_path)
 
 
 for model in models:

convert_legacy_imatrix_to_gguf.py

@@ -0,0 +1,122 @@
+#!/usr/bin/env python3
+
+from __future__ import annotations
+
+import os
+import sys
+import logging
+import argparse
+
+from typing import Any
+from pathlib import Path
+from dataclasses import dataclass
+
+import numpy as np
+import numpy.typing as npt
+
+if 'NO_LOCAL_GGUF' not in os.environ:
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
+import gguf
+
+
+logger = logging.getLogger("imatrix-to-gguf")
+
+
+class IMatrixWriter(gguf.GGUFWriter):
+    def add_architecture(self) -> None:
+        # no arch is stored in imatrix files
+        pass
+
+
+@dataclass
+class IMatrixEntry:
+    values: np.ndarray[Any, np.dtype[np.float32]]
+    counts: np.ndarray[Any, np.dtype[np.float32]]
+
+
+class IMatrixReader:
+    chunk_size: int = 512  # guess
+    offset: int = 0
+    data: np.ndarray[Any, np.dtype[np.uint8]]
+    n_enties: int
+    entries: dict[str, IMatrixEntry]
+    chunk_count: int
+    dataset: str
+
+    def _get(self, dtype: npt.DTypeLike, count: int = 1) -> npt.NDArray[Any]:
+        count = int(count)
+        itemsize = int(np.empty([], dtype=dtype).itemsize)
+        offset = self.offset
+        self.offset = offset + itemsize * count
+        return self.data[offset:self.offset].view(dtype=dtype)[:count]
+
+    def __init__(self, imatrix: Path):
+        self.offset = 0
+        self.entries = {}
+        self.data = np.memmap(imatrix)
+        n_entries = self._get(np.int32).item()
+        assert n_entries >= 0
+        for _ in range(n_entries):
+            len = self._get(np.int32).item()
+            name = self._get(np.uint8, len).tobytes().decode("utf-8")
+            ncall = self._get(np.int32).item()
+            nval = self._get(np.int32).item()
+            data = self._get(np.float32, nval)
+            assert name not in self.entries, f"duplicated name: {name!r}"
+
+            self.entries[name] = IMatrixEntry(data * np.float32(self.chunk_size), np.array([ncall * self.chunk_size], dtype=np.float32))
+
+        self.chunk_count = self._get(np.int32).item()
+        dataset_len = self._get(np.int32).item()
+        self.dataset = self._get(np.uint8, dataset_len).tobytes().decode("utf-8")
+
+    def to_writer(self, outfile: Path) -> IMatrixWriter:
+        writer = IMatrixWriter(path=outfile, arch="")
+
+        writer.add_type(gguf.GGUFType.IMATRIX)
+        writer.add_key_value(gguf.Keys.IMatrix.CHUNK_COUNT, self.chunk_count, gguf.GGUFValueType.UINT32)
+        writer.add_key_value(gguf.Keys.IMatrix.CHUNK_SIZE, self.chunk_size, gguf.GGUFValueType.UINT32)
+        writer.add_key_value(gguf.Keys.IMatrix.DATASET, self.dataset, gguf.GGUFValueType.STRING)
+
+        for name, entry in self.entries.items():
+            writer.add_tensor(name + ".sums", entry.values)
+            writer.add_tensor(name + ".counts", entry.counts)
+
+        return writer
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Convert an old imatrix.dat file to a GGUF compatible file")
+    parser.add_argument(
+        "--outfile", type=Path,
+        help="path to write to; default: based on input.",
+    )
+    parser.add_argument(
+        "--verbose", action="store_true",
+        help="increase output verbosity",
+    )
+    parser.add_argument(
+        "imatrix", type=Path,
+        help="path to an imatrix file",
+    )
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
+
+    if args.outfile is None:
+        input_file: Path = args.imatrix
+        if input_file.suffix != ".gguf":
+            args.outfile = input_file.with_suffix(".gguf")
+            if args.outfile.exists():
+                logger.error(f"default file exists, specify with --outfile to overwrite: {args.outfile}")
+                exit(1)
+
+    writer = IMatrixReader(args.imatrix).to_writer(args.outfile)
+
+    writer.write_header_to_file(args.outfile)
+    writer.write_kv_data_to_file()
+    writer.write_tensors_to_file()

examples/imatrix/imatrix.cpp

@@ -6,12 +6,11 @@
 #include <cstdio>
 #include <cstring>
 #include <ctime>
-#include <sstream>
 #include <thread>
 #include <mutex>
 #include <vector>
-#include <fstream>
 #include <unordered_map>
+#include <map>
 #include <algorithm>
 
 #if defined(_MSC_VER)
@@ -21,16 +20,27 @@
 static void print_usage(int, char ** argv) {
     LOG_TEE("\nexample usage:\n");
     LOG_TEE("\n    %s \\\n"
-            "       -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] [--verbosity 1] \\\n"
+            "       -m model.gguf -f some-text.txt [-o imatrix.gguf] [--process-output] [--verbosity 1] \\\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" , argv[0]);
+            "       [--in-file imatrix-prev-0.gguf --in-file imatrix-prev-1.gguf ...]\n" , argv[0]);
     LOG_TEE("\n");
 }
 
+static bool str_remove_suffix(std::string & str, const std::string & suffix) {
+    bool has_suffix = str.size() >= suffix.size() && str.compare(str.size() - suffix.size(), str.size(), suffix) == 0;
+    if (has_suffix) {
+        str = str.substr(0, str.size() - suffix.size());
+    }
+    return has_suffix;
+}
+
+static const char * const LLM_KV_IMATRIX_DATASET     = "imatrix.dataset";
+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 {
@@ -38,13 +48,13 @@ public:
     IMatrixCollector() = default;
     void set_params(gpt_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;
+    void save_imatrix(int32_t n_chunk = -1) const;
     bool load_imatrix(const char * file_name);
 private:
     std::unordered_map<std::string, Stats> m_stats;
     gpt_params                             m_params;
     std::mutex                             m_mutex;
-    int                                    m_last_call = 0;
+    int32_t                                m_last_chunk = 0;
     std::vector<float>                     m_src1_data;
     std::vector<char>                      m_ids; // the expert ids from ggml_mul_mat_id
 };
@@ -118,18 +128,24 @@ 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) {
             fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", 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) {
+            fprintf(stderr, "Oops: inconsistent expert count for %s (%d vs %d)\n", wname.c_str(), (int)e.counts.size(), (int)n_as);
+            exit(1); //GGML_ABORT("fatal error");
+        }
         if (m_params.verbosity > 1) {
-            printf("%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);
+            printf("%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) {
@@ -147,23 +163,26 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
                     const int64_t i12 = row;
                     const float * x = (const float *)((const char *)data + i11*src1->nb[1] + i12*src1->nb[2]);
 
+                    e.counts[ex]++;
+
                     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])) {
-                            fprintf(stderr, "%f detected in %s\n", e.values[e_start + j], wname.c_str());
+                        e.values[e_start + j] = std::fma(x[j], x[j], e.values[e_start + j]);
+                        if (!std::isfinite((float)e.values[e_start + j])) {
+                            fprintf(stderr, "%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] / (m_params.n_ctx / m_params.n_parallel);
+            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);
                 }
             }
         }
@@ -171,34 +190,40 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
         auto & e = m_stats[wname];
         if (e.values.empty()) {
             e.values.resize(src1->ne[0], 0);
-            e.counts.resize(src1->ne[0], 0);
+            e.counts.resize(1, 0);
         }
         else if (e.values.size() != (size_t)src1->ne[0]) {
             fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]);
             exit(1); //GGML_ABORT("fatal error");
         }
-        ++e.ncall;
-        if (m_params.verbosity > 1) {
-            printf("%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);
+        else if (e.counts.size() != 1) {
+            fprintf(stderr, "Oops: inconsistent expert count for %s (%d vs %d)\n", wname.c_str(), (int)e.counts.size(), 1);
+            exit(1); //GGML_ABORT("fatal error");
         }
+        if (m_params.verbosity > 1) {
+            printf("%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[1], (int)src1->type);
+        }
+        // TODO: higher dimensions
         for (int row = 0; row < (int)src1->ne[1]; ++row) {
             const float * x = data + row * src1->ne[0];
+            e.counts[0]++;
             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])) {
-                    fprintf(stderr, "%f detected in %s\n", e.values[j], wname.c_str());
+                e.values[j] = std::fma(x[j], x[j], e.values[j]);
+                if (!std::isfinite((float)e.values[j])) {
+                    fprintf(stderr, "%f detected in %s\n", (float)e.values[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[0] / (m_params.n_ctx / m_params.n_parallel);
+        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);
             }
         }
     }
@@ -206,22 +231,22 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
     return true;
 }
 
-void IMatrixCollector::save_imatrix(int ncall) const {
+void IMatrixCollector::save_imatrix(int32_t n_chunk) const {
     auto fname = m_params.out_file;
     if (fname.empty()) {
-        fname = "imatrix.dat";
+        fname = "imatrix.gguf";
     }
 
-    if (ncall > 0) {
+    if (n_chunk > 0) {
         fname += ".at_";
-        fname += std::to_string(ncall);
+        fname += std::to_string(n_chunk);
     }
 
     // avoid 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;
     std::vector<std::string> to_store;
+    size_t data_size = 0;
 
     bool is_first = true; // for printing
     for (const auto & kv : m_stats) {
@@ -253,102 +278,158 @@ void IMatrixCollector::save_imatrix(int ncall) const {
             continue;
         }
 
-        n_entries++;
         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);
     }
 
     if (to_store.size() < m_stats.size()) {
         fprintf(stderr, "%s: warning: storing only %zu out of %zu entries\n", __func__, to_store.size(), m_stats.size());
     }
 
-    std::ofstream out(fname, std::ios::binary);
-    out.write((const char *) &n_entries, sizeof(n_entries));
+    // 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();
+
+    gguf_set_val_str(ctx_gguf, "general.type", "imatrix");
+    // Write the input filename to later on specify it in quantize
+    gguf_set_val_str(ctx_gguf, LLM_KV_IMATRIX_DATASET, m_params.prompt_file.c_str());
+    // 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);
-        int 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();
-        out.write((const char *) &nval, sizeof(nval));
+        const int32_t nval = (int32_t) stat.values.size();
+        const int32_t nmat = (int32_t) stat.counts.size();
         if (nval > 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);
+            struct ggml_tensor * sums = 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(sums, "%s.sums", name.c_str());
+            ggml_format_name(counts, "%s.counts", name.c_str());
+
+            for (int32_t j = 0; j < nval; ++j) {
+                ((float *) sums->data)[j] = (float) stat.values[j];
             }
-            out.write((const char*)tmp.data(), nval*sizeof(float));
+            for (int32_t j = 0; j < nmat; ++j) {
+                ((float *) counts->data)[j] = (float) stat.counts[j];
+            }
+
+            gguf_add_tensor(ctx_gguf, sums);
+            gguf_add_tensor(ctx_gguf, counts);
         }
     }
 
-    // Write the number of call the matrix was computed with
-    out.write((const char *) &m_last_call, sizeof(m_last_call));
-
-    // Write the input filename at the end of the file to later on specify it in quantize
-    {
-        int len = m_params.prompt_file.size();
-        out.write((const char *) &len, sizeof(len));
-        out.write(m_params.prompt_file.c_str(), len);
-    }
+    gguf_write_to_file(ctx_gguf, fname.c_str(), false);
 
     if (m_params.verbosity > 0) {
-        fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname.c_str());
+        fprintf(stderr, "\n%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(const char * fname) {
-    std::ifstream in(fname, std::ios::binary);
-    if (!in) {
-        printf("%s: failed to open %s\n",__func__, fname);
+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 false;
     }
-    int n_entries;
-    in.read((char*)&n_entries, sizeof(n_entries));
-    if (in.fail() || n_entries < 1) {
-        printf("%s: no data in file %s\n", __func__, fname);
+    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__, file_name);
+        gguf_free(ctx_gguf);
+        ggml_free(ctx);
         return false;
     }
-    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);
-        if (in.fail()) {
-            printf("%s: failed reading name for entry %d from %s\n",__func__,i+1, fname);
+
+    const std::string sums_suffix{".sums"};
+    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 (str_remove_suffix(name, sums_suffix)) {
+            // sums
+            sums_counts_for[name].first = cur;
+        } else if (str_remove_suffix(name, counts_suffix)) {
+            // counts
+            sums_counts_for[name].second = cur;
+        } else {
+            fprintf(stderr, "%s: invalid imatrix tensor name: %s\n", __func__, name.c_str());
+            gguf_free(ctx_gguf);
+            ggml_free(ctx);
             return false;
         }
-        name_as_vec[len] = 0;
-        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));
-        if (in.fail() || nval < 1) {
-            printf("%s: failed reading number of values for entry %d\n",__func__,i);
-            m_stats = {};
+    }
+
+    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);
             return false;
         }
 
+        auto & e = m_stats[name];
+
+        int64_t nval = ggml_nelements(sums);
         if (e.values.empty()) {
             e.values.resize(nval, 0);
-            e.counts.resize(nval, 0);
-        }
-
-        std::vector<float> tmp(nval);
-        in.read((char*)tmp.data(), nval*sizeof(float));
-        if (in.fail()) {
-            printf("%s: failed reading data for entry %d\n",__func__,i);
-            m_stats = {};
+        } else if ((size_t) nval != e.values.size()) {
+            fprintf(stderr, "%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;
         }
 
-        // Recreate the state as expected by save_imatrix(), and corerct for weighted sum.
-        for (int i = 0; i < nval; i++) {
-            e.values[i] += tmp[i];
-            e.counts[i] += ncall;
+        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()) {
+            fprintf(stderr, "%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;
         }
-        e.ncall += ncall;
 
+        // Recreate the state as expected by save_imatrix()
+        for (int64_t j = 0; j < nval; j++) {
+            e.values[j] += ((const float *) sums->data)[j];
+        }
+        for (int64_t j = 0; j < ncounts; j++) {
+            e.counts[j] += std::lround(((const float *) counts->data)[j]);
+        }
     }
+    gguf_free(ctx_gguf);
+    ggml_free(ctx);
     return true;
 }
 
@@ -431,10 +512,9 @@ static void process_logits(
     }
 }
 
-static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
+static bool compute_imatrix(llama_context * ctx, const gpt_params & params, const int32_t n_ctx) {
     const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
     GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx)));
-    const int n_ctx = llama_n_ctx(ctx);
 
     auto tim1 = std::chrono::high_resolution_clock::now();
     fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
@@ -478,22 +558,28 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
     double nll = 0.0;
     double nll2 = 0.0;
 
-    fprintf(stderr, "%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) {
+    fprintf(stderr, "%s: computing over %d chunks, n_ctx=%d, batch_size=%d, n_seq=%d\n", __func__, n_chunk, n_ctx, n_batch, n_seq);
+
+    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();
 
@@ -504,35 +590,50 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
             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];
+            // clear the batch
+            llama_batch_clear(batch);
 
-            // add BOS token for the first batch of each chunk
-            if (add_bos && j == 0) {
-                tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
+            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_token_bos(llama_get_model(ctx));
+                }
+
+                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)
+                    llama_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;
             }
 
-            // TODO: use batch.logits to save computations instead of relying on logits_all == true
-            if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
+            if (llama_decode(ctx, batch)) {
                 fprintf(stderr, "%s : failed to eval\n", __func__);
                 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);
             }
         }
 
-        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();
             fprintf(stderr, "%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) {
                 fprintf(stderr, "%d hours ", total_seconds / (60*60));
                 total_seconds = total_seconds % (60*60);
@@ -542,12 +643,21 @@ static bool compute_imatrix(llama_context * ctx, const gpt_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);
 
-            printf("[%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;
+
+                printf("[%d]%.4lf,", i + seq + 1, std::exp(nll / count));
+            }
             fflush(stdout);
 
             logits.clear();
@@ -582,7 +692,22 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    params.n_batch = std::min(params.n_batch, params.n_ctx);
+    const int32_t n_ctx = params.n_ctx;
+
+    if (n_ctx <= 0) {
+        fprintf(stderr, "%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);
 
@@ -630,7 +755,7 @@ int main(int argc, char ** argv) {
         fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str());
     }
 
-    if (!compute_imatrix(ctx, params)) {
+    if (!compute_imatrix(ctx, params, n_ctx)) {
         return 1;
     }
 

examples/llava/minicpmv-cli.cpp

@@ -18,8 +18,8 @@ struct llava_context {
 };
 
 static void show_additional_info(int /*argc*/, char ** argv) {
-    LOG_TEE("\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
-    LOG_TEE("  note: a lower temperature value like 0.1 is recommended for better quality.\n");
+    LOG_TEE("\nexample usage:\n\n%s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
+    LOG_TEE("\nnote: a lower temperature value like 0.1 is recommended for better quality.\n");
 }
 
 static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
@@ -255,7 +255,7 @@ int main(int argc, char ** argv) {
 
     gpt_params params;
 
-    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, show_additional_info)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, show_additional_info)) {
         return 1;
     }
 

examples/quantize/quantize.cpp

@@ -6,8 +6,7 @@
 #include <vector>
 #include <string>
 #include <unordered_map>
-#include <fstream>
-#include <cmath>
+#include <map>
 
 struct quant_option {
     std::string name;
@@ -63,6 +62,11 @@ 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_DATASET     = "imatrix.dataset";
+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 try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std::string & ftype_str_out) {
     std::string ftype_str;
 
@@ -122,67 +126,114 @@ static void usage(const char * executable) {
     exit(1);
 }
 
+// TODO: share with implementation in imatrix.cpp
+static bool str_remove_suffix(std::string & str, const std::string & suffix) {
+    bool has_suffix = str.size() >= suffix.size() && str.compare(str.size() - suffix.size(), str.size(), suffix) == 0;
+    if (has_suffix) {
+        str = str.substr(0, str.size() - suffix.size());
+    }
+    return has_suffix;
+}
+
 static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_dataset, 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());
+
+    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: if this is an older imatrix file, make sure to convert it to the GGUF-based imatrix format\n", __func__);
         exit(1);
     }
-    int n_entries;
-    in.read((char *)&n_entries, sizeof(n_entries));
-    if (in.fail() || n_entries < 1) {
-        printf("%s: no data in file %s\n", __func__, imatrix_file.c_str());
+    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);
     }
-    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);
-        if (in.fail()) {
-            printf("%s: failed reading name for entry %d from %s\n", __func__, i+1, imatrix_file.c_str());
+
+    const int dataset_idx     = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_DATASET);
+    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{".sums"};
+    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 (str_remove_suffix(name, sums_suffix)) {
+            // sums
+            sums_counts_for[name].first = cur;
+        } else if (str_remove_suffix(name, counts_suffix)) {
+            // counts
+            sums_counts_for[name].second = cur;
+        } else {
+            fprintf(stderr, "%s: invalid imatrix tensor name: %s\n", __func__, name.c_str());
+            gguf_free(ctx_gguf);
+            ggml_free(ctx);
             exit(1);
         }
-        name_as_vec[len] = 0;
-        std::string name{name_as_vec.data()};
+    }
+
+    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];
-        int ncall;
-        in.read((char *)&ncall, sizeof(ncall));
-        int nval;
-        in.read((char *)&nval, sizeof(nval));
-        if (in.fail() || nval < 1) {
-            printf("%s: failed reading number of values for entry %d\n", __func__, i);
-            imatrix_data = {};
-            exit(1);
+        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];
+            for (int64_t i = 0; i < ne0; ++i) {
+                e[j*ne0 + i] = ((const float *) sums->data)[j*ne0 + i] / count;
+            }
+            if (count > max_count) {
+                max_count = count;
+            }
         }
-        e.resize(nval);
-        in.read((char *)e.data(), nval*sizeof(float));
-        if (in.fail()) {
-            printf("%s: failed reading data for entry %d\n", __func__, i);
-            imatrix_data = {};
-            exit(1);
-        }
-        if (ncall > 0) {
-            for (auto& v : e) v /= ncall;
-        }
-
         if (getenv("LLAMA_TRACE")) {
-            printf("%s: loaded data (size = %6d, ncall = %6d) for '%s'\n", __func__, int(e.size()), ncall, name.c_str());
+            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());
         }
     }
 
-    // latest 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));
-        int dataset_len;
-        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());
-    }
-    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;
+    int m_last_chunk = gguf_get_val_u32(ctx_gguf, chunk_count_idx);
+    imatrix_dataset = gguf_get_val_str(ctx_gguf, dataset_idx);
+
+    printf("%s: imatrix dataset='%s'\n", __func__, imatrix_dataset.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_chunk);
+
+    gguf_free(ctx_gguf);
+    ggml_free(ctx);
+
+    return m_last_chunk;
 }
 
 static int prepare_imatrix(const std::string & imatrix_file,

ggml/src/ggml-cuda/mmq.cu

@@ -26,7 +26,11 @@ void ggml_cuda_op_mul_mat_q(
     // nrows_dst == nrows of the matrix that the kernel writes into
     const int64_t nrows_dst = id == ctx.device ? ne0 : row_diff;
 
-    const mmq_args args = {src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stride00, src1_padded_row_size, src1_ncols, ne11, nrows_dst};
+    // The stream-k decomposition is only faster for recent NVIDIA GPUs.
+    // Also its fixup needs to allocate a temporary buffer in the memory pool.
+    // There are multiple parallel CUDA streams for src1_ncols != ne11 which would introduce a race condition for this buffer.
+    const bool use_stream_k = compute_capability >= CC_VOLTA && compute_capability < CC_OFFSET_AMD && src1_ncols == ne11;
+    const mmq_args args = {src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stride00, src1_padded_row_size, src1_ncols, ne11, nrows_dst, use_stream_k};
 
     switch (src0->type) {
         case GGML_TYPE_Q4_0:

ggml/src/ggml-cuda/mmq.cuh

@@ -2742,6 +2742,7 @@ struct mmq_args {
     int64_t ne00; int64_t ne01; int64_t stride01;
     int64_t ne10; int64_t ne11; int64_t stride11;
     int64_t ne0;
+    bool use_stream_k;
 };
 
 template<ggml_type type>
@@ -2777,8 +2778,7 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a
     const int ntx = (args.ne11 + mmq_x - 1) / mmq_x;
     const dim3 block_nums_xy_tiling(nty, ntx, 1);
 
-    const bool use_stream_k = cc >= CC_VOLTA && cc < CC_OFFSET_AMD;
-    if (!use_stream_k) {
+    if (!args.use_stream_k) {
         if (args.ne01 % mmq_y == 0) {
             constexpr bool need_check = false;
             mul_mat_q<type, mmq_x, MMQ_NWARPS, need_check><<<block_nums_xy_tiling, block_dims, shmem, stream>>>

gguf-py/gguf/constants.py

@@ -174,6 +174,12 @@ class Keys:
         TYPE       = "adapter.type"
         LORA_ALPHA = "adapter.lora.alpha"
 
+    class IMatrix:
+        CHUNK_COUNT = "imatrix.chunk_count"
+        CHUNK_SIZE  = "imatrix.chunk_size"
+        DATASET     = "imatrix.dataset"
+
+
 #
 # recommended mapping of model tensor names for storage in gguf
 #
@@ -182,6 +188,7 @@ class Keys:
 class GGUFType:
     MODEL   = "model"
     ADAPTER = "adapter"
+    IMATRIX = "imatrix"
 
 
 class MODEL_ARCH(IntEnum):

requirements.txt

@@ -8,5 +8,6 @@
 
 -r ./requirements/requirements-convert_hf_to_gguf.txt
 -r ./requirements/requirements-convert_hf_to_gguf_update.txt
+-r ./requirements/requirements-convert_legacy_imatrix_to_gguf.txt
 -r ./requirements/requirements-convert_llama_ggml_to_gguf.txt
 -r ./requirements/requirements-convert_lora_to_gguf.txt

requirements/requirements-convert_legacy_imatrix_to_gguf.txt

@@ -0,0 +1 @@
+-r ./requirements-convert_legacy_llama.txt