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@ -941,11 +941,37 @@ struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_p
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#ifdef LLAMA_USE_CURL
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#define CURL_MAX_RETRY 3
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#define CURL_RETRY_DELAY_SECONDS 2
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static bool starts_with(const std::string & str, const std::string & prefix) {
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// While we wait for C++20's std::string::starts_with...
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return str.rfind(prefix, 0) == 0;
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}
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static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_attempts, int retry_delay_seconds) {
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int remaining_attempts = max_attempts;
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while (remaining_attempts > 0) {
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fprintf(stderr, "%s: Trying to download from %s (attempt %d of %d)...\n", __func__ , url.c_str(), max_attempts - remaining_attempts + 1, max_attempts);
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CURLcode res = curl_easy_perform(curl);
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if (res == CURLE_OK) {
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return true;
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}
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int exponential_backoff_delay = std::pow(retry_delay_seconds, max_attempts - remaining_attempts) * 1000;
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fprintf(stderr, "%s: curl_easy_perform() failed: %s, retrying after %d milliseconds...\n", __func__, curl_easy_strerror(res), exponential_backoff_delay);
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remaining_attempts--;
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std::this_thread::sleep_for(std::chrono::milliseconds(exponential_backoff_delay));
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}
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fprintf(stderr, "%s: curl_easy_perform() failed after %d attempts\n", __func__, max_attempts);
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return false;
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}
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static bool llama_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
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// Initialize libcurl
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@ -1049,9 +1075,8 @@ static bool llama_download_file(const std::string & url, const std::string & pat
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curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
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curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
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CURLcode res = curl_easy_perform(curl.get());
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if (res != CURLE_OK) {
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fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
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bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS);
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if (!was_perform_successful) {
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return false;
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}
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@ -1126,11 +1151,10 @@ static bool llama_download_file(const std::string & url, const std::string & pat
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};
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// start the download
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fprintf(stderr, "%s: downloading from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
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llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
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auto res = curl_easy_perform(curl.get());
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if (res != CURLE_OK) {
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fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
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fprintf(stderr, "%s: trying to download model from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
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llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
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bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS);
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if (!was_perform_successful) {
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return false;
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}
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|
@ -31,6 +31,7 @@ import re
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import requests
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import sys
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import json
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import shutil
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from hashlib import sha256
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from enum import IntEnum, auto
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@ -125,12 +126,27 @@ def download_model(model):
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if tokt == TOKENIZER_TYPE.UGM:
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files.append("spiece.model")
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for file in files:
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save_path = f"models/tokenizers/{name}/{file}"
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if os.path.isfile(save_path):
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logger.info(f"{name}: File {save_path} already exists - skipping")
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continue
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download_file_with_auth(f"{repo}/resolve/main/{file}", token, save_path)
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if os.path.isdir(repo):
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# If repo is a path on the file system, copy the directory
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for file in files:
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src_path = os.path.join(repo, file)
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dst_path = f"models/tokenizers/{name}/{file}"
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if os.path.isfile(dst_path):
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logger.info(f"{name}: File {dst_path} already exists - skipping")
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continue
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if os.path.isfile(src_path):
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shutil.copy2(src_path, dst_path)
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logger.info(f"{name}: Copied {src_path} to {dst_path}")
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else:
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logger.warning(f"{name}: Source file {src_path} does not exist")
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else:
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# If repo is a URL, download the files
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for file in files:
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save_path = f"models/tokenizers/{name}/{file}"
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if os.path.isfile(save_path):
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logger.info(f"{name}: File {save_path} already exists - skipping")
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continue
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download_file_with_auth(f"{repo}/resolve/main/{file}", token, save_path)
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for model in models:
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122
convert_legacy_imatrix_to_gguf.py
Normal file
122
convert_legacy_imatrix_to_gguf.py
Normal file
@ -0,0 +1,122 @@
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#!/usr/bin/env python3
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from __future__ import annotations
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import os
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import sys
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import logging
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import argparse
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from typing import Any
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from pathlib import Path
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from dataclasses import dataclass
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import numpy as np
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import numpy.typing as npt
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if 'NO_LOCAL_GGUF' not in os.environ:
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sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
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import gguf
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logger = logging.getLogger("imatrix-to-gguf")
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class IMatrixWriter(gguf.GGUFWriter):
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def add_architecture(self) -> None:
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# no arch is stored in imatrix files
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pass
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@dataclass
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class IMatrixEntry:
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values: np.ndarray[Any, np.dtype[np.float32]]
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counts: np.ndarray[Any, np.dtype[np.float32]]
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class IMatrixReader:
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chunk_size: int = 512 # guess
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offset: int = 0
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data: np.ndarray[Any, np.dtype[np.uint8]]
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n_enties: int
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entries: dict[str, IMatrixEntry]
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chunk_count: int
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dataset: str
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def _get(self, dtype: npt.DTypeLike, count: int = 1) -> npt.NDArray[Any]:
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count = int(count)
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itemsize = int(np.empty([], dtype=dtype).itemsize)
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offset = self.offset
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self.offset = offset + itemsize * count
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return self.data[offset:self.offset].view(dtype=dtype)[:count]
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def __init__(self, imatrix: Path):
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self.offset = 0
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self.entries = {}
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self.data = np.memmap(imatrix)
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n_entries = self._get(np.int32).item()
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assert n_entries >= 0
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for _ in range(n_entries):
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len = self._get(np.int32).item()
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name = self._get(np.uint8, len).tobytes().decode("utf-8")
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ncall = self._get(np.int32).item()
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nval = self._get(np.int32).item()
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data = self._get(np.float32, nval)
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assert name not in self.entries, f"duplicated name: {name!r}"
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self.entries[name] = IMatrixEntry(data * np.float32(self.chunk_size), np.array([ncall * self.chunk_size], dtype=np.float32))
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self.chunk_count = self._get(np.int32).item()
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dataset_len = self._get(np.int32).item()
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self.dataset = self._get(np.uint8, dataset_len).tobytes().decode("utf-8")
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def to_writer(self, outfile: Path) -> IMatrixWriter:
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writer = IMatrixWriter(path=outfile, arch="")
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writer.add_type(gguf.GGUFType.IMATRIX)
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writer.add_key_value(gguf.Keys.IMatrix.CHUNK_COUNT, self.chunk_count, gguf.GGUFValueType.UINT32)
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writer.add_key_value(gguf.Keys.IMatrix.CHUNK_SIZE, self.chunk_size, gguf.GGUFValueType.UINT32)
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writer.add_key_value(gguf.Keys.IMatrix.DATASET, self.dataset, gguf.GGUFValueType.STRING)
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for name, entry in self.entries.items():
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writer.add_tensor(name + ".sums", entry.values)
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writer.add_tensor(name + ".counts", entry.counts)
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return writer
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def parse_args():
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parser = argparse.ArgumentParser(
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description="Convert an old imatrix.dat file to a GGUF compatible file")
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parser.add_argument(
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"--outfile", type=Path,
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help="path to write to; default: based on input.",
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)
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parser.add_argument(
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"--verbose", action="store_true",
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help="increase output verbosity",
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)
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parser.add_argument(
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"imatrix", type=Path,
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help="path to an imatrix file",
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)
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return parser.parse_args()
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if __name__ == "__main__":
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args = parse_args()
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logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
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if args.outfile is None:
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input_file: Path = args.imatrix
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if input_file.suffix != ".gguf":
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args.outfile = input_file.with_suffix(".gguf")
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if args.outfile.exists():
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logger.error(f"default file exists, specify with --outfile to overwrite: {args.outfile}")
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exit(1)
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writer = IMatrixReader(args.imatrix).to_writer(args.outfile)
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writer.write_header_to_file(args.outfile)
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writer.write_kv_data_to_file()
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writer.write_tensors_to_file()
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@ -6,12 +6,11 @@
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#include <cstdio>
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#include <cstring>
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#include <ctime>
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#include <sstream>
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#include <thread>
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#include <mutex>
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#include <vector>
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#include <fstream>
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#include <unordered_map>
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#include <map>
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#include <algorithm>
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#if defined(_MSC_VER)
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@ -21,16 +20,27 @@
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static void print_usage(int, char ** argv) {
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LOG_TEE("\nexample usage:\n");
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LOG_TEE("\n %s \\\n"
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" -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] [--verbosity 1] \\\n"
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" -m model.gguf -f some-text.txt [-o imatrix.gguf] [--process-output] [--verbosity 1] \\\n"
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" [--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \\\n"
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" [--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...]\n" , argv[0]);
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" [--in-file imatrix-prev-0.gguf --in-file imatrix-prev-1.gguf ...]\n" , argv[0]);
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LOG_TEE("\n");
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}
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static bool str_remove_suffix(std::string & str, const std::string & suffix) {
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bool has_suffix = str.size() >= suffix.size() && str.compare(str.size() - suffix.size(), str.size(), suffix) == 0;
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if (has_suffix) {
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str = str.substr(0, str.size() - suffix.size());
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}
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return has_suffix;
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}
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static const char * const LLM_KV_IMATRIX_DATASET = "imatrix.dataset";
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static const char * const LLM_KV_IMATRIX_CHUNK_COUNT = "imatrix.chunk_count";
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static const char * const LLM_KV_IMATRIX_CHUNK_SIZE = "imatrix.chunk_size";
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struct Stats {
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std::vector<float> values;
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std::vector<int> counts;
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int ncall = 0;
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std::vector<float> values;
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std::vector<int64_t> counts;
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};
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class IMatrixCollector {
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@ -38,13 +48,13 @@ public:
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IMatrixCollector() = default;
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void set_params(gpt_params params) { m_params = std::move(params); }
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bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
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void save_imatrix(int ncall = -1) const;
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void save_imatrix(int32_t n_chunk = -1) const;
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bool load_imatrix(const char * file_name);
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private:
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std::unordered_map<std::string, Stats> m_stats;
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gpt_params m_params;
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std::mutex m_mutex;
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int m_last_call = 0;
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int32_t m_last_chunk = 0;
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std::vector<float> m_src1_data;
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std::vector<char> m_ids; // the expert ids from ggml_mul_mat_id
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};
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@ -118,18 +128,24 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
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auto & e = m_stats[wname];
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++e.ncall;
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if (e.counts.size() == 1 && n_as > 1) {
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// broadcast, when loading an old imatrix
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e.counts.resize(n_as, e.counts[0]);
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}
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if (e.values.empty()) {
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e.values.resize(src1->ne[0]*n_as, 0);
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e.counts.resize(src1->ne[0]*n_as, 0);
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e.counts.resize(n_as, 0);
|
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}
|
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else if (e.values.size() != (size_t)src1->ne[0]*n_as) {
|
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fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]*n_as);
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exit(1); //GGML_ABORT("fatal error");
|
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}
|
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else if (e.counts.size() != (size_t)n_as) {
|
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fprintf(stderr, "Oops: inconsistent expert count for %s (%d vs %d)\n", wname.c_str(), (int)e.counts.size(), (int)n_as);
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exit(1); //GGML_ABORT("fatal error");
|
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}
|
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if (m_params.verbosity > 1) {
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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);
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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);
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}
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// loop over all possible experts, regardless if they are used or not in the batch
|
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for (int ex = 0; ex < n_as; ++ex) {
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@ -147,23 +163,26 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
|
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const int64_t i12 = row;
|
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const float * x = (const float *)((const char *)data + i11*src1->nb[1] + i12*src1->nb[2]);
|
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|
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e.counts[ex]++;
|
||||
|
||||
for (int j = 0; j < (int)src1->ne[0]; ++j) {
|
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e.values[e_start + j] += x[j]*x[j];
|
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e.counts[e_start + j]++;
|
||||
if (!std::isfinite(e.values[e_start + j])) {
|
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fprintf(stderr, "%f detected in %s\n", e.values[e_start + j], wname.c_str());
|
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e.values[e_start + j] = std::fma(x[j], x[j], e.values[e_start + j]);
|
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if (!std::isfinite((float)e.values[e_start + j])) {
|
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fprintf(stderr, "%f detected in %s\n", (float)e.values[e_start + j], wname.c_str());
|
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exit(1);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
if (e.ncall > m_last_call) {
|
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m_last_call = e.ncall;
|
||||
if (m_last_call % m_params.n_out_freq == 0) {
|
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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;
|
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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;
|
||||
}
|
||||
|
||||
|
@ -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;
|
||||
}
|
||||
|
||||
|
@ -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,
|
||||
|
@ -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:
|
||||
|
@ -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>>>
|
||||
|
@ -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):
|
||||
|
@ -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
|
||||
|
@ -0,0 +1 @@
|
||||
-r ./requirements-convert_legacy_llama.txt
|
Loading…
Reference in New Issue
Block a user