diff --git a/convert_legacy_imatrix_to_gguf.py b/convert_legacy_imatrix_to_gguf.py new file mode 100644 index 0000000000000..939d3695b23ce --- /dev/null +++ b/convert_legacy_imatrix_to_gguf.py @@ -0,0 +1,118 @@ +#!/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.array([ncall * self.chunk_size], dtype=np.float32)) + + self.chunk_count = self._get(np.int32).item() + self.dataset = self._get(np.uint8, self._get(np.int32).item()).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") + + 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() diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index 6135f00a7e8c1..2314a035d04fe 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -5,11 +5,9 @@ #include #include #include -#include #include #include #include -#include #include #include @@ -22,16 +20,19 @@ static void print_usage(int argc, char ** argv, const gpt_params & params) { 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 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 values; - std::vector counts; - int ncall = 0; + std::vector values; + std::vector counts; }; class IMatrixCollector { @@ -39,13 +40,13 @@ class IMatrixCollector { 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 m_stats; gpt_params m_params; std::mutex m_mutex; - int m_last_call = 0; + int32_t m_last_chunk = 0; std::vector m_src1_data; std::vector m_ids; // the expert ids from ggml_mul_mat_id }; @@ -119,18 +120,24 @@ 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) { @@ -148,23 +155,26 @@ 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()); + 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); } } } @@ -172,34 +182,40 @@ 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; + 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_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (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[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()); + 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); } } } @@ -207,15 +223,15 @@ 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 @@ -223,6 +239,7 @@ void IMatrixCollector::save_imatrix(int ncall) const { int n_entries = 0; std::vector to_store; + size_t data_size = 0; bool is_first = true; // for printing for (const auto & kv : m_stats) { @@ -256,100 +273,132 @@ void IMatrixCollector::save_imatrix(int ncall) const { 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)); + 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 tmp(nval); - for (int i = 0; i < nval; i++) { - tmp[i] = (stat.values[i] / static_cast(stat.counts[i])) * static_cast(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_set_name(sums, (name + ".sums").c_str()); + ggml_set_name(counts, (name + ".counts").c_str()); + + for (int32_t j = 0; j < nval; ++j) { + ((float *) sums->data)[j] = (float) stat.values[j]; + } + for (int32_t j = 0; j < nmat; ++j) { + ((float *) counts->data)[j] = (float) stat.counts[j]; } - out.write((const char*)tmp.data(), nval*sizeof(float)); + + 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 < 2) { + 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 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"}; + + // TODO: allow loading from mis-ordered imatrix files + for (int32_t i = 0; i < n_entries - 1; i += 2) { + std::string sums_name{gguf_get_tensor_name(ctx_gguf, i + 0)}; + std::string counts_name{gguf_get_tensor_name(ctx_gguf, i + 1)}; + + if (sums_name.size() < sums_suffix.size() || + counts_name.size() < counts_suffix.size() || + !std::equal(sums_name.begin(), sums_name.end() - sums_suffix.size(), counts_name.begin()) || + !std::equal(sums_suffix.rbegin(), sums_suffix.rend(), sums_name.rbegin()) || + !std::equal(counts_suffix.rbegin(), counts_suffix.rend(), counts_name.rbegin())) { + fprintf(stderr, "%s: mismatched sums and counts for entry %d\n", __func__, i / 2); + 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 = {}; + + struct ggml_tensor * sums = ggml_get_tensor(ctx, sums_name.c_str()); + struct ggml_tensor * counts = ggml_get_tensor(ctx, counts_name.c_str()); + if (!sums || !counts) { + fprintf(stderr, "%s: failed reading data for entry %d\n", __func__, i / 2); + gguf_free(ctx_gguf); + ggml_free(ctx); return false; } + std::string name = sums_name.substr(0, sums_name.size() - sums_suffix.size()); + auto & e = m_stats[name]; + + int32_t nval = ggml_nelements(sums); if (e.values.empty()) { e.values.resize(nval, 0); - e.counts.resize(nval, 0); } - - std::vector 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 = {}; - return false; + int32_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]); } - // 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; + // Recreate the state as expected by save_imatrix() + for (int32_t j = 0; j < nval; j++) { + e.values[j] += ((const float *) sums->data)[j]; + } + for (int32_t j = 0; j < ncounts; j++) { + e.counts[j] += std::lround(((const float *) counts->data)[j]); } - e.ncall += ncall; - } + gguf_free(ctx_gguf); + ggml_free(ctx); return true; } diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index 7312309aeef98..2df073d45e8f1 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -6,8 +6,6 @@ #include #include #include -#include -#include struct quant_option { std::string name; @@ -61,6 +59,11 @@ 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; @@ -121,66 +124,92 @@ static void usage(const char * executable) { } static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_dataset, std::unordered_map> & 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) { 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 < 2) { + 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 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()); - exit(1); - } - name_as_vec[len] = 0; - std::string name{name_as_vec.data()}; - 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 = {}; + + 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"}; + + // TODO: allow loading from mis-ordered imatrix files + for (int32_t i = 0; i < n_entries - 1; i += 2) { + std::string sums_name{gguf_get_tensor_name(ctx_gguf, i + 0)}; + std::string counts_name{gguf_get_tensor_name(ctx_gguf, i + 1)}; + + if (sums_name.size() < sums_suffix.size() || + counts_name.size() < counts_suffix.size() || + !std::equal(sums_name.begin(), sums_name.end() - sums_suffix.size(), counts_name.begin()) || + !std::equal(sums_suffix.rbegin(), sums_suffix.rend(), sums_name.rbegin()) || + !std::equal(counts_suffix.rbegin(), counts_suffix.rend(), counts_name.rbegin())) { + fprintf(stderr, "%s: mismatched sums and counts for entry %d\n", __func__, i / 2); + gguf_free(ctx_gguf); + ggml_free(ctx); exit(1); } - 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 = {}; + + struct ggml_tensor * sums = ggml_get_tensor(ctx, sums_name.c_str()); + struct ggml_tensor * counts = ggml_get_tensor(ctx, counts_name.c_str()); + if (!sums || !counts) { + fprintf(stderr, "%s: failed reading data for entry %d\n", __func__, i / 2); + gguf_free(ctx_gguf); + ggml_free(ctx); exit(1); } - if (ncall > 0) { - for (auto& v : e) v /= ncall; - } + const int64_t ne0 = sums->ne[0]; + const int64_t ne1 = sums->ne[1]; + std::string name = sums_name.substr(0, sums_name.size() - sums_suffix.size()); + auto & e = imatrix_data[name]; + e.resize(ggml_nelements(sums)); + float max_count = 0.0f; + for (int64_t j = 0; j < ne1; ++j) { + const float count = ((const float *) counts->data)[ne1]; + for (int64_t i = 0; i < ne0; ++i) { + e[ne1*ne0 + ne0] = ((const float *) sums->data)[ne1*ne0 + ne0] / count; + } + if (count > max_count) { + max_count = count; + } + } 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, ncall = %6d) for '%s'\n", __func__, int(e.size()), int(max_count / chunk_size), name.c_str()); } } + gguf_free(ctx_gguf); + ggml_free(ctx); - // 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 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); + return m_last_chunk; } static int prepare_imatrix(const std::string & imatrix_file, diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 5541972ce52b0..4fdeddb7c6648 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -167,6 +167,12 @@ class Adapter: 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 # @@ -175,6 +181,7 @@ class Adapter: class GGUFType: MODEL = "model" ADAPTER = "adapter" + IMATRIX = "imatrix" class MODEL_ARCH(IntEnum):