diff --git a/convert_legacy_imatrix_to_gguf.py b/convert_legacy_imatrix_to_gguf.py new file mode 100644 index 0000000000000..bd72655bf2cc7 --- /dev/null +++ b/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() diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index 15a3f0d147fb9..0e4cc8e683ec4 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -5,12 +5,11 @@ #include #include #include -#include #include #include #include -#include #include +#include #include #if defined(_MSC_VER) @@ -20,16 +19,27 @@ 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 values; - std::vector counts; - int ncall = 0; + std::vector values; + std::vector counts; }; class IMatrixCollector { @@ -37,13 +47,13 @@ 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 }; @@ -117,18 +127,24 @@ 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) { @@ -146,23 +162,26 @@ 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); } } } @@ -170,34 +189,40 @@ 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()); + 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); } } } @@ -205,22 +230,22 @@ 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 to_store; + size_t data_size = 0; bool is_first = true; // for printing for (const auto & kv : m_stats) { @@ -252,102 +277,158 @@ 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 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_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]; + } + 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 < 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 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> 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); + } 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; } - 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 = {}; + 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; } - // 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 (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]); } - e.ncall += ncall; - } + gguf_free(ctx_gguf); + ggml_free(ctx); return true; } @@ -430,10 +511,9 @@ 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__); @@ -477,22 +557,28 @@ 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 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 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 logits; + const int n_seq_batch = std::min(n_seq, n_chunk - i); const auto t_start = std::chrono::high_resolution_clock::now(); @@ -503,35 +589,50 @@ 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); + + 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[batch_start] = llama_token_bos(llama_get_model(ctx)); + // 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(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); @@ -541,12 +642,21 @@ 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); + + 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); - printf("[%d]%.4lf,", i + 1, std::exp(nll / count)); + 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; } diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index a23bfb86b350f..0cde695ed5046 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -6,8 +6,7 @@ #include #include #include -#include -#include +#include 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> & 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 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"}; + + // Using an ordered map to get a deterministic iteration order. + std::map> 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); } - 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 = {}; + } + + 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); } - if (ncall > 0) { - for (auto& v : e) v /= ncall; - } + const int64_t ne0 = sums->ne[0]; + const int64_t ne1 = sums->ne[1]; + + auto & e = imatrix_data[name]; + e.resize(ggml_nelements(sums)); + float max_count = 0.0f; + for (int64_t j = 0; j < ne1; ++j) { + const float count = ((const float *) counts->data)[j]; + 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; + } + } 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 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, diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index c87d087822a9a..ae90d70a69736 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -174,6 +174,12 @@ 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 # @@ -182,6 +188,7 @@ class Adapter: class GGUFType: MODEL = "model" ADAPTER = "adapter" + IMATRIX = "imatrix" class MODEL_ARCH(IntEnum): diff --git a/requirements.txt b/requirements.txt index 9e190ae27de38..98c53db8179e2 100644 --- a/requirements.txt +++ b/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 diff --git a/requirements/requirements-convert_legacy_imatrix_to_gguf.txt b/requirements/requirements-convert_legacy_imatrix_to_gguf.txt new file mode 100644 index 0000000000000..afe2747d448d4 --- /dev/null +++ b/requirements/requirements-convert_legacy_imatrix_to_gguf.txt @@ -0,0 +1 @@ +-r ./requirements-convert_legacy_llama.txt