#!/usr/bin/env python3
from __future__ import annotations
import logging
import argparse
import os
import struct
import sys
from enum import IntEnum
from pathlib import Path
import numpy as np
if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
import gguf
logger = logging.getLogger("ggml-to-gguf")
class GGMLFormat(IntEnum):
GGML = 0
GGMF = 1
GGJT = 2
class GGMLFType(IntEnum):
ALL_F32 = 0
MOSTLY_F16 = 1
MOSTLY_Q4_0 = 2
MOSTLY_Q4_1 = 3
MOSTLY_Q4_1_SOME_F16 = 4
MOSTLY_Q8_0 = 7
MOSTLY_Q5_0 = 8
MOSTLY_Q5_1 = 9
MOSTLY_Q2_K = 10
MOSTLY_Q3_K_S = 11
MOSTLY_Q3_K_M = 12
MOSTLY_Q3_K_L = 13
MOSTLY_Q4_K_S = 14
MOSTLY_Q4_K_M = 15
MOSTLY_Q5_K_S = 16
MOSTLY_Q5_K_M = 17
MOSTLY_Q6_K = 18
class Hyperparameters:
def __init__(self):
self.n_vocab = self.n_embd = self.n_mult = self.n_head = 0
self.n_layer = self.n_rot = self.n_ff = 0
self.ftype = GGMLFType.ALL_F32
def set_n_ff(self, model):
ff_tensor_idx = model.tensor_map.get(b'layers.0.feed_forward.w1.weight')
assert ff_tensor_idx is not None, 'Missing layer 0 FF tensor'
ff_tensor = model.tensors[ff_tensor_idx]
self.n_ff = ff_tensor.dims[1]
def load(self, data, offset):
(
self.n_vocab,
self.n_embd,
self.n_mult,
self.n_head,
self.n_layer,
self.n_rot,
ftype,
) = struct.unpack('<7I', data[offset:offset + (4 * 7)])
try:
self.ftype = GGMLFType(ftype)
except ValueError:
raise ValueError(f'Invalid ftype {ftype}')
return 4 * 7
def __str__(self):
return f'<Hyperparameters: n_vocab={self.n_vocab}, n_embd={self.n_embd}, n_mult={self.n_mult}, n_head={self.n_head}, n_layer={self.n_layer}, n_rot={self.n_rot}, n_ff={self.n_ff}, ftype={self.ftype.name}>'
class Vocab:
def __init__(self, load_scores = True):
self.items = []
self.load_scores = load_scores
def load(self, data, offset, n_vocab):
orig_offset = offset
for _ in range(n_vocab):
itemlen = struct.unpack('<I', data[offset:offset + 4])[0]
assert itemlen < 4096, 'Absurd vocab item length'
offset += 4
item_text = bytes(data[offset:offset + itemlen])
offset += itemlen
if self.load_scores:
item_score = struct.unpack('<f', data[offset:offset + 4])[0]
offset += 4
else:
item_score = 0.0
self.items.append((item_text, item_score))
return offset - orig_offset
class Tensor:
def __init__(self, use_padding = True):
self.name = None
self.dims: tuple[int, ...] = ()
self.dtype = None
self.start_offset = 0
self.len_bytes = np.int64(0)
self.use_padding = use_padding
def load(self, data, offset):
orig_offset = offset
(n_dims, name_len, dtype) = struct.unpack('<3I', data[offset:offset + 12])
assert n_dims >= 0 and n_dims <= 4, f'Invalid tensor dimensions {n_dims}'
assert name_len < 4096, 'Absurd tensor name length'
self.dtype = gguf.GGMLQuantizationType(dtype)
quant = gguf.GGML_QUANT_SIZES.get(self.dtype)
assert quant is not None, 'Unknown tensor type'
(blksize, tysize) = quant
offset += 12
self.dims = struct.unpack(f'<{n_dims}I', data[offset:offset + (4 * n_dims)])
offset += 4 * n_dims
self.name = bytes(data[offset:offset + name_len])
offset += name_len
pad = ((offset + 31) & ~31) - offset if self.use_padding else 0
offset += pad
n_elems = np.prod(self.dims)
n_bytes = np.int64(np.int64(n_elems) * np.int64(tysize)) // np.int64(blksize)
self.start_offset = offset
self.len_bytes = n_bytes
offset += n_bytes
return offset - orig_offset
class GGMLModel:
file_format: GGMLFormat
format_version: int
def __init__(self):
self.hyperparameters = None
self.vocab = None
self.tensor_map = {}
self.tensors = []
def validate_header(self, data, offset):
magic = bytes(data[offset:offset + 4])
if magic == b'GGUF':
raise ValueError('File is already in GGUF format.')
if magic == b'lmgg':
self.file_format = GGMLFormat.GGML
self.format_version = 1
return 4
version = struct.unpack('<I', data[offset + 4:offset + 8])[0]
if magic == b'fmgg':
if version != 1:
raise ValueError(f'Cannot handle unexpected GGMF file version {version}')
self.file_format = GGMLFormat.GGMF
self.format_version = version
return 8
if magic == b'tjgg':
if version < 1 or version > 3:
raise ValueError(f'Cannot handle unexpected GGJT file version {version}')
self.file_format = GGMLFormat.GGJT
self.format_version = version
return 8
raise ValueError(f"Unexpected file magic {magic!r}! This doesn't look like a GGML format file.")
def validate_conversion(self, ftype):
err = ''
if (self.file_format < GGMLFormat.GGJT or self.format_version < 2):
if ftype not in (GGMLFType.ALL_F32, GGMLFType.MOSTLY_F16):
err = 'Quantizations changed in GGJTv2. Can only convert unquantized GGML files older than GGJTv2.'
elif (self.file_format == GGMLFormat.GGJT and self.format_version == 2):
if ftype in (GGMLFType.MOSTLY_Q4_0, GGMLFType.MOSTLY_Q4_1,
GGMLFType.MOSTLY_Q4_1_SOME_F16, GGMLFType.MOSTLY_Q8_0):
err = 'Q4 and Q8 quantizations changed in GGJTv3.'
if len(err) > 0:
raise ValueError(f'{err} Sorry, your {self.file_format.name}v{self.format_version} file of type {ftype.name} is not eligible for conversion.')
def load(self, data, offset):
offset += self.validate_header(data, offset)
hp = Hyperparameters()
offset += hp.load(data, offset)
logger.info(f'* File format: {self.file_format.name}v{self.format_version} with ftype {hp.ftype.name}')
self.validate_conversion(hp.ftype)
vocab = Vocab(load_scores = self.file_format > GGMLFormat.GGML)
offset += vocab.load(data, offset, hp.n_vocab)
tensors: list[Tensor] = []
tensor_map = {}
while offset < len(data):
tensor = Tensor(use_padding = self.file_format > GGMLFormat.GGMF)
offset += tensor.load(data, offset)
tensor_map[tensor.name] = len(tensors)
tensors.append(tensor)
self.hyperparameters = hp
self.vocab = vocab
self.tensors = tensors
self.tensor_map = tensor_map
hp.set_n_ff(self)
return offset
class GGMLToGGUF:
def __init__(self, ggml_model, data, cfg, params_override = None, vocab_override = None, special_vocab = None):
hp = ggml_model.hyperparameters
self.model = ggml_model
self.data = data
self.cfg = cfg
self.params_override = params_override
self.vocab_override = vocab_override
self.special_vocab = special_vocab
if params_override is not None:
n_kv_head = params_override.n_head_kv
else:
if cfg.gqa == 1:
n_kv_head = hp.n_head
else:
gqa = float(cfg.gqa)
n_kv_head = None
for x in range(1, 256):
if float(hp.n_head) / float(x) == gqa:
n_kv_head = x
assert n_kv_head is not None, "Couldn't determine n_kv_head from GQA param"
logger.info(f'- Guessed n_kv_head = {n_kv_head} based on GQA {cfg.gqa}')
self.n_kv_head = n_kv_head
self.name_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.LLAMA, ggml_model.hyperparameters.n_layer)
def save(self):
logger.info('* Preparing to save GGUF file')
gguf_writer = gguf.GGUFWriter(
self.cfg.output,
gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA],
use_temp_file = False)
self.add_params(gguf_writer)
self.add_vocab(gguf_writer)
if self.special_vocab is not None:
self.special_vocab.add_to_gguf(gguf_writer)
self.add_tensors(gguf_writer)
logger.info(" gguf: write header")
gguf_writer.write_header_to_file()
logger.info(" gguf: write metadata")
gguf_writer.write_kv_data_to_file()
logger.info(" gguf: write tensors")
gguf_writer.write_tensors_to_file()
gguf_writer.close()
def add_params(self, gguf_writer):
hp = self.model.hyperparameters
cfg = self.cfg
if cfg.desc is not None:
desc = cfg.desc
else:
desc = f'converted from legacy {self.model.file_format.name}v{self.model.format_version} {hp.ftype.name} format'
try:
# Filenames aren't necessarily valid UTF8.
name = cfg.name if cfg.name is not None else cfg.input.name
except UnicodeDecodeError:
name = None
logger.info('* Adding model parameters and KV items')
if name is not None:
gguf_writer.add_name(name)
gguf_writer.add_description(desc)
gguf_writer.add_file_type(int(hp.ftype))
if self.params_override is not None:
po = self.params_override
assert po.n_embd == hp.n_embd, 'Model hyperparams mismatch'
assert po.n_layer == hp.n_layer, 'Model hyperparams mismatch'
assert po.n_head == hp.n_head, 'Model hyperparams mismatch'
gguf_writer.add_context_length (po.n_ctx)
gguf_writer.add_embedding_length (po.n_embd)
gguf_writer.add_block_count (po.n_layer)
gguf_writer.add_feed_forward_length (po.n_ff)
gguf_writer.add_rope_dimension_count(po.n_embd // po.n_head)
gguf_writer.add_head_count (po.n_head)
gguf_writer.add_head_count_kv (po.n_head_kv)
gguf_writer.add_layer_norm_rms_eps (po.f_norm_eps)
return
gguf_writer.add_context_length(cfg.context_length)
gguf_writer.add_embedding_length(hp.n_embd)
gguf_writer.add_block_count(hp.n_layer)
gguf_writer.add_feed_forward_length(hp.n_ff)
gguf_writer.add_rope_dimension_count(hp.n_embd // hp.n_head)
gguf_writer.add_head_count(hp.n_head)
gguf_writer.add_head_count_kv(self.n_kv_head)
gguf_writer.add_layer_norm_rms_eps(float(cfg.eps))
def add_vocab(self, gguf_writer):
hp = self.model.hyperparameters
gguf_writer.add_tokenizer_model('llama')
gguf_writer.add_tokenizer_pre('default')
tokens = []
scores = []
toktypes = []
if self.vocab_override is not None:
vo = self.vocab_override
logger.info('* Adding vocab item(s)')
for (_, (vbytes, score, ttype)) in enumerate(vo.all_tokens()):
tokens.append(vbytes)
scores.append(score)
toktypes.append(ttype)
assert len(tokens) == hp.n_vocab, \
f'Override vocab has a different number of items than hyperparameters - override = {len(tokens)} but n_vocab={hp.n_vocab}'
gguf_writer.add_token_list(tokens)
gguf_writer.add_token_scores(scores)
if len(toktypes) > 0:
gguf_writer.add_token_types(toktypes)
return
logger.info(f'* Adding {hp.n_vocab} vocab item(s)')
assert len(self.model.vocab.items) >= 3, 'Cannot handle unexpectedly short model vocab'
for (tokid, (vbytes, vscore)) in enumerate(self.model.vocab.items):
tt = 1 # Normal
# Special handling for UNK, BOS, EOS tokens.
if tokid <= 2:
if tokid == 0:
vbytes = b'<unk>'
tt = 2
elif tokid == 1:
vbytes = b'<s>'
tt = 3
else:
vbytes = b'</s>'
tt = 3
elif len(vbytes) == 0:
tt = 3 # Control
elif tokid >= 3 and tokid <= 258 and len(vbytes) == 1:
vbytes = bytes(f'<0x{vbytes[0]:02X}>', encoding = 'UTF-8')
tt = 6 # Byte
else:
vbytes = vbytes.replace(b' ', b'\xe2\x96\x81')
toktypes.append(tt)
tokens.append(vbytes)
scores.append(vscore)
gguf_writer.add_token_list(tokens)
gguf_writer.add_token_scores(scores)
gguf_writer.add_token_types(toktypes)
gguf_writer.add_unk_token_id(0)
gguf_writer.add_bos_token_id(1)
gguf_writer.add_eos_token_id(2)
def add_tensors(self, gguf_writer):
tensor_map = self.name_map
data = self.data
logger.info(f'* Adding {len(self.model.tensors)} tensor(s)')
for tensor in self.model.tensors:
name = str(tensor.name, 'UTF-8')
mapped_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
assert mapped_name is not None, f'Bad name {name}'
tempdims = list(tensor.dims[:])
if len(tempdims) > 1:
temp = tempdims[1]
tempdims[1] = tempdims[0]
tempdims[0] = temp
gguf_writer.add_tensor(
mapped_name,
data[tensor.start_offset:tensor.start_offset + tensor.len_bytes],
raw_shape = tempdims,
raw_dtype = tensor.dtype)
def handle_metadata(cfg, hp):
import examples.convert_legacy_llama as convert
assert cfg.model_metadata_dir.is_dir(), 'Metadata dir is not a directory'
hf_config_path = cfg.model_metadata_dir / "config.json"
orig_config_path = cfg.model_metadata_dir / "params.json"
# We pass a fake model here. "original" mode will check the shapes of some
# tensors if information is missing in the .json file: other than that, the
# model data isn't used so this should be safe (at least for now).
fakemodel = {
'tok_embeddings.weight': convert.LazyTensor.__new__(convert.LazyTensor),
'layers.0.feed_forward.w1.weight': convert.LazyTensor.__new__(convert.LazyTensor),
}
fakemodel['tok_embeddings.weight'].shape = [hp.n_vocab]
fakemodel['layers.0.feed_forward.w1.weight'].shape = [hp.n_ff]
if hf_config_path.exists():
params = convert.Params.loadHFTransformerJson(fakemodel, hf_config_path)
elif orig_config_path.exists():
params = convert.Params.loadOriginalParamsJson(fakemodel, orig_config_path)
else:
raise ValueError('Unable to load metadata')
vocab_path = Path(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir)
vocab_factory = convert.VocabFactory(vocab_path)
vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype.split(","), cfg.model_metadata_dir)
convert.check_vocab_size(params, vocab)
return params, vocab, special_vocab
def handle_args():
parser = argparse.ArgumentParser(description = 'Convert GGML models to GGUF')
parser.add_argument('--input', '-i', type = Path, required = True,
help = 'Input GGMLv3 filename')
parser.add_argument('--output', '-o', type = Path, required = True,
help ='Output GGUF filename')
parser.add_argument('--name',
help = 'Set model name')
parser.add_argument('--desc',
help = 'Set model description')
parser.add_argument('--gqa', type = int, default = 1,
help = 'grouped-query attention factor (use 8 for LLaMA2 70B)')
parser.add_argument('--eps', default = '5.0e-06',
help = 'RMS norm eps: Use 1e-6 for LLaMA1 and OpenLLaMA, use 1e-5 for LLaMA2')
parser.add_argument('--context-length', '-c', type=int, default = 2048,
help = 'Default max context length: LLaMA1 is typically 2048, LLaMA2 is typically 4096')
parser.add_argument('--model-metadata-dir', '-m', type = Path,
help ='Load HuggingFace/.pth vocab and metadata from the specified directory')
parser.add_argument("--vocab-dir", type=Path,
help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir")
parser.add_argument("--vocabtype", default="spm,hfft",
help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm,hfft)")
parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
return parser.parse_args()
def main():
cfg = handle_args()
logging.basicConfig(level=logging.DEBUG if cfg.verbose else logging.INFO)
logger.info(f'* Using config: {cfg}')
logger.warning('=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===')
if cfg.model_metadata_dir is None and (cfg.gqa == 1 or cfg.eps == '5.0e-06'):
logger.info('- Note: If converting LLaMA2, specifying "--eps 1e-5" is required. 70B models also need "--gqa 8".')
data = np.memmap(cfg.input, mode = 'r')
model = GGMLModel()
logger.info('* Scanning GGML input file')
offset = model.load(data, 0) # noqa
logger.info(f'* GGML model hyperparameters: {model.hyperparameters}')
vocab_override = None
params_override = None
special_vocab = None
if cfg.model_metadata_dir is not None:
(params_override, vocab_override, special_vocab) = handle_metadata(cfg, model.hyperparameters)
logger.info('!! Note: When overriding params the --gqa, --eps and --context-length options are ignored.')
logger.info(f'* Overriding params: {params_override}')
logger.info(f'* Overriding vocab: {vocab_override}')
logger.info(f'* Special vocab: {special_vocab}')
else:
logger.warning('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n')
if model.file_format == GGMLFormat.GGML:
logger.info('! This is a very old GGML file that does not contain vocab scores. Strongly recommend using model metadata!')
converter = GGMLToGGUF(
model, data, cfg,
params_override = params_override,
vocab_override = vocab_override,
special_vocab = special_vocab
)
converter.save()
logger.info(f'* Successful completion. Output saved to: {cfg.output}')
if __name__ == '__main__':
main()