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Merge branch 'master' into concedo_experimental
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# Conflicts:
#	CMakeLists.txt
#	Makefile
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@LostRuins
LostRuins committed Sep 7, 2023
2 parents a8673be + 178b185 commit 0bf75b0
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Showing 10 changed files with 380 additions and 282 deletions.
188 changes: 94 additions & 94 deletions common/common.cpp
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16 changes: 8 additions & 8 deletions common/log.h
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Original file line number Diff line number Diff line change
Expand Up @@ -513,16 +513,16 @@ inline bool log_param_pair_parse(bool check_but_dont_parse, const std::string &

inline void log_print_usage()
{
fprintf(stdout, "log options:\n");
printf("log options:\n");
/* format
fprintf(stdout, " -h, --help show this help message and exit\n");*/
printf(" -h, --help show this help message and exit\n");*/
/* spacing
fprintf(stdout, "__-param----------------Description\n");*/
fprintf(stdout, " --log-test Run simple logging test\n");
fprintf(stdout, " --log-disable Disable trace logs\n");
fprintf(stdout, " --log-enable Enable trace logs\n");
fprintf(stdout, " --log-file Specify a log filename (without extension)\n");
fprintf(stdout, " Log file will be tagged with unique ID and written as \"<name>.<ID>.log\"\n"); /* */
printf("__-param----------------Description\n");*/
printf(" --log-test Run simple logging test\n");
printf(" --log-disable Disable trace logs\n");
printf(" --log-enable Enable trace logs\n");
printf(" --log-file Specify a log filename (without extension)\n");
printf(" Log file will be tagged with unique ID and written as \"<name>.<ID>.log\"\n"); /* */
}

#define log_dump_cmdline(argc, argv) log_dump_cmdline_impl(argc, argv)
Expand Down
168 changes: 133 additions & 35 deletions convert-llama-ggmlv3-to-gguf.py → convert-llama-ggml-to-gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
import math
import struct
import sys
from enum import IntEnum
from pathlib import Path

import numpy as np
Expand Down Expand Up @@ -34,10 +35,35 @@
gguf.GGMLQuantizationType.Q8_K : (256, 4 + QK_K + QK_K // 8),
}

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 = self.n_layer = self.n_rot = self.ftype = 0
self.n_ff = 0
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')
Expand All @@ -53,37 +79,46 @@ def load(self, data, offset):
self.n_head,
self.n_layer,
self.n_rot,
self.ftype,
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}>'
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):
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
vocab = bytes(data[offset:offset + itemlen])
item_text = bytes(data[offset:offset + itemlen])
offset += itemlen
score = struct.unpack('<f', data[offset:offset + 4])[0]
offset += 4
self.items.append((vocab, score))
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):
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
Expand All @@ -99,7 +134,7 @@ def load(self, data, offset):
offset += 4 * n_dims
self.name = bytes(data[offset:offset + name_len])
offset += name_len
pad = ((offset + 31) & ~31) - offset
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)
Expand All @@ -109,28 +144,60 @@ def load(self, data, offset):
# print(n_dims, name_len, dtype, self.dims, self.name, pad)
return offset - orig_offset

class GGMLV3Model:
class GGMLModel:
def __init__(self):
self.hyperparameters = None
self.vocab = None
self.tensor_map = {}
self.tensors = []

def validate_header(self, data, offset):
if bytes(data[offset:offset + 4]) != b'tjgg' or struct.unpack('<I', data[offset + 4:offset + 8])[0] != 3:
raise ValueError('Only GGJTv3 supported')
return 8
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)
vocab = Vocab()
print(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()
tensor = Tensor(use_padding = self.file_format > GGMLFormat.GGMF)
offset += tensor.load(data, offset)
tensor_map[tensor.name] = len(tensors)
tensors.append(tensor)
Expand Down Expand Up @@ -168,7 +235,10 @@ def __init__(self, ggml_model, data, cfg, params_override = None, vocab_override

def save(self):
print('* Preparing to save GGUF file')
gguf_writer = gguf.GGUFWriter(self.cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], use_temp_file = False)
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:
Expand All @@ -185,7 +255,10 @@ def save(self):
def add_params(self, gguf_writer):
hp = self.model.hyperparameters
cfg = self.cfg
desc = cfg.desc if cfg.desc is not None else 'converted from legacy GGJTv3 format'
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
Expand All @@ -195,6 +268,7 @@ def add_params(self, gguf_writer):
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'
Expand Down Expand Up @@ -231,7 +305,8 @@ def add_vocab(self, gguf_writer):
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}'
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:
Expand Down Expand Up @@ -283,7 +358,11 @@ def add_tensors(self, gguf_writer):
tempdims[1] = tempdims[0]
tempdims[0] = temp
# print(f'+ {tensor.name} | {mapped_name} {tensor.dims} :: {tempdims}')
gguf_writer.add_tensor(mapped_name, data[tensor.start_offset:tensor.start_offset + tensor.len_bytes], raw_shape = tempdims, raw_dtype = tensor.dtype)
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 convert
Expand All @@ -305,32 +384,46 @@ def handle_metadata(cfg, hp):
params = convert.Params.loadOriginalParamsJson(fakemodel, orig_config_path)
else:
raise ValueError('Unable to load metadata')
vocab = convert.load_vocab(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir, cfg.vocabtype)
vocab = convert.load_vocab(
cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir,
cfg.vocabtype )
# FIXME: Respect cfg.vocab_dir?
svocab = gguf.SpecialVocab(cfg.model_metadata_dir)
convert.check_vocab_size(params, vocab)
return (params, vocab, svocab)

def handle_args():
parser = argparse.ArgumentParser(description = 'Convert GGMLv3 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", choices=["spm", "bpe"], help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)", default="spm")
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", choices=["spm", "bpe"], default="spm",
help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)")
return parser.parse_args()

def main():
cfg = handle_args()
print(f'* Using config: {cfg}')
print('\n=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===\n')
if cfg.model_metadata_dir is None and (cfg.gqa == 1 or cfg.eps == '5.0e-06'):
print('- Note: If converting LLaMA2, specifying "--eps 1e-5" is required. 70B models also need "--gqa 8".')
data = np.memmap(cfg.input, mode = 'r')
model = GGMLV3Model()
model = GGMLModel()
print('* Scanning GGML input file')
offset = model.load(data, 0)
print(f'* GGML model hyperparameters: {model.hyperparameters}')
Expand All @@ -345,7 +438,12 @@ def main():
print(f'* Special vocab: {special_vocab}')
else:
print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n')
converter = GGMLToGGUF(model, data, cfg, params_override = params_override, vocab_override = vocab_override, special_vocab = special_vocab)
if model.file_format == GGMLFormat.GGML:
print('! 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()
print(f'* Successful completion. Output saved to: {cfg.output}')

Expand Down
3 changes: 1 addition & 2 deletions convert.py
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Original file line number Diff line number Diff line change
Expand Up @@ -673,7 +673,7 @@ def persistent_load(self, pid: Any) -> Any:
assert isinstance(pid[1], LazyStorageKind)
data_type = pid[1].data_type
filename_stem = pid[2]
filename = self.data_base_path + '/' + filename_stem
filename = f'{self.data_base_path}/{filename_stem}'
info = self.zip_file.getinfo(filename)

def load(offset: int, elm_count: int) -> NDArray:
Expand All @@ -689,7 +689,6 @@ def load(offset: int, elm_count: int) -> NDArray:

@staticmethod
def lazy_rebuild_tensor_v2(storage: Any, storage_offset: Any, size: Any, stride: Any,
# pyright: ignore[reportSelfClsParameterName]
requires_grad: Any, backward_hooks: Any, metadata: Any = None) -> LazyTensor:
assert isinstance(storage, LazyStorage)

Expand Down
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