vbatts/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Improve some informational output

Browse source 
Formatting changes to clean up some long lines
This commit is contained in:
KerfuffleV2 2023-09-06 01:52:42 -06:00
parent 645b6a2e47
commit 69de44d441
1 changed files with 57 additions and 22 deletions
Download patch file Download diff file Expand all files Collapse all files

79
convert-llama-ggml-to-gguf.py
View file

@ -61,7 +61,8 @@ class GGMLFType(IntEnum):
class Hyperparameters: class Hyperparameters:
def __init__(self): def __init__(self):
self.n_vocab = self.n_embd = self.n_mult = self.n_head = self.n_layer = self.n_rot = 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 self.ftype = GGMLFType.ALL_F32
def set_n_ff(self, model): def set_n_ff(self, model):
@ -174,10 +175,16 @@ class GGMLModel:
raise ValueError(f"Unexpected file magic {magic!r}! This doesn't look like a GGML format file.") raise ValueError(f"Unexpected file magic {magic!r}! This doesn't look like a GGML format file.")
def validate_conversion(self, ftype): def validate_conversion(self, ftype):
if (self.file_format < GGMLFormat.GGJT or self.format_version < 2) and ftype not in (GGMLFType.ALL_F32, GGMLFType.MOSTLY_F16): err = ''
raise ValueError(f'Quantizations changed in GGJTv2. Can only convert unquantized GGML files older than GGJTv2. Sorry, your {self.file_format.name}v{self.format_version} file of type {ftype.name} is not eligible for conversion.') if (self.file_format < GGMLFormat.GGJT or self.format_version < 2):
if (self.file_format == GGMLFormat.GGJT and self.format_version == 2) and ftype in (GGMLFType.MOSTLY_Q4_0, GGMLFType.MOSTLY_Q4_1, GGMLFType.MOSTLY_Q4_1_SOME_F16, GGMLFType.MOSTLY_Q8_0): if ftype not in (GGMLFType.ALL_F32, GGMLFType.MOSTLY_F16):
raise ValueError(f'Q4 and Q8 quantizations changed in GGJTv3. Sorry, your {self.file_format.name}v{self.format_version} file of type {ftype.name} is not eligible for conversion.') 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): def load(self, data, offset):
offset += self.validate_header(data, offset) offset += self.validate_header(data, offset)
@ -228,7 +235,10 @@ class GGMLToGGUF:
def save(self): def save(self):
print('* Preparing to save GGUF file') 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_params(gguf_writer)
self.add_vocab(gguf_writer) self.add_vocab(gguf_writer)
if self.special_vocab is not None: if self.special_vocab is not None:
@ -245,7 +255,10 @@ class GGMLToGGUF:
def add_params(self, gguf_writer): def add_params(self, gguf_writer):
hp = self.model.hyperparameters hp = self.model.hyperparameters
cfg = self.cfg cfg = self.cfg
desc = cfg.desc if cfg.desc is not None else f'converted from legacy {self.model.file_format.name}v{self.model.format_version} {hp.ftype.name} 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: try:
# Filenames aren't necessarily valid UTF8. # Filenames aren't necessarily valid UTF8.
name = cfg.name if cfg.name is not None else cfg.input.name name = cfg.name if cfg.name is not None else cfg.input.name
@ -292,7 +305,8 @@ class GGMLToGGUF:
tokens.append(vbytes) tokens.append(vbytes)
scores.append(score) scores.append(score)
toktypes.append(ttype) 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_list(tokens)
gguf_writer.add_token_scores(scores) gguf_writer.add_token_scores(scores)
if len(toktypes) > 0: if len(toktypes) > 0:
@ -344,7 +358,11 @@ class GGMLToGGUF:
tempdims[1] = tempdims[0] tempdims[1] = tempdims[0]
tempdims[0] = temp tempdims[0] = temp
# print(f'+ {tensor.name} | {mapped_name} {tensor.dims} :: {tempdims}') # 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): def handle_metadata(cfg, hp):
import convert import convert
@ -366,30 +384,44 @@ def handle_metadata(cfg, hp):
params = convert.Params.loadOriginalParamsJson(fakemodel, orig_config_path) params = convert.Params.loadOriginalParamsJson(fakemodel, orig_config_path)
else: else:
raise ValueError('Unable to load metadata') 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? # FIXME: Respect cfg.vocab_dir?
svocab = gguf.SpecialVocab(cfg.model_metadata_dir) svocab = gguf.SpecialVocab(cfg.model_metadata_dir)
convert.check_vocab_size(params, vocab) convert.check_vocab_size(params, vocab)
return (params, vocab, svocab) return (params, vocab, svocab)
def handle_args(): def handle_args():
parser = argparse.ArgumentParser(description = 'Convert GGMLv3 models to GGUF') 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('--input', '-i', type = Path, required = True,
parser.add_argument('--output', '-o', type = Path, required = True, help ='Output GGUF filename') help = 'Input GGMLv3 filename')
parser.add_argument('--name', help = 'Set model name') parser.add_argument('--output', '-o', type = Path, required = True,
parser.add_argument('--desc', help = 'Set model description') help ='Output GGUF filename')
parser.add_argument('--gqa', type = int, default = 1, help = 'grouped-query attention factor (use 8 for LLaMA2 70B)') parser.add_argument('--name',
parser.add_argument('--eps', default = '5.0e-06', help = 'RMS norm eps: Use 1e-6 for LLaMA1 and OpenLLaMA, use 1e-5 for LLaMA2') help = 'Set model name')
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('--desc',
parser.add_argument('--model-metadata-dir', '-m', type = Path, help ='Load HuggingFace/.pth vocab and metadata from the specified directory') help = 'Set model description')
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('--gqa', type = int, default = 1,
parser.add_argument("--vocabtype", choices=["spm", "bpe"], help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)", default="spm") 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() return parser.parse_args()
def main(): def main():
cfg = handle_args() cfg = handle_args()
print(f'* Using config: {cfg}') 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') 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') data = np.memmap(cfg.input, mode = 'r')
model = GGMLModel() model = GGMLModel()
print('* Scanning GGML input file') print('* Scanning GGML input file')
@ -408,7 +440,10 @@ def main():
print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n') print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n')
if model.file_format == GGMLFormat.GGML: 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!') 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 = GGMLToGGUF(model, data, cfg,
params_override = params_override,
vocab_override = vocab_override,
special_vocab = special_vocab )
converter.save() converter.save()
print(f'* Successful completion. Output saved to: {cfg.output}') print(f'* Successful completion. Output saved to: {cfg.output}')