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Uniform args parsing and vocab only mode for convert examples
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@KerfuffleV2
KerfuffleV2 committed Aug 29, 2023
1 parent 2ea1338 commit 58fa4dc
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Showing 4 changed files with 204 additions and 183 deletions.
5 changes: 2 additions & 3 deletions convert-falcon-hf-to-gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,11 +33,10 @@ def bytes_to_unicode():
bs.append(b)
cs.append(2**8+n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
return dict(zip(bs, (chr(n) for n in cs)))


def count_model_parts(dir_model: str) -> int:
def count_model_parts(dir_model: Path) -> int:
num_parts = 0
for filename in os.listdir(dir_model):
if filename.startswith("pytorch_model-"):
Expand Down
117 changes: 63 additions & 54 deletions convert-gptneox-hf-to-gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@
import json
import numpy as np
import torch
import argparse

from typing import Any, List
from pathlib import Path
Expand Down Expand Up @@ -37,7 +38,7 @@ def bytes_to_unicode():
return dict(zip(bs, (chr(n) for n in cs)))


def count_model_parts(dir_model: str) -> int:
def count_model_parts(dir_model: Path) -> int:
num_parts = 0
for filename in os.listdir(dir_model):
if filename.startswith("pytorch_model-"):
Expand All @@ -48,16 +49,21 @@ def count_model_parts(dir_model: str) -> int:
return num_parts


if len(sys.argv) < 3:
print(f"Usage: python {sys.argv[0]} dir-model ftype\n")
print(" ftype == 0 -> float32")
print(" ftype == 1 -> float16")
sys.exit(1)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Convert a GPT-NeoX model to a GGML compatible file")
parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)")
parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1)
return parser.parse_args()

args = parse_args()

# output in the same directory as the model
dir_model = sys.argv[1]
last_dir = os.path.basename(os.path.normpath(dir_model))
dir_model = args.model
ftype = args.ftype
if not dir_model.is_dir():
print(f'Error: {args.model} is not a directory', file = sys.stderr)
sys.exit(1)

# possible tensor data types
# ftype == 0 -> float32
Expand All @@ -66,19 +72,15 @@ def count_model_parts(dir_model: str) -> int:
# map from ftype to string
ftype_str = ["f32", "f16"]

ftype = 1
if len(sys.argv) > 2:
ftype = int(sys.argv[2])
if ftype < 0 or ftype > 1:
print("Invalid ftype: " + str(ftype))

sys.exit(1)

fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
if args.outfile is not None:
fname_out = args.outfile
else:
# output in the same directory as the model by default
fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'

print("gguf: loading model "+last_dir)
print("gguf: loading model "+dir_model.name)

with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
with open(dir_model / "config.json", "r", encoding="utf-8") as f:
hparams = json.load(f)

if hparams["architectures"][0] != "GPTNeoXForCausalLM":
Expand All @@ -96,7 +98,7 @@ def count_model_parts(dir_model: str) -> int:

block_count = hparams["num_hidden_layers"]

gguf_writer.add_name(last_dir)
gguf_writer.add_name(dir_model.name)
gguf_writer.add_context_length(hparams["max_position_embeddings"])
gguf_writer.add_embedding_length(hparams["hidden_size"])
gguf_writer.add_block_count(block_count)
Expand All @@ -112,45 +114,49 @@ def count_model_parts(dir_model: str) -> int:

tokens: List[bytearray] = []

if Path(dir_model + "/tokenizer.json").is_file():
# gpt2 tokenizer
gguf_writer.add_tokenizer_model("gpt2")
tokenizer_json_file = dir_model / 'tokenizer.json'
if not tokenizer_json_file.is_file():
print(f'Error: Missing {tokenizer_json_file}', file = sys.stderr)
sys.exit(1)

# gpt2 tokenizer
gguf_writer.add_tokenizer_model("gpt2")

with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
tokenizer_json = json.load(f)
with open(tokenizer_json_file, "r", encoding="utf-8") as f:
tokenizer_json = json.load(f)

print("gguf: get gpt2 tokenizer vocab")
print("gguf: get gpt2 tokenizer vocab")

vocab_size = len(tokenizer_json["model"]["vocab"])
vocab_size = len(tokenizer_json["model"]["vocab"])

# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
tokenizer = AutoTokenizer.from_pretrained(dir_model)
# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
tokenizer = AutoTokenizer.from_pretrained(dir_model)

reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
byte_encoder = bytes_to_unicode()
byte_decoder = {v: k for k, v in byte_encoder.items()}
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
byte_encoder = bytes_to_unicode()
byte_decoder = {v: k for k, v in byte_encoder.items()}

for i in range(vocab_size):
if i in reverse_vocab:
try:
text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
except KeyError:
text = bytearray()
for c in reverse_vocab[i]:
if ord(c) < 256: # single byte character
text.append(byte_decoder[ord(c)])
else: # multibyte special token character
text.extend(c.encode('utf-8'))
else:
print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
pad_token = f"[PAD{i}]".encode("utf8")
text = bytearray(pad_token)
for i in range(vocab_size):
if i in reverse_vocab:
try:
text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
except KeyError:
text = bytearray()
for c in reverse_vocab[i]:
if ord(c) < 256: # single byte character
text.append(byte_decoder[ord(c)])
else: # multibyte special token character
text.extend(c.encode('utf-8'))
else:
print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
pad_token = f"[PAD{i}]".encode("utf8")
text = bytearray(pad_token)

tokens.append(text)
tokens.append(text)

gguf_writer.add_token_list(tokens)
gguf_writer.add_token_list(tokens)

special_vocab = gguf.SpecialVocab(Path(dir_model), load_merges = True)
special_vocab = gguf.SpecialVocab(dir_model, load_merges = True)
special_vocab.add_to_gguf(gguf_writer)

# TENSORS
Expand All @@ -168,6 +174,8 @@ def count_model_parts(dir_model: str) -> int:
)

for part_name in part_names:
if args.vocab_only:
break
print("gguf: loading model part '" + part_name + "'")
model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")

Expand Down Expand Up @@ -216,10 +224,11 @@ def count_model_parts(dir_model: str) -> int:
gguf_writer.write_header_to_file()
print("gguf: write metadata")
gguf_writer.write_kv_data_to_file()
print("gguf: write tensors")
gguf_writer.write_tensors_to_file()
if not args.vocab_only:
print("gguf: write tensors")
gguf_writer.write_tensors_to_file()

gguf_writer.close()

print("gguf: model successfully exported to '" + fname_out + "'")
print(f"gguf: model successfully exported to '{fname_out}'")
print("")
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