Spaces:
Build error
Build error
File size: 4,721 Bytes
f8c5b0d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 |
import sys
import struct
import json
import numpy as np
from transformers import AutoModelForCausalLM, AutoTokenizer
import sentencepiece.sentencepiece_model_pb2 as model
# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a signficant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8+n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
if len(sys.argv) < 3:
print("Usage: convert-h5-to-ggml.py dir-model [use-f32]\n")
print(" ftype == 0 -> float32")
print(" ftype == 1 -> float16")
sys.exit(1)
# output in the same directory as the model
dir_model = sys.argv[1]
fname_out = sys.argv[1] + "/ggml-model.bin"
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
hparams = json.load(f)
# possible data types
# ftype == 0 -> float32
# ftype == 1 -> float16
#
# 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] + ".bin"
tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
dir_model, low_cpu_mem_usage=True, trust_remote_code=True
)
# print (model)
# print(tokenizer.encode('I believe the meaning of life is'))
list_vars = model.state_dict()
for name in list_vars.keys():
print(name, list_vars[name].shape, list_vars[name].dtype)
fout = open(fname_out, "wb")
print(hparams)
fout.write(struct.pack("i", 0x67676D6C)) # magic: ggml in hex
fout.write(struct.pack("i", hparams["d_model"]))
fout.write(struct.pack("i", hparams["max_seq_len"]))
fout.write(struct.pack("i", hparams["n_heads"]))
fout.write(struct.pack("i", hparams["n_layers"]))
fout.write(struct.pack("i", hparams["vocab_size"]))
fout.write(struct.pack("f", hparams["attn_config"]["alibi_bias_max"]))
fout.write(struct.pack("f", hparams["attn_config"]["clip_qkv"] or 0.0))
fout.write(struct.pack("i", ftype))
vocab_size = hparams["vocab_size"]
encoder = tokenizer.vocab
# Add added_tokens (special tokens) to the encoder
encoder.update(tokenizer.get_added_vocab())
byte_encoder = bytes_to_unicode()
byte_decoder = {v:k for k, v in byte_encoder.items()}
counter = 0
# sort by value
for key in sorted(encoder, key=encoder.get):
# workaround for key error when c not found
text=""
for c in key:
if c not in byte_decoder:
text += c
else:
text += chr(byte_decoder[c] )
text = bytearray( text, encoding="utf-8" )
fout.write(struct.pack("i", len(text)))
fout.write(text)
counter += 1
# Repeat last token until vocab_size
while counter < vocab_size:
fout.write(struct.pack("i", len(text)))
fout.write(text)
counter += 1
# assert counter == config.vocab_size
for name in list_vars.keys():
data = list_vars[name].squeeze().numpy()
print("Processing variable: " + name + " with shape: ", data.shape)
n_dims = len(data.shape)
# ftype == 0 -> float32, ftype == 1 -> float16
ftype_cur = 0
if ftype != 0:
if name[-7:] == ".weight" and n_dims == 2:
print(" Converting to float16")
data = data.astype(np.float16)
ftype_cur = 1
else:
print(" Converting to float32")
data = data.astype(np.float32)
ftype_cur = 0
else:
if data.dtype != np.float32:
print(" Converting to float32")
data = data.astype(np.float32)
ftype_cur = 0
# header
str = name.encode("utf-8")
fout.write(struct.pack("iii", n_dims, len(str), ftype_cur))
for i in range(n_dims):
fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
fout.write(str)
# data
data.tofile(fout)
fout.close()
print("Done. Output file: " + fname_out)
print("") |