forked from yeyupiaoling/Whisper-Finetune
-
Notifications
You must be signed in to change notification settings - Fork 0
/
convert-ggml.py
173 lines (144 loc) · 5.9 KB
/
convert-ggml.py
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
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
import argparse
import functools
import json
import os
import struct
import numpy as np
import torch
from transformers import WhisperForConditionalGeneration
from utils.utils import add_arguments, print_arguments
parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
add_arg("model_dir", type=str, default="models/whisper-tiny-finetune", help="需要转换的模型路径")
add_arg("output_path", type=str, default="models/ggml-model.bin", help="转换保存模型的路径")
add_arg("use_f16", type=bool, default=True, help="是否量化为半精度")
args = parser.parse_args()
print_arguments(args)
conv_map = {
'self_attn.k_proj': 'attn.key',
'self_attn.q_proj': 'attn.query',
'self_attn.v_proj': 'attn.value',
'self_attn.out_proj': 'attn.out',
'self_attn_layer_norm': 'attn_ln',
'encoder_attn.q_proj': 'cross_attn.query',
'encoder_attn.v_proj': 'cross_attn.value',
'encoder_attn.out_proj': 'cross_attn.out',
'encoder_attn_layer_norm': 'cross_attn_ln',
'fc1': 'mlp.0',
'fc2': 'mlp.2',
'final_layer_norm': 'mlp_ln',
'encoder.layer_norm.bias': 'encoder.ln_post.bias',
'encoder.layer_norm.weight': 'encoder.ln_post.weight',
'encoder.embed_positions.weight': 'encoder.positional_embedding',
'decoder.layer_norm.bias': 'decoder.ln.bias',
'decoder.layer_norm.weight': 'decoder.ln.weight',
'decoder.embed_positions.weight': 'decoder.positional_embedding',
'decoder.embed_tokens.weight': 'decoder.token_embedding.weight',
'proj_out.weight': 'decoder.proj.weight',
}
def bytes_to_unicode():
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))
encoder = json.load(open(f"{args.model_dir}/vocab.json", "r", encoding="utf8"))
encoder_added = json.load(open(f"{args.model_dir}/added_tokens.json", "r", encoding="utf8"))
hparams = json.load(open(f"{args.model_dir}/config.json", "r", encoding="utf8"))
# 支持large-v3模型
if "max_length" not in hparams.keys():
hparams["max_length"] = hparams["max_target_positions"]
model = WhisperForConditionalGeneration.from_pretrained(args.model_dir)
n_mels = hparams["num_mel_bins"]
with np.load(f"tools/mel_filters.npz") as f:
filters = torch.from_numpy(f[f"mel_{n_mels}"])
tokens = json.load(open(f"{args.model_dir}/vocab.json", "r", encoding="utf8"))
os.makedirs(os.path.dirname(args.output_path), exist_ok=True)
fout = open(args.output_path, "wb")
fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
fout.write(struct.pack("i", hparams["vocab_size"]))
fout.write(struct.pack("i", hparams["max_source_positions"]))
fout.write(struct.pack("i", hparams["d_model"]))
fout.write(struct.pack("i", hparams["encoder_attention_heads"]))
fout.write(struct.pack("i", hparams["encoder_layers"]))
fout.write(struct.pack("i", hparams["max_length"]))
fout.write(struct.pack("i", hparams["d_model"]))
fout.write(struct.pack("i", hparams["decoder_attention_heads"]))
fout.write(struct.pack("i", hparams["decoder_layers"]))
fout.write(struct.pack("i", hparams["num_mel_bins"]))
fout.write(struct.pack("i", args.use_f16))
fout.write(struct.pack("i", filters.shape[0]))
fout.write(struct.pack("i", filters.shape[1]))
for i in range(filters.shape[0]):
for j in range(filters.shape[1]):
fout.write(struct.pack("f", filters[i][j]))
byte_encoder = bytes_to_unicode()
byte_decoder = {v: k for k, v in byte_encoder.items()}
fout.write(struct.pack("i", len(tokens)))
tokens = sorted(tokens.items(), key=lambda x: x[1])
for key in tokens:
text = bytearray([byte_decoder[c] for c in key[0]])
fout.write(struct.pack("i", len(text)))
fout.write(text)
list_vars = model.state_dict()
for name in list_vars.keys():
# this seems to not be used
if name == "proj_out.weight":
print('Skipping', name)
continue
src = name
nn = name
if name != "proj_out.weight":
nn = nn.split(".")[1:]
else:
nn = nn.split(".")
if nn[1] == "layers":
nn[1] = "blocks"
if ".".join(nn[3:-1]) == "encoder_attn.k_proj":
mapped = "attn.key" if nn[0] == "encoder" else "cross_attn.key"
else:
mapped = conv_map[".".join(nn[3:-1])]
name = ".".join(nn[:3] + [mapped] + nn[-1:])
else:
name = ".".join(nn)
name = conv_map[name] if name in conv_map else name
print(src, ' -> ', name)
data = list_vars[src].squeeze().numpy()
data = data.astype(np.float16)
# reshape conv bias from [n] to [n, 1]
if name in ["encoder.conv1.bias", "encoder.conv2.bias"]:
data = data.reshape(data.shape[0], 1)
print(" Reshaped variable: ", name, " to shape: ", data.shape)
n_dims = len(data.shape)
print(name, n_dims, data.shape)
# looks like the whisper models are in f16 by default
# so we need to convert the small tensors to f32 until we fully support f16 in ggml
# ftype == 0 -> float32, ftype == 1 -> float16
ftype = 1
if args.use_f16:
if n_dims < 2 or \
name == "encoder.conv1.bias" or \
name == "encoder.conv2.bias" or \
name == "encoder.positional_embedding" or \
name == "decoder.positional_embedding":
print(" Converting to float32")
data = data.astype(np.float32)
ftype = 0
else:
data = data.astype(np.float32)
ftype = 0
# header
str_ = name.encode('utf-8')
fout.write(struct.pack("iii", n_dims, len(str_), ftype))
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(f"导出模型: {args.output_path}")