whisper.cpp/models/convert-pt-to-ggml.py
2022-10-22 18:54:01 +03:00

329 lines
9.2 KiB
Python

# Convert Whisper transformer model from PyTorch to ggml format
#
# Usage: python convert-pt-to-ggml.py ~/.cache/whisper/medium.pt ~/path/to/repo/whisper/ ./models/whisper-medium
#
# You need to clone the original repo in ~/path/to/repo/whisper/
#
# git clone https://github.com/openai/whisper ~/path/to/repo/whisper/
#
# It is used to various assets needed by the algorithm:
#
# - tokenizer
# - mel filters
#
# Also, you need to have the original models in ~/.cache/whisper/
# See the original repo for more details.
#
# This script loads the specified model and whisper assets and saves them in ggml format.
# The output is a single binary file containing the following information:
#
# - hparams
# - mel filters
# - tokenizer vocab
# - model variables
#
# For each variable, write the following:
#
# - Number of dimensions (int)
# - Name length (int)
# - Dimensions (int[n_dims])
# - Name (char[name_length])
# - Data (float[n_dims])
#
import io
import os
import sys
import struct
import json
import code
import torch
import numpy as np
from transformers import GPTJForCausalLM
from transformers import GPT2TokenizerFast
# ref: https://github.com/openai/whisper/blob/8cf36f3508c9acd341a45eb2364239a3d81458b9/whisper/tokenizer.py#L10-L110
LANGUAGES = {
"en": "english",
"zh": "chinese",
"de": "german",
"es": "spanish",
"ru": "russian",
"ko": "korean",
"fr": "french",
"ja": "japanese",
"pt": "portuguese",
"tr": "turkish",
"pl": "polish",
"ca": "catalan",
"nl": "dutch",
"ar": "arabic",
"sv": "swedish",
"it": "italian",
"id": "indonesian",
"hi": "hindi",
"fi": "finnish",
"vi": "vietnamese",
"iw": "hebrew",
"uk": "ukrainian",
"el": "greek",
"ms": "malay",
"cs": "czech",
"ro": "romanian",
"da": "danish",
"hu": "hungarian",
"ta": "tamil",
"no": "norwegian",
"th": "thai",
"ur": "urdu",
"hr": "croatian",
"bg": "bulgarian",
"lt": "lithuanian",
"la": "latin",
"mi": "maori",
"ml": "malayalam",
"cy": "welsh",
"sk": "slovak",
"te": "telugu",
"fa": "persian",
"lv": "latvian",
"bn": "bengali",
"sr": "serbian",
"az": "azerbaijani",
"sl": "slovenian",
"kn": "kannada",
"et": "estonian",
"mk": "macedonian",
"br": "breton",
"eu": "basque",
"is": "icelandic",
"hy": "armenian",
"ne": "nepali",
"mn": "mongolian",
"bs": "bosnian",
"kk": "kazakh",
"sq": "albanian",
"sw": "swahili",
"gl": "galician",
"mr": "marathi",
"pa": "punjabi",
"si": "sinhala",
"km": "khmer",
"sn": "shona",
"yo": "yoruba",
"so": "somali",
"af": "afrikaans",
"oc": "occitan",
"ka": "georgian",
"be": "belarusian",
"tg": "tajik",
"sd": "sindhi",
"gu": "gujarati",
"am": "amharic",
"yi": "yiddish",
"lo": "lao",
"uz": "uzbek",
"fo": "faroese",
"ht": "haitian creole",
"ps": "pashto",
"tk": "turkmen",
"nn": "nynorsk",
"mt": "maltese",
"sa": "sanskrit",
"lb": "luxembourgish",
"my": "myanmar",
"bo": "tibetan",
"tl": "tagalog",
"mg": "malagasy",
"as": "assamese",
"tt": "tatar",
"haw": "hawaiian",
"ln": "lingala",
"ha": "hausa",
"ba": "bashkir",
"jw": "javanese",
"su": "sundanese",
}
# ref: https://github.com/openai/whisper/blob/8cf36f3508c9acd341a45eb2364239a3d81458b9/whisper/tokenizer.py#L273-L292
def build_tokenizer(path_to_whisper_repo: str, name: str = "gpt2"):
os.environ["TOKENIZERS_PARALLELISM"] = "false"
path = os.path.join(path_to_whisper_repo, "whisper/assets", name)
tokenizer = GPT2TokenizerFast.from_pretrained(path)
specials = [
"<|startoftranscript|>",
*[f"<|{lang}|>" for lang in LANGUAGES.keys()],
"<|translate|>",
"<|transcribe|>",
"<|startoflm|>",
"<|startofprev|>",
"<|nocaptions|>",
"<|notimestamps|>",
]
tokenizer.add_special_tokens(dict(additional_special_tokens=specials))
return tokenizer
# 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) < 4:
print("Usage: convert-pt-to-ggml.py model.pt path-to-whisper-repo dir-output [use-f32]\n")
sys.exit(1)
fname_inp = sys.argv[1]
dir_whisper = sys.argv[2]
dir_out = sys.argv[3]
# try to load PyTorch binary data
try:
model_bytes = open(fname_inp, "rb").read()
with io.BytesIO(model_bytes) as fp:
checkpoint = torch.load(fp, map_location="cpu")
except:
print("Error: failed to load PyTorch model file: %s" % fname_inp)
sys.exit(1)
hparams = checkpoint["dims"]
print("hparams:", hparams)
list_vars = checkpoint["model_state_dict"]
#print(list_vars['encoder.positional_embedding'])
#print(list_vars['encoder.conv1.weight'])
#print(list_vars['encoder.conv1.weight'].shape)
# load mel filters
n_mels = hparams["n_mels"]
with np.load(os.path.join(dir_whisper, "whisper/assets", "mel_filters.npz")) as f:
filters = torch.from_numpy(f[f"mel_{n_mels}"])
#print (filters)
#code.interact(local=locals())
multilingual = hparams["n_vocab"] == 51865
tokenizer = build_tokenizer(dir_whisper, multilingual and "multilingual" or "gpt2")
#print(tokenizer)
#print(tokenizer.name_or_path)
#print(len(tokenizer.additional_special_tokens))
dir_tokenizer = tokenizer.name_or_path
# output in the same directory as the model
fname_out = dir_out + "/ggml-model.bin"
with open(dir_tokenizer + "/vocab.json", "r") as f:
tokens = json.load(f)
# use 16-bit or 32-bit floats
use_f16 = True
if len(sys.argv) > 4:
use_f16 = False
fname_out = dir_out + "/ggml-model-f32.bin"
fout = open(fname_out, "wb")
fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
fout.write(struct.pack("i", hparams["n_vocab"]))
fout.write(struct.pack("i", hparams["n_audio_ctx"]))
fout.write(struct.pack("i", hparams["n_audio_state"]))
fout.write(struct.pack("i", hparams["n_audio_head"]))
fout.write(struct.pack("i", hparams["n_audio_layer"]))
fout.write(struct.pack("i", hparams["n_text_ctx"]))
fout.write(struct.pack("i", hparams["n_text_state"]))
fout.write(struct.pack("i", hparams["n_text_head"]))
fout.write(struct.pack("i", hparams["n_text_layer"]))
fout.write(struct.pack("i", hparams["n_mels"]))
fout.write(struct.pack("i", use_f16))
# write mel filters
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)))
for key in tokens:
text = bytearray([byte_decoder[c] for c in key])
fout.write(struct.pack("i", len(text)))
fout.write(text)
for name in list_vars.keys():
data = list_vars[name].squeeze().numpy()
print("Processing variable: " + name + " with shape: ", data.shape)
# reshape conv bias from [n] to [n, 1]
if name == "encoder.conv1.bias" or \
name == "encoder.conv2.bias":
data = data.reshape(data.shape[0], 1)
print(" Reshaped variable: " + name + " to shape: ", data.shape)
n_dims = len(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 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":
ftype = 0
data = data.astype(np.float32)
print(" Converting to float32")
data = data.astype(np.float32)
ftype = 0
else:
data = data.astype(np.float32)
ftype = 0
#if name.startswith("encoder"):
# if name.endswith("mlp.0.weight") or \
# name.endswith("mlp.2.weight"):
# print(" Transposing")
# data = data.transpose()
# 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("Done. Output file: " + fname_out)
print("")