convert-pt-to-ggml.py

# 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:
        if n_dims < 3 and data.dtype != np.float32:
            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("")