otherarch/tools/convert_hf_mpt.py

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("")