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Refactoring convert-pth-to-ggml.py: more concise and readable #109

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merged 6 commits into from
Mar 19, 2023
Merged

Refactoring convert-pth-to-ggml.py: more concise and readable #109

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94f368f
Refactor get_n_parts function to simplify code and improve readability
qunash Mar 13, 2023
d8aba05
Use f-strings instead of concatenation
qunash Mar 13, 2023
c2af311
Refactoring: more concise and readable
qunash Mar 14, 2023
e1b1e12
modularize
qunash Mar 14, 2023
c2577fd
Merge branch 'master' into master
qunash Mar 16, 2023
6535332
Merge branch 'master' into master
ggerganov Mar 19, 2023
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192 changes: 84 additions & 108 deletions convert-pth-to-ggml.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,145 +16,99 @@
# At the start of the ggml file we write the model parameters
# and vocabulary.
#
import os
import argparse
import sys
import json
import struct
import numpy as np
import torch
from sentencepiece import SentencePieceProcessor

if len(sys.argv) < 3:
print("Usage: convert-ckpt-to-ggml.py dir-model ftype\n")
print(" ftype == 0 -> float32")
print(" ftype == 1 -> float16")
sys.exit(1)
def parse_args():

# output in the same directory as the model
dir_model = sys.argv[1]

fname_hparams = sys.argv[1] + "/params.json"
fname_tokenizer = sys.argv[1] + "/../tokenizer.model"
parser = argparse.ArgumentParser(description='Convert a LLaMA model checkpoint to a ggml compatible file')
parser.add_argument('dir_model', help='directory containing the model checkpoint')
parser.add_argument('ftype', type=int, choices=[0, 1], default=1, help='file type (0: float32, 1: float16)')
return parser.parse_args()

def get_n_parts(dim):
if dim == 4096:
return 1
elif dim == 5120:
return 2
elif dim == 6656:
return 4
elif dim == 8192:
return 8
else:
print("Invalid dim: " + str(dim))

mappings = {4096: 1, 5120: 2, 6656: 4, 8192: 8}
n_parts = mappings.get(dim)
if n_parts is None:
print(f"Invalid dim: {dim}")
sys.exit(1)

# 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"

if os.path.exists(fname_out):
print(f"Skip conversion, it already exists: {fname_out}")
sys.exit(0)

with open(fname_hparams, "r") as f:
hparams = json.load(f)
print(f"n_parts = {n_parts}\n")
return n_parts

tokenizer = SentencePieceProcessor(fname_tokenizer)
def load_hparams_and_tokenizer(dir_model):

fname_hparams = f"{dir_model}/params.json"
fname_tokenizer = f"{dir_model}/../tokenizer.model"

hparams.update({"vocab_size": tokenizer.vocab_size()})
with open(fname_hparams, "r") as f:
hparams = json.load(f)
print(hparams)

n_parts = get_n_parts(hparams["dim"])
tokenizer = SentencePieceProcessor(fname_tokenizer)
hparams.update({"vocab_size": tokenizer.vocab_size()})

print(hparams)
print('n_parts = ', n_parts)
return hparams, tokenizer

for p in range(n_parts):
print('Processing part ', p)
def write_header(fout, hparams, ftype):

keys = ["vocab_size", "dim", "multiple_of", "n_heads", "n_layers"]
values = [
0x67676d6c, # magic: ggml in hex
*[hparams[key] for key in keys],
hparams["dim"] // hparams["n_heads"], # rot (obsolete)
ftype
]
fout.write(struct.pack("i" * len(values), *values))

#fname_model = sys.argv[1] + "/consolidated.00.pth"
fname_model = sys.argv[1] + "/consolidated.0" + str(p) + ".pth"
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin"
if (p > 0):
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin" + "." + str(p)
def write_tokens(fout, tokenizer):

model = torch.load(fname_model, map_location="cpu")

fout = open(fname_out, "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["dim"]))
fout.write(struct.pack("i", hparams["multiple_of"]))
fout.write(struct.pack("i", hparams["n_heads"]))
fout.write(struct.pack("i", hparams["n_layers"]))
fout.write(struct.pack("i", hparams["dim"] // hparams["n_heads"])) # rot (obsolete)
fout.write(struct.pack("i", ftype))

# Is this correct??
for i in range(tokenizer.vocab_size()):
if tokenizer.is_unknown(i):
# "<unk>" token (translated as ??)
text = " \u2047 ".encode("utf-8")
fout.write(struct.pack("i", len(text)))
fout.write(text)
elif tokenizer.is_control(i):
# "<s>"/"</s>" tokens
fout.write(struct.pack("i", 0))
text = b""
elif tokenizer.is_byte(i):
# "<U+XX>" tokens (which may be invalid UTF-8)
piece = tokenizer.id_to_piece(i)
if len(piece) != 6:
print("Invalid token: " + piece)
print(f"Invalid token: {piece}")
sys.exit(1)
byte_value = int(piece[3:-1], 16)
fout.write(struct.pack("i", 1))
fout.write(struct.pack("B", byte_value))
text = struct.pack("B", byte_value)
else:
# normal token. Uses U+2581 (LOWER ONE EIGHTH BLOCK) to represent spaces.
text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8")
fout.write(struct.pack("i", len(text)))
fout.write(text)
fout.write(struct.pack("i", len(text)))
fout.write(text)

for k, v in model.items():
name = k
shape = v.shape
def process_and_write_variables(fout, model, ftype):

# skip layers.X.attention.inner_attention.rope.freqs
if name[-5:] == "freqs":
for name, data in model.items():

if name.endswith("freqs"):
continue

print("Processing variable: " + name + " with shape: ", shape, " and type: ", v.dtype)

#data = tf.train.load_variable(dir_model, name).squeeze()
data = v.numpy().squeeze()
n_dims = len(data.shape);

shape = data.shape

print(f"Processing variable: {name} with shape: {shape} and type: {data.dtype}\n")

data = np.squeeze(data)
n_dims = len(shape)

# for efficiency - transpose some matrices
# "model/h.*/attn/c_attn/w"
# "model/h.*/attn/c_proj/w"
# "model/h.*/mlp/c_fc/w"
# "model/h.*/mlp/c_proj/w"
#if name[-14:] == "/attn/c_attn/w" or \
# name[-14:] == "/attn/c_proj/w" or \
# name[-11:] == "/mlp/c_fc/w" or \
# name[-13:] == "/mlp/c_proj/w":
# print(" Transposing")
#if name.endswith(("/attn/c_attn/w", "/attn/c_proj/w", "/mlp/c_fc/w", "/mlp/c_proj/w")):
# print("Transposing")
# data = data.transpose()

dshape = data.shape

# default type is fp16
ftype_cur = 1
if ftype == 0 or n_dims == 1:
Expand All @@ -164,18 +118,40 @@ def get_n_parts(dim):

# header
sname = name.encode('utf-8')
fout.write(struct.pack("iii", n_dims, len(sname), ftype_cur))
for i in range(n_dims):
fout.write(struct.pack("i", dshape[n_dims - 1 - i]))
fout.write(sname);

fout.write(struct.pack("iii", len(data.shape), len(sname), ftype_cur))
for dim in reversed(data.shape):
fout.write(struct.pack("i", dim))
fout.write(sname)
# data
data.tofile(fout)

# I hope this deallocates the memory ..
model = None
def main():

args = parse_args()
dir_model = args.dir_model
ftype = args.ftype
ftype_str = ["f32", "f16"]

hparams, tokenizer = load_hparams_and_tokenizer(dir_model)
n_parts = get_n_parts(hparams["dim"])

for p in range(n_parts):

print(f"Processing part {p}\n")

fname_model = f"{dir_model}/consolidated.0{p}.pth"
fname_out = f"{dir_model}/ggml-model-{ftype_str[ftype]}.bin{'' if p == 0 else '.' + str(p)}"

model = torch.load(fname_model, map_location="cpu")

with open(fname_out, "wb") as fout:
write_header(fout, hparams, ftype)
write_tokens(fout, tokenizer)
process_and_write_variables(fout, model, ftype)

fout.close()
del model
print(f"Done. Output file: {fname_out}, (part {p})\n")

print("Done. Output file: " + fname_out + ", (part ", p, ")")
print("")
if __name__ == "__main__":
main()