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

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* Refactor get_n_parts function to simplify code and improve readability

* Use f-strings instead of concatenation

* Refactoring: more concise and readable

* modularize

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
pull/294/head
qunash 1 year ago committed by GitHub
parent 70f01cb863
commit 467b149761
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1 changed files with 84 additions and 108 deletions
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convert-pth-to-ggml.py
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@ -16,7 +16,7 @@
# At the start of the ggml file we write the model parameters
# and vocabulary.
#
import os
import argparse
import sys
import json
import struct
@ -24,137 +24,91 @@ 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:
@ -164,18 +118,40 @@ for p in range(n_parts):
# 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()

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