mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 13:58:46 +01:00
Refactoring convert-pth-to-ggml.py: more concise and readable (#109)
* 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>
This commit is contained in:
qunash
GitHub
parent
70f01cb863
commit
467b149761
@ -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"]
|
||||
print(f"n_parts = {n_parts}\n")
|
||||
return n_parts
|
||||
|
||||
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"
|
||||
def load_hparams_and_tokenizer(dir_model):
|
||||
|
||||
fname_hparams = f"{dir_model}/params.json"
|
||||
fname_tokenizer = f"{dir_model}/../tokenizer.model"
|
||||
|
||||
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(hparams)
|
||||
|
||||
with open(fname_hparams, "r") as f:
|
||||
hparams = json.load(f)
|
||||
tokenizer = SentencePieceProcessor(fname_tokenizer)
|
||||
hparams.update({"vocab_size": tokenizer.vocab_size()})
|
||||
|
||||
tokenizer = SentencePieceProcessor(fname_tokenizer)
|
||||
return hparams, tokenizer
|
||||
|
||||
hparams.update({"vocab_size": tokenizer.vocab_size()})
|
||||
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))
|
||||
|
||||
n_parts = get_n_parts(hparams["dim"])
|
||||
def write_tokens(fout, tokenizer):
|
||||
|
||||
print(hparams)
|
||||
print('n_parts = ', n_parts)
|
||||
|
||||
for p in range(n_parts):
|
||||
print('Processing part ', p)
|
||||
|
||||
#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)
|
||||
|
||||
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():
|
||||
|
||||
fout.close()
|
||||
args = parse_args()
|
||||
dir_model = args.dir_model
|
||||
ftype = args.ftype
|
||||
ftype_str = ["f32", "f16"]
|
||||
|
||||
print("Done. Output file: " + fname_out + ", (part ", p, ")")
|
||||
print("")
|
||||
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)
|
||||
|
||||
del model
|
||||
print(f"Done. Output file: {fname_out}, (part {p})\n")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user