mirror of
https://github.com/nomic-ai/gpt4all
synced 2024-11-02 09:40:42 +00:00
103 lines
2.9 KiB
Python
103 lines
2.9 KiB
Python
import sys
|
|
import struct
|
|
import json
|
|
import torch
|
|
import numpy as np
|
|
|
|
from transformers import AutoModel, AutoTokenizer
|
|
|
|
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 + "/tokenizer.json", "r", encoding="utf-8") as f:
|
|
encoder = json.load(f)
|
|
|
|
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
|
|
hparams = json.load(f)
|
|
|
|
with open(dir_model + "/vocab.txt", "r", encoding="utf-8") as f:
|
|
vocab = f.readlines()
|
|
# 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)
|
|
model = AutoModel.from_pretrained(dir_model, low_cpu_mem_usage=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", 0x62657274)) # magic: ggml in hex
|
|
fout.write(struct.pack("i", hparams["vocab_size"]))
|
|
fout.write(struct.pack("i", hparams["max_position_embeddings"]))
|
|
fout.write(struct.pack("i", hparams["hidden_size"]))
|
|
fout.write(struct.pack("i", hparams["intermediate_size"]))
|
|
fout.write(struct.pack("i", hparams["num_attention_heads"]))
|
|
fout.write(struct.pack("i", hparams["num_hidden_layers"]))
|
|
fout.write(struct.pack("i", ftype))
|
|
|
|
for i in range(hparams["vocab_size"]):
|
|
text = vocab[i][:-1] # strips newline at the end
|
|
#print(f"{i}:{text}")
|
|
data = bytes(text, 'utf-8')
|
|
fout.write(struct.pack("i", len(data)))
|
|
fout.write(data)
|
|
|
|
for name in list_vars.keys():
|
|
data = list_vars[name].squeeze().numpy()
|
|
if name in ['embeddings.position_ids', 'pooler.dense.weight', 'pooler.dense.bias']:
|
|
continue
|
|
print("Processing variable: " + name + " with shape: ", data.shape)
|
|
|
|
n_dims = len(data.shape);
|
|
|
|
# ftype == 0 -> float32, ftype == 1 -> float16
|
|
if ftype == 1 and name[-7:] == ".weight" and n_dims == 2:
|
|
print(" Converting to float16")
|
|
data = data.astype(np.float16)
|
|
l_type = 1
|
|
else:
|
|
l_type = 0
|
|
|
|
# header
|
|
str = name.encode('utf-8')
|
|
fout.write(struct.pack("iii", n_dims, len(str), l_type))
|
|
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("")
|