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import sys
import struct
import json
import numpy as np
from transformers import AutoModelForCausalLM, 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 + "/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)
model = AutoModelForCausalLM.from_pretrained(dir_model, low_cpu_mem_usage=True)
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["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["num_attention_heads"]))
fout.write(struct.pack("i", hparams["num_hidden_layers"]))
fout.write(struct.pack("i", int(hparams["rotary_pct"]*(hparams["hidden_size"]//hparams["num_attention_heads"]))))
fout.write(struct.pack("i", hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True))
fout.write(struct.pack("i", ftype))
# TODO: temporary hack to not deal with implementing the tokenizer
for i in range(hparams["vocab_size"]):
text = tokenizer.decode([i]).encode('utf-8')
fout.write(struct.pack("i", len(text)))
fout.write(text)
for name in list_vars.keys():
data = list_vars[name].squeeze().numpy()
print("Processing variable: " + name + " with shape: ", data.shape)
# we don't need these
if name.endswith(".attention.masked_bias") or \
name.endswith(".attention.bias") or \
name.endswith(".attention.rotary_emb.inv_freq"):
print(" Skipping variable: " + name)
continue
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("")
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