def convert_gpt2_checkpoint_to_pytorch(gpt2_checkpoint_path, full,
gpt2_config_file,
pytorch_dump_folder_path):
#putting requirements here so users can see usage info before it errors out on missing modules
from io import open
from shutil import copyfile
import logging
logging.basicConfig(level=logging.INFO)
from pathlib import Path
import torch
#WEIGHTS_NAME = "pytorch_model.bin"
#CONFIG_NAME = "config.json"
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
GPT2Config,
GPT2Model,
load_tf_weights_in_gpt2,
)
gpt2_checkpoint_path = Path(gpt2_checkpoint_path)
print(gpt2_checkpoint_path.name)
if pytorch_dump_folder_path == '':
prefix = '32BIT-' if full else '16BIT-'
pytorch_dump_folder_path = 'pytorch-' + prefix + gpt2_checkpoint_path.name
pytorch_dump_folder_path = Path(pytorch_dump_folder_path)
pytorch_dump_folder_path.mkdir(exist_ok=True)
# Construct model
if gpt2_config_file == "":
#This doesn't seem to work. We will use the hparams.json file that seems to be included in
#config = GPT2Config()
gpt2_config_file = gpt2_checkpoint_path / 'hparams.json'
config = GPT2Config.from_json_file(gpt2_config_file)
model = GPT2Model(config)
# Load weights from numpy
load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path)
if not full:
model.half()
# Save pytorch-model
pytorch_weights_dump_path = pytorch_dump_folder_path / WEIGHTS_NAME
pytorch_config_dump_path = pytorch_dump_folder_path / CONFIG_NAME
print("Save PyTorch model to {}".format(str(pytorch_weights_dump_path)))
torch.save(model.state_dict(), pytorch_weights_dump_path)
print("Save configuration file to: " + str(pytorch_config_dump_path))
with pytorch_config_dump_path.open("w", encoding="utf-8") as f:
f.write(config.to_json_string())
copyfile(gpt2_checkpoint_path / 'vocab.bpe',
pytorch_dump_folder_path / 'merges.txt')
copyfile(gpt2_checkpoint_path / 'encoder.json',
pytorch_dump_folder_path / 'vocab.json')
def convert_gpt2_checkpoint_to_pytorch(gpt2_checkpoint_path, gpt2_config_file, pytorch_dump_folder_path):
# Construct model
if gpt2_config_file == "":
config = GPT2Config()
else:
config = GPT2Config.from_json_file(gpt2_config_file)
model = GPT2Model(config)
# Load weights from numpy
load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path)
# Save pytorch-model
pytorch_weights_dump_path = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME
print("Save PyTorch model to {}".format(pytorch_weights_dump_path))
torch.save(model.state_dict(), pytorch_weights_dump_path)
print("Save configuration file to {}".format(pytorch_config_dump_path))
with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
f.write(config.to_json_string())