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huggingface_gptj_convert.py
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huggingface_gptj_convert.py
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# Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved.
# Modified by Brendan Dolan-Gavitt, 2022
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import configparser
import multiprocessing
import numpy as np
from pathlib import Path
import torch
import os
import sys
from transformers import GPTJForCausalLM
dir_path = os.path.dirname(os.path.realpath(__file__))
sys.path.append(dir_path + "/../../../..")
sys.path.append(dir_path)
def get_weight_data_type(data_type):
if data_type == "fp32":
return np.float32
elif data_type == "fp16":
return np.float16
else:
assert False, f"Invalid weight data type {data_type}"
def split_and_convert_process(i, saved_dir, factor, key, val):
if key.find("input_layernorm.weight") != -1 or key.find("input_layernorm.bias") != -1 or \
key.find("attention.dense.bias") != -1 or key.find("post_attention_layernorm.weight") != -1 or \
key.find("post_attention_layernorm.bias") != -1 or key.find("mlp.dense_4h_to_h.bias") != -1 or \
key.find("final_layernorm.weight") != -1 or key.find("final_layernorm.bias") != -1:
# shared weights, only need to convert the weights of rank 0
if i == 0:
saved_path = saved_dir + "/model." + key + ".bin"
val.tofile(saved_path)
elif key.find("attention.dense.weight") != -1 or key.find("mlp.dense_4h_to_h.weight") != -1:
split_vals = np.split(val, factor, axis=0)
for j in range(factor):
saved_path = saved_dir + "/model." + key + ".%d.bin" % (i * factor + j)
split_vals[j].tofile(saved_path)
elif key.find("mlp.dense_h_to_4h.weight") != -1 or key.find("mlp.dense_h_to_4h.bias") != -1:
split_vals = np.split(val, factor, axis=-1)
for j in range(factor):
saved_path = saved_dir + "/model." + key + ".%d.bin" % (i * factor + j)
split_vals[j].tofile(saved_path)
elif key.find("attention.query_key_value.weight") != -1:
split_vals = np.split(val, factor, axis=-1)
for j in range(factor):
saved_path = saved_dir + "/model." + key + ".%d.bin" % (i * factor + j)
split_vals[j].tofile(saved_path)
else:
print("[ERROR] cannot find key '{}'".format(key))
def split_and_convert(args):
saved_dir = args.saved_dir + "/%d-gpu/" % args.infer_gpu_num
if os.path.exists(saved_dir) is False:
os.makedirs(saved_dir)
t_gpu_num = args.trained_gpu_num
i_gpu_num = args.infer_gpu_num
assert (i_gpu_num % t_gpu_num == 0)
factor = (int)(i_gpu_num / t_gpu_num)
model = GPTJForCausalLM.from_pretrained(args.in_file)
try:
config = configparser.ConfigParser()
config["gpt"] = {}
for key in vars(args):
config["gpt"][key] = f"{vars(args)[key]}"
for k, v in vars(model.config).items():
config["gpt"][k] = f"{v}"
config["gpt"]["weight_data_type"] = args.weight_data_type
with open((Path(saved_dir) / "config.ini").as_posix(), 'w') as configfile:
config.write(configfile)
except Exception:
print("Fail to save the config in config.ini.")
np_weight_data_type = get_weight_data_type(args.weight_data_type)
huggingface_model_name_pattern = [
"ln_1.bias",
"ln_1.weight",
"attn.q_proj.weight",
"attn.out_proj.weight",
"mlp.fc_in.bias",
"mlp.fc_in.weight",
"mlp.fc_out.bias",
"mlp.fc_out.weight",
]
ft_model_name_pattern = [
"input_layernorm.bias",
"input_layernorm.weight",
"attention.query_key_value.weight",
"attention.dense.weight",
"mlp.dense_h_to_4h.bias",
"mlp.dense_h_to_4h.weight",
"mlp.dense_4h_to_h.bias",
"mlp.dense_4h_to_h.weight",
]
torch.multiprocessing.set_start_method("spawn")
pool = multiprocessing.Pool(args.processes)
for name, param in model.named_parameters():
if name.find("weight") == -1 and name.find("bias") == -1:
continue
print(name)
if name == 'transformer.wte.weight':
param.detach().cpu().numpy().astype(np_weight_data_type).tofile(saved_dir + "model.wte.bin")
elif name == 'transformer.ln_f.bias':
param.detach().cpu().numpy().astype(np_weight_data_type).tofile(
saved_dir + "model.final_layernorm.bias.bin")
elif name == 'transformer.ln_f.weight':
param.detach().cpu().numpy().astype(np_weight_data_type).tofile(
saved_dir + "model.final_layernorm.weight.bin")
elif name == 'lm_head.weight':
param.detach().cpu().numpy().astype(np_weight_data_type).tofile(saved_dir + "model.lm_head.weight.bin")
elif name == 'lm_head.bias':
param.detach().cpu().numpy().astype(np_weight_data_type).tofile(saved_dir + "model.lm_head.bias.bin")
else:
for i in range(len(huggingface_model_name_pattern)):
if name.find(huggingface_model_name_pattern[i]) != -1:
# Special case for QKV weights
if name.find("attn.q_proj.weight") != -1:
layer = name.split('.')[2]
base_k = f'transformer.h.{layer}.'
w = model.state_dict()
QKV_w = torch.stack([
w[base_k + "attn.q_proj.weight"],
w[base_k + "attn.k_proj.weight"],
w[base_k + "attn.v_proj.weight"],
]) # [qkv, n_heads * dim_head, latent_space]
QKV_w = QKV_w.permute(2, 0, 1)
weights = QKV_w.detach().cpu().numpy().astype(np_weight_data_type)
else:
weights = param.detach().cpu().numpy().astype(np_weight_data_type)
# Some weights need to be transposed
if name.find("mlp.fc_in.weight") != -1 or name.find("mlp.fc_out.weight") != -1 or \
name.find("attn.out_proj.weight") != -1:
weights = weights.T
new_name = name.replace("transformer.h.", "layers.").replace(huggingface_model_name_pattern[i],
ft_model_name_pattern[i])
pool.starmap(split_and_convert_process,
[(0, saved_dir, factor, new_name,
weights)], )
pool.close()
pool.join()
if __name__ == "__main__":
parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('-saved_dir', '-o', type=str, help='file name of output file', required=True)
parser.add_argument('-in_file', '-i', type=str, help='HF model name or directory', required=True)
parser.add_argument('-trained_gpu_num', '-t_g', type=int, help='How many gpus for training', default=1)
parser.add_argument('-infer_gpu_num', '-i_g', type=int, help='How many gpus for inference', required=True)
parser.add_argument("-processes", "-p", type=int, help="How many processes to spawn for conversion (default: 4)",
default=4)
parser.add_argument("-weight_data_type", type=str, default="fp32", choices=["fp32", "fp16"],
help="output weight data type")
args = parser.parse_args()
print("\n=============== Argument ===============")
for key in vars(args):
print("{}: {}".format(key, vars(args)[key]))
print("========================================")
split_and_convert(args)