Convert HuggingFace GPT-J model to FasterTransformers

huggingface_gptj_convert.py

# 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,args, 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) == False):
        os.makedirs(saved_dir)
    ckpt_name = args.in_file

    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) / f"config.ini").as_posix(), 'w') as configfile:
            config.write(configfile)
    except:
        print(f"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, args,
                                    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)