How to train a distilled gpt2
Author: cloudygoose
Pull request: no

Hi! Thanks for everything.
I'm interested in training a distilled version of gpt2, because I want it to be even smaller than the distilled gpt2 model.
In https://github.com/huggingface/transformers/tree/master/examples/distillation , there is a tutorial about how to get a distilled bert. Could you give instructions about how to train a distilled gpt2? 
Thanks!

Comments:
很简单哦。看我的代码：


"""
Training the distilled model.
Supported architectures include: BERT -> DistilBERT, RoBERTa -> DistilRoBERTa, GPT2 -> DistilGPT2.
"""
import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch

from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPT2Config,
    GPT2LMHeadModel,
    GPT2Tokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


MODEL_CLASSES = {
    "distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    "roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    "bert": (BertConfig, BertForMaskedLM, BertTokenizer),
    "gpt2": (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer),
}


def sanity_checks(args):
    """
    A bunch of args sanity checks to perform even starting...
    """
    assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
    assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
    if args.mlm:
        assert os.path.isfile(args.token_counts)
        assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
    else:
        assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

    assert args.teacher_type == args.student_type or (
        args.student_type == "distilbert" and args.teacher_type == "bert"
    )
    assert os.path.isfile(args.student_config)
    if args.student_pretrained_weights is not None:
        assert os.path.isfile(args.student_pretrained_weights)

    if args.freeze_token_type_embds:
        assert args.student_type in ["roberta"]

    assert args.alpha_ce >= 0.0
    assert args.alpha_mlm >= 0.0
    assert args.alpha_clm >= 0.0
    assert args.alpha_mse >= 0.0
    assert args.alpha_cos >= 0.0
    assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0


def freeze_pos_embeddings(student, args):
    if args.student_type == "roberta":
        student.roberta.embeddings.position_embeddings.weight.requires_grad = False
    elif args.student_type == "gpt2":
        student.transformer.wpe.weight.requires_grad = False


def freeze_token_type_embeddings(student, args):
    if args.student_type == "roberta":
        student.roberta.embeddings.token_type_embeddings.weight.requires_grad = False


def main():
    parser = argparse.ArgumentParser(description="Training")
    parser.add_argument("--force", 
                        action="store_true", 
                        default=True,
                        help="Overwrite dump_path if it already exists.")

    parser.add_argument(
        "--dump_path", 
        type=str, 
        #required=True, 
        default=r'D:\2020.03.02distillgpt2' ,
        help="The output directory (log, checkpoints, parameters, etc.)"
    )
    parser.add_argument(
        "--data_file",
        type=str,
        #required=True,
        default=r'scripts\gpt2.pickle' ,
        help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence.",
    )

    parser.add_argument(
        "--student_type",
        type=str,
        choices=["distilbert", "roberta", "gpt2"],
        #required=True,
        default='gpt2',
        help="The student type (DistilBERT, RoBERTa).",
    )
    parser.add_argument("--student_config", 
                        type=str, 
                        #required=True,
                        default=r'training_configs\distilgpt2.json',
                        help="Path to the student configuration.")
    parser.add_argument(
        "--student_pretrained_weights", default=None, type=str, help="Load student initialization checkpoint."
    )

    parser.add_argument(
        "--teacher_type", 
        choices=["bert", "roberta", "gpt2"], 
        #required=True, 
        default='gpt2',
        help="Teacher type (BERT, RoBERTa)."
    )
    parser.add_argument("--teacher_name", 
                        type=str, 
                        #required=True,
                        default= r'D:\checkpoint-652500',
                        help="The teacher model.")

    parser.add_argument("--temperature", 
                        default=1.5, 
                        type=float, help="Temperature for the softmax temperature.")
    
    parser.add_argument(
        "--alpha_ce", 
        default=0.5,
        type=float, 
        help="Linear weight for the distillation loss. Must be >=0."
    )
    parser.add_argument(
        "--alpha_mlm",
        default=0.0,
        type=float,
        help="Linear weight for the MLM loss. Must be >=0. Should be used in coonjunction with `mlm` flag.",
    )
    parser.add_argument("--alpha_clm", default=0.5, type=float, help="Linear weight for the CLM loss. Must be >=0.")
    parser.add_argument("--alpha_mse", default=0.0, type=float, help="Linear weight of the MSE loss. Must be >=0.")
    parser.add_argument(
        "--alpha_cos", default=0.0, type=float, help="Linear weight of the cosine embedding loss. Must be >=0."
    )

    parser.add_argument(
        "--mlm", action="store_true", help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."
    )
    parser.add_argument(
        "--mlm_mask_prop",
        default=0.15,
        type=float,
        help="Proportion of tokens for which we need to make a prediction.",
    )
    parser.add_argument("--word_mask", default=0.8, type=float, help="Proportion of tokens to mask out.")
    parser.add_argument("--word_keep", default=0.1, type=float, help="Proportion of tokens to keep.")
    parser.add_argument("--word_rand", default=0.1, type=float, help="Proportion of tokens to randomly replace.")
    parser.add_argument(
        "--mlm_smoothing",
        default=0.7,
        type=float,
        help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).",
    )
    parser.add_argument("--token_counts", 
                        type=str, 
                         default=r'scripts\gpt2_token_counts.pickle' ,
                        help="The token counts in the data_file for MLM.")

    parser.add_argument(
        "--restrict_ce_to_mask",
        action="store_true",
        help="If true, compute the distilation loss only the [MLM] prediction distribution.",
    )
    parser.add_argument(
        "--freeze_pos_embs",
        action="store_true",
        help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only.",
    )
    parser.add_argument(
        "--freeze_token_type_embds",
        action="store_true",
        help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only.",
    )

    parser.add_argument("--n_epoch", type=int, default=3, help="Number of pass on the whole dataset.")
    parser.add_argument("--batch_size", type=int, default=4, help="Batch size (for each process).")
    parser.add_argument(
        "--group_by_size",
        action="store_false",
        help="If true, group sequences that have similar length into the same batch. Default is true.",
    )

    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=50,
        help="Gradient accumulation for larger training batches.",
    )
    parser.add_argument("--warmup_prop", default=0.05, type=float, help="Linear warmup proportion.")
    parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight deay if we apply some.")
    parser.add_argument("--learning_rate", default=5e-4, type=float, help="The initial learning rate for Adam.")
    parser.add_argument("--adam_epsilon", default=1e-6, type=float, help="Epsilon for Adam optimizer.")
    parser.add_argument("--max_grad_norm", default=5.0, type=float, help="Max gradient norm.")
    parser.add_argument("--initializer_range", default=0.02, type=float, help="Random initialization range.")

    parser.add_argument(
        "--fp16",
        action="store_true",
        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
    )
    parser.add_argument(
        "--fp16_opt_level",
        type=str,
        default="O1",
        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
        "See details at https://nvidia.github.io/apex/amp.html",
    )
    parser.add_argument("--n_gpu", type=int, default=1, help="Number of GPUs in the node.")
    parser.add_argument("--local_rank", type=int, default=-1, help="Distributed training - Local rank")
    parser.add_argument("--seed", type=int, default=2020, help="Random seed")

    parser.add_argument("--log_interval", type=int, default=500, help="Tensorboard logging interval.")
    parser.add_argument("--checkpoint_interval", type=int, default=1500, help="Checkpoint interval.")
    args = parser.parse_args([])
    sanity_checks(args)

    # ARGS #
    init_gpu_params(args)
    set_seed(args)
    if args.is_master:
        if os.path.exists(args.dump_path):
            if not args.force:
                raise ValueError(
                    f"Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite it"
                    "Use `--force` if you want to overwrite it"
                )
            else:
                shutil.rmtree(args.dump_path)

        if not os.path.exists(args.dump_path):
            os.makedirs(args.dump_path)
        logger.info(f"Experiment will be dumped and logged in {args.dump_path}")

        # SAVE PARAMS #
        logger.info(f"Param: {args}")
        with open(os.path.join(args.dump_path, "parameters.json"), "w",encoding = 'utf-8') as f:
            json.dump(vars(args), f, indent=4)
        #git_log(args.dump_path)

    student_config_class, student_model_class, _ = MODEL_CLASSES[args.student_type]
    teacher_config_class, teacher_model_class, teacher_tokenizer_class = MODEL_CLASSES[args.teacher_type]

    # TOKENIZER #
    from transformers import BertTokenizer 
    tokenizer = BertTokenizer(
        vocab_file = r"scripts\vocab.txt",
        unk_token='<unk>',
        sep_token='<sep>',
        pad_token='<pad>',
        cls_token='
',
        mask_token='<mask>') 
    
    special_tokens_dict = {"bos_token": "
", "eos_token": "
"}
    tokenizer.add_special_tokens(special_tokens_dict)
    special_tok_ids = {}
    for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
        idx = tokenizer.all_special_tokens.index(tok_symbol)
        special_tok_ids[tok_name] = tokenizer.all_special_ids[idx]
    logger.info(f"Special tokens {special_tok_ids}")
    args.special_tok_ids = special_tok_ids
    args.max_model_input_size = 512

    # DATA LOADER #
    logger.info(f"Loading data from {args.data_file}")
    with open(args.data_file, "rb") as fp:
        data = pickle.load(fp)

    if args.mlm:
        logger.info(f"Loading token counts from {args.token_counts} (already pre-computed)")
        with open(args.token_counts, "rb") as fp:
            counts = pickle.load(fp)

        token_probs = np.maximum(counts, 1) ** -args.mlm_smoothing
        for idx in special_tok_ids.values():
            token_probs[idx] = 0.0  # do not predict special tokens
        token_probs = torch.from_numpy(token_probs)
    else:
        token_probs = None

    train_lm_seq_dataset = LmSeqsDataset(params=args, data=data)
    logger.info(f"Data loader created.")

    # STUDENT #
    logger.info(f"Loading student config from {args.student_config}")
    stu_architecture_config = student_config_class.from_pretrained(args.student_config)
    stu_architecture_config.output_hidden_states = True

    if args.student_pretrained_weights is not None:
        logger.info(f"Loading pretrained weights from {args.student_pretrained_weights}")
        student = student_model_class.from_pretrained(args.student_pretrained_weights, config=stu_architecture_config)
    else:
        student = student_model_class(stu_architecture_config)

    if args.n_gpu > 0:
        student.to(f"cuda:{args.local_rank}")
    logger.info(f"Student loaded.")

    # TEACHER #
    teacher = teacher_model_class.from_pretrained(args.teacher_name, output_hidden_states=True)
    teacher.resize_token_embeddings(len(tokenizer))  # Update the model embeddings with the new vocabulary size
    teacher.to('cuda')
    teacher.eval()
    if args.n_gpu > 0:
        teacher.to(f"cuda:{args.local_rank}")
    logger.info(f"Teacher loaded from {args.teacher_name}.")

    # FREEZING #
    if args.freeze_pos_embs:
        freeze_pos_embeddings(student, args)
    if args.freeze_token_type_embds:
        freeze_token_type_embeddings(student, args)

    # SANITY CHECKS #
    assert student.config.vocab_size == teacher.config.vocab_size
    assert student.config.hidden_size == teacher.config.hidden_size
    assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
    if args.mlm:
        assert token_probs.size(0) == stu_architecture_config.vocab_size

    # DISTILLER #
    torch.cuda.empty_cache()
    distiller = Distiller(
        params=args, dataset=train_lm_seq_dataset, token_probs=token_probs, student=student, teacher=teacher
    )
    distiller.train()
    logger.info("Let's go get some drinks.")


if __name__ == "__main__":
    main()


Hi, not sure where to put this but there might be some small errors in the training code provided in this repo.
1. `n_gpus` is used when initializing GPUs but the actual name of this variable is `gpus`
2. It seems that now `return_dict` is default to `True`, so the function`step` fails because the results are unpacked to keys rather than values. The easiest fix I guess is to turn off `return_dict` in `train.py` like the following
   ```python
   student.config.update(dict(return_dict=False))
   teacher.config.update(dict(return_dict=False))
   ```

I followed the guide in README to train a distill gpt2, but the performance of my distilgpt2 is not good as huggingface, actually, the performance is bad. Did you trained a nice performance distilgpt2?