784 lines
34 KiB
Python
784 lines
34 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa).
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GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned
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using a masked language modeling (MLM) loss.
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"""
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import argparse
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import glob
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import logging
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import os
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import pickle
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import random
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import re
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import shutil
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from typing import Dict, List, Tuple
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import numpy as np
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import torch
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from torch.nn.utils.rnn import pad_sequence
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from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
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from torch.utils.data.distributed import DistributedSampler
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from tqdm import tqdm, trange
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from transformers import (
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MODEL_WITH_LM_HEAD_MAPPING,
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WEIGHTS_NAME,
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AdamW,
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AutoConfig,
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AutoModelWithLMHead,
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AutoTokenizer,
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PreTrainedModel,
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PreTrainedTokenizer,
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get_linear_schedule_with_warmup,
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)
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try:
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from torch.utils.tensorboard import SummaryWriter
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except ImportError:
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from tensorboardX import SummaryWriter
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logger = logging.getLogger(__name__)
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MODEL_CONFIG_CLASSES = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
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MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
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class TextDataset(Dataset):
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def __init__(self, tokenizer: PreTrainedTokenizer, args, file_path: str, block_size=512):
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assert os.path.isfile(file_path)
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block_size = block_size - (tokenizer.max_len - tokenizer.max_len_single_sentence)
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directory, filename = os.path.split(file_path)
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cached_features_file = os.path.join(
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directory, args.model_type + "_cached_lm_" + str(block_size) + "_" + filename
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)
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if os.path.exists(cached_features_file) and not args.overwrite_cache:
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logger.info("Loading features from cached file %s", cached_features_file)
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with open(cached_features_file, "rb") as handle:
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self.examples = pickle.load(handle)
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else:
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logger.info("Creating features from dataset file at %s", directory)
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self.examples = []
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with open(file_path, encoding="utf-8") as f:
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text = f.read()
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tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text))
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for i in range(0, len(tokenized_text) - block_size + 1, block_size): # Truncate in block of block_size
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self.examples.append(tokenizer.build_inputs_with_special_tokens(tokenized_text[i : i + block_size]))
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# Note that we are loosing the last truncated example here for the sake of simplicity (no padding)
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# If your dataset is small, first you should loook for a bigger one :-) and second you
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# can change this behavior by adding (model specific) padding.
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logger.info("Saving features into cached file %s", cached_features_file)
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with open(cached_features_file, "wb") as handle:
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pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)
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def __len__(self):
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return len(self.examples)
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def __getitem__(self, item):
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return torch.tensor(self.examples[item], dtype=torch.long)
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class LineByLineTextDataset(Dataset):
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def __init__(self, tokenizer: PreTrainedTokenizer, args, file_path: str, block_size=512):
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assert os.path.isfile(file_path)
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# Here, we do not cache the features, operating under the assumption
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# that we will soon use fast multithreaded tokenizers from the
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# `tokenizers` repo everywhere =)
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logger.info("Creating features from dataset file at %s", file_path)
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with open(file_path, encoding="utf-8") as f:
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lines = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]
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self.examples = tokenizer.batch_encode_plus(lines, add_special_tokens=True, max_length=block_size)["input_ids"]
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def __len__(self):
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return len(self.examples)
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def __getitem__(self, i):
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return torch.tensor(self.examples[i], dtype=torch.long)
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def load_and_cache_examples(args, tokenizer, evaluate=False):
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file_path = args.eval_data_file if evaluate else args.train_data_file
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if args.line_by_line:
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return LineByLineTextDataset(tokenizer, args, file_path=file_path, block_size=args.block_size)
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else:
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return TextDataset(tokenizer, args, file_path=file_path, block_size=args.block_size)
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def set_seed(args):
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random.seed(args.seed)
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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if args.n_gpu > 0:
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torch.cuda.manual_seed_all(args.seed)
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def _sorted_checkpoints(args, checkpoint_prefix="checkpoint", use_mtime=False) -> List[str]:
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ordering_and_checkpoint_path = []
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glob_checkpoints = glob.glob(os.path.join(args.output_dir, "{}-*".format(checkpoint_prefix)))
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for path in glob_checkpoints:
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if use_mtime:
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ordering_and_checkpoint_path.append((os.path.getmtime(path), path))
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else:
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regex_match = re.match(".*{}-([0-9]+)".format(checkpoint_prefix), path)
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if regex_match and regex_match.groups():
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ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))
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checkpoints_sorted = sorted(ordering_and_checkpoint_path)
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checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
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return checkpoints_sorted
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def _rotate_checkpoints(args, checkpoint_prefix="checkpoint", use_mtime=False) -> None:
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if not args.save_total_limit:
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return
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if args.save_total_limit <= 0:
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return
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# Check if we should delete older checkpoint(s)
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checkpoints_sorted = _sorted_checkpoints(args, checkpoint_prefix, use_mtime)
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if len(checkpoints_sorted) <= args.save_total_limit:
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return
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number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - args.save_total_limit)
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checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
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for checkpoint in checkpoints_to_be_deleted:
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logger.info("Deleting older checkpoint [{}] due to args.save_total_limit".format(checkpoint))
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shutil.rmtree(checkpoint)
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def mask_tokens(inputs: torch.Tensor, tokenizer: PreTrainedTokenizer, args) -> Tuple[torch.Tensor, torch.Tensor]:
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""" Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. """
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if tokenizer.mask_token is None:
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raise ValueError(
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"This tokenizer does not have a mask token which is necessary for masked language modeling. Remove the --mlm flag if you want to use this tokenizer."
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)
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labels = inputs.clone()
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# We sample a few tokens in each sequence for masked-LM training (with probability args.mlm_probability defaults to 0.15 in Bert/RoBERTa)
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probability_matrix = torch.full(labels.shape, args.mlm_probability)
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special_tokens_mask = [
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tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
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]
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probability_matrix.masked_fill_(torch.tensor(special_tokens_mask, dtype=torch.bool), value=0.0)
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if tokenizer._pad_token is not None:
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padding_mask = labels.eq(tokenizer.pad_token_id)
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probability_matrix.masked_fill_(padding_mask, value=0.0)
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masked_indices = torch.bernoulli(probability_matrix).bool()
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labels[~masked_indices] = -100 # We only compute loss on masked tokens
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# 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
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indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices
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inputs[indices_replaced] = tokenizer.convert_tokens_to_ids(tokenizer.mask_token)
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# 10% of the time, we replace masked input tokens with random word
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indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced
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random_words = torch.randint(len(tokenizer), labels.shape, dtype=torch.long)
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inputs[indices_random] = random_words[indices_random]
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# The rest of the time (10% of the time) we keep the masked input tokens unchanged
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return inputs, labels
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def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedTokenizer) -> Tuple[int, float]:
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""" Train the model """
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if args.local_rank in [-1, 0]:
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tb_writer = SummaryWriter()
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args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
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def collate(examples: List[torch.Tensor]):
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if tokenizer._pad_token is None:
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return pad_sequence(examples, batch_first=True)
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return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)
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train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
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train_dataloader = DataLoader(
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train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, collate_fn=collate
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)
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if args.max_steps > 0:
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t_total = args.max_steps
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args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
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else:
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t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
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model = model.module if hasattr(model, "module") else model # Take care of distributed/parallel training
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model.resize_token_embeddings(len(tokenizer))
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# Prepare optimizer and schedule (linear warmup and decay)
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no_decay = ["bias", "LayerNorm.weight"]
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optimizer_grouped_parameters = [
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{
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"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
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"weight_decay": args.weight_decay,
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},
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{"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
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]
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optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
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scheduler = get_linear_schedule_with_warmup(
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optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
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)
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# Check if saved optimizer or scheduler states exist
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if (
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args.model_name_or_path
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and os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt"))
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and os.path.isfile(os.path.join(args.model_name_or_path, "scheduler.pt"))
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):
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# Load in optimizer and scheduler states
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optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
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scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))
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if args.fp16:
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try:
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from apex import amp
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except ImportError:
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raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
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model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
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# multi-gpu training (should be after apex fp16 initialization)
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if args.n_gpu > 1:
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model = torch.nn.DataParallel(model)
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# Distributed training (should be after apex fp16 initialization)
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if args.local_rank != -1:
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model = torch.nn.parallel.DistributedDataParallel(
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model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
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)
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# Train!
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logger.info("***** Running training *****")
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logger.info(" Num examples = %d", len(train_dataset))
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logger.info(" Num Epochs = %d", args.num_train_epochs)
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logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
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logger.info(
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" Total train batch size (w. parallel, distributed & accumulation) = %d",
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args.train_batch_size
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* args.gradient_accumulation_steps
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* (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
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)
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logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
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logger.info(" Total optimization steps = %d", t_total)
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global_step = 0
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epochs_trained = 0
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steps_trained_in_current_epoch = 0
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# Check if continuing training from a checkpoint
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if args.model_name_or_path and os.path.exists(args.model_name_or_path):
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try:
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# set global_step to gobal_step of last saved checkpoint from model path
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checkpoint_suffix = args.model_name_or_path.split("-")[-1].split("/")[0]
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global_step = int(checkpoint_suffix)
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epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)
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steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)
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logger.info(" Continuing training from checkpoint, will skip to saved global_step")
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logger.info(" Continuing training from epoch %d", epochs_trained)
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logger.info(" Continuing training from global step %d", global_step)
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logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)
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except ValueError:
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logger.info(" Starting fine-tuning.")
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tr_loss, logging_loss = 0.0, 0.0
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model.zero_grad()
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train_iterator = trange(
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epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]
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)
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set_seed(args) # Added here for reproducibility
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for _ in train_iterator:
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epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
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for step, batch in enumerate(epoch_iterator):
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# Skip past any already trained steps if resuming training
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if steps_trained_in_current_epoch > 0:
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steps_trained_in_current_epoch -= 1
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continue
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inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch)
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inputs = inputs.to(args.device)
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labels = labels.to(args.device)
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model.train()
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outputs = model(inputs, masked_lm_labels=labels) if args.mlm else model(inputs, labels=labels)
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loss = outputs[0] # model outputs are always tuple in transformers (see doc)
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if args.n_gpu > 1:
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loss = loss.mean() # mean() to average on multi-gpu parallel training
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if args.gradient_accumulation_steps > 1:
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loss = loss / args.gradient_accumulation_steps
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if args.fp16:
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with amp.scale_loss(loss, optimizer) as scaled_loss:
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scaled_loss.backward()
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else:
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loss.backward()
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tr_loss += loss.item()
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if (step + 1) % args.gradient_accumulation_steps == 0:
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if args.fp16:
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torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
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else:
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torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
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optimizer.step()
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scheduler.step() # Update learning rate schedule
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model.zero_grad()
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global_step += 1
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if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
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# Log metrics
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if (
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args.local_rank == -1 and args.evaluate_during_training
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): # Only evaluate when single GPU otherwise metrics may not average well
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results = evaluate(args, model, tokenizer)
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for key, value in results.items():
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tb_writer.add_scalar("eval_{}".format(key), value, global_step)
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tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
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tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step)
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logging_loss = tr_loss
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if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
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checkpoint_prefix = "checkpoint"
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# Save model checkpoint
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output_dir = os.path.join(args.output_dir, "{}-{}".format(checkpoint_prefix, global_step))
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os.makedirs(output_dir, exist_ok=True)
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model_to_save = (
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model.module if hasattr(model, "module") else model
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) # Take care of distributed/parallel training
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model_to_save.save_pretrained(output_dir)
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tokenizer.save_pretrained(output_dir)
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torch.save(args, os.path.join(output_dir, "training_args.bin"))
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logger.info("Saving model checkpoint to %s", output_dir)
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_rotate_checkpoints(args, checkpoint_prefix)
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torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
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torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
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logger.info("Saving optimizer and scheduler states to %s", output_dir)
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if args.max_steps > 0 and global_step > args.max_steps:
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epoch_iterator.close()
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break
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if args.max_steps > 0 and global_step > args.max_steps:
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train_iterator.close()
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break
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if args.local_rank in [-1, 0]:
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tb_writer.close()
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return global_step, tr_loss / global_step
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def evaluate(args, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, prefix="") -> Dict:
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# Loop to handle MNLI double evaluation (matched, mis-matched)
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eval_output_dir = args.output_dir
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eval_dataset = load_and_cache_examples(args, tokenizer, evaluate=True)
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if args.local_rank in [-1, 0]:
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os.makedirs(eval_output_dir, exist_ok=True)
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args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
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# Note that DistributedSampler samples randomly
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def collate(examples: List[torch.Tensor]):
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if tokenizer._pad_token is None:
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return pad_sequence(examples, batch_first=True)
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return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)
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eval_sampler = SequentialSampler(eval_dataset)
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eval_dataloader = DataLoader(
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eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=collate
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)
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# multi-gpu evaluate
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if args.n_gpu > 1:
|
|
model = torch.nn.DataParallel(model)
|
|
|
|
# Eval!
|
|
logger.info("***** Running evaluation {} *****".format(prefix))
|
|
logger.info(" Num examples = %d", len(eval_dataset))
|
|
logger.info(" Batch size = %d", args.eval_batch_size)
|
|
eval_loss = 0.0
|
|
nb_eval_steps = 0
|
|
model.eval()
|
|
|
|
for batch in tqdm(eval_dataloader, desc="Evaluating"):
|
|
inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch)
|
|
inputs = inputs.to(args.device)
|
|
labels = labels.to(args.device)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(inputs, masked_lm_labels=labels) if args.mlm else model(inputs, labels=labels)
|
|
lm_loss = outputs[0]
|
|
eval_loss += lm_loss.mean().item()
|
|
nb_eval_steps += 1
|
|
|
|
eval_loss = eval_loss / nb_eval_steps
|
|
perplexity = torch.exp(torch.tensor(eval_loss))
|
|
|
|
result = {"perplexity": perplexity}
|
|
|
|
output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
|
|
with open(output_eval_file, "w") as writer:
|
|
logger.info("***** Eval results {} *****".format(prefix))
|
|
for key in sorted(result.keys()):
|
|
logger.info(" %s = %s", key, str(result[key]))
|
|
writer.write("%s = %s\n" % (key, str(result[key])))
|
|
|
|
return result
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser()
|
|
|
|
# Required parameters
|
|
parser.add_argument(
|
|
"--train_data_file", default=None, type=str, required=True, help="The input training data file (a text file)."
|
|
)
|
|
parser.add_argument(
|
|
"--output_dir",
|
|
type=str,
|
|
required=True,
|
|
help="The output directory where the model predictions and checkpoints will be written.",
|
|
)
|
|
parser.add_argument(
|
|
"--model_type", type=str, required=True, help="The model architecture to be trained or fine-tuned.",
|
|
)
|
|
|
|
# Other parameters
|
|
parser.add_argument(
|
|
"--eval_data_file",
|
|
default=None,
|
|
type=str,
|
|
help="An optional input evaluation data file to evaluate the perplexity on (a text file).",
|
|
)
|
|
parser.add_argument(
|
|
"--line_by_line",
|
|
action="store_true",
|
|
help="Whether distinct lines of text in the dataset are to be handled as distinct sequences.",
|
|
)
|
|
parser.add_argument(
|
|
"--should_continue", action="store_true", help="Whether to continue from latest checkpoint in output_dir"
|
|
)
|
|
parser.add_argument(
|
|
"--model_name_or_path",
|
|
default=None,
|
|
type=str,
|
|
help="The model checkpoint for weights initialization. Leave None if you want to train a model from scratch.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--mlm", action="store_true", help="Train with masked-language modeling loss instead of language modeling."
|
|
)
|
|
parser.add_argument(
|
|
"--mlm_probability", type=float, default=0.15, help="Ratio of tokens to mask for masked language modeling loss"
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--config_name",
|
|
default=None,
|
|
type=str,
|
|
help="Optional pretrained config name or path if not the same as model_name_or_path. If both are None, initialize a new config.",
|
|
)
|
|
parser.add_argument(
|
|
"--tokenizer_name",
|
|
default=None,
|
|
type=str,
|
|
help="Optional pretrained tokenizer name or path if not the same as model_name_or_path. If both are None, initialize a new tokenizer.",
|
|
)
|
|
parser.add_argument(
|
|
"--cache_dir",
|
|
default=None,
|
|
type=str,
|
|
help="Optional directory to store the pre-trained models downloaded from s3 (instead of the default one)",
|
|
)
|
|
parser.add_argument(
|
|
"--block_size",
|
|
default=-1,
|
|
type=int,
|
|
help="Optional input sequence length after tokenization."
|
|
"The training dataset will be truncated in block of this size for training."
|
|
"Default to the model max input length for single sentence inputs (take into account special tokens).",
|
|
)
|
|
parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
|
|
parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
|
|
parser.add_argument(
|
|
"--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step."
|
|
)
|
|
|
|
parser.add_argument("--per_gpu_train_batch_size", default=4, type=int, help="Batch size per GPU/CPU for training.")
|
|
parser.add_argument(
|
|
"--per_gpu_eval_batch_size", default=4, type=int, help="Batch size per GPU/CPU for evaluation."
|
|
)
|
|
parser.add_argument(
|
|
"--gradient_accumulation_steps",
|
|
type=int,
|
|
default=1,
|
|
help="Number of updates steps to accumulate before performing a backward/update pass.",
|
|
)
|
|
parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
|
|
parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
|
|
parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
|
|
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
|
|
parser.add_argument(
|
|
"--num_train_epochs", default=1.0, type=float, help="Total number of training epochs to perform."
|
|
)
|
|
parser.add_argument(
|
|
"--max_steps",
|
|
default=-1,
|
|
type=int,
|
|
help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
|
|
)
|
|
parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
|
|
|
|
parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.")
|
|
parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.")
|
|
parser.add_argument(
|
|
"--save_total_limit",
|
|
type=int,
|
|
default=None,
|
|
help="Limit the total amount of checkpoints, delete the older checkpoints in the output_dir, does not delete by default",
|
|
)
|
|
parser.add_argument(
|
|
"--eval_all_checkpoints",
|
|
action="store_true",
|
|
help="Evaluate all checkpoints starting with the same prefix as model_name_or_path ending and ending with step number",
|
|
)
|
|
parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
|
|
parser.add_argument(
|
|
"--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory"
|
|
)
|
|
parser.add_argument(
|
|
"--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
|
|
)
|
|
parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
|
|
|
|
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("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
|
|
parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.")
|
|
parser.add_argument("--server_port", type=str, default="", help="For distant debugging.")
|
|
args = parser.parse_args()
|
|
|
|
if args.model_type in ["bert", "roberta", "distilbert", "camembert"] and not args.mlm:
|
|
raise ValueError(
|
|
"BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the --mlm "
|
|
"flag (masked language modeling)."
|
|
)
|
|
if args.eval_data_file is None and args.do_eval:
|
|
raise ValueError(
|
|
"Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file "
|
|
"or remove the --do_eval argument."
|
|
)
|
|
if args.should_continue:
|
|
sorted_checkpoints = _sorted_checkpoints(args)
|
|
if len(sorted_checkpoints) == 0:
|
|
raise ValueError("Used --should_continue but no checkpoint was found in --output_dir.")
|
|
else:
|
|
args.model_name_or_path = sorted_checkpoints[-1]
|
|
|
|
if (
|
|
os.path.exists(args.output_dir)
|
|
and os.listdir(args.output_dir)
|
|
and args.do_train
|
|
and not args.overwrite_output_dir
|
|
and not args.should_continue
|
|
):
|
|
raise ValueError(
|
|
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
|
|
args.output_dir
|
|
)
|
|
)
|
|
|
|
# Setup distant debugging if needed
|
|
if args.server_ip and args.server_port:
|
|
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
|
|
import ptvsd
|
|
|
|
print("Waiting for debugger attach")
|
|
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
|
|
ptvsd.wait_for_attach()
|
|
|
|
# Setup CUDA, GPU & distributed training
|
|
if args.local_rank == -1 or args.no_cuda:
|
|
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
|
|
args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
|
|
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
|
|
torch.cuda.set_device(args.local_rank)
|
|
device = torch.device("cuda", args.local_rank)
|
|
torch.distributed.init_process_group(backend="nccl")
|
|
args.n_gpu = 1
|
|
args.device = device
|
|
|
|
# Setup logging
|
|
logging.basicConfig(
|
|
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
|
datefmt="%m/%d/%Y %H:%M:%S",
|
|
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
|
|
)
|
|
logger.warning(
|
|
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
|
|
args.local_rank,
|
|
device,
|
|
args.n_gpu,
|
|
bool(args.local_rank != -1),
|
|
args.fp16,
|
|
)
|
|
|
|
# Set seed
|
|
set_seed(args)
|
|
|
|
# Load pretrained model and tokenizer
|
|
if args.local_rank not in [-1, 0]:
|
|
torch.distributed.barrier() # Barrier to make sure only the first process in distributed training download model & vocab
|
|
|
|
if args.config_name:
|
|
config = AutoConfig.from_pretrained(args.config_name, cache_dir=args.cache_dir)
|
|
elif args.model_name_or_path:
|
|
config = AutoConfig.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)
|
|
else:
|
|
# When we release a pip version exposing CONFIG_MAPPING,
|
|
# we can do `config = CONFIG_MAPPING[args.model_type]()`.
|
|
raise ValueError(
|
|
"You are instantiating a new config instance from scratch. This is not supported, but you can do it from another script, save it,"
|
|
"and load it from here, using --config_name"
|
|
)
|
|
|
|
if args.tokenizer_name:
|
|
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, cache_dir=args.cache_dir)
|
|
elif args.model_name_or_path:
|
|
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)
|
|
else:
|
|
raise ValueError(
|
|
"You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another script, save it,"
|
|
"and load it from here, using --tokenizer_name"
|
|
)
|
|
|
|
if args.block_size <= 0:
|
|
args.block_size = tokenizer.max_len
|
|
# Our input block size will be the max possible for the model
|
|
else:
|
|
args.block_size = min(args.block_size, tokenizer.max_len)
|
|
|
|
if args.model_name_or_path:
|
|
model = AutoModelWithLMHead.from_pretrained(
|
|
args.model_name_or_path,
|
|
from_tf=bool(".ckpt" in args.model_name_or_path),
|
|
config=config,
|
|
cache_dir=args.cache_dir,
|
|
)
|
|
else:
|
|
logger.info("Training new model from scratch")
|
|
model = AutoModelWithLMHead.from_config(config)
|
|
|
|
model.to(args.device)
|
|
|
|
if args.local_rank == 0:
|
|
torch.distributed.barrier() # End of barrier to make sure only the first process in distributed training download model & vocab
|
|
|
|
logger.info("Training/evaluation parameters %s", args)
|
|
|
|
# Training
|
|
if args.do_train:
|
|
if args.local_rank not in [-1, 0]:
|
|
torch.distributed.barrier() # Barrier to make sure only the first process in distributed training process the dataset, and the others will use the cache
|
|
|
|
train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False)
|
|
|
|
if args.local_rank == 0:
|
|
torch.distributed.barrier()
|
|
|
|
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
|
|
logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
|
|
|
|
# Saving best-practices: if you use save_pretrained for the model and tokenizer, you can reload them using from_pretrained()
|
|
if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
|
|
# Create output directory if needed
|
|
if args.local_rank in [-1, 0]:
|
|
os.makedirs(args.output_dir, exist_ok=True)
|
|
|
|
logger.info("Saving model checkpoint to %s", args.output_dir)
|
|
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
|
|
# They can then be reloaded using `from_pretrained()`
|
|
model_to_save = (
|
|
model.module if hasattr(model, "module") else model
|
|
) # Take care of distributed/parallel training
|
|
model_to_save.save_pretrained(args.output_dir)
|
|
tokenizer.save_pretrained(args.output_dir)
|
|
|
|
# Good practice: save your training arguments together with the trained model
|
|
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
|
|
|
|
# Load a trained model and vocabulary that you have fine-tuned
|
|
model = AutoModelWithLMHead.from_pretrained(args.output_dir)
|
|
tokenizer = AutoTokenizer.from_pretrained(args.output_dir)
|
|
model.to(args.device)
|
|
|
|
# Evaluation
|
|
results = {}
|
|
if args.do_eval and args.local_rank in [-1, 0]:
|
|
checkpoints = [args.output_dir]
|
|
if args.eval_all_checkpoints:
|
|
checkpoints = list(
|
|
os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
|
|
)
|
|
logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging
|
|
logger.info("Evaluate the following checkpoints: %s", checkpoints)
|
|
for checkpoint in checkpoints:
|
|
global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
|
|
prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else ""
|
|
|
|
model = AutoModelWithLMHead.from_pretrained(checkpoint)
|
|
model.to(args.device)
|
|
result = evaluate(args, model, tokenizer, prefix=prefix)
|
|
result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
|
|
results.update(result)
|
|
|
|
return results
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|