|
- # coding=utf-8
- # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
- # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
- #
- # 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.
- """
- Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa).
- GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned
- using a masked language modeling (MLM) loss.
- """
-
-
- import argparse
- import glob
- import logging
- import os
- import pickle
- import random
- import re
- import shutil
- from typing import Dict, List, Tuple
-
- import numpy as np
- import torch
- from torch.nn.utils.rnn import pad_sequence
- from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
- from torch.utils.data.distributed import DistributedSampler
- from tqdm import tqdm, trange
-
- from transformers import (
- WEIGHTS_NAME,
- AdamW,
- BertConfig,
- BertForMaskedLM,
- BertTokenizer,
- CamembertConfig,
- CamembertForMaskedLM,
- CamembertTokenizer,
- DistilBertConfig,
- DistilBertForMaskedLM,
- DistilBertTokenizer,
- GPT2Config,
- GPT2LMHeadModel,
- GPT2Tokenizer,
- OpenAIGPTConfig,
- OpenAIGPTLMHeadModel,
- OpenAIGPTTokenizer,
- PreTrainedModel,
- PreTrainedTokenizer,
- RobertaConfig,
- RobertaForMaskedLM,
- RobertaTokenizer,
- get_linear_schedule_with_warmup,
- )
-
-
- try:
- from torch.utils.tensorboard import SummaryWriter
- except ImportError:
- from tensorboardX import SummaryWriter
-
-
- logger = logging.getLogger(__name__)
-
-
- MODEL_CLASSES = {
- "gpt2": (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer),
- "openai-gpt": (OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
- "bert": (BertConfig, BertForMaskedLM, BertTokenizer),
- "roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
- "distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
- "camembert": (CamembertConfig, CamembertForMaskedLM, CamembertTokenizer),
- }
-
-
- class TextDataset(Dataset):
- def __init__(self, tokenizer: PreTrainedTokenizer, args, file_path: str, block_size=512):
- assert os.path.isfile(file_path)
-
- block_size = block_size - (tokenizer.max_len - tokenizer.max_len_single_sentence)
-
- directory, filename = os.path.split(file_path)
- cached_features_file = os.path.join(
- directory, args.model_type + "_cached_lm_" + str(block_size) + "_" + filename
- )
-
- if os.path.exists(cached_features_file) and not args.overwrite_cache:
- logger.info("Loading features from cached file %s", cached_features_file)
- with open(cached_features_file, "rb") as handle:
- self.examples = pickle.load(handle)
- else:
- logger.info("Creating features from dataset file at %s", directory)
-
- self.examples = []
- with open(file_path, encoding="utf-8") as f:
- text = f.read()
-
- tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text))
-
- for i in range(0, len(tokenized_text) - block_size + 1, block_size): # Truncate in block of block_size
- self.examples.append(tokenizer.build_inputs_with_special_tokens(tokenized_text[i : i + block_size]))
- # Note that we are loosing the last truncated example here for the sake of simplicity (no padding)
- # If your dataset is small, first you should loook for a bigger one :-) and second you
- # can change this behavior by adding (model specific) padding.
-
- logger.info("Saving features into cached file %s", cached_features_file)
- with open(cached_features_file, "wb") as handle:
- pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)
-
- def __len__(self):
- return len(self.examples)
-
- def __getitem__(self, item):
- return torch.tensor(self.examples[item], dtype=torch.long)
-
-
- class LineByLineTextDataset(Dataset):
- def __init__(self, tokenizer: PreTrainedTokenizer, args, file_path: str, block_size=512):
- assert os.path.isfile(file_path)
- # Here, we do not cache the features, operating under the assumption
- # that we will soon use fast multithreaded tokenizers from the
- # `tokenizers` repo everywhere =)
- logger.info("Creating features from dataset file at %s", file_path)
-
- with open(file_path, encoding="utf-8") as f:
- lines = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]
-
- self.examples = tokenizer.batch_encode_plus(lines, add_special_tokens=True, max_length=block_size)["input_ids"]
-
- def __len__(self):
- return len(self.examples)
-
- def __getitem__(self, i):
- return torch.tensor(self.examples[i], dtype=torch.long)
-
-
- def load_and_cache_examples(args, tokenizer, evaluate=False):
- file_path = args.eval_data_file if evaluate else args.train_data_file
- if args.line_by_line:
- return LineByLineTextDataset(tokenizer, args, file_path=file_path, block_size=args.block_size)
- else:
- return TextDataset(tokenizer, args, file_path=file_path, block_size=args.block_size)
-
-
- def set_seed(args):
- random.seed(args.seed)
- np.random.seed(args.seed)
- torch.manual_seed(args.seed)
- if args.n_gpu > 0:
- torch.cuda.manual_seed_all(args.seed)
-
-
- def _sorted_checkpoints(args, checkpoint_prefix="checkpoint", use_mtime=False) -> List[str]:
- ordering_and_checkpoint_path = []
-
- glob_checkpoints = glob.glob(os.path.join(args.output_dir, "{}-*".format(checkpoint_prefix)))
-
- for path in glob_checkpoints:
- if use_mtime:
- ordering_and_checkpoint_path.append((os.path.getmtime(path), path))
- else:
- regex_match = re.match(".*{}-([0-9]+)".format(checkpoint_prefix), path)
- if regex_match and regex_match.groups():
- ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))
-
- checkpoints_sorted = sorted(ordering_and_checkpoint_path)
- checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
- return checkpoints_sorted
-
-
- def _rotate_checkpoints(args, checkpoint_prefix="checkpoint", use_mtime=False) -> None:
- if not args.save_total_limit:
- return
- if args.save_total_limit <= 0:
- return
-
- # Check if we should delete older checkpoint(s)
- checkpoints_sorted = _sorted_checkpoints(args, checkpoint_prefix, use_mtime)
- if len(checkpoints_sorted) <= args.save_total_limit:
- return
-
- number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - args.save_total_limit)
- checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
- for checkpoint in checkpoints_to_be_deleted:
- logger.info("Deleting older checkpoint [{}] due to args.save_total_limit".format(checkpoint))
- shutil.rmtree(checkpoint)
-
-
- def mask_tokens(inputs: torch.Tensor, tokenizer: PreTrainedTokenizer, args) -> Tuple[torch.Tensor, torch.Tensor]:
- """ Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. """
-
- if tokenizer.mask_token is None:
- raise ValueError(
- "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."
- )
-
- labels = inputs.clone()
- # We sample a few tokens in each sequence for masked-LM training (with probability args.mlm_probability defaults to 0.15 in Bert/RoBERTa)
- probability_matrix = torch.full(labels.shape, args.mlm_probability)
- special_tokens_mask = [
- tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
- ]
- probability_matrix.masked_fill_(torch.tensor(special_tokens_mask, dtype=torch.bool), value=0.0)
- if tokenizer._pad_token is not None:
- padding_mask = labels.eq(tokenizer.pad_token_id)
- probability_matrix.masked_fill_(padding_mask, value=0.0)
- masked_indices = torch.bernoulli(probability_matrix).bool()
- labels[~masked_indices] = -100 # We only compute loss on masked tokens
-
- # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
- indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices
- inputs[indices_replaced] = tokenizer.convert_tokens_to_ids(tokenizer.mask_token)
-
- # 10% of the time, we replace masked input tokens with random word
- indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced
- random_words = torch.randint(len(tokenizer), labels.shape, dtype=torch.long)
- inputs[indices_random] = random_words[indices_random]
-
- # The rest of the time (10% of the time) we keep the masked input tokens unchanged
- return inputs, labels
-
-
- def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedTokenizer) -> Tuple[int, float]:
- """ Train the model """
- if args.local_rank in [-1, 0]:
- tb_writer = SummaryWriter()
-
- args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
-
- def collate(examples: List[torch.Tensor]):
- if tokenizer._pad_token is None:
- return pad_sequence(examples, batch_first=True)
- return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)
-
- train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
- train_dataloader = DataLoader(
- train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, collate_fn=collate
- )
-
- if args.max_steps > 0:
- t_total = args.max_steps
- args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
- else:
- t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
-
- # Prepare optimizer and schedule (linear warmup and decay)
- no_decay = ["bias", "LayerNorm.weight"]
- optimizer_grouped_parameters = [
- {
- "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
- "weight_decay": args.weight_decay,
- },
- {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
- ]
- optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
- scheduler = get_linear_schedule_with_warmup(
- optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
- )
-
- # Check if saved optimizer or scheduler states exist
- if (
- args.model_name_or_path
- and os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt"))
- and os.path.isfile(os.path.join(args.model_name_or_path, "scheduler.pt"))
- ):
- # Load in optimizer and scheduler states
- optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
- scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))
-
- if args.fp16:
- try:
- from apex import amp
- except ImportError:
- raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
- model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
-
- # multi-gpu training (should be after apex fp16 initialization)
- if args.n_gpu > 1:
- model = torch.nn.DataParallel(model)
-
- # Distributed training (should be after apex fp16 initialization)
- if args.local_rank != -1:
- model = torch.nn.parallel.DistributedDataParallel(
- model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
- )
-
- # Train!
- logger.info("***** Running training *****")
- logger.info(" Num examples = %d", len(train_dataset))
- logger.info(" Num Epochs = %d", args.num_train_epochs)
- logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
- logger.info(
- " Total train batch size (w. parallel, distributed & accumulation) = %d",
- args.train_batch_size
- * args.gradient_accumulation_steps
- * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
- )
- logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
- logger.info(" Total optimization steps = %d", t_total)
-
- global_step = 0
- epochs_trained = 0
- steps_trained_in_current_epoch = 0
- # Check if continuing training from a checkpoint
- if args.model_name_or_path and os.path.exists(args.model_name_or_path):
- try:
- # set global_step to gobal_step of last saved checkpoint from model path
- checkpoint_suffix = args.model_name_or_path.split("-")[-1].split("/")[0]
- global_step = int(checkpoint_suffix)
- epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)
- steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)
-
- logger.info(" Continuing training from checkpoint, will skip to saved global_step")
- logger.info(" Continuing training from epoch %d", epochs_trained)
- logger.info(" Continuing training from global step %d", global_step)
- logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)
- except ValueError:
- logger.info(" Starting fine-tuning.")
-
- tr_loss, logging_loss = 0.0, 0.0
-
- model_to_resize = model.module if hasattr(model, "module") else model # Take care of distributed/parallel training
- model_to_resize.resize_token_embeddings(len(tokenizer))
-
- model.zero_grad()
- train_iterator = trange(
- epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]
- )
- set_seed(args) # Added here for reproducibility
- for _ in train_iterator:
- epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
- for step, batch in enumerate(epoch_iterator):
-
- # Skip past any already trained steps if resuming training
- if steps_trained_in_current_epoch > 0:
- steps_trained_in_current_epoch -= 1
- continue
-
- inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch)
- inputs = inputs.to(args.device)
- labels = labels.to(args.device)
- model.train()
- outputs = model(inputs, masked_lm_labels=labels) if args.mlm else model(inputs, labels=labels)
- loss = outputs[0] # model outputs are always tuple in transformers (see doc)
-
- if args.n_gpu > 1:
- loss = loss.mean() # mean() to average on multi-gpu parallel training
- if args.gradient_accumulation_steps > 1:
- loss = loss / args.gradient_accumulation_steps
-
- if args.fp16:
- with amp.scale_loss(loss, optimizer) as scaled_loss:
- scaled_loss.backward()
- else:
- loss.backward()
-
- tr_loss += loss.item()
- if (step + 1) % args.gradient_accumulation_steps == 0:
- if args.fp16:
- torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
- else:
- torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
- optimizer.step()
- scheduler.step() # Update learning rate schedule
- model.zero_grad()
- global_step += 1
-
- if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
- # Log metrics
- if (
- args.local_rank == -1 and args.evaluate_during_training
- ): # Only evaluate when single GPU otherwise metrics may not average well
- results = evaluate(args, model, tokenizer)
- logger.info(results)
- for key, value in results.items():
- tb_writer.add_scalar("eval_{}".format(key), value, global_step)
- tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
- tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step)
- logging_loss = tr_loss
-
- if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
- checkpoint_prefix = "checkpoint"
- # Save model checkpoint
- output_dir = os.path.join(args.output_dir, "{}-{}".format(checkpoint_prefix, global_step))
- os.makedirs(output_dir, exist_ok=True)
- model_to_save = (
- model.module if hasattr(model, "module") else model
- ) # Take care of distributed/parallel training
- model_to_save.save_pretrained(output_dir)
- tokenizer.save_pretrained(output_dir)
-
- torch.save(args, os.path.join(output_dir, "training_args.bin"))
- logger.info("Saving model checkpoint to %s", output_dir)
-
- _rotate_checkpoints(args, checkpoint_prefix)
-
- torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
- torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
- logger.info("Saving optimizer and scheduler states to %s", output_dir)
-
- if args.max_steps > 0 and global_step > args.max_steps:
- epoch_iterator.close()
- break
- if args.max_steps > 0 and global_step > args.max_steps:
- train_iterator.close()
- break
-
- if args.local_rank in [-1, 0]:
- tb_writer.close()
-
- return global_step, tr_loss / global_step
-
-
- def evaluate(args, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, prefix="") -> Dict:
- # Loop to handle MNLI double evaluation (matched, mis-matched)
- eval_output_dir = args.output_dir
-
- eval_dataset = load_and_cache_examples(args, tokenizer, evaluate=True)
-
- if args.local_rank in [-1, 0]:
- os.makedirs(eval_output_dir, exist_ok=True)
-
- args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
- # Note that DistributedSampler samples randomly
-
- def collate(examples: List[torch.Tensor]):
- if tokenizer._pad_token is None:
- return pad_sequence(examples, batch_first=True)
- return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)
-
- eval_sampler = SequentialSampler(eval_dataset)
- eval_dataloader = DataLoader(
- eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=collate
- )
-
- # multi-gpu evaluate
- 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
- ):
- 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
-
- config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
-
- if args.config_name:
- config = config_class.from_pretrained(args.config_name, cache_dir=args.cache_dir)
- elif args.model_name_or_path:
- config = config_class.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)
- else:
- config = config_class()
-
- if args.tokenizer_name:
- tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name, cache_dir=args.cache_dir)
- elif args.model_name_or_path:
- tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)
- else:
- raise ValueError(
- "You are instantiating a new {} tokenizer. This is not supported, but you can do it from another script, save it,"
- "and load it from here, using --tokenizer_name".format(tokenizer_class.__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 = model_class.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 = model_class(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 = model_class.from_pretrained(args.output_dir)
- tokenizer = tokenizer_class.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 = model_class.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()
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