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#!/usr/bin/env python | |
# coding=utf-8 | |
# Copyright 2021 The HuggingFace Inc. team. 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 masked language modeling (BERT, ALBERT, RoBERTa...) | |
on a text file or a dataset without using HuggingFace Trainer. | |
Here is the full list of checkpoints on the hub that can be fine-tuned by this script: | |
https://huggingface.co/models?filter=fill-mask | |
""" | |
# You can also adapt this script on your own mlm task. Pointers for this are left as comments. | |
import argparse | |
import json | |
import logging | |
import math | |
import os | |
import random | |
from itertools import chain | |
from pathlib import Path | |
import datasets | |
import torch | |
from accelerate import Accelerator, DistributedType | |
from accelerate.logging import get_logger | |
from accelerate.utils import set_seed | |
from datasets import load_dataset | |
from huggingface_hub import Repository, create_repo | |
from torch.utils.data import DataLoader | |
from tqdm.auto import tqdm | |
import transformers | |
from transformers import ( | |
CONFIG_MAPPING, | |
MODEL_MAPPING, | |
AutoConfig, | |
AutoModelForMaskedLM, | |
AutoTokenizer, | |
DataCollatorForLanguageModeling, | |
SchedulerType, | |
get_scheduler, | |
) | |
from transformers.utils import check_min_version, send_example_telemetry | |
from transformers.utils.versions import require_version | |
# Will error if the minimal version of Transformers is not installed. Remove at your own risks. | |
check_min_version("4.34.0.dev0") | |
logger = get_logger(__name__) | |
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt") | |
MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys()) | |
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) | |
def parse_args(): | |
parser = argparse.ArgumentParser(description="Finetune a transformers model on a Masked Language Modeling task") | |
parser.add_argument( | |
"--dataset_name", | |
type=str, | |
default=None, | |
help="The name of the dataset to use (via the datasets library).", | |
) | |
parser.add_argument( | |
"--dataset_config_name", | |
type=str, | |
default=None, | |
help="The configuration name of the dataset to use (via the datasets library).", | |
) | |
parser.add_argument( | |
"--train_file", type=str, default=None, help="A csv or a json file containing the training data." | |
) | |
parser.add_argument( | |
"--validation_file", type=str, default=None, help="A csv or a json file containing the validation data." | |
) | |
parser.add_argument( | |
"--validation_split_percentage", | |
default=5, | |
help="The percentage of the train set used as validation set in case there's no validation split", | |
) | |
parser.add_argument( | |
"--pad_to_max_length", | |
action="store_true", | |
help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.", | |
) | |
parser.add_argument( | |
"--model_name_or_path", | |
type=str, | |
help="Path to pretrained model or model identifier from huggingface.co/models.", | |
required=False, | |
) | |
parser.add_argument( | |
"--config_name", | |
type=str, | |
default=None, | |
help="Pretrained config name or path if not the same as model_name", | |
) | |
parser.add_argument( | |
"--tokenizer_name", | |
type=str, | |
default=None, | |
help="Pretrained tokenizer name or path if not the same as model_name", | |
) | |
parser.add_argument( | |
"--use_slow_tokenizer", | |
action="store_true", | |
help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).", | |
) | |
parser.add_argument( | |
"--per_device_train_batch_size", | |
type=int, | |
default=8, | |
help="Batch size (per device) for the training dataloader.", | |
) | |
parser.add_argument( | |
"--per_device_eval_batch_size", | |
type=int, | |
default=8, | |
help="Batch size (per device) for the evaluation dataloader.", | |
) | |
parser.add_argument( | |
"--learning_rate", | |
type=float, | |
default=5e-5, | |
help="Initial learning rate (after the potential warmup period) to use.", | |
) | |
parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") | |
parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") | |
parser.add_argument( | |
"--max_train_steps", | |
type=int, | |
default=None, | |
help="Total number of training steps to perform. If provided, overrides num_train_epochs.", | |
) | |
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( | |
"--lr_scheduler_type", | |
type=SchedulerType, | |
default="linear", | |
help="The scheduler type to use.", | |
choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], | |
) | |
parser.add_argument( | |
"--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." | |
) | |
parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") | |
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") | |
parser.add_argument( | |
"--model_type", | |
type=str, | |
default=None, | |
help="Model type to use if training from scratch.", | |
choices=MODEL_TYPES, | |
) | |
parser.add_argument( | |
"--max_seq_length", | |
type=int, | |
default=None, | |
help=( | |
"The maximum total input sequence length after tokenization. Sequences longer than this will be truncated." | |
), | |
) | |
parser.add_argument( | |
"--line_by_line", | |
type=bool, | |
default=False, | |
help="Whether distinct lines of text in the dataset are to be handled as distinct sequences.", | |
) | |
parser.add_argument( | |
"--preprocessing_num_workers", | |
type=int, | |
default=None, | |
help="The number of processes to use for the preprocessing.", | |
) | |
parser.add_argument( | |
"--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets" | |
) | |
parser.add_argument( | |
"--mlm_probability", type=float, default=0.15, help="Ratio of tokens to mask for masked language modeling loss" | |
) | |
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") | |
parser.add_argument( | |
"--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." | |
) | |
parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") | |
parser.add_argument( | |
"--trust_remote_code", | |
type=bool, | |
default=False, | |
help=( | |
"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option" | |
"should only be set to `True` for repositories you trust and in which you have read the code, as it will" | |
"execute code present on the Hub on your local machine." | |
), | |
) | |
parser.add_argument( | |
"--checkpointing_steps", | |
type=str, | |
default=None, | |
help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", | |
) | |
parser.add_argument( | |
"--resume_from_checkpoint", | |
type=str, | |
default=None, | |
help="If the training should continue from a checkpoint folder.", | |
) | |
parser.add_argument( | |
"--with_tracking", | |
action="store_true", | |
help="Whether to enable experiment trackers for logging.", | |
) | |
parser.add_argument( | |
"--report_to", | |
type=str, | |
default="all", | |
help=( | |
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' | |
' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' | |
"Only applicable when `--with_tracking` is passed." | |
), | |
) | |
parser.add_argument( | |
"--low_cpu_mem_usage", | |
action="store_true", | |
help=( | |
"It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded." | |
"If passed, LLM loading time and RAM consumption will be benefited." | |
), | |
) | |
args = parser.parse_args() | |
# Sanity checks | |
if args.dataset_name is None and args.train_file is None and args.validation_file is None: | |
raise ValueError("Need either a dataset name or a training/validation file.") | |
else: | |
if args.train_file is not None: | |
extension = args.train_file.split(".")[-1] | |
if extension not in ["csv", "json", "txt"]: | |
raise ValueError("`train_file` should be a csv, json or txt file.") | |
if args.validation_file is not None: | |
extension = args.validation_file.split(".")[-1] | |
if extension not in ["csv", "json", "txt"]: | |
raise ValueError("`validation_file` should be a csv, json or txt file.") | |
if args.push_to_hub: | |
assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." | |
return args | |
def main(): | |
args = parse_args() | |
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The | |
# information sent is the one passed as arguments along with your Python/PyTorch versions. | |
send_example_telemetry("run_mlm_no_trainer", args) | |
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example. | |
# If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers | |
# in the environment | |
accelerator_log_kwargs = {} | |
if args.with_tracking: | |
accelerator_log_kwargs["log_with"] = args.report_to | |
accelerator_log_kwargs["project_dir"] = args.output_dir | |
accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs) | |
# Make one log on every process with the configuration for debugging. | |
logging.basicConfig( | |
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", | |
datefmt="%m/%d/%Y %H:%M:%S", | |
level=logging.INFO, | |
) | |
logger.info(accelerator.state, main_process_only=False) | |
if accelerator.is_local_main_process: | |
datasets.utils.logging.set_verbosity_warning() | |
transformers.utils.logging.set_verbosity_info() | |
else: | |
datasets.utils.logging.set_verbosity_error() | |
transformers.utils.logging.set_verbosity_error() | |
# If passed along, set the training seed now. | |
if args.seed is not None: | |
set_seed(args.seed) | |
# Handle the repository creation | |
if accelerator.is_main_process: | |
if args.push_to_hub: | |
# Retrieve of infer repo_name | |
repo_name = args.hub_model_id | |
if repo_name is None: | |
repo_name = Path(args.output_dir).absolute().name | |
# Create repo and retrieve repo_id | |
repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id | |
# Clone repo locally | |
repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token) | |
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: | |
if "step_*" not in gitignore: | |
gitignore.write("step_*\n") | |
if "epoch_*" not in gitignore: | |
gitignore.write("epoch_*\n") | |
elif args.output_dir is not None: | |
os.makedirs(args.output_dir, exist_ok=True) | |
accelerator.wait_for_everyone() | |
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) | |
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ | |
# (the dataset will be downloaded automatically from the datasets Hub). | |
# | |
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called | |
# 'text' is found. You can easily tweak this behavior (see below). | |
# | |
# In distributed training, the load_dataset function guarantee that only one local process can concurrently | |
# download the dataset. | |
if args.dataset_name is not None: | |
# Downloading and loading a dataset from the hub. | |
raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name) | |
if "validation" not in raw_datasets.keys(): | |
raw_datasets["validation"] = load_dataset( | |
args.dataset_name, | |
args.dataset_config_name, | |
split=f"train[:{args.validation_split_percentage}%]", | |
) | |
raw_datasets["train"] = load_dataset( | |
args.dataset_name, | |
args.dataset_config_name, | |
split=f"train[{args.validation_split_percentage}%:]", | |
) | |
else: | |
data_files = {} | |
if args.train_file is not None: | |
data_files["train"] = args.train_file | |
if args.validation_file is not None: | |
data_files["validation"] = args.validation_file | |
extension = args.train_file.split(".")[-1] | |
if extension == "txt": | |
extension = "text" | |
raw_datasets = load_dataset(extension, data_files=data_files) | |
# If no validation data is there, validation_split_percentage will be used to divide the dataset. | |
if "validation" not in raw_datasets.keys(): | |
raw_datasets["validation"] = load_dataset( | |
extension, | |
data_files=data_files, | |
split=f"train[:{args.validation_split_percentage}%]", | |
) | |
raw_datasets["train"] = load_dataset( | |
extension, | |
data_files=data_files, | |
split=f"train[{args.validation_split_percentage}%:]", | |
) | |
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at | |
# https://huggingface.co/docs/datasets/loading_datasets.html. | |
# Load pretrained model and tokenizer | |
# | |
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently | |
# download model & vocab. | |
if args.config_name: | |
config = AutoConfig.from_pretrained(args.config_name, trust_remote_code=args.trust_remote_code) | |
elif args.model_name_or_path: | |
config = AutoConfig.from_pretrained(args.model_name_or_path, trust_remote_code=args.trust_remote_code) | |
else: | |
config = CONFIG_MAPPING[args.model_type]() | |
logger.warning("You are instantiating a new config instance from scratch.") | |
if args.tokenizer_name: | |
tokenizer = AutoTokenizer.from_pretrained( | |
args.tokenizer_name, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code | |
) | |
elif args.model_name_or_path: | |
tokenizer = AutoTokenizer.from_pretrained( | |
args.model_name_or_path, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code | |
) | |
else: | |
raise ValueError( | |
"You are instantiating a new tokenizer from scratch. This is not supported by this script." | |
"You can do it from another script, save it, and load it from here, using --tokenizer_name." | |
) | |
if args.model_name_or_path: | |
model = AutoModelForMaskedLM.from_pretrained( | |
args.model_name_or_path, | |
from_tf=bool(".ckpt" in args.model_name_or_path), | |
config=config, | |
low_cpu_mem_usage=args.low_cpu_mem_usage, | |
trust_remote_code=args.trust_remote_code, | |
) | |
else: | |
logger.info("Training new model from scratch") | |
model = AutoModelForMaskedLM.from_config(config, trust_remote_code=args.trust_remote_code) | |
# We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch | |
# on a small vocab and want a smaller embedding size, remove this test. | |
embedding_size = model.get_input_embeddings().weight.shape[0] | |
if len(tokenizer) > embedding_size: | |
model.resize_token_embeddings(len(tokenizer)) | |
# Preprocessing the datasets. | |
# First we tokenize all the texts. | |
column_names = raw_datasets["train"].column_names | |
text_column_name = "text" if "text" in column_names else column_names[0] | |
if args.max_seq_length is None: | |
max_seq_length = tokenizer.model_max_length | |
if max_seq_length > 1024: | |
logger.warning( | |
"The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value" | |
" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can" | |
" override this default with `--block_size xxx`." | |
) | |
max_seq_length = 1024 | |
else: | |
if args.max_seq_length > tokenizer.model_max_length: | |
logger.warning( | |
f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the" | |
f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." | |
) | |
max_seq_length = min(args.max_seq_length, tokenizer.model_max_length) | |
if args.line_by_line: | |
# When using line_by_line, we just tokenize each nonempty line. | |
padding = "max_length" if args.pad_to_max_length else False | |
def tokenize_function(examples): | |
# Remove empty lines | |
examples[text_column_name] = [ | |
line for line in examples[text_column_name] if len(line) > 0 and not line.isspace() | |
] | |
return tokenizer( | |
examples[text_column_name], | |
padding=padding, | |
truncation=True, | |
max_length=max_seq_length, | |
# We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it | |
# receives the `special_tokens_mask`. | |
return_special_tokens_mask=True, | |
) | |
with accelerator.main_process_first(): | |
tokenized_datasets = raw_datasets.map( | |
tokenize_function, | |
batched=True, | |
num_proc=args.preprocessing_num_workers, | |
remove_columns=[text_column_name], | |
load_from_cache_file=not args.overwrite_cache, | |
desc="Running tokenizer on dataset line_by_line", | |
) | |
else: | |
# Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts. | |
# We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more | |
# efficient when it receives the `special_tokens_mask`. | |
def tokenize_function(examples): | |
return tokenizer(examples[text_column_name], return_special_tokens_mask=True) | |
with accelerator.main_process_first(): | |
tokenized_datasets = raw_datasets.map( | |
tokenize_function, | |
batched=True, | |
num_proc=args.preprocessing_num_workers, | |
remove_columns=column_names, | |
load_from_cache_file=not args.overwrite_cache, | |
desc="Running tokenizer on every text in dataset", | |
) | |
# Main data processing function that will concatenate all texts from our dataset and generate chunks of | |
# max_seq_length. | |
def group_texts(examples): | |
# Concatenate all texts. | |
concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()} | |
total_length = len(concatenated_examples[list(examples.keys())[0]]) | |
# We drop the small remainder, and if the total_length < max_seq_length we exclude this batch and return an empty dict. | |
# We could add padding if the model supported it instead of this drop, you can customize this part to your needs. | |
total_length = (total_length // max_seq_length) * max_seq_length | |
# Split by chunks of max_len. | |
result = { | |
k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)] | |
for k, t in concatenated_examples.items() | |
} | |
return result | |
# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a | |
# remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value | |
# might be slower to preprocess. | |
# | |
# To speed up this part, we use multiprocessing. See the documentation of the map method for more information: | |
# https://huggingface.co/docs/datasets/process#map | |
with accelerator.main_process_first(): | |
tokenized_datasets = tokenized_datasets.map( | |
group_texts, | |
batched=True, | |
num_proc=args.preprocessing_num_workers, | |
load_from_cache_file=not args.overwrite_cache, | |
desc=f"Grouping texts in chunks of {max_seq_length}", | |
) | |
train_dataset = tokenized_datasets["train"] | |
eval_dataset = tokenized_datasets["validation"] | |
# Conditional for small test subsets | |
if len(train_dataset) > 3: | |
# Log a few random samples from the training set: | |
for index in random.sample(range(len(train_dataset)), 3): | |
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") | |
# Data collator | |
# This one will take care of randomly masking the tokens. | |
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=args.mlm_probability) | |
# DataLoaders creation: | |
train_dataloader = DataLoader( | |
train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size | |
) | |
eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size) | |
# Optimizer | |
# Split weights in two groups, one with weight decay and the other not. | |
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 = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) | |
# Note -> the training dataloader needs to be prepared before we grab his length below (cause its length will be | |
# shorter in multiprocess) | |
# Scheduler and math around the number of training steps. | |
overrode_max_train_steps = False | |
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) | |
if args.max_train_steps is None: | |
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch | |
overrode_max_train_steps = True | |
lr_scheduler = get_scheduler( | |
name=args.lr_scheduler_type, | |
optimizer=optimizer, | |
num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps, | |
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, | |
) | |
# Prepare everything with our `accelerator`. | |
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( | |
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler | |
) | |
# On TPU, the tie weights in our model have been disconnected, so we need to restore the ties. | |
if accelerator.distributed_type == DistributedType.TPU: | |
model.tie_weights() | |
# We need to recalculate our total training steps as the size of the training dataloader may have changed. | |
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) | |
if overrode_max_train_steps: | |
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch | |
# Afterwards we recalculate our number of training epochs | |
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) | |
# Figure out how many steps we should save the Accelerator states | |
checkpointing_steps = args.checkpointing_steps | |
if checkpointing_steps is not None and checkpointing_steps.isdigit(): | |
checkpointing_steps = int(checkpointing_steps) | |
# We need to initialize the trackers we use, and also store our configuration. | |
# The trackers initializes automatically on the main process. | |
if args.with_tracking: | |
experiment_config = vars(args) | |
# TensorBoard cannot log Enums, need the raw value | |
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value | |
accelerator.init_trackers("mlm_no_trainer", experiment_config) | |
# Train! | |
total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps | |
logger.info("***** Running training *****") | |
logger.info(f" Num examples = {len(train_dataset)}") | |
logger.info(f" Num Epochs = {args.num_train_epochs}") | |
logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") | |
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") | |
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") | |
logger.info(f" Total optimization steps = {args.max_train_steps}") | |
# Only show the progress bar once on each machine. | |
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) | |
completed_steps = 0 | |
starting_epoch = 0 | |
# Potentially load in the weights and states from a previous save | |
if args.resume_from_checkpoint: | |
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": | |
checkpoint_path = args.resume_from_checkpoint | |
path = os.path.basename(args.resume_from_checkpoint) | |
else: | |
# Get the most recent checkpoint | |
dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] | |
dirs.sort(key=os.path.getctime) | |
path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last | |
checkpoint_path = path | |
path = os.path.basename(checkpoint_path) | |
accelerator.print(f"Resumed from checkpoint: {checkpoint_path}") | |
accelerator.load_state(path) | |
# Extract `epoch_{i}` or `step_{i}` | |
training_difference = os.path.splitext(path)[0] | |
if "epoch" in training_difference: | |
starting_epoch = int(training_difference.replace("epoch_", "")) + 1 | |
resume_step = None | |
completed_steps = starting_epoch * num_update_steps_per_epoch | |
else: | |
# need to multiply `gradient_accumulation_steps` to reflect real steps | |
resume_step = int(training_difference.replace("step_", "")) * args.gradient_accumulation_steps | |
starting_epoch = resume_step // len(train_dataloader) | |
completed_steps = resume_step // args.gradient_accumulation_steps | |
resume_step -= starting_epoch * len(train_dataloader) | |
# update the progress_bar if load from checkpoint | |
progress_bar.update(completed_steps) | |
for epoch in range(starting_epoch, args.num_train_epochs): | |
model.train() | |
if args.with_tracking: | |
total_loss = 0 | |
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None: | |
# We skip the first `n` batches in the dataloader when resuming from a checkpoint | |
active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step) | |
else: | |
active_dataloader = train_dataloader | |
for step, batch in enumerate(active_dataloader): | |
with accelerator.accumulate(model): | |
outputs = model(**batch) | |
loss = outputs.loss | |
# We keep track of the loss at each epoch | |
if args.with_tracking: | |
total_loss += loss.detach().float() | |
accelerator.backward(loss) | |
optimizer.step() | |
lr_scheduler.step() | |
optimizer.zero_grad() | |
# Checks if the accelerator has performed an optimization step behind the scenes | |
if accelerator.sync_gradients: | |
progress_bar.update(1) | |
completed_steps += 1 | |
if isinstance(checkpointing_steps, int): | |
if completed_steps % checkpointing_steps == 0: | |
output_dir = f"step_{completed_steps}" | |
if args.output_dir is not None: | |
output_dir = os.path.join(args.output_dir, output_dir) | |
accelerator.save_state(output_dir) | |
if completed_steps >= args.max_train_steps: | |
break | |
model.eval() | |
losses = [] | |
for step, batch in enumerate(eval_dataloader): | |
with torch.no_grad(): | |
outputs = model(**batch) | |
loss = outputs.loss | |
losses.append(accelerator.gather_for_metrics(loss.repeat(args.per_device_eval_batch_size))) | |
losses = torch.cat(losses) | |
try: | |
eval_loss = torch.mean(losses) | |
perplexity = math.exp(eval_loss) | |
except OverflowError: | |
perplexity = float("inf") | |
logger.info(f"epoch {epoch}: perplexity: {perplexity}") | |
if args.with_tracking: | |
accelerator.log( | |
{ | |
"perplexity": perplexity, | |
"eval_loss": eval_loss, | |
"train_loss": total_loss.item() / len(train_dataloader), | |
"epoch": epoch, | |
"step": completed_steps, | |
}, | |
step=completed_steps, | |
) | |
if args.push_to_hub and epoch < args.num_train_epochs - 1: | |
accelerator.wait_for_everyone() | |
unwrapped_model = accelerator.unwrap_model(model) | |
unwrapped_model.save_pretrained( | |
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save | |
) | |
if accelerator.is_main_process: | |
tokenizer.save_pretrained(args.output_dir) | |
repo.push_to_hub( | |
commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True | |
) | |
if args.checkpointing_steps == "epoch": | |
output_dir = f"epoch_{epoch}" | |
if args.output_dir is not None: | |
output_dir = os.path.join(args.output_dir, output_dir) | |
accelerator.save_state(output_dir) | |
if args.with_tracking: | |
accelerator.end_training() | |
if args.output_dir is not None: | |
accelerator.wait_for_everyone() | |
unwrapped_model = accelerator.unwrap_model(model) | |
unwrapped_model.save_pretrained( | |
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save | |
) | |
if accelerator.is_main_process: | |
tokenizer.save_pretrained(args.output_dir) | |
if args.push_to_hub: | |
repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) | |
with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: | |
json.dump({"perplexity": perplexity}, f) | |
if __name__ == "__main__": | |
main() | |