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# 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. | |
""" Finetuning a 🤗 Transformers model for sequence classification on GLUE.""" | |
import argparse | |
import json | |
import logging | |
import math | |
import os | |
import random | |
from pathlib import Path | |
import datasets | |
import evaluate | |
import torch | |
from accelerate import Accelerator | |
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 ( | |
AutoConfig, | |
AutoModelForSequenceClassification, | |
AutoTokenizer, | |
DataCollatorWithPadding, | |
PretrainedConfig, | |
SchedulerType, | |
default_data_collator, | |
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/text-classification/requirements.txt") | |
task_to_keys = { | |
"cola": ("sentence", None), | |
"mnli": ("premise", "hypothesis"), | |
"mrpc": ("sentence1", "sentence2"), | |
"qnli": ("question", "sentence"), | |
"qqp": ("question1", "question2"), | |
"rte": ("sentence1", "sentence2"), | |
"sst2": ("sentence", None), | |
"stsb": ("sentence1", "sentence2"), | |
"wnli": ("sentence1", "sentence2"), | |
} | |
def parse_args(): | |
parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task") | |
parser.add_argument( | |
"--task_name", | |
type=str, | |
default=None, | |
help="The name of the glue task to train on.", | |
choices=list(task_to_keys.keys()), | |
) | |
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( | |
"--max_length", | |
type=int, | |
default=128, | |
help=( | |
"The maximum total input sequence length after tokenization. Sequences longer than this will be truncated," | |
" sequences shorter will be padded if `--pad_to_max_length` is passed." | |
), | |
) | |
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=True, | |
) | |
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("--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( | |
"--ignore_mismatched_sizes", | |
action="store_true", | |
help="Whether or not to enable to load a pretrained model whose head dimensions are different.", | |
) | |
args = parser.parse_args() | |
# Sanity checks | |
if args.task_name is None and args.train_file is None and args.validation_file is None: | |
raise ValueError("Need either a task name or a training/validation file.") | |
else: | |
if args.train_file is not None: | |
extension = args.train_file.split(".")[-1] | |
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." | |
if args.validation_file is not None: | |
extension = args.validation_file.split(".")[-1] | |
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json 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_glue_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 = ( | |
Accelerator(log_with=args.report_to, project_dir=args.output_dir) if args.with_tracking else Accelerator() | |
) | |
# 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 training and evaluation files (see below) | |
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub). | |
# For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the | |
# sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named | |
# label if at least two columns are provided. | |
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this | |
# single column. 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.task_name is not None: | |
# Downloading and loading a dataset from the hub. | |
raw_datasets = load_dataset("glue", args.task_name) | |
else: | |
# Loading the dataset from local csv or json file. | |
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 if args.train_file is not None else args.validation_file).split(".")[-1] | |
raw_datasets = load_dataset(extension, data_files=data_files) | |
# See more about loading any type of standard or custom dataset at | |
# https://huggingface.co/docs/datasets/loading_datasets.html. | |
# Labels | |
if args.task_name is not None: | |
is_regression = args.task_name == "stsb" | |
if not is_regression: | |
label_list = raw_datasets["train"].features["label"].names | |
num_labels = len(label_list) | |
else: | |
num_labels = 1 | |
else: | |
# Trying to have good defaults here, don't hesitate to tweak to your needs. | |
is_regression = raw_datasets["train"].features["label"].dtype in ["float32", "float64"] | |
if is_regression: | |
num_labels = 1 | |
else: | |
# A useful fast method: | |
# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique | |
label_list = raw_datasets["train"].unique("label") | |
label_list.sort() # Let's sort it for determinism | |
num_labels = len(label_list) | |
# Load pretrained model and tokenizer | |
# | |
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently | |
# download model & vocab. | |
config = AutoConfig.from_pretrained( | |
args.model_name_or_path, | |
num_labels=num_labels, | |
finetuning_task=args.task_name, | |
trust_remote_code=args.trust_remote_code, | |
) | |
tokenizer = AutoTokenizer.from_pretrained( | |
args.model_name_or_path, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code | |
) | |
model = AutoModelForSequenceClassification.from_pretrained( | |
args.model_name_or_path, | |
from_tf=bool(".ckpt" in args.model_name_or_path), | |
config=config, | |
ignore_mismatched_sizes=args.ignore_mismatched_sizes, | |
trust_remote_code=args.trust_remote_code, | |
) | |
# Preprocessing the datasets | |
if args.task_name is not None: | |
sentence1_key, sentence2_key = task_to_keys[args.task_name] | |
else: | |
# Again, we try to have some nice defaults but don't hesitate to tweak to your use case. | |
non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"] | |
if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names: | |
sentence1_key, sentence2_key = "sentence1", "sentence2" | |
else: | |
if len(non_label_column_names) >= 2: | |
sentence1_key, sentence2_key = non_label_column_names[:2] | |
else: | |
sentence1_key, sentence2_key = non_label_column_names[0], None | |
# Some models have set the order of the labels to use, so let's make sure we do use it. | |
label_to_id = None | |
if ( | |
model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id | |
and args.task_name is not None | |
and not is_regression | |
): | |
# Some have all caps in their config, some don't. | |
label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()} | |
if sorted(label_name_to_id.keys()) == sorted(label_list): | |
logger.info( | |
f"The configuration of the model provided the following label correspondence: {label_name_to_id}. " | |
"Using it!" | |
) | |
label_to_id = {i: label_name_to_id[label_list[i]] for i in range(num_labels)} | |
else: | |
logger.warning( | |
"Your model seems to have been trained with labels, but they don't match the dataset: ", | |
f"model labels: {sorted(label_name_to_id.keys())}, dataset labels: {sorted(label_list)}." | |
"\nIgnoring the model labels as a result.", | |
) | |
elif args.task_name is None and not is_regression: | |
label_to_id = {v: i for i, v in enumerate(label_list)} | |
if label_to_id is not None: | |
model.config.label2id = label_to_id | |
model.config.id2label = {id: label for label, id in config.label2id.items()} | |
elif args.task_name is not None and not is_regression: | |
model.config.label2id = {l: i for i, l in enumerate(label_list)} | |
model.config.id2label = {id: label for label, id in config.label2id.items()} | |
padding = "max_length" if args.pad_to_max_length else False | |
def preprocess_function(examples): | |
# Tokenize the texts | |
texts = ( | |
(examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key]) | |
) | |
result = tokenizer(*texts, padding=padding, max_length=args.max_length, truncation=True) | |
if "label" in examples: | |
if label_to_id is not None: | |
# Map labels to IDs (not necessary for GLUE tasks) | |
result["labels"] = [label_to_id[l] for l in examples["label"]] | |
else: | |
# In all cases, rename the column to labels because the model will expect that. | |
result["labels"] = examples["label"] | |
return result | |
with accelerator.main_process_first(): | |
processed_datasets = raw_datasets.map( | |
preprocess_function, | |
batched=True, | |
remove_columns=raw_datasets["train"].column_names, | |
desc="Running tokenizer on dataset", | |
) | |
train_dataset = processed_datasets["train"] | |
eval_dataset = processed_datasets["validation_matched" if args.task_name == "mnli" else "validation"] | |
# 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]}.") | |
# DataLoaders creation: | |
if args.pad_to_max_length: | |
# If padding was already done ot max length, we use the default data collator that will just convert everything | |
# to tensors. | |
data_collator = default_data_collator | |
else: | |
# Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by padding to the maximum length of | |
# the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple | |
# of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). | |
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None)) | |
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) | |
# 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, | |
num_training_steps=args.max_train_steps, | |
) | |
# Prepare everything with our `accelerator`. | |
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( | |
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler | |
) | |
# 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("glue_no_trainer", experiment_config) | |
# Get the metric function | |
if args.task_name is not None: | |
metric = evaluate.load("glue", args.task_name) | |
else: | |
metric = evaluate.load("accuracy") | |
# 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): | |
outputs = model(**batch) | |
loss = outputs.loss | |
# We keep track of the loss at each epoch | |
if args.with_tracking: | |
total_loss += loss.detach().float() | |
loss = loss / args.gradient_accumulation_steps | |
accelerator.backward(loss) | |
if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1: | |
optimizer.step() | |
lr_scheduler.step() | |
optimizer.zero_grad() | |
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() | |
samples_seen = 0 | |
for step, batch in enumerate(eval_dataloader): | |
with torch.no_grad(): | |
outputs = model(**batch) | |
predictions = outputs.logits.argmax(dim=-1) if not is_regression else outputs.logits.squeeze() | |
predictions, references = accelerator.gather((predictions, batch["labels"])) | |
# If we are in a multiprocess environment, the last batch has duplicates | |
if accelerator.num_processes > 1: | |
if step == len(eval_dataloader) - 1: | |
predictions = predictions[: len(eval_dataloader.dataset) - samples_seen] | |
references = references[: len(eval_dataloader.dataset) - samples_seen] | |
else: | |
samples_seen += references.shape[0] | |
metric.add_batch( | |
predictions=predictions, | |
references=references, | |
) | |
eval_metric = metric.compute() | |
logger.info(f"epoch {epoch}: {eval_metric}") | |
if args.with_tracking: | |
accelerator.log( | |
{ | |
"accuracy" if args.task_name is not None else "glue": eval_metric, | |
"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) | |
if args.task_name == "mnli": | |
# Final evaluation on mismatched validation set | |
eval_dataset = processed_datasets["validation_mismatched"] | |
eval_dataloader = DataLoader( | |
eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size | |
) | |
eval_dataloader = accelerator.prepare(eval_dataloader) | |
model.eval() | |
for step, batch in enumerate(eval_dataloader): | |
outputs = model(**batch) | |
predictions = outputs.logits.argmax(dim=-1) | |
metric.add_batch( | |
predictions=accelerator.gather(predictions), | |
references=accelerator.gather(batch["labels"]), | |
) | |
eval_metric = metric.compute() | |
logger.info(f"mnli-mm: {eval_metric}") | |
if args.output_dir is not None: | |
all_results = {f"eval_{k}": v for k, v in eval_metric.items()} | |
with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: | |
json.dump(all_results, f) | |
if __name__ == "__main__": | |
main() | |