601 lines
25 KiB
Python
601 lines
25 KiB
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.
|
|
""" Finetuning a 🤗 Transformers model for sequence classification on GLUE."""
|
|
import argparse
|
|
import logging
|
|
import math
|
|
import os
|
|
import random
|
|
from pathlib import Path
|
|
from typing import Union
|
|
|
|
import datasets
|
|
from datasets import load_dataset, load_metric, Metric
|
|
from torch.utils.data import DataLoader
|
|
from tqdm.auto import tqdm
|
|
|
|
import transformers
|
|
from accelerate import Accelerator
|
|
from huggingface_hub import Repository
|
|
from transformers import (
|
|
AdamW,
|
|
AutoConfig,
|
|
AutoModelForSequenceClassification,
|
|
AutoTokenizer,
|
|
DataCollatorWithPadding,
|
|
PretrainedConfig,
|
|
SchedulerType,
|
|
default_data_collator,
|
|
get_scheduler,
|
|
set_seed,
|
|
RobertaForSequenceClassification,
|
|
GPT2ForSequenceClassification,
|
|
)
|
|
from transformers.file_utils import get_full_repo_name
|
|
from transformers.utils.versions import require_version
|
|
from torch import nn
|
|
|
|
from gpt2 import (
|
|
GPT2ForSequenceClassificationCustomSimple,
|
|
GPT2ForSequenceClassificationCustom,
|
|
)
|
|
from roberta import (
|
|
RobertaForSequenceClassificationCustomSimple,
|
|
RobertaForSequenceClassificationCustom,
|
|
RobertaForSequenceClassificationCustomAlternative,
|
|
)
|
|
|
|
MODEL_TYPE = Union[
|
|
GPT2ForSequenceClassificationCustomSimple, GPT2ForSequenceClassificationCustom,
|
|
RobertaForSequenceClassificationCustomSimple, RobertaForSequenceClassificationCustom,
|
|
RobertaForSequenceClassificationCustomAlternative,
|
|
RobertaForSequenceClassification, GPT2ForSequenceClassification
|
|
]
|
|
|
|
|
|
logger = logging.getLogger(__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(
|
|
"--test_file", type=str, default=None, help="A csv or a json file containing the test 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_lengh` 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(
|
|
"--custom_model",
|
|
action="store_true",
|
|
help="If passed, will used custom implementation of model.",
|
|
)
|
|
parser.add_argument(
|
|
"--freeze_model",
|
|
action="store_true",
|
|
help="If passed, will freeze model weights (not classification head).",
|
|
)
|
|
parser.add_argument(
|
|
"--return_hidden_states",
|
|
action="store_true",
|
|
help="If passed, will return the hidden states of all layers.",
|
|
)
|
|
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.")
|
|
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.test_file is not None:
|
|
extension = args.test_file.split(".")[-1]
|
|
assert extension in ["csv", "json"], "`test_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 do_evaluate(
|
|
model: MODEL_TYPE, dataloader: DataLoader,
|
|
accelerator: Accelerator, metric: Metric,
|
|
output_hidden_states: bool,
|
|
is_regression: bool, logger_msg: str,
|
|
tqdm_msg: str = 'Evaluate',
|
|
disable_tqdm: bool = True,
|
|
):
|
|
model.eval()
|
|
for step, batch in enumerate(tqdm(dataloader, total=len(dataloader), desc=tqdm_msg, disable=disable_tqdm)):
|
|
outputs = model(**batch, output_hidden_states=output_hidden_states)
|
|
predictions = outputs.logits.argmax(dim=-1) if not is_regression else outputs.logits.squeeze()
|
|
metric.add_batch(
|
|
predictions=accelerator.gather(predictions),
|
|
references=accelerator.gather(batch["labels"]),
|
|
)
|
|
eval_metric = metric.compute()
|
|
logger.info(f'{logger_msg}: {eval_metric}')
|
|
|
|
|
|
def freeze_model(model: nn.Module) -> None:
|
|
for param in model.parameters():
|
|
param.requires_grad = False
|
|
|
|
|
|
def main():
|
|
args = parse_args()
|
|
|
|
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
|
|
accelerator = 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)
|
|
|
|
# Setup logging, we only want one process per machine to log things on the screen.
|
|
# accelerator.is_local_main_process is only True for one process per machine.
|
|
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
|
|
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:
|
|
if args.hub_model_id is None:
|
|
repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
|
|
else:
|
|
repo_name = args.hub_model_id
|
|
repo = Repository(args.output_dir, clone_from=repo_name)
|
|
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
|
|
if args.test_file is not None:
|
|
data_files["test"] = args.test_file
|
|
extension = (args.train_file if args.train_file is not None else args.valid_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)
|
|
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
|
|
|
|
output_hidden_states = args.return_hidden_states
|
|
logger.info(f'Return hidden states from model: {output_hidden_states}')
|
|
custom_model = args.custom_model
|
|
custom_kwargs = {}
|
|
if custom_model:
|
|
# Uncomment code to select type of custom model
|
|
if 'roberta' in args.model_name_or_path:
|
|
# Simple implementation
|
|
# model_cls = RobertaForSequenceClassificationCustomSimple
|
|
|
|
# Custom implementation with custom forward method and additional argument
|
|
# model_cls = RobertaForSequenceClassificationCustom
|
|
# custom_kwargs = {'use_hidden_states': output_hidden_states}
|
|
|
|
# Custom implementation with custom forward method
|
|
model_cls = RobertaForSequenceClassificationCustomAlternative
|
|
|
|
elif 'gpt2' in args.model_name_or_path:
|
|
# Simple implementation
|
|
# model_cls = GPT2ForSequenceClassificationCustomSimple
|
|
|
|
# Custom implementation with custom forward method
|
|
model_cls = GPT2ForSequenceClassificationCustom
|
|
|
|
else:
|
|
logger.warning(f'Not found custom implementation for: {args.model_name_or_path}')
|
|
model_cls = AutoModelForSequenceClassification
|
|
else:
|
|
model_cls = AutoModelForSequenceClassification
|
|
logger.info(f'Using implementation from: {model_cls.__name__}')
|
|
model = model_cls.from_pretrained(
|
|
args.model_name_or_path,
|
|
from_tf=bool(".ckpt" in args.model_name_or_path),
|
|
config=config,
|
|
**custom_kwargs
|
|
)
|
|
|
|
if args.freeze_model:
|
|
logger.info('Freezing model weights')
|
|
if isinstance(model, RobertaForSequenceClassification):
|
|
freeze_model(model.roberta)
|
|
|
|
elif isinstance(model, GPT2ForSequenceClassification):
|
|
freeze_model(model.transformer)
|
|
|
|
else:
|
|
raise RuntimeError(f'Cannot freeze weights for {model.__class__.__name__}')
|
|
|
|
# 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 list(sorted(label_name_to_id.keys())) == list(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: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
|
|
"\nIgnoring the model labels as a result.",
|
|
)
|
|
elif args.task_name is None:
|
|
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
|
|
if 'gpt2' in tokenizer.name_or_path and tokenizer.pad_token is None:
|
|
logger.info(f'Set PAD token to EOS: {tokenizer.eos_token}')
|
|
tokenizer._pad_token = tokenizer.eos_token
|
|
model.config.pad_token_id = model.config.eos_token_id
|
|
|
|
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"]
|
|
test_dataset = processed_datasets["test"]
|
|
|
|
# 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)
|
|
test_dataloader = DataLoader(test_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 = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
|
|
|
|
# Prepare everything with our `accelerator`.
|
|
model, optimizer, train_dataloader, eval_dataloader, test_dataloader = accelerator.prepare(
|
|
model, optimizer, train_dataloader, eval_dataloader, test_dataloader
|
|
)
|
|
|
|
# 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.
|
|
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
|
|
else:
|
|
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
|
|
|
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,
|
|
)
|
|
|
|
# Get the metric function
|
|
if args.task_name is not None:
|
|
metric = load_metric("glue", args.task_name)
|
|
else:
|
|
metric = load_metric("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
|
|
|
|
for epoch in range(args.num_train_epochs):
|
|
model.train()
|
|
for step, batch in enumerate(train_dataloader):
|
|
outputs = model(**batch, output_hidden_states=output_hidden_states)
|
|
loss = outputs.loss
|
|
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 completed_steps >= args.max_train_steps:
|
|
break
|
|
|
|
do_evaluate(model, eval_dataloader, accelerator, metric, output_hidden_states,
|
|
is_regression, f'Epoch {epoch}', tqdm_msg='Validating')
|
|
|
|
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, 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)
|
|
|
|
do_evaluate(model, test_dataloader, accelerator, metric, output_hidden_states,
|
|
is_regression, 'Test-set evaluation', tqdm_msg='Evaluate test-set')
|
|
|
|
if args.output_dir is not None:
|
|
accelerator.wait_for_everyone()
|
|
unwrapped_model = accelerator.unwrap_model(model)
|
|
unwrapped_model.save_pretrained(args.output_dir, 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")
|
|
|
|
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 __name__ == "__main__":
|
|
main() |