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# Before running, install required packages:
{% if notebook %}
!
{%- else %}
#
{%- endif %}
pip install datasets transformers[sentencepiece] accelerate
import collections
import logging
import math
import datasets
import numpy as np
import torch
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from codecarbon import EmissionsTracker
from datasets import load_dataset
from torch.optim import {{ optimizer }}
from torch.utils.data import DataLoader
from torch.utils.data.dataloader import DataLoader
from tqdm.auto import tqdm
from transformers import (AutoConfig, AutoModelForMaskedLM, AutoTokenizer,
DataCollatorForLanguageModeling, Trainer,
TrainingArguments, default_data_collator,
get_scheduler)
from transformers.utils.versions import require_version
{{ header("Setup") }}
tracker = EmissionsTracker(log_level='error')
tracker.start()
logger = get_logger(__name__)
require_version("datasets>=1.8.0")
accelerator = Accelerator()
set_seed({{ seed }})
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.ERROR,
)
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()
{{ header("Load model and dataset") }}
{% if subset == 'default' %}
datasets = load_dataset('{{dataset}}')
{% else %}
datasets = load_dataset('{{dataset}}', '{{ subset }}')
{% endif %}
model_checkpoint = "{{model_checkpoint}}"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)
{% if pretrained %}
model = AutoModelFor{{task}}.from_pretrained(model_checkpoint)
{% else %}
config = AutoConfig.from_pretrained(model_checkpoint)
model = AutoModelFor{{task}}.from_config(config)
{% endif %}
model.resize_token_embeddings(len(tokenizer))
model_name = model_checkpoint.split("/")[-1]
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
{{ header("Preprocessing") }}
def tokenize_function(examples):
result = tokenizer(examples["{{ feature }}"])
{% if task=="MaskedLM" %}
{% if whole_word_masking %}
if tokenizer.is_fast:
result["word_ids"] = [result.word_ids(i) for i in range(len(result["input_ids"]))]
{% endif %}
{% endif %}
return result
with accelerator.main_process_first():
tokenized_datasets = datasets.map(tokenize_function, batched=True, num_proc=4, remove_columns=list(set(sum(list(datasets.column_names.values()),[]))), desc="Running tokenizer on dataset"
)
block_size = {{ block_size }}
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, 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 // block_size) * block_size
# Split by chunks of max_len.
result = {
k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
for k, t in concatenated_examples.items()
}
result["labels"] = result["input_ids"].copy()
return result
with accelerator.main_process_first():
lm_datasets = tokenized_datasets.map(
group_texts,
batched=True,
batch_size=1000,
num_proc=4,
desc=f"Grouping texts in chunks of {block_size}",
)
{% if whole_word_masking %}
def whole_word_masking_data_collator(features):
for feature in features:
word_ids = feature.pop("word_ids")
# Create a map between words and corresponding token indices
mapping = collections.defaultdict(list)
current_word_index = -1
current_word = None
for idx, word_id in enumerate(word_ids):
if word_id is not None:
if word_id != current_word:
current_word = word_id
current_word_index += 1
mapping[current_word_index].append(idx)
# Randomly mask words
wwm_probability = {{ mlm_probability }}
mask = np.random.binomial(1, wwm_probability, (len(mapping),))
input_ids = feature["input_ids"]
labels = feature["labels"]
new_labels = [-100] * len(labels)
for word_id in np.where(mask)[0]:
word_id = word_id.item()
for idx in mapping[word_id]:
new_labels[idx] = labels[idx]
input_ids[idx] = tokenizer.mask_token_id
return default_data_collator(features)
data_collator = whole_word_masking_data_collator
{% else %}
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability={{ mlm_probability }})
{% endif %}
def insert_random_mask(batch):
features = [dict(zip(batch, t)) for t in zip(*batch.values())]
masked_inputs = data_collator(features)
# Create a new "masked" column for each column in the dataset
return {"masked_" + k: v.numpy() for k, v in masked_inputs.items()}
{% if whole_word_masking %}
lm_datasetst = lm_datasets.remove_columns(["word_ids"])
{% endif %}
with accelerator.main_process_first():
eval_dataset = lm_datasets["{{ validation }}"].map(
insert_random_mask,
batched=True,
remove_columns=lm_datasets["{{ validation }}"].column_names,
desc="Inserting a random mask on eval dataset"
)
eval_dataset = eval_dataset.rename_columns(
{
name: name.split('masked_')[1] for name in eval_dataset.features.keys()
}
)
batch_size = {{ batch_size }}
train_dataloader = DataLoader(
lm_datasets["{{ train }}"],
shuffle=True,
batch_size=batch_size,
collate_fn=data_collator,
)
eval_dataloader = DataLoader(
eval_dataset, batch_size=batch_size, collate_fn=default_data_collator
)
{{ header("Training") }}
{% if use_weight_decay %}
weight_decay = {{ weight_decay }}
def get_grouped_params(model, no_decay=["bias", "LayerNorm.weight"]):
params_with_wd, params_without_wd = [], []
for n, p in model.named_parameters():
if any(nd in n for nd in no_decay):
params_without_wd.append(p)
else:
params_with_wd.append(p)
return [
{"params": params_with_wd, "weight_decay": weight_decay},
{"params": params_without_wd, "weight_decay": 0.0},
]
optimizer = {{ optimizer }}(get_grouped_params(model), lr={{ lr }})
{% else %}
optimizer = {{ optimizer }}(model.parameters(), lr={{ lr }})
{% endif %}
accelerator = Accelerator()
model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
model, optimizer, train_dataloader, eval_dataloader
)
num_train_epochs = {{ num_epochs }}
gradient_accumulation_steps = {{ gradient_accumulation_steps }}
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / gradient_accumulation_steps)
max_train_steps = num_train_epochs * num_update_steps_per_epoch
output_dir=f"{model_name}-finetuned"
lr_scheduler = get_scheduler(
'{{ lr_scheduler_type }}',
optimizer=optimizer,
num_warmup_steps={{ num_warmup_steps }},
num_training_steps=max_train_steps,
)
progress_bar = tqdm(range(max_train_steps), disable=not accelerator.is_local_main_process)
for epoch in range(num_train_epochs):
# Training
model.train()
for step, batch in enumerate(train_dataloader):
outputs = model(**batch)
loss = outputs.loss / gradient_accumulation_steps
accelerator.backward(loss)
if step % gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
#TODO Let the user decide on clip grad norm
accelerator.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)
# Evaluation
model.eval()
losses = []
for step, batch in enumerate(eval_dataloader):
with torch.no_grad():
outputs = model(**batch)
loss = outputs.loss
losses.append(accelerator.gather(loss.repeat(batch_size)))
losses = torch.cat(losses)
losses = losses[: len(eval_dataset)]
try:
eval_loss = torch.mean(losses)
perplexity = math.exp(eval_loss)
except OverflowError:
perplexity = float("inf")
accelerator.print({"loss/eval": eval_loss, "perplexity": perplexity})
model.train()
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)
if accelerator.is_main_process:
tokenizer.save_pretrained(output_dir)
emissions = tracker.stop()
accelerator.print(f'Emissions: {emissions} kg') |