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import datetime
import json
import os
import torch
import wandb
from datasets import load_from_disk
from transformers import (
EarlyStoppingCallback,
PretrainedConfig,
Trainer,
TrainingArguments,
)
from methformer import Methformer, MethformerCollator
def compute_metrics(eval_preds):
logits, labels = eval_preds
logits = torch.tensor(logits)
labels = torch.tensor(labels)
# Only evaluate masked positions (label == -1.0 was masked during input)
mask = labels != -1.0
masked_mse = torch.mean((logits[mask] - labels[mask]) ** 2).item()
masked_mae = torch.mean(torch.abs(logits[mask] - labels[mask])).item()
return {
"masked_mse": masked_mse,
"masked_mae": masked_mae,
}
device = (
"cuda"
if torch.cuda.is_available()
else "mps"
if torch.backends.mps.is_available()
else "cpu"
)
dataset = load_from_disk("/home/ubuntu/project/MethFormer/data/methformer_pretrain_binned")
train_dataset = dataset["train"].shuffle(seed=42)
eval_dataset = dataset["validation"]
def train():
wandb.init(
group="methformer_pretrain_sweep",
job_type="pretrain_sweep",
name=f"mf_{datetime.datetime.now().strftime('%Y-%m-%d_%H%M')}",
dir="/home/ubuntu/project/MethFormer/output/methformer_pretrain_sweep",
reinit="finish_previous",
)
config = wandb.config
run_name = f"mf_{datetime.datetime.now().strftime('%Y-%m-%d_%H%M')}"
out_dir = f"/home/ubuntu/project/MethFormer/output/methformer_pretrain_sweep/{run_name}"
os.makedirs(out_dir, exist_ok=True)
model_config = PretrainedConfig(
input_dim=2,
hidden_dim=config.hidden_dim,
num_hidden_layers=config.num_hidden_layers,
num_attention_heads=config.num_attention_heads,
hidden_dropout_prob=config.hidden_dropout_prob,
)
model = Methformer(model_config)
model.to(device)
training_args = TrainingArguments(
run_name=run_name,
output_dir=os.path.join(out_dir, "checkpoints"),
eval_on_start=True,
per_device_train_batch_size=128,
per_device_eval_batch_size=256,
gradient_accumulation_steps=1,
max_grad_norm=1.0,
learning_rate=1e-5,
warmup_ratio=0.05,
lr_scheduler_type="cosine",
num_train_epochs=20,
logging_dir=os.path.join(out_dir, "logs"),
save_strategy="steps",
save_total_limit=1,
eval_strategy="steps",
logging_steps=500,
eval_steps=5000,
save_steps=5000,
metric_for_best_model="masked_mse",
greater_is_better=False,
report_to="wandb",
disable_tqdm=False,
dataloader_num_workers=8,
remove_unused_columns=False,
fp16=not torch.backends.mps.is_available(),
load_best_model_at_end=True,
seed=42,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
compute_metrics=compute_metrics,
data_collator=MethformerCollator(masking_ratio=config.masking_ratio),
callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
)
trainer.train()
# Save the final model
model.save_pretrained(os.path.join(out_dir, "model"))
model.config.save_pretrained(os.path.join(out_dir, "model"))
with open("/home/ubuntu/project/MethFormer/config/pretrain_sweep_config.json", "r") as f:
sweep_config = json.load(f)
sweep_id = wandb.sweep(
sweep=sweep_config,
project="MethFormer",
)
wandb.agent(sweep_id, train, count=20)
# After the sweep
api = wandb.Api()
sweep_path = f"{wandb.run.entity}/{wandb.run.project}/{sweep_id}"
sweep = api.sweep(sweep_path)
# Filter only finished runs with masked_r2
runs = [
run for run in sweep.runs if run.state == "finished" and "masked_r2" in run.summary
]
# Find best run by highest masked_r2
best_run = max(runs, key=lambda r: r.summary["masked_r2"])
# Save best config
best_config = {k: v for k, v in best_run.config.items() if not k.startswith("_")}
with open("/home/ubuntu/project/MethFormer/config/best_config.json", "w") as f:
json.dump(best_config, f, indent=2)
print(f"Best run ID: {best_run.id}")
print(f"Best masked_r2: {best_run.summary['masked_r2']}")
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