File size: 5,707 Bytes
1e9aaa3 939f41f 1e9aaa3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 | import argparse
import os
import sys
from dataclasses import dataclass
from typing import Dict, List, Optional
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
from transformers import EarlyStoppingCallback, Trainer, TrainingArguments
from transformers.trainer_utils import EvalPrediction
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from modeling_virtual_cell_distil import (
VirtualCellDistilConfig,
VirtualCellDistilForSequenceClassification,
)
@dataclass
class BulkCollator:
def __call__(self, features: List[Dict]) -> Dict[str, torch.Tensor]:
return {
"input_ids": torch.stack([
torch.tensor(f["bulk_expression"], dtype=torch.float32) for f in features
]),
"labels": torch.tensor([f["labels"] for f in features], dtype=torch.long),
}
def compute_metrics(eval_pred: EvalPrediction) -> Dict[str, float]:
logits = eval_pred.predictions
if isinstance(logits, tuple):
logits = logits[0]
labels = eval_pred.label_ids
preds = np.argmax(logits, axis=1)
return {
"accuracy": accuracy_score(labels, preds),
"f1_macro": f1_score(labels, preds, average="macro", zero_division=0),
"precision": precision_score(labels, preds, average="macro", zero_division=0),
"recall": recall_score(labels, preds, average="macro", zero_division=0),
}
def parse_args():
p = argparse.ArgumentParser()
p.add_argument("--dataset_path", required=True,
help="HF dataset ID or local path with train (and optionally validation) splits")
p.add_argument("--model_name_or_path", default="ConvergeBio/virtual-cell-distil-bulk")
p.add_argument("--hf_token", default=None)
p.add_argument("--output_dir", default="./vc_distil_output")
p.add_argument("--num_classes", type=int, default=None)
p.add_argument("--freeze_encoder", action="store_true",
help="Freeze the pretrained encoder and train the classification head only")
p.add_argument("--num_train_epochs", type=int, default=15)
p.add_argument("--per_device_train_batch_size", type=int, default=32)
p.add_argument("--per_device_eval_batch_size", type=int, default=32)
p.add_argument("--learning_rate", type=float, default=1e-4)
p.add_argument("--weight_decay", type=float, default=0.05)
p.add_argument("--warmup_ratio", type=float, default=0.1)
p.add_argument("--lr_scheduler_type", default="cosine")
p.add_argument("--patience", type=int, default=5)
p.add_argument("--num_workers", type=int, default=4)
p.add_argument("--prefetch_factor", type=int, default=2)
p.add_argument("--wandb_project", default=None)
p.add_argument("--run_name", default=None)
return p.parse_args()
def main():
args = parse_args()
if os.path.isdir(args.dataset_path):
ds = DatasetDict.load_from_disk(args.dataset_path)
else:
ds = load_dataset(args.dataset_path,
num_proc=args.num_workers or None,
token=args.hf_token or True)
train_ds = ds["train"]
val_ds: Optional[object] = ds.get("validation")
hf_kwargs = {"trust_remote_code": True}
if args.hf_token:
hf_kwargs["token"] = args.hf_token
config = VirtualCellDistilConfig.from_pretrained(args.model_name_or_path, **hf_kwargs)
if args.num_classes is not None:
config.num_labels = args.num_classes
config.id2label = {str(i): str(i) for i in range(args.num_classes)}
config.label2id = {str(i): i for i in range(args.num_classes)}
model = VirtualCellDistilForSequenceClassification.from_pretrained(
args.model_name_or_path,
config=config,
ignore_mismatched_sizes=True,
**hf_kwargs,
)
if args.freeze_encoder:
for param in model.encoder.parameters():
param.requires_grad = False
if args.wandb_project:
os.environ["WANDB_PROJECT"] = args.wandb_project
has_val = val_ds is not None
training_args = TrainingArguments(
output_dir=args.output_dir,
num_train_epochs=args.num_train_epochs,
per_device_train_batch_size=args.per_device_train_batch_size,
per_device_eval_batch_size=args.per_device_eval_batch_size,
learning_rate=args.learning_rate,
weight_decay=args.weight_decay,
warmup_ratio=args.warmup_ratio,
lr_scheduler_type=args.lr_scheduler_type,
eval_strategy="epoch" if has_val else "no",
save_strategy="epoch",
load_best_model_at_end=has_val,
metric_for_best_model="eval_loss" if has_val else None,
greater_is_better=False,
report_to="wandb" if args.wandb_project else "none",
run_name=args.run_name,
dataloader_num_workers=args.num_workers,
dataloader_prefetch_factor=args.prefetch_factor if args.num_workers > 0 else None, # prefetch batches in background for CPU loading speedup
remove_unused_columns=False,
)
callbacks = [EarlyStoppingCallback(args.patience)] if has_val else []
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_ds,
eval_dataset=val_ds,
data_collator=BulkCollator(),
compute_metrics=compute_metrics if has_val else None,
callbacks=callbacks,
)
trainer.train()
trainer.save_model(args.output_dir)
if __name__ == "__main__":
main() |