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run.py
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run.py
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from pathlib import Path
import torch
from datasets import DatasetDict, load_from_disk
from simple_parsing import parse
from transformers import (
TrainingArguments,
)
from underspec.ds_registry import load_and_process_dataset
from underspec.model import ModelConfig
from underspec.sft import train
from underspec.sft_config import SFTConfig
from underspec.utils import get_config_foldername
def run_train(cfg: SFTConfig):
splits = load_and_process_dataset(
cfg.dataset, cfg.n_train, cfg.n_val, cfg.n_test, cfg.n_predict
)
cols = ["hard_label", "txt"]
splits = splits.select_columns(cols).rename_column("hard_label", "labels")
print(
f"Example:\n\n{splits['train'][0]['txt']}\n\nLabel: {splits['train'][0]['labels']}"
)
root = Path(cfg.results_folder) / cfg.run_name
cfg_name = get_config_foldername(vars(cfg))
train_args: dict = dict(
num_train_epochs=cfg.n_epochs,
adam_beta2=0.95,
gradient_accumulation_steps=cfg.batch_size // cfg.minibatch_size,
eval_strategy="steps",
label_names=["labels"],
load_best_model_at_end=cfg.load_best_model_at_end,
logging_steps=25,
metric_for_best_model=cfg.metric_for_best_model,
greater_is_better=cfg.greater_is_better,
per_device_train_batch_size=cfg.minibatch_size,
per_device_eval_batch_size=cfg.minibatch_size,
save_strategy="steps",
save_total_limit=cfg.save_total_limit,
tf32=True, # Use Tensor Cores even for fp32 matmuls
warmup_steps=cfg.n_warmup_steps,
weight_decay=cfg.weight_decay,
learning_rate=cfg.lr,
lr_scheduler_type=cfg.lr_schedule,
eval_steps=cfg.eval_every,
save_steps=cfg.save_every,
)
def get_model_and_run_name(model_name, current_name):
model_last = model_name.split("/")[-1]
model_cfg = ModelConfig(name=model_name, enable_lora=not cfg.disable_lora)
run_name = f"{current_name}-{cfg.run_name}-{cfg.dataset}-{model_last}"
return model_cfg, run_name
# train weak floor, get predictions
print("\n\033[32m===== Training weak model =====\033[0m")
model_cfg, run_name = get_model_and_run_name(cfg.weak_model_name, "weak")
train_args["run_name"] = run_name
train_args["output_dir"] = str(root / cfg_name / "weak")
weak_ds_dict = DatasetDict(
{
"train": splits["train"],
"val": splits["val"],
"test": splits["test"],
}
)
weak_predict_dict = {"train": splits["train"], "val": splits["val"]}
train(
weak_ds_dict,
model_cfg,
TrainingArguments(**train_args),
cfg.to_dict(),
predict_dict=weak_predict_dict,
)
# train strong ceil
print("\n\033[32m===== Training strong model =====\033[0m")
model_cfg, run_name = get_model_and_run_name(cfg.strong_model_name, "strong")
train_args["run_name"] = run_name
train_args["output_dir"] = str(root / cfg_name / "strong")
strong_ds_dict = DatasetDict(
{
"train": splits["train"],
"val": splits["val"],
"test": splits["test"],
}
)
train(strong_ds_dict, model_cfg, TrainingArguments(**train_args), cfg.to_dict())
# load weak predictions
weak_preds_root = root / cfg_name / "weak" / "predictions"
weak_train_preds_ds = load_from_disk(str(weak_preds_root / "train"))
weak_val_preds_ds = load_from_disk(str(weak_preds_root / "val"))
# train w2s with logconf, get predictions
print("\n\033[32m===== Training w2s model =====\033[0m")
model_cfg, run_name = get_model_and_run_name(cfg.strong_model_name, "w2s")
train_args["run_name"] = run_name
train_args["output_dir"] = str(root / cfg_name / "w2s")
w2s_ds_dict = DatasetDict(
{
"train": (
splits["train"]
.remove_columns("labels")
.add_column("labels", weak_train_preds_ds["soft_pred"]) # type: ignore
),
"val": (
splits["val"]
.remove_columns("labels")
.add_column("labels", weak_val_preds_ds["soft_pred"])
), # type: ignore
"test": splits["test"],
}
)
# assert (weak_train_preds_ds["id"] == w2s_ds_dict["train"]["id"])
# assert (weak_val_preds_ds["id"] == w2s_ds_dict["val"]["id"])
w2s_predict_dict = {"train": splits["train"], "val": splits["val"]}
train(
w2s_ds_dict,
model_cfg,
TrainingArguments(**train_args),
cfg.to_dict(),
predict_dict=w2s_predict_dict,
logconf_weight=cfg.logconf_weight,
logconf_warmup_steps=cfg.logconf_warmup_steps,
)
# load w2s predictions, and balanced-harden them
print("\n\033[32m===== Training (s+w)2s model =====\033[0m")
w2s_preds_root = root / cfg_name / "w2s" / "predictions"
w2s_train_preds_ds = load_from_disk(str(w2s_preds_root / "train")).with_format(
type="torch", columns=["soft_pred"]
)
w2s_val_preds_ds = load_from_disk(str(w2s_preds_root / "val")).with_format(
type="torch", columns=["soft_pred"]
)
prior = torch.tensor(splits["train"]["labels"]).float().mean()
thresh = torch.quantile(w2s_train_preds_ds["soft_pred"], 1 - prior) # type: ignore
# set the label column of train to be (1 - a) * weak + a * hard_w2s
sw2s_train_labels = (
(
(1 - cfg.strong_weight) * torch.tensor(weak_train_preds_ds["soft_pred"]) # type: ignore
+ cfg.strong_weight * (w2s_train_preds_ds["soft_pred"] > thresh).float()
)
.float()
.tolist()
)
sw2s_val_labels = (
(
(1 - cfg.strong_weight) * torch.tensor(weak_val_preds_ds["soft_pred"]) # type: ignore
+ cfg.strong_weight * (w2s_val_preds_ds["soft_pred"] > thresh).float()
)
.float()
.tolist()
)
# train sw2s on train with logconf
model_cfg, run_name = get_model_and_run_name(cfg.strong_model_name, "sw2s")
train_args["run_name"] = run_name
train_args["output_dir"] = str(root / cfg_name / "sw2s")
sw2s_ds_dict = DatasetDict(
{
"train": (
splits["train"]
.remove_columns("labels")
.add_column("labels", sw2s_train_labels) # type: ignore
),
"val": (
splits["val"]
.remove_columns("labels")
.add_column("labels", sw2s_val_labels) # type: ignore
),
"test": splits["test"],
}
)
# assert (w2s_train_preds_ds["id"] == sw2s_ds_dict["train"]["id"])
# assert (w2s_val_preds_ds["id"] == sw2s_ds_dict["val"]["id"])
train(
sw2s_ds_dict,
model_cfg,
TrainingArguments(**train_args),
cfg.to_dict(),
logconf_weight=cfg.logconf_weight,
logconf_warmup_steps=0,
)
if __name__ == "__main__":
run_train(parse(SFTConfig))