add pre-training script

src/train_pt.py

@@ -0,0 +1,80 @@
+# coding=utf-8
+# Implements several parameter-efficient pre-training method for LLaMA.
+# This code is inspired by
+# https://github.com/huggingface/transformers/blob/v4.29.2/examples/pytorch/language-modeling/run_clm.py
+
+
+import math
+from utils import (
+    load_pretrained,
+    prepare_args,
+    prepare_data,
+    preprocess_data,
+    DataCollatorForLLaMA,
+    PeftTrainer,
+    LogCallback,
+    plot_loss
+)
+
+
+def main():
+
+    # Prepare pretrained model and dataset
+    model_args, data_args, training_args, finetuning_args = prepare_args(stage="pt")
+    dataset = prepare_data(model_args, data_args)
+    model, tokenizer = load_pretrained(model_args, finetuning_args, training_args.do_train, stage="pt")
+    dataset = preprocess_data(dataset, tokenizer, data_args, training_args, stage="pt")
+    data_collator = DataCollatorForLLaMA(tokenizer, model, data_args.ignore_pad_token_for_loss)
+
+    # Split the dataset
+    if training_args.do_train:
+        if data_args.dev_ratio > 1e-6:
+            dataset = dataset.train_test_split(test_size=data_args.dev_ratio)
+            trainer_kwargs = {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
+        else:
+            trainer_kwargs = {"train_dataset": dataset}
+    else: # do_eval or do_predict
+        trainer_kwargs = {"eval_dataset": dataset}
+
+    # Initialize our Trainer
+    trainer = PeftTrainer(
+        finetuning_args=finetuning_args,
+        model=model,
+        args=training_args,
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+        callbacks=[LogCallback()],
+        **trainer_kwargs
+    )
+
+    # Training
+    if training_args.do_train:
+        train_result = trainer.train()
+        trainer.log_metrics("train", train_result.metrics)
+        trainer.save_metrics("train", train_result.metrics)
+        trainer.save_state()
+        trainer.save_model()
+        if trainer.is_world_process_zero() and model_args.plot_loss:
+            plot_loss(training_args, keys=["loss", "eval_loss"])
+
+    # Evaluation
+    if training_args.do_eval:
+        metrics = trainer.evaluate(metric_key_prefix="eval")
+
+        try:
+            perplexity = math.exp(metrics["eval_loss"])
+        except OverflowError:
+            perplexity = float("inf")
+        metrics["perplexity"] = perplexity
+
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+
+def _mp_fn(index):
+    # For xla_spawn (TPUs)
+    main()
+
+
+if __name__ == "__main__":
+    main()