tests/test_full.py

# Copyright Lightning AI. Licensed under the Apache License 2.0, see LICENSE file.

import os
from contextlib import redirect_stdout
from io import StringIO
from unittest import mock
from unittest.mock import Mock

import torch
import yaml

import litgpt.finetune.full as module
from litgpt.args import EvalArgs, TrainArgs
from litgpt.data import Alpaca


@mock.patch.dict(os.environ, {"LT_ACCELERATOR": "cpu"})
def test_full_script(tmp_path, fake_checkpoint_dir, monkeypatch, alpaca_path):
    model_config = dict(block_size=128, n_layer=2, n_embd=8, n_head=4, padded_vocab_size=8)
    (fake_checkpoint_dir / "model_config.yaml").write_text(yaml.dump(model_config))
    monkeypatch.setattr(module, "load_checkpoint", Mock())

    tokenizer_mock = Mock()
    tokenizer_mock.return_value = tokenizer_mock
    tokenizer_mock.encode = lambda *_, **__: torch.tensor([3, 2, 1])
    monkeypatch.setattr(module, "Tokenizer", tokenizer_mock)

    out_dir = tmp_path / "out"
    setup_args = (fake_checkpoint_dir,)
    setup_kwargs = dict(
        data=Alpaca(download_dir=alpaca_path.parent, file_name=alpaca_path.name, val_split_fraction=0.5, num_workers=0),
        out_dir=out_dir,
        precision="32-true",
        train=TrainArgs(global_batch_size=1, save_interval=2, epochs=1, max_steps=6, micro_batch_size=1),
        eval=EvalArgs(interval=2, max_iters=2, max_new_tokens=1),
    )
    stdout = StringIO()
    with redirect_stdout(stdout), mock.patch("sys.argv", ["full.py", str(fake_checkpoint_dir)]):
        module.setup(*setup_args, **setup_kwargs)

    out_dir_contents = set(os.listdir(out_dir))
    checkpoint_dirs = {"step-000002", "step-000004", "step-000006", "final"}
    assert checkpoint_dirs.issubset(out_dir_contents)
    assert all((out_dir / p).is_dir() for p in checkpoint_dirs)
    for checkpoint_dir in checkpoint_dirs:
        assert set(os.listdir(out_dir / checkpoint_dir)) == {
            "lit_model.pth",
            "model_config.yaml",
            "tokenizer_config.json",
            "tokenizer.json",
            "hyperparameters.yaml",
            "prompt_style.yaml",
        }
    assert (out_dir / "logs" / "csv" / "version_0" / "metrics.csv").is_file()

    logs = stdout.getvalue()
    assert logs.count("(step)") == 6
    assert logs.count("val loss") == 4  # 3 validations + 1 final validation
    assert logs.count("Final evaluation") == 1
    assert "of trainable parameters: 1,888" in logs

    # Resume training and do 2 steps more
    setup_kwargs["train"].max_steps = 8
    setup_kwargs["resume"] = True
    stdout = StringIO()
    with redirect_stdout(stdout), mock.patch("sys.argv", ["full.py", str(fake_checkpoint_dir)]):
        module.setup(*setup_args, **setup_kwargs)
    logs = stdout.getvalue()
    assert f"Resuming training from {out_dir / 'step-000006' / 'lit_model.pth'}" in logs
    assert logs.count("(step)") == 2
    assert out_dir / "step-000008" in set(out_dir.iterdir())