tests/test_utils.py

# Copyright Lightning AI. Licensed under the Apache License 2.0, see LICENSE file.
import os
from contextlib import redirect_stderr
from dataclasses import asdict
from io import StringIO
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from unittest import mock

import pytest
import torch
import torch.nn.functional as F
import yaml
from lightning import Fabric
from lightning.fabric.loggers import CSVLogger, TensorBoardLogger
from lightning.fabric.plugins import BitsandbytesPrecision
from lightning.pytorch.loggers import MLFlowLogger, WandbLogger
from lightning_utilities.core.imports import RequirementCache

from litgpt import GPT
from litgpt.args import TrainArgs
from litgpt.utils import (
    CLI,
    CycleIterator,
    _RunIf,
    capture_hparams,
    check_file_size_on_cpu_and_warn,
    check_nvlink_connectivity,
    check_valid_checkpoint_dir,
    choose_logger,
    chunked_cross_entropy,
    copy_config_files,
    extend_checkpoint_dir,
    find_resume_path,
    fix_and_load_json,
    incremental_save,
    init_out_dir,
    instantiate_bnb_optimizer,
    instantiate_torch_optimizer,
    num_parameters,
    parse_devices,
    save_hyperparameters,
    select_sft_generate_example,
)


# match fails on windows. why did they have to use backslashes?
@_RunIf(skip_windows=True)
def test_check_valid_checkpoint_dir(tmp_path):
    os.chdir(tmp_path)

    out = StringIO()
    with pytest.raises(SystemExit), redirect_stderr(out):
        check_valid_checkpoint_dir(tmp_path)
    out = out.getvalue().strip()
    expected = f"""
checkpoint_dir '{str(tmp_path.absolute())}' is missing the files: ['lit_model.pth', 'model_config.yaml', 'tokenizer.json OR tokenizer.model', 'tokenizer_config.json'].
Find download instructions at https://github.com/Lightning-AI/litgpt/blob/main/tutorials

See all download options by running:
 litgpt download
    """.strip()
    assert out == expected

    out = StringIO()
    checkpoint_dir = tmp_path / "checkpoints" / "stabilityai" / "stablelm-base-alpha-3b"
    with pytest.raises(SystemExit), redirect_stderr(out):
        check_valid_checkpoint_dir(checkpoint_dir)
    out = out.getvalue().strip()
    expected = f"""
checkpoint_dir '{str(checkpoint_dir.absolute())}' is not a checkpoint directory.
Find download instructions at https://github.com/Lightning-AI/litgpt/blob/main/tutorials

See all download options by running:
 litgpt download
    """.strip()
    assert out == expected

    out = StringIO()
    checkpoint_dir.mkdir(parents=True)
    foo_checkpoint_dir = tmp_path / "foo"
    with pytest.raises(SystemExit), redirect_stderr(out):
        check_valid_checkpoint_dir(foo_checkpoint_dir)
    out = out.getvalue().strip()
    expected = f"""
checkpoint_dir '{str(foo_checkpoint_dir.absolute())}' is not a checkpoint directory.
Find download instructions at https://github.com/Lightning-AI/litgpt/blob/main/tutorials

You have downloaded locally:
'{str(checkpoint_dir.absolute())}'

See all download options by running:
 litgpt download
    """.strip()
    assert out == expected


def test_incremental_write(tmp_path):
    sd = {str(k): torch.randn(5, 10) for k in range(3)}
    sd["0"].someattr = 1
    sd_expected = {k: v.clone() for k, v in sd.items()}
    fn = str(tmp_path / "test.pt")
    with incremental_save(fn) as f:
        sd["0"] = f.store_early(sd["0"])
        sd["2"] = f.store_early(sd["2"])
        f.save(sd)
    sd_actual = torch.load(fn)
    assert sd_actual.keys() == sd_expected.keys()
    assert sd_actual["0"].someattr == 1  # requires PyTorch 2.0+
    for k, v_expected in sd_expected.items():
        v_actual = sd_actual[k]
        torch.testing.assert_close(v_expected, v_actual)
    sd_actual = torch.load(fn, weights_only=True)
    assert sd_actual.keys() == sd_expected.keys()
    assert sd_actual["0"].someattr == 1  # requires PyTorch 2.0+
    for k, v_expected in sd_expected.items():
        v_actual = sd_actual[k]
        torch.testing.assert_close(v_expected, v_actual)


@pytest.mark.parametrize("B", (1, 2))
@pytest.mark.parametrize("ignore_index", (None, -1, -2, -100))
def test_chunked_cross_entropy(ignore_index, B):
    V = 50
    T = 25
    regular_logits = torch.randn(B, T, V)
    targets = torch.randint(0, V, (B, T))

    if ignore_index is not None:
        targets[:, [1, 4, 10, 19]] = ignore_index

    baseline_loss = F.cross_entropy(
        regular_logits.reshape(-1, regular_logits.size(-1)),
        targets.reshape(-1),
        ignore_index=(ignore_index if ignore_index is not None else -100),
    )

    ignore_index = ignore_index if ignore_index is not None else -100
    regular_loss = chunked_cross_entropy(regular_logits, targets, chunk_size=0, ignore_index=ignore_index)
    assert torch.equal(baseline_loss, regular_loss)
    assert regular_loss.numel() == 1

    chunked_loss = chunked_cross_entropy(regular_logits, targets, chunk_size=10, ignore_index=ignore_index)
    torch.testing.assert_close(chunked_loss, regular_loss)
    torch.testing.assert_close(chunked_loss, baseline_loss)

    logit_chunk_size = 6
    assert T % logit_chunk_size != 0  # ensure leftover
    chunked_logits = list(regular_logits.split(logit_chunk_size, dim=1))
    chunked_loss = chunked_cross_entropy(chunked_logits, targets, chunk_size=0, ignore_index=ignore_index)
    torch.testing.assert_close(chunked_loss, regular_loss)
    torch.testing.assert_close(chunked_loss, baseline_loss)

    chunked_loss = chunked_cross_entropy(chunked_logits, targets, chunk_size=10, ignore_index=ignore_index)
    torch.testing.assert_close(chunked_loss, regular_loss)
    torch.testing.assert_close(chunked_loss, baseline_loss)


def test_num_parameters():
    model = torch.nn.Linear(2, 2)
    assert num_parameters(model) == 6
    assert num_parameters(model, requires_grad=True) == 6
    assert num_parameters(model, requires_grad=False) == 0

    model = torch.nn.Linear(2, 2)
    model.bias.requires_grad = False
    assert num_parameters(model) == 6
    assert num_parameters(model, requires_grad=True) == 4
    assert num_parameters(model, requires_grad=False) == 2


@_RunIf(min_cuda_gpus=1)
@pytest.mark.parametrize("mode", ["nf4", "nf4-dq", "fp4", "fp4-dq", "int8", "int8-training"])
def test_num_parameters_bitsandbytes(mode):
    plugin = BitsandbytesPrecision(mode=mode)
    fabric = Fabric(plugins=plugin, accelerator="cuda", devices=1)

    model = torch.nn.Linear(10, 10)
    model = fabric.setup(model)
    assert num_parameters(model) == 110

    with fabric.init_module(empty_init=True):
        model = GPT.from_name("pythia-14m")
    assert num_parameters(model) == 14067712


def test_cycle_iterator():
    iterator = CycleIterator([])
    with pytest.raises(StopIteration):
        next(iterator)

    iterator = CycleIterator(range(3))
    assert iterator.epoch == 0
    assert next(iterator) == 0
    assert iterator.epoch == 0
    assert next(iterator) == 1
    assert iterator.epoch == 0
    assert next(iterator) == 2
    assert iterator.epoch == 0
    assert next(iterator) == 0
    assert iterator.epoch == 1


def test_parse_devices():
    with pytest.raises(ValueError, match="must be 'auto' or a positive integer"):
        assert parse_devices(0)
    with pytest.raises(ValueError, match="must be 'auto' or a positive integer"):
        assert parse_devices(-2)

    with mock.patch("litgpt.utils.torch.cuda.device_count", return_value=0):
        assert parse_devices("auto") == 1  # CPU
        assert parse_devices(10) == 10  # leave validation up to Fabric later on
    with mock.patch("litgpt.utils.torch.cuda.device_count", return_value=1):
        assert parse_devices("auto") == 1  # CUDA
    with mock.patch("litgpt.utils.torch.cuda.device_count", return_value=3):
        assert parse_devices("auto") == 3
        assert parse_devices(-1) == 3

    assert parse_devices(5) == 5


def test_copy_config_files(fake_checkpoint_dir, tmp_path):
    copy_config_files(fake_checkpoint_dir, tmp_path)
    expected = {"model_config.yaml", "tokenizer_config.json", "tokenizer.json"}
    contents = set(os.listdir(tmp_path))
    assert expected.issubset(contents)


def test_capture_hparams():
    integer = 1
    string = "string"
    boolean = True
    none = None
    path = Path("/path")
    dataclass = TrainArgs()
    other = torch.nn.Linear(1, 1)
    hparams = capture_hparams()
    assert hparams == {
        "integer": integer,
        "string": string,
        "boolean": boolean,
        "none": none,
        "path": path,
        "dataclass": asdict(dataclass),
        "other": str(other),
    }


def _test_function(out_dir: Path, foo: bool = False, bar: int = 1):
    save_hyperparameters(_test_function, out_dir)


def test_save_hyperparameters(tmp_path):
    with mock.patch("sys.argv", ["any.py", str(tmp_path), "--foo", "True"]):
        CLI(_test_function)

    with open(tmp_path / "hyperparameters.yaml", encoding="utf-8") as file:
        hparams = yaml.full_load(file)

    assert hparams["out_dir"] == str(tmp_path)
    assert hparams["foo"] is True
    assert hparams["bar"] == 1


def _test_function2(out_dir: Path, foo: bool = False, bar: int = 1):
    assert False, "I only exist as a signature, but I should not run."


@pytest.mark.parametrize(
    "command",
    [
        "any.py",
        "litgpt finetune",
        "litgpt finetune_full",
        "litgpt finetune_lora",
        "litgpt finetune_adapter",
        "litgpt finetune_adapter_v2",
        "litgpt pretrain",
    ],
)
def test_save_hyperparameters_known_commands(command, tmp_path):
    with mock.patch("sys.argv", [*command.split(" "), str(tmp_path), "--foo", "True"]):
        save_hyperparameters(_test_function2, tmp_path)

    with open(tmp_path / "hyperparameters.yaml", encoding="utf-8") as file:
        hparams = yaml.full_load(file)

    assert hparams["out_dir"] == str(tmp_path)
    assert hparams["foo"] is True
    assert hparams["bar"] == 1


def test_choose_logger(tmp_path):
    assert isinstance(choose_logger("csv", out_dir=tmp_path, name="csv"), CSVLogger)
    if RequirementCache("tensorboard"):
        assert isinstance(choose_logger("tensorboard", out_dir=tmp_path, name="tb"), TensorBoardLogger)
    if RequirementCache("wandb"):
        assert isinstance(choose_logger("wandb", out_dir=tmp_path, name="wandb"), WandbLogger)
    if RequirementCache("mlflow") or RequirementCache("mlflow-skinny"):
        assert isinstance(choose_logger("mlflow", out_dir=tmp_path, name="wandb"), MLFlowLogger)
    with pytest.raises(ValueError, match="`--logger_name=foo` is not a valid option."):
        choose_logger("foo", out_dir=tmp_path, name="foo")


@pytest.mark.parametrize(
    "path_type, input_path, expected",
    [
        ("relative", "some/relative/path", "some/relative/path"),
        ("absolute", "/usr/absolute/path", "/usr/absolute/path"),
        ("env_relative", "some/relative/path", "prefix/some/relative/path"),
        ("env_absolute", "/usr/absolute/path", "/usr/absolute/path"),
    ],
)
def test_init_out_dir(path_type, input_path, expected):
    if path_type.startswith("env_"):
        with mock.patch.dict(os.environ, {"LIGHTNING_ARTIFACTS_DIR": "prefix"}):
            result = init_out_dir(input_path)
            assert result == Path(expected), f"Failed for {path_type} with input {input_path} (result {result})"
    else:
        result = init_out_dir(input_path)
        if "LIGHTNING_ARTIFACTS_DIR" not in os.environ:
            assert result == Path(expected), f"Failed for {path_type} with input {input_path} (result {result})"
        else:
            assert result == Path(os.getenv("LIGHTNING_ARTIFACTS_DIR")) / expected, (
                f"Failed for {path_type} with input {input_path} (result {result})"
            )


def test_find_resume_path(tmp_path):
    assert find_resume_path(resume=None, out_dir=Path("does/not/exist")) is None
    assert find_resume_path(resume=Path("does/not/exist"), out_dir=Path("does/not/matter")) == Path("does/not/exist")
    assert find_resume_path(resume=(tmp_path / "checkpoint.pt"), out_dir=Path("does/not/matter")) == (
        tmp_path / "checkpoint.pt"
    )

    # `resume='auto'` does not enforce the checkpoint to exist
    assert find_resume_path(resume="auto", out_dir=Path("does/not/exist")) is None

    # `resume=True` requires a checkpoint to exist
    with pytest.raises(FileNotFoundError, match="You passed `--resume=True`, but no checkpoint file was found"):
        find_resume_path(resume=True, out_dir=Path("does/not/exist"))
    with pytest.raises(FileNotFoundError, match="You passed `--resume=True`, but no checkpoint file was found"):
        find_resume_path(resume=True, out_dir=tmp_path)

    (tmp_path / "step-001").mkdir()
    (tmp_path / "step-001" / "lit_model.pth").touch()
    (tmp_path / "step-002").mkdir()
    (tmp_path / "step-002" / "lit_model.pth").touch()
    (tmp_path / "step-003").mkdir()
    (tmp_path / "step-003" / "lit_model.pth").touch()

    assert find_resume_path(resume=True, out_dir=tmp_path) == (tmp_path / "step-003" / "lit_model.pth")
    assert find_resume_path(resume="auto", out_dir=tmp_path) == (tmp_path / "step-003" / "lit_model.pth")


@pytest.fixture
def model_parameters():
    return [torch.nn.Parameter(torch.randn(2, 2))]


def test_instantiate_bnb_optimizer_with_str(model_parameters):
    import bitsandbytes as bnb

    with mock.patch("litgpt.utils.get_argument_names", return_value={"lr", "eps", "weight_decay"}):
        optimizer = instantiate_bnb_optimizer("AdamW", model_parameters)
        assert isinstance(optimizer, bnb.optim.adamw.PagedAdamW)


def test_instantiate_bnb_optimizer_with_dict(model_parameters):
    import bitsandbytes as bnb

    optimizer_dict = {"class_path": "AdamW", "init_args": {"lr": 0.01}}
    with mock.patch("litgpt.utils.get_argument_names", return_value={"lr", "eps", "weight_decay"}):
        optimizer = instantiate_bnb_optimizer(optimizer_dict, model_parameters)
        assert isinstance(optimizer, bnb.optim.adamw.PagedAdamW)
        assert optimizer.param_groups[0]["lr"] == 0.01


def test_instantiate_bnb_optimizer_with_invalid_str(model_parameters):
    with pytest.raises(ValueError, match="only supports the AdamW"):
        instantiate_bnb_optimizer("SGD", model_parameters)


def test_instantiate_torch_optimizer_with_str(model_parameters):
    optimizer = instantiate_torch_optimizer("Adam", model_parameters, lr=0.01)
    assert isinstance(optimizer, torch.optim.Adam)
    assert optimizer.param_groups[0]["lr"] == 0.01


def test_instantiate_torch_optimizer_with_class(model_parameters):
    optimizer = instantiate_torch_optimizer(
        {"class_path": "torch.optim.Adam", "init_args": {"lr": 123}}, model_parameters, lr=0.02
    )
    assert isinstance(optimizer, torch.optim.Adam)
    # init args gets overridden
    assert optimizer.param_groups[0]["lr"] == 0.02


@pytest.mark.parametrize(
    "input_path, expected",
    [
        (Path("checkpoints/my_model"), Path("checkpoints/my_model")),
        (Path("checkpoints/my_model"), Path("./checkpoints/my_model")),
    ],
)
def test_extend_checkpoint_dir_is_prefixed(input_path, expected):
    original_dir = Path.cwd()  # Save the current directory
    with TemporaryDirectory() as tmp_dir:
        os.chdir(tmp_dir)

        try:
            if not input_path.is_absolute():
                input_path = Path(tmp_dir) / input_path
            if not expected.is_absolute():
                expected = Path(tmp_dir) / expected
            input_path.parent.mkdir(parents=True, exist_ok=True)
            input_path.touch(exist_ok=True)
            assert extend_checkpoint_dir(input_path) == expected
        finally:
            os.chdir(original_dir)  # Reset the current directory


@pytest.mark.parametrize(
    "input_path, expected",
    [
        (Path("my_model"), Path("checkpoints/my_model")),
        (Path("my_model"), Path("./checkpoints/my_model")),
    ],
)
def test_extend_checkpoint_dir(input_path, expected):
    original_dir = Path.cwd()  # Save the current directory
    with TemporaryDirectory() as tmp_dir:
        os.chdir(tmp_dir)

        try:
            if not input_path.is_absolute():
                input_path = Path(tmp_dir) / "checkpoints" / input_path
            if not expected.is_absolute():
                expected = Path(tmp_dir) / expected
            input_path.parent.mkdir(parents=True, exist_ok=True)
            input_path.touch(exist_ok=True)
            assert extend_checkpoint_dir(input_path) == expected
        finally:
            os.chdir(original_dir)  # Reset the current directory


@pytest.mark.parametrize(
    "input_path, expected",
    [
        (Path("my_model"), Path("my_model")),
        (Path("/my_model"), Path("/my_model")),
    ],
)
def test_extend_checkpoint_dir_dont_exist(input_path, expected):
    assert extend_checkpoint_dir(input_path) == expected


def test_file_size_below_limit_on_cpu():
    # Test file size below limit on CPU
    with NamedTemporaryFile() as temp_file:
        with mock.patch("os.path.getsize", return_value=4_000_000_000):
            size = check_file_size_on_cpu_and_warn(temp_file.name, "cpu")
            assert size == 4_000_000_000


def test_file_size_above_limit_on_cpu():
    # Test file size above limit on CPU
    with NamedTemporaryFile() as temp_file:
        with mock.patch("os.path.getsize", return_value=4_600_000_000):
            with pytest.warns(UserWarning) as record:
                size = check_file_size_on_cpu_and_warn(temp_file.name, "cpu")
            assert size == 4_600_000_000
            assert "over 4.2 GB" in str(record[0].message)


def test_file_size_above_limit_on_gpu():
    # Test file size above limit on GPU should not warn
    with NamedTemporaryFile() as temp_file:
        with mock.patch("os.path.getsize", return_value=4_600_000_000):
            size = check_file_size_on_cpu_and_warn(temp_file.name, "gpu")
            assert size == 4_600_000_000


@pytest.fixture
def mock_cuda_is_available_true(monkeypatch):
    """Fixture to mock torch.cuda.is_available() to return True."""
    monkeypatch.setattr(torch.cuda, "is_available", lambda: True)


@pytest.fixture
def mock_nvidia_device_properties(monkeypatch):
    """Fixture to mock torch.cuda.get_device_properties() for NVIDIA GPUs."""
    mock_device_properties = mock.MagicMock(name="GPU Device", spec=["name"])
    mock_device_properties.name = "NVIDIA RTX A6000"
    monkeypatch.setattr(torch.cuda, "get_device_properties", lambda idx: mock_device_properties)


@pytest.fixture
def mock_amd_device_properties(monkeypatch):
    """Fixture to mock torch.cuda.get_device_properties() for AMD GPUs."""
    mock_device_properties = mock.MagicMock(name="GPU Device", spec=["name"])
    mock_device_properties.name = "AMD Instinct MI250X"
    monkeypatch.setattr(torch.cuda, "get_device_properties", lambda idx: mock_device_properties)


@pytest.fixture
def all_nvlink_connected_output():
    return mock.MagicMock(
        stdout="""        GPU0	GPU1	GPU2	GPU3
GPU0	X	NV12	NV12	NV12
GPU1	NV12	X	NV12	NV12
GPU2	NV12	NV12	X	NV12
GPU3	NV12	NV12	NV12	X""",
        returncode=0,
    )


@mock.patch("subprocess.run")
def test_all_nvlink_connected(
    mock_run, all_nvlink_connected_output, mock_cuda_is_available_true, mock_nvidia_device_properties
):
    mock_run.return_value = all_nvlink_connected_output
    with mock.patch("builtins.print") as mock_print:
        check_nvlink_connectivity()
        mock_print.assert_any_call("All GPUs are fully connected via NVLink.")


@pytest.fixture
def nvlink_partially_connected_output():
    return mock.MagicMock(
        stdout="""        GPU0    GPU1    GPU2    GPU3    CPU Affinity
GPU0     X      NV1     SYS     SYS     0-7
GPU1    NV1      X      SYS     SYS     0-7
GPU2    SYS     SYS      X      NV1     8-15
GPU3    SYS     SYS     NV1      X      8-15

Legend:
  X   = Self
  NV1 = Connected via NVLink with 1 hop
  SYS = Connected via the PCIe or CPU subsystem""",
        returncode=0,
    )


@mock.patch("subprocess.run")
def test_nvlink_partially_connected_output(
    mock_run, nvlink_partially_connected_output, mock_cuda_is_available_true, mock_nvidia_device_properties
):
    mock_run.return_value = nvlink_partially_connected_output
    with mock.patch("builtins.print") as mock_print:
        check_nvlink_connectivity()
        mock_print.assert_any_call(
            "Warning: Not all GPUs are fully connected via NVLink. Some GPUs are connected via slower interfaces. "
            "It is recommended to switch to a different machine with faster GPU connections for optimal multi-GPU training performance."
        )


@pytest.fixture
def nvlink_not_connected_output():
    return mock.MagicMock(
        stdout="""        GPU0    GPU1    GPU2    GPU3    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      PHB     PHB     PHB     0-47    0               N/A
GPU1    PHB      X      PHB     PHB     0-47    0               N/A
GPU2    PHB     PHB      X      PHB     0-47    0               N/A
GPU3    PHB     PHB     PHB      X      0-47    0               N/A

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks""",
        returncode=0,
    )


@mock.patch("subprocess.run")
def test_nvlink_not_connected_output(
    mock_run, nvlink_not_connected_output, mock_cuda_is_available_true, mock_nvidia_device_properties
):
    mock_run.return_value = nvlink_not_connected_output
    with mock.patch("builtins.print") as mock_print:
        check_nvlink_connectivity()
        mock_print.assert_any_call(
            "Warning: Not all GPUs are fully connected via NVLink. Some GPUs are connected via slower interfaces. "
            "It is recommended to switch to a different machine with faster GPU connections for optimal multi-GPU training performance."
        )


@pytest.fixture
def nvlink_all_gpu_connected_but_other_connected_output():
    return mock.MagicMock(
        stdout="""	GPU0	GPU1	GPU2	GPU3	GPU4	GPU5	GPU6	GPU7	NIC0	NIC1	NIC2	NIC3	NIC4	NIC5	NIC6	NIC7	NIC8	NIC9	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	X 	NV12	NV12	NV12	NV12	NV12	NV12	NV12	SYS	SYS	PXB	PXB	SYS	SYS	SYS	SYS	SYS	SYS	0-63,128-191	0		N/A
GPU1	NV12	X 	NV12	NV12	NV12	NV12	NV12	NV12	SYS	SYS	PXB	PXB	SYS	SYS	SYS	SYS	SYS	SYS	0-63,128-191	0		N/A
GPU2	NV12	NV12	X 	NV12	NV12	NV12	NV12	NV12	PXB	PXB	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	0-63,128-191	0		N/A
GPU3	NV12	NV12	NV12	X 	NV12	NV12	NV12	NV12	PXB	PXB	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	0-63,128-191	0		N/A
GPU4	NV12	NV12	NV12	NV12	X 	NV12	NV12	NV12	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PXB	PXB	64-127,192-254	1		N/A
GPU5	NV12	NV12	NV12	NV12	NV12	X 	NV12	NV12	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PXB	PXB	64-127,192-254	1		N/A
GPU6	NV12	NV12	NV12	NV12	NV12	NV12	X 	NV12	SYS	SYS	SYS	SYS	PXB	PXB	SYS	SYS	SYS	SYS	64-127,192-254	1		N/A
GPU7	NV12	NV12	NV12	NV12	NV12	NV12	NV12	X 	SYS	SYS	SYS	SYS	PXB	PXB	SYS	SYS	SYS	SYS	64-127,192-254	1		N/A
NIC0	SYS	SYS	PXB	PXB	SYS	SYS	SYS	SYS	X 	PIX	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS
NIC1	SYS	SYS	PXB	PXB	SYS	SYS	SYS	SYS	PIX	X 	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS
NIC2	PXB	PXB	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	X 	PXB	SYS	SYS	SYS	SYS	SYS	SYS
NIC3	PXB	PXB	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PXB	X 	SYS	SYS	SYS	SYS	SYS	SYS
NIC4	SYS	SYS	SYS	SYS	SYS	SYS	PXB	PXB	SYS	SYS	SYS	SYS	X 	PXB	SYS	SYS	SYS	SYS
NIC5	SYS	SYS	SYS	SYS	SYS	SYS	PXB	PXB	SYS	SYS	SYS	SYS	PXB	X 	SYS	SYS	SYS	SYS
NIC6	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	X 	PIX	SYS	SYS
NIC7	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PIX	X 	SYS	SYS
NIC8	SYS	SYS	SYS	SYS	PXB	PXB	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	X 	PXB
NIC9	SYS	SYS	SYS	SYS	PXB	PXB	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	SYS	PXB	X

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

NIC Legend:

  NIC0: mlx5_0
  NIC1: mlx5_1
  NIC2: mlx5_2
  NIC3: mlx5_3
  NIC4: mlx5_4
  NIC5: mlx5_5
  NIC6: mlx5_6
  NIC7: mlx5_7
  NIC8: mlx5_8
  NIC9: mlx5_9

""",
        returncode=0,
    )


@mock.patch("subprocess.run")
def test_nvlink_all_gpu_connected_but_other_connected_output(
    mock_run,
    nvlink_all_gpu_connected_but_other_connected_output,
    mock_cuda_is_available_true,
    mock_nvidia_device_properties,
):
    mock_run.return_value = nvlink_all_gpu_connected_but_other_connected_output
    with mock.patch("builtins.print") as mock_print:
        check_nvlink_connectivity()
    mock_print.assert_any_call("All GPUs are fully connected via NVLink.")


@pytest.fixture
def nvidia_smi_nvlink_output_dual_gpu_no_numa():
    return mock.MagicMock(
        stdout="""
        GPU0    GPU1    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV1     0-15    0               N/A
GPU1    NV1      X      0-15    0               N/A

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks
    """,
        returncode=0,
    )


@mock.patch("subprocess.run")
def test_check_nvlink_connectivity__returns_fully_connected_when_nvidia_all_nvlink_two_gpus(
    mock_run, nvidia_smi_nvlink_output_dual_gpu_no_numa, mock_cuda_is_available_true, mock_nvidia_device_properties
):
    mock_run.return_value = nvidia_smi_nvlink_output_dual_gpu_no_numa
    with mock.patch("builtins.print") as mock_print:
        check_nvlink_connectivity()
        mock_print.assert_any_call("All GPUs are fully connected via NVLink.")


@pytest.fixture
def rocm_smi_xgmi_output_multi_gpu():
    """
    rocm-smi --showtopotype on ROCm 6.0.3+
    """
    return mock.MagicMock(
        stdout="""
=============================== ROCm System Management Interface ============================
=============================== Link Type between two GPUs ===============================
       GPU0         GPU1         GPU2         GPU3         GPU4         GPU5         GPU6         GPU7
GPU0   0            XGMI         XGMI         XGMI         XGMI         XGMI         XGMI         XGMI
GPU1   XGMI         0            XGMI         XGMI         XGMI         XGMI         XGMI         XGMI
GPU2   XGMI         XGMI         0            XGMI         XGMI         XGMI         XGMI         XGMI
GPU3   XGMI         XGMI         XGMI         0            XGMI         XGMI         XGMI         XGMI
GPU4   XGMI         XGMI         XGMI         XGMI         0            XGMI         XGMI         XGMI
GPU5   XGMI         XGMI         XGMI         XGMI         XGMI         0            XGMI         XGMI
GPU6   XGMI         XGMI         XGMI         XGMI         XGMI         XGMI         0            XGMI
GPU7   XGMI         XGMI         XGMI         XGMI         XGMI         XGMI         XGMI         0
================================== End of ROCm SMI Log ===================================
    """,
        returncode=0,
    )


@mock.patch("subprocess.run")
def test_check_nvlink_connectivity__returns_fully_connected_when_amd_all_xgmi_8_gpus(
    mock_run, rocm_smi_xgmi_output_multi_gpu, mock_cuda_is_available_true, mock_amd_device_properties
):
    mock_run.return_value = rocm_smi_xgmi_output_multi_gpu
    with mock.patch("builtins.print") as mock_print:
        check_nvlink_connectivity()
        mock_print.assert_any_call("All GPUs are fully connected via XGMI.")


@mock.patch("subprocess.run")
def test_check_nvlink_connectivity__returns_no_gpus_when_no_gpus(mock_run, monkeypatch):
    monkeypatch.setattr(torch.cuda, "is_available", lambda: False)
    with mock.patch("builtins.print") as mock_print:
        check_nvlink_connectivity()
        mock_print.assert_any_call("No GPUs available")


@mock.patch("subprocess.run")
def test_check_nvlink_connectivity__returns_unrecognized_vendor_when_unrecognized_vendor(
    mock_run, monkeypatch, mock_cuda_is_available_true
):
    mock_device_properties = mock.MagicMock(name="GPU Device", spec=["name"])
    mock_device_properties.name = "GARAGE DIY HYPERSCALER GPU"
    monkeypatch.setattr(torch.cuda, "get_device_properties", lambda idx: mock_device_properties)
    with mock.patch("builtins.print") as mock_print:
        check_nvlink_connectivity()
        mock_print.assert_any_call("Unrecognized GPU vendor: GARAGE DIY HYPERSCALER GPU")


def test_fix_and_load_json():
    # Test 1: Invalid JSON string with a trailing comma
    invalid_json_trailing_comma = """
    {
      "_from_model_config": true,
      "bos_token_id": 128000,
      "eos_token_id": 128001,
      "transformers_version": "4.45.0.dev0",
      "do_sample": true,
      "temperature": 0.6,
      "top_p": 0.9,
    }
    """

    expected_output_trailing_comma = {
        "_from_model_config": True,
        "bos_token_id": 128000,
        "eos_token_id": 128001,
        "transformers_version": "4.45.0.dev0",
        "do_sample": True,
        "temperature": 0.6,
        "top_p": 0.9,
    }

    result_trailing_comma = fix_and_load_json(invalid_json_trailing_comma)
    assert result_trailing_comma == expected_output_trailing_comma

    # Test 2: Invalid JSON string with missing commas between properties
    invalid_json_missing_commas = """
    {
      "_from_model_config": true,
      "bos_token_id": 128000,
      "eos_token_id": 128001,
      "transformers_version": "4.45.0.dev0"
      "do_sample": true,
      "temperature": 0.6,
      "top_p": 0.9,
    }
    """

    expected_output_missing_commas = {
        "_from_model_config": True,
        "bos_token_id": 128000,
        "eos_token_id": 128001,
        "transformers_version": "4.45.0.dev0",
        "do_sample": True,
        "temperature": 0.6,
        "top_p": 0.9,
    }

    result_missing_commas = fix_and_load_json(invalid_json_missing_commas)
    assert result_missing_commas == expected_output_missing_commas


def test_select_sft_generate_example():
    eval_mock = mock.MagicMock()
    data_mock = mock.MagicMock()

    test_dataset = {"data": [{"instruction": "Test instruction 1"}, {"instruction": "Test instruction 2"}]}
    train_dataset = {"data": [{"instruction": "Train instruction 1"}, {"instruction": "Train instruction 2"}]}

    data_mock.test_dataset.data = test_dataset["data"]
    data_mock.train_dataset.data = train_dataset["data"]

    # Test "first" instruction from test dataset
    eval_mock.evaluate_example = "first"
    instruction = select_sft_generate_example(eval_mock, data_mock)
    assert instruction == "Test instruction 1"

    # Test "first" instruction from train dataset when test dataset is empty
    data_mock.test_dataset.data = []
    instruction = select_sft_generate_example(eval_mock, data_mock)
    assert instruction == "Train instruction 1"

    # Test random selection from test dataset
    eval_mock.evaluate_example = "random"
    data_mock.test_dataset.data = [{"instruction": "Test instruction 1"}, {"instruction": "Test instruction 2"}]
    with mock.patch("random.randint", return_value=1):
        instruction = select_sft_generate_example(eval_mock, data_mock)
        assert instruction == "Test instruction 2"

    # Test random selection from train dataset when test dataset is empty
    data_mock.test_dataset.data = []
    with mock.patch("random.randint", return_value=1):
        instruction = select_sft_generate_example(eval_mock, data_mock)
        assert instruction == "Train instruction 2"

    # Test specific index from test dataset
    eval_mock.evaluate_example = 1
    data_mock.test_dataset.data = [{"instruction": "Test instruction 1"}, {"instruction": "Test instruction 2"}]
    instruction = select_sft_generate_example(eval_mock, data_mock)
    assert instruction == "Test instruction 2"

    # Test specific index from train dataset when test dataset has fewer elements
    data_mock.test_dataset.data = [{"instruction": "Test instruction 1"}]
    instruction = select_sft_generate_example(eval_mock, data_mock)
    assert instruction == "Train instruction 2"

    # Test out-of-range index
    eval_mock.evaluate_example = 2
    data_mock.test_dataset.data = [{"instruction": "Test instruction 1"}]
    data_mock.train_dataset.data = [{"instruction": "Train instruction 1"}]
    with pytest.raises(IndexError):
        select_sft_generate_example(eval_mock, data_mock)

    # Test unknown evaluation type
    eval_mock.evaluate_example = "unknown"
    with pytest.raises(ValueError):
        select_sft_generate_example(eval_mock, data_mock)