Model-Optimizer/tests/examples/speculative_decoding/test_eagle.py

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import os
from pathlib import Path

import pytest
import safetensors.torch
import torch
from _test_utils.examples.run_command import MODELOPT_ROOT, run_example_command
from packaging.version import Version
from transformers import AutoConfig

from modelopt.torch.export.plugins.hf_spec_export import LLAMA_EAGLE_SINGLE_LAYER

EAGLE3_YAML = str(
    MODELOPT_ROOT / "modelopt_recipes" / "general" / "speculative_decoding" / "eagle3.yaml"
)

# Tiny EAGLE architecture overrides (dotlist entries)
_TINY_EAGLE_ARCH = [
    "eagle.eagle_architecture_config.max_position_embeddings=128",
    "eagle.eagle_architecture_config.num_hidden_layers=1",
    "eagle.eagle_architecture_config.intermediate_size=64",
    "eagle.eagle_architecture_config.num_attention_heads=2",
    "eagle.eagle_architecture_config.num_key_value_heads=2",
    "eagle.eagle_architecture_config.head_dim=64",
]


def generate_offline_pt_data(
    output_dir,
    num_files: int = 8,
    seq_len: int = 128,
    hidden_size: int = 512,
    vocab_size: int = 32000,
    num_aux_layers: int = 2,
) -> Path:
    """Generate fake offline training .pt files for EAGLE3 offline training tests.

    Each file contains the keys expected by OfflineSupervisedDataset:
      - input_ids:         LongTensor of shape (seq_len,)
      - hidden_states:     FloatTensor of shape (seq_len, hidden_size)
      - aux_hidden_states: FloatTensor of shape (seq_len, hidden_size*num_aux_layers)

    Args:
        output_dir: Directory to write .pt files into.
        num_files: Number of .pt files to generate.
        seq_len: Sequence length. Defaults to 128.
        hidden_size: Hidden size matching the base model. Defaults to 512 (tiny_llama).
        vocab_size: Vocabulary size matching the base model. Defaults to 32000 (tiny_llama).
        num_aux_layers: Number of auxiliary layers. Defaults to 2.
    Returns:
        Path to the output directory.
    """
    output_dir = Path(output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)
    torch.manual_seed(42)
    for i in range(num_files):
        sample = {
            "input_ids": torch.randint(0, vocab_size, (seq_len,)),
            "hidden_states": torch.randn(seq_len, hidden_size),
            "aux_hidden_states": torch.randn(seq_len, hidden_size * num_aux_layers),
        }
        torch.save(sample, output_dir / f"sample_{i:04d}.pt")
    return output_dir


@pytest.fixture(scope="module")
def eagle_output_dir(tmp_path_factory):
    """Eagle output directory shared in this module."""
    return tmp_path_factory.mktemp("eagle_output_dir")


@pytest.fixture(scope="module")
def draft_vocab_cache_dir(tmp_path_factory):
    """Eagle output directory shared in this module."""
    return tmp_path_factory.mktemp("eagle_output_dir")


def test_calibrate_draft_vocab(tiny_llama_path, tiny_daring_anteater_path, draft_vocab_cache_dir):
    """Test calibration of draft vocabulary."""
    draft_vocab_size = 80  # tiny tokenizer has vocab size 128 only
    run_example_command(
        [
            "python",
            "./scripts/calibrate_draft_vocab.py",
            "--model",
            tiny_llama_path,
            "--data",
            tiny_daring_anteater_path,
            "--draft_vocab_size",
            str(draft_vocab_size),
            "--save_dir",
            draft_vocab_cache_dir,
        ],
        "speculative_decoding",
    )

    model_name = os.path.basename(os.path.normpath(tiny_llama_path))
    d2t = torch.load(os.path.join(draft_vocab_cache_dir, model_name, "d2t.pt"))
    assert d2t.shape[0] == draft_vocab_size, (
        f"Expected draft vocab size {draft_vocab_size}, got {d2t.shape[0]}"
    )


# fmt: off
@pytest.mark.parametrize(("cp_size", "mix_hidden_states"), [(1, False), (2, False), (1, True), (2, True)])
def test_llama_eagle3(tiny_llama_path,
                      tiny_daring_anteater_path,
                      eagle_output_dir,
                      cp_size,
                      mix_hidden_states,
                      num_gpus):
    """Test Eagle3 training with a tiny llama model, using different cp_size values."""
    if cp_size == 2 and num_gpus < 2:
        pytest.skip(f"cp_size=2 requires at least 2 GPUs, but only {num_gpus} found.")
    if cp_size == 2 and not Version(torch.__version__) >= Version("2.10.0"):
        pytest.skip("cp_size=2 requires torch 2.10.0")

    output_dir = str(eagle_output_dir / f"eagle-tinyllama-cp{cp_size}-mix{mix_hidden_states}")
    overrides = [
        f"model.model_name_or_path={tiny_llama_path}",
        f"data.data_path={tiny_daring_anteater_path}",
        f"training.output_dir={output_dir}",
        "data.sample_size=64",
        "training.num_train_epochs=0.25",
        "training.learning_rate=1e-5",
        "training.training_seq_len=128",
        f"training.cp_size={cp_size}",
        f"eagle.eagle_mix_hidden_states={mix_hidden_states}",
        f"eagle.eagle_use_torch_compile={not mix_hidden_states}", #mhs fail for compile. TODO: fix this
        *_TINY_EAGLE_ARCH,
    ]

    run_example_command(
        ["./launch_train.sh", "--config", EAGLE3_YAML, *overrides],
        "speculative_decoding",
    )


def test_resume_training(tiny_daring_anteater_path, eagle_output_dir):
    """Test resume training of Eagle3."""
    # Resume the compile-free checkpoint (mixTrue) so this test doesn't pay torch.compile;
    # the compiled path is smoke-tested once by test_llama_eagle3[1-False].
    checkpoint_dir = str(eagle_output_dir / "eagle-tinyllama-cp1-mixTrue")
    overrides = [
        f"model.model_name_or_path={checkpoint_dir}",
        f"data.data_path={tiny_daring_anteater_path}",
        f"training.output_dir={checkpoint_dir}",
        "data.sample_size=64",
        "training.num_train_epochs=0.5",
        "training.learning_rate=1e-5",
        "training.training_seq_len=128",
        "eagle.eagle_use_torch_compile=false",
    ]
    run_example_command(
        ["./launch_train.sh", "--config", EAGLE3_YAML, *overrides],
        "speculative_decoding",
    )


def test_ar_validate(eagle_output_dir):
    """Test in-framework AR evaluation."""
    run_example_command(
        [
            "python", "./scripts/ar_validate.py",
            # Validate the compile-free checkpoint so AR generation doesn't trigger torch.compile.
            "--model_path", eagle_output_dir / "eagle-tinyllama-cp1-mixTrue",
            "--osl", "10",
            "--num_samples", "5",
            "--steps", "3"
        ],
        "speculative_decoding",
    )


def test_export_hf_checkpoint(eagle_output_dir):
    """Test export of Eagle3 checkpoint."""
    run_example_command(
        [
            "python", "./scripts/export_hf_checkpoint.py",
            "--model_path", eagle_output_dir / "eagle-tinyllama-cp1-mixTrue",
            "--export_path", eagle_output_dir / "eagle-tinyllama-export",
        ],
        "speculative_decoding",
    )
    # Check the exported checkpoints have required keys
    state_dict = safetensors.torch.load_file(eagle_output_dir / "eagle-tinyllama-export" / "model.safetensors")
    for required_key in LLAMA_EAGLE_SINGLE_LAYER["required"]:
        assert f"{required_key}.weight" in state_dict, f"Missing key '{required_key}.weight' in state_dict"


def test_convert_to_vllm_ckpt(tiny_llama_path, eagle_output_dir):
    """Test conversion of Eagle3 checkpoint to VLLM one-model checkpoint."""
    run_example_command(
        [
            "python", "./scripts/convert_to_vllm_ckpt.py",
            "--input", eagle_output_dir / "eagle-tinyllama-export",
            "--verifier", tiny_llama_path,
            "--output", eagle_output_dir / "eagle-tinyllama-export-vllm-one-ckpt",
        ],
        "speculative_decoding",
    )
# fmt: on


@pytest.mark.parametrize(
    ("model_source", "use_fake_base"),
    [
        (None, False),  # tiny_llama (from fixture), no FakeBase
        pytest.param("moonshotai/Kimi-K2.5", True),  # remote HF repo, FakeBaseModel
        pytest.param(  # remote HF repo, no FakeBaseModel
            "moonshotai/Kimi-K2-Thinking",
            True,
            marks=pytest.mark.manual(reason="skip redundand test, too slow"),
        ),
        pytest.param(
            "MiniMaxAI/MiniMax-M2.5",
            True,
            marks=pytest.mark.manual(reason="skip redundand test, too slow"),
        ),
    ],
    ids=["tinyllama", "kimi-k2.5", "kimi-k2-thinking", "minimax-m2.5"],
)
def test_offline_eagle3_training(
    tiny_llama_path,
    tiny_daring_anteater_path,
    tmp_path,
    eagle_output_dir,
    model_source,
    use_fake_base,
):
    """Test Eagle3 training with pre-computed hidden states (offline mode / FakeBaseModel)."""
    model_path = tiny_llama_path if model_source is None else model_source
    model_id = "tinyllama" if model_source is None else model_source.split("/")[-1]
    output_subdir = eagle_output_dir / f"eagle-{model_id}-offline"

    cfg = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
    if hasattr(cfg, "text_config"):  # vlm: get text_config
        cfg = cfg.text_config

    offline_data_dir = generate_offline_pt_data(
        tmp_path / "offline_data",
        hidden_size=cfg.hidden_size,
        vocab_size=cfg.vocab_size,
        num_aux_layers=min(cfg.num_hidden_layers, 3),
    )

    overrides = [
        f"model.model_name_or_path={model_path}",
        "model.trust_remote_code=true",
        f"model.use_fake_base_for_offline={str(use_fake_base).lower()}",
        f"data.data_path={tiny_daring_anteater_path}",
        f"data.offline_data_path={offline_data_dir}",
        f"training.output_dir={output_subdir}",
        "training.num_train_epochs=0.1",
        "training.learning_rate=1e-5",
        "training.training_seq_len=64",
        # torch.compile is smoke-tested once by test_llama_eagle3[1-False]; skip its ~2min
        # warmup here (the recipe default is true).
        "eagle.eagle_use_torch_compile=false",
        *_TINY_EAGLE_ARCH,
    ]
    run_example_command(
        ["./launch_train.sh", "--config", EAGLE3_YAML, *overrides],
        "speculative_decoding",
    )
    assert os.path.exists(output_subdir / "config.json")


def test_eagle3_dry_run(tiny_llama_path, tmp_path, eagle_output_dir):
    """Test --dry_run: load + mtsp.convert + save_pretrained, no training, then export.

    Exercises the full dry-run pipeline (FakeBaseModel + skip-train + export) so users
    can smoke-test convert→save→export plumbing without paying for an actual training run.
    The exported checkpoint has untrained draft-head weights but a valid EAGLE3 structure.
    """
    output_subdir = eagle_output_dir / "eagle-tinyllama-dryrun"
    export_subdir = eagle_output_dir / "eagle-tinyllama-dryrun-export"

    overrides = [
        f"model.model_name_or_path={tiny_llama_path}",
        "model.use_fake_base_for_offline=true",
        # offline_data_path is required to route into the FakeBaseModel path
        # (use_offline_training=True), but is never actually read in --dry_run mode.
        f"data.offline_data_path={tmp_path / 'dummy_offline'}",
        f"training.output_dir={output_subdir}",
        *_TINY_EAGLE_ARCH,
    ]
    run_example_command(
        ["./launch_train.sh", "--config", EAGLE3_YAML, "--dry_run", *overrides],
        "speculative_decoding",
    )
    assert (output_subdir / "modelopt_state.pth").exists()
    assert (output_subdir / "config.json").exists()

    # The dry-run checkpoint must be exportable to the deployment format.
    run_example_command(
        [
            "python",
            "./scripts/export_hf_checkpoint.py",
            "--model_path",
            output_subdir,
            "--export_path",
            export_subdir,
        ],
        "speculative_decoding",
    )
    state_dict = safetensors.torch.load_file(export_subdir / "model.safetensors")
    for required_key in LLAMA_EAGLE_SINGLE_LAYER["required"]:
        assert f"{required_key}.weight" in state_dict, (
            f"Missing key '{required_key}.weight' in state_dict"
        )


def test_offline_resume_training_kimi(tiny_daring_anteater_path, tmp_path, eagle_output_dir):
    """Test resume of offline Eagle3 training from a FakeBaseModel checkpoint (Kimi-K2.5).

    Depends on test_offline_eagle3_training["kimi-k2.5"] having run first.
    Exercises AutoModelForCausalLM.from_pretrained with model_type='fake_base_model'.
    """
    checkpoint_dir = eagle_output_dir / "eagle-Kimi-K2.5-offline"
    config = AutoConfig.from_pretrained(checkpoint_dir, trust_remote_code=True)

    offline_data_dir = generate_offline_pt_data(
        tmp_path / "offline_data_resume",
        hidden_size=config.hidden_size,
        vocab_size=config.vocab_size,
        num_aux_layers=min(config.num_hidden_layers, 3),
    )

    overrides = [
        f"model.model_name_or_path={checkpoint_dir}",
        "model.trust_remote_code=true",
        "model.use_fake_base_for_offline=true",
        f"data.data_path={tiny_daring_anteater_path}",
        f"data.offline_data_path={offline_data_dir}",
        f"training.output_dir={checkpoint_dir}",
        "training.num_train_epochs=0.2",
        "training.learning_rate=1e-5",
        "training.training_seq_len=64",
        "eagle.eagle_use_torch_compile=false",
    ]
    run_example_command(
        ["./launch_train.sh", "--config", EAGLE3_YAML, *overrides],
        "speculative_decoding",
    )