test_eagle_offline_ptq.py

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"""End-to-end CI test for the offline speculative decoding PTQ workflow.

Covers the three-stage pipeline:
  1. Collect hidden states from the base model  → .pt files
  2. Train an offline EAGLE draft model         → ModelOpt checkpoint
  3. PTQ the offline checkpoint                 → quantized export

Running all three stages in sequence validates that the data format produced
by stage 1 is correctly consumed by stage 2 and that the checkpoint produced
by stage 2 is correctly quantized in stage 3.
"""

import pytest
import safetensors.torch
import torch
from _test_utils.examples.run_command import MODELOPT_ROOT, run_example_command

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",
]


@pytest.fixture(scope="module")
def offline_ptq_dirs(tmp_path_factory):
    """Shared output directories for all stages."""
    return {
        "hidden_states": tmp_path_factory.mktemp("hidden_states"),
        "eagle_ckpt": tmp_path_factory.mktemp("eagle_ckpt"),
        "ptq_export": tmp_path_factory.mktemp("ptq_export"),
    }


def test_collect_hidden_states(tiny_llama_path, tiny_conversations_path, offline_ptq_dirs):
    """Stage 1: generate .pt hidden state files from the base model."""
    run_example_command(
        [
            "python",
            "collect_hidden_states/compute_hidden_states_hf.py",
            "--model",
            tiny_llama_path,
            "--input-data",
            str(tiny_conversations_path),
            "--output-dir",
            str(offline_ptq_dirs["hidden_states"]),
            "--debug-max-num-conversations",
            "2",
            "--max-seq-len",
            "32",
        ],
        "speculative_decoding",
    )

    pt_files = list(offline_ptq_dirs["hidden_states"].glob("*.pt"))
    assert len(pt_files) > 0, "No .pt files generated by compute_hidden_states_hf.py"

    # Validate the format expected by OfflineSupervisedDataset
    sample = torch.load(str(pt_files[0]))
    assert "input_ids" in sample, "Missing 'input_ids' in .pt file"
    assert "hidden_states" in sample, "Missing 'hidden_states' in .pt file"


def test_offline_eagle_training(tiny_llama_path, tiny_daring_anteater_path, offline_ptq_dirs):
    """Stage 2: train an EAGLE3 draft model using the offline hidden states."""
    output_dir = offline_ptq_dirs["eagle_ckpt"] / "trained"

    overrides = [
        f"model.model_name_or_path={tiny_llama_path}",
        f"data.data_path={tiny_daring_anteater_path}",
        f"data.offline_data_path={offline_ptq_dirs['hidden_states']}",
        f"training.output_dir={output_dir}",
        "training.num_train_epochs=1",
        "training.learning_rate=1e-5",
        "training.training_seq_len=64",
        "training.save_steps=1",
        *_TINY_EAGLE_ARCH,
    ]

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

    assert output_dir.exists(), "EAGLE training did not produce an output directory"


def test_offline_ptq(offline_ptq_dirs):
    """Stage 3: run PTQ on the offline EAGLE checkpoint using the hidden state dataset."""
    run_example_command(
        [
            "python",
            "hf_ptq.py",
            "--pyt_ckpt_path",
            str(offline_ptq_dirs["eagle_ckpt"] / "trained"),
            "--qformat",
            "fp8",
            "--calib_size",
            "2",
            "--batch_size",
            "1",
            "--specdec_offline_dataset",
            str(offline_ptq_dirs["hidden_states"]),
            "--export_path",
            str(offline_ptq_dirs["ptq_export"]),
        ],
        "llm_ptq",
    )

    # Verify the exported checkpoint exists and has the expected EAGLE keys
    export_dir = offline_ptq_dirs["ptq_export"]
    assert (export_dir / "model.safetensors").exists(), "PTQ export missing model.safetensors"
    assert (export_dir / "config.json").exists(), "PTQ export missing config.json"

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

    state_dict = safetensors.torch.load_file(export_dir / "model.safetensors")
    for key in LLAMA_EAGLE_SINGLE_LAYER["required"] - {"fc", "layers.0.hidden_norm"}:
        assert f"{key}.weight" in state_dict, f"Missing key '{key}.weight' in exported state dict"