gmnet/code/journal_exp/tests/test_training_audit.py

"""Regression tests for long-run training audit safeguards."""

from __future__ import annotations

import random
from unittest import mock

import numpy as np
import pytest
import torch
from torch import nn

from gmnet.data.build import _build_manifest, _validate_expected_samples
from gmnet.engine import state_dict_schema_sha256
from gmnet.models import SmoothClippedSelfGate
from gmnet.train import (
    capture_rng_state,
    checkpoint_path_for_progress,
    collect_rng_states,
    fail_if_nonfinite,
    parameter_groups,
    make_scheduler,
    reduce_batchnorm_statistics,
    restore_rng_state,
    should_evaluate_epoch,
    smooth_clip_diagnostics,
    stable_config_fingerprint,
    validate_resume_checkpoint,
    validate_resume_data_manifest,
    validate_resume_training_progress,
)


class _Parameters(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.weight = nn.Parameter(torch.ones(2, 2))
        self.raw_clip = nn.Parameter(torch.ones(1, 2, 1, 1))
        self.bias = nn.Parameter(torch.ones(2))


def test_raw_clip_pattern_disables_weight_decay() -> None:
    model = _Parameters()
    groups = parameter_groups(model, 0.03, ["raw_clip"])
    assert [id(value) for value in groups[0]["params"]] == [id(model.weight)]
    assert {id(value) for value in groups[1]["params"]} == {
        id(model.raw_clip),
        id(model.bias),
    }
    assert groups[1]["weight_decay"] == 0.0


def test_no_weight_decay_pattern_supports_glob() -> None:
    model = nn.Sequential(_Parameters())
    groups = parameter_groups(model, 0.03, ["*.raw_clip"])
    assert id(model[0].raw_clip) in {id(value) for value in groups[1]["params"]}


def test_config_fingerprint_is_stable_and_sensitive() -> None:
    first = {"train": {"epochs": 300}, "seed": 1}
    reordered = {"seed": 1, "train": {"epochs": 300}}
    changed = {"seed": 2, "train": {"epochs": 300}}
    assert stable_config_fingerprint(first) == stable_config_fingerprint(reordered)
    assert stable_config_fingerprint(first) != stable_config_fingerprint(changed)


def test_strict_resume_checks_all_experiment_identity_fields() -> None:
    checkpoint = {
        "config_fingerprint": "abc",
        "run_name": "run",
        "seed": 7,
        "world_size": 8,
        "epoch_complete": True,
        "rng_state_by_rank": [{} for _ in range(8)],
    }
    validate_resume_checkpoint(
        checkpoint, config_fingerprint="abc", run_name="run", seed=7, world_size=8
    )
    with pytest.raises(ValueError, match="config_fingerprint.*run_name.*seed.*world_size"):
        validate_resume_checkpoint(
            checkpoint,
            config_fingerprint="different",
            run_name="other",
            seed=8,
            world_size=4,
        )
    with pytest.raises(ValueError, match="missing config_fingerprint"):
        validate_resume_checkpoint(
            {}, config_fingerprint="abc", run_name="run", seed=7, world_size=8
        )


def test_strict_resume_requires_a_complete_consistent_epoch() -> None:
    checkpoint = {
        "epoch": 2,
        "global_step": 30,
        "epoch_complete": True,
        "steps_in_epoch": 10,
        "expected_steps_per_epoch": 10,
        "training_complete": False,
    }
    validate_resume_training_progress(
        checkpoint, expected_steps_per_epoch=10, epochs=4
    )
    with pytest.raises(ValueError, match="steps_in_epoch.*global_step"):
        validate_resume_training_progress(
            {**checkpoint, "steps_in_epoch": 1, "global_step": 21},
            expected_steps_per_epoch=10,
            epochs=4,
        )


def test_partial_epoch_uses_recovery_checkpoint_only(tmp_path) -> None:
    assert checkpoint_path_for_progress(
        tmp_path, epoch_complete=False
    ) == tmp_path / "checkpoint_recovery.pt"
    assert checkpoint_path_for_progress(
        tmp_path, epoch_complete=True
    ) == tmp_path / "checkpoint_last.pt"


def test_state_schema_hash_tracks_names_shapes_and_dtypes() -> None:
    first = {"weight": torch.zeros(2, 3), "count": torch.zeros((), dtype=torch.int64)}
    same = {"weight": torch.ones(2, 3), "count": torch.ones((), dtype=torch.int64)}
    changed = {"weight": torch.ones(3, 2), "count": torch.ones((), dtype=torch.int64)}
    assert state_dict_schema_sha256(first) == state_dict_schema_sha256(same)
    assert state_dict_schema_sha256(first) != state_dict_schema_sha256(changed)


def test_scheduler_supports_epoch_steps_and_cooldown() -> None:
    parameter = nn.Parameter(torch.ones(()))
    optimizer = torch.optim.SGD([parameter], lr=1.0)
    scheduler = make_scheduler(
        optimizer,
        epochs=6,
        steps_per_epoch=10,
        warmup_epochs=1,
        warmup_lr=0.1,
        base_lr=1.0,
        min_lr=0.01,
        step_unit="epoch",
        cooldown_epochs=2,
    )
    observed = [optimizer.param_groups[0]["lr"]]
    for _ in range(6):
        optimizer.step()
        scheduler.step()
        observed.append(optimizer.param_groups[0]["lr"])
    assert observed[0] == pytest.approx(0.1)
    assert observed[1] == pytest.approx(1.0)
    assert observed[4:] == pytest.approx([0.01, 0.01, 0.01])


def test_scheduler_rejects_invalid_protocol() -> None:
    optimizer = torch.optim.SGD([nn.Parameter(torch.ones(()))], lr=1.0)
    with pytest.raises(ValueError, match="step_unit"):
        make_scheduler(
            optimizer,
            epochs=2,
            steps_per_epoch=1,
            warmup_epochs=0,
            warmup_lr=0.0,
            base_lr=1.0,
            min_lr=0.0,
            step_unit="batch",
        )


def test_strict_resume_checks_data_manifest() -> None:
    manifest = {"manifest_sha256": "same"}
    validate_resume_data_manifest({"data_manifest": manifest}, manifest)
    with pytest.raises(ValueError, match="data manifest changed"):
        validate_resume_data_manifest(
            {"data_manifest": {"manifest_sha256": "old"}}, manifest
        )


def test_rank_local_rng_state_round_trip() -> None:
    generator = torch.Generator().manual_seed(41)
    random.seed(11)
    np.random.seed(21)
    torch.manual_seed(31)
    with mock.patch("gmnet.train.torch.cuda.is_available", return_value=False):
        state = capture_rng_state(generator)
        expected = (
            random.random(),
            float(np.random.random()),
            float(torch.rand(())),
            float(torch.rand((), generator=generator)),
        )
        random.random()
        np.random.random()
        torch.rand(())
        torch.rand((), generator=generator)
        restore_rng_state(
            {"rng_state_by_rank": [state]},
            rank=0,
            world_size=1,
            generator=generator,
        )
    actual = (
        random.random(),
        float(np.random.random()),
        float(torch.rand(())),
        float(torch.rand((), generator=generator)),
    )
    assert actual == pytest.approx(expected)


def test_rng_collection_keeps_one_state_per_rank() -> None:
    local = {"rank": 0}

    def gather(output: list[object], value: object) -> None:
        assert value is local
        output[:] = [value, {"rank": 1}]

    with (
        mock.patch("gmnet.train.capture_rng_state", return_value=local),
        mock.patch("gmnet.train.dist.all_gather_object", side_effect=gather),
    ):
        states = collect_rng_states(
            torch.Generator(), distributed=True, world_size=2
        )
    assert states == [{"rank": 0}, {"rank": 1}]


def test_rng_restore_rejects_wrong_world_size_or_schema() -> None:
    with pytest.raises(ValueError, match="exactly 2 entries"):
        restore_rng_state(
            {"rng_state_by_rank": [{}]},
            rank=0,
            world_size=2,
            generator=torch.Generator(),
        )
    with pytest.raises(ValueError, match="rank-local RNG state schema"):
        restore_rng_state(
            {"rng_state_by_rank": [{}]},
            rank=0,
            world_size=1,
            generator=torch.Generator(),
        )


def test_eval_interval_always_evaluates_final_or_stopped_epoch() -> None:
    assert not should_evaluate_epoch(
        epoch=0, epochs=12, eval_interval=5, stop_after_epoch=False
    )
    assert should_evaluate_epoch(
        epoch=4, epochs=12, eval_interval=5, stop_after_epoch=False
    )
    assert should_evaluate_epoch(
        epoch=11, epochs=12, eval_interval=5, stop_after_epoch=False
    )
    assert should_evaluate_epoch(
        epoch=2, epochs=12, eval_interval=5, stop_after_epoch=True
    )
    with pytest.raises(ValueError, match="positive"):
        should_evaluate_epoch(
            epoch=0, epochs=1, eval_interval=0, stop_after_epoch=False
        )


def test_nonfinite_guard_rejects_nonfinite_gradient() -> None:
    model = nn.Linear(2, 1)
    loss = model(torch.ones(1, 2)).sum()
    loss.backward()
    fail_if_nonfinite(loss, model, epoch=1, step=2)
    model.weight.grad[0, 0] = torch.inf
    with pytest.raises(FloatingPointError, match="weight"):
        fail_if_nonfinite(loss, model, epoch=1, step=2)


def test_bn_reduction_averages_running_statistics() -> None:
    model = nn.BatchNorm1d(2)
    model.running_mean.copy_(torch.tensor([2.0, 4.0]))
    model.running_var.copy_(torch.tensor([6.0, 8.0]))

    def double(tensor: torch.Tensor, **_: object) -> None:
        tensor.mul_(2)

    with mock.patch("gmnet.train.dist.all_reduce", side_effect=double) as reduce:
        reduce_batchnorm_statistics(model, world_size=2)
    assert reduce.call_count == 2
    assert torch.equal(model.running_mean, torch.tensor([2.0, 4.0]))
    assert torch.equal(model.running_var, torch.tensor([6.0, 8.0]))


def test_smooth_clip_diagnostics_include_stage_statistics() -> None:
    model = nn.Module()
    model.stages = nn.ModuleList(
        [
            nn.Sequential(SmoothClippedSelfGate(2, init_clip=2.0)),
            nn.Sequential(SmoothClippedSelfGate(3, init_clip=3.0)),
        ]
    )
    result = smooth_clip_diagnostics(model)
    assert result is not None
    assert result["module_count"] == 2
    assert result["overall"]["count"] == 5
    assert set(result["by_stage"]) == {"stage1", "stage2"}
    assert result["by_stage"]["stage1"]["count"] == 2


def test_expected_sample_counts_are_strict() -> None:
    _validate_expected_samples(
        {"expected_samples": {"train": 10, "val": 4}},
        train_samples=10,
        val_samples=4,
    )
    with pytest.raises(ValueError, match="train dataset has 10"):
        _validate_expected_samples(
            {"expected_train_samples": 11}, train_samples=10, val_samples=4
        )


def test_imagefolder_manifest_is_root_independent(tmp_path) -> None:
    from PIL import Image
    from torchvision import datasets

    manifests = []
    for root_name in ("first", "second"):
        root = tmp_path / root_name
        for split in ("train", "val"):
            directory = root / split / "class0"
            directory.mkdir(parents=True)
            Image.new("RGB", (2, 2), color="white").save(directory / "a.png")
        train = datasets.ImageFolder(root / "train")
        val = datasets.ImageFolder(root / "val")
        manifests.append(
            _build_manifest(
                dataset_name="imagefolder",
                train_split="train",
                val_split="val",
                train_dataset=train,
                val_dataset=val,
                train_root=root / "train",
                val_root=root / "val",
                class_to_idx=train.class_to_idx,
            )
        )
    assert manifests[0] == manifests[1]
    assert manifests[0]["schema_version"] == 2
    assert manifests[0]["sampled_content_samples"] == {"train": 1, "val": 1}


def test_imagefolder_manifest_detects_sampled_file_content_change(tmp_path) -> None:
    from PIL import Image
    from torchvision import datasets

    root = tmp_path / "data"
    for split in ("train", "val"):
        directory = root / split / "class0"
        directory.mkdir(parents=True)
        Image.new("RGB", (2, 2), color="white").save(directory / "a.png")

    def build() -> dict[str, object]:
        train = datasets.ImageFolder(root / "train")
        val = datasets.ImageFolder(root / "val")
        return _build_manifest(
            dataset_name="imagefolder",
            train_split="train",
            val_split="val",
            train_dataset=train,
            val_dataset=val,
            train_root=root / "train",
            val_root=root / "val",
            class_to_idx=train.class_to_idx,
        )

    before = build()
    Image.new("RGB", (2, 2), color="black").save(
        root / "val" / "class0" / "a.png"
    )
    after = build()
    assert before["sample_index_sha256"] == after["sample_index_sha256"]
    assert before["sampled_content_sha256"]["train"] == after[
        "sampled_content_sha256"
    ]["train"]
    assert before["sampled_content_sha256"]["val"] != after[
        "sampled_content_sha256"
    ]["val"]
    assert before["manifest_sha256"] != after["manifest_sha256"]