File size: 6,820 Bytes
90884df | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 | from __future__ import annotations
import copy
import pytest
import torch
from torch import nn
from music3lab.codec.external_finetune import (
ExternalSource,
FineTuneMetrics,
WarmupCosineSchedule,
deterministic_guarded_crop,
evaluate_gates,
load_source_splits,
mixed_batch_loss,
select_checkpoint,
serialize_source_splits,
split_external_sources,
)
USER22 = tuple(f"user22-{index:02d}" for index in range(22))
def _sources() -> tuple[ExternalSource, ...]:
"""Two thousand admissible sources plus user music that must stay OOD."""
return tuple(
ExternalSource(source_id=f"external-{index:04d}", samples=44100 * 12)
for index in range(2000)
) + tuple(
ExternalSource(source_id=source_id, samples=44100 * 12, user_provided=True)
for source_id in USER22
)
def test_source_exclusive_1600_200_200_split_and_guarded_crops_are_deterministic() -> None:
first = split_external_sources(_sources(), seed=73)
second = split_external_sources(_sources(), seed=73)
assert {name: len(rows) for name, rows in first.items()} == {
"train": 1600,
"validation": 200,
"heldout": 200,
}
assert first == second
owners = {
row.source_id: name for name, rows in first.items() for row in rows
}
assert len(owners) == 2000
assert not set(USER22) & set(owners)
crop = deterministic_guarded_crop(
first["train"][0], split="train", epoch=3, item_index=7, seed=73
)
assert crop == deterministic_guarded_crop(
first["train"][0], split="train", epoch=3, item_index=7, seed=73
)
assert crop.samples == 44032
assert 44100 * 5 <= crop.start_sample
assert crop.start_sample + crop.samples <= 44100 * 7
assert crop.source_id in owners and owners[crop.source_id] == "train"
def test_manifest_loader_rejects_user_audio_from_train_or_checkpoint_selection(tmp_path) -> None:
manifest = split_external_sources(_sources(), seed=9)
path = tmp_path / "sources.json"
path.write_text(serialize_source_splits(manifest), encoding="utf-8")
loaded = load_source_splits(path)
assert loaded == manifest
assert not set(USER22) & {
row.source_id for rows in loaded.values() for row in rows
}
with pytest.raises(ValueError, match="user-provided"):
split_external_sources(
_sources(), seed=9, train_source_ids=("external-0000", USER22[0])
)
def test_mixed_loss_uses_six_external_two_music3_and_exact_weights() -> None:
teacher_nmse = torch.tensor(2.0)
teacher_ruler = torch.tensor(3.0)
external_ruler = torch.tensor(5.0)
prior = torch.tensor(7.0)
result = mixed_batch_loss(
teacher_nmse=teacher_nmse,
teacher_ruler=teacher_ruler,
external_ruler=external_ruler,
external_prior=prior,
external_count=6,
music3_count=2,
)
assert result.external_count == 6
assert result.music3_count == 2
assert float(result.total) == pytest.approx(
0.70 * 5.0 + 0.30 * (2.0 + 0.05 * 3.0) + 0.005 * 7.0
)
assert result.teacher_weight == pytest.approx(0.30)
assert result.external_weight == pytest.approx(0.70)
def test_only_encoder_receives_gradients_and_frozen_decoder_is_unchanged() -> None:
encoder = nn.Linear(2, 2, bias=False)
decoder = nn.Linear(2, 2, bias=False)
for parameter in decoder.parameters():
parameter.requires_grad_(False)
decoder_before = copy.deepcopy(decoder.state_dict())
teacher_audio = torch.tensor([[1.0, -1.0], [0.5, 0.25]])
external_audio = torch.tensor([[0.25, -0.5]]).repeat(6, 1)
predicted = encoder(torch.cat((teacher_audio, external_audio), dim=0))
rendered = decoder(predicted)
result = mixed_batch_loss(
teacher_nmse=(predicted[:2] - teacher_audio).square().mean(),
teacher_ruler=(rendered[:2] - teacher_audio).square().mean(),
external_ruler=(rendered[2:] - external_audio).square().mean(),
external_prior=predicted[2:].square().mean(),
external_count=6,
music3_count=2,
)
result.total.backward()
assert all(parameter.grad is not None for parameter in encoder.parameters())
assert all(parameter.grad is None for parameter in decoder.parameters())
assert decoder.state_dict().keys() == decoder_before.keys()
assert all(torch.equal(value, decoder_before[key]) for key, value in decoder.state_dict().items())
def test_warmup_cosine_endpoints_are_frozen() -> None:
schedule = WarmupCosineSchedule(
warmup_steps=10,
total_steps=100,
maximum_learning_rate=1e-3,
minimum_learning_rate=1e-5,
)
assert schedule(0) == pytest.approx(0.0)
assert schedule(10) == pytest.approx(1e-3)
assert schedule(100) == pytest.approx(1e-5)
assert schedule(50) < schedule(10)
def test_synthetic_training_improves_external_ruler_without_teacher_regression() -> None:
scale = nn.Parameter(torch.zeros(()))
optimizer = torch.optim.SGD([scale], lr=0.15)
baseline = FineTuneMetrics(teacher_ruler=1.0, external_ruler=1.1025)
for _ in range(50):
optimizer.zero_grad()
loss = mixed_batch_loss(
teacher_nmse=(scale - 1.0).square(),
teacher_ruler=(scale - 1.0).square(),
external_ruler=(scale - 1.05).square(),
external_prior=scale.square(),
external_count=6,
music3_count=2,
)
loss.total.backward()
optimizer.step()
candidate = FineTuneMetrics(
teacher_ruler=float((scale.detach() - 1.0).square()),
external_ruler=float((scale.detach() - 1.05).square()),
)
gate = evaluate_gates(baseline=baseline, candidate=candidate)
assert gate.external_ruler_improved is True
assert gate.teacher_regression_fraction <= 0.05
assert gate.teacher_regression_within_limit is True
assert gate.passes is True
def test_heldout_gates_and_user22_ood_report_cannot_select_a_checkpoint() -> None:
baseline = FineTuneMetrics(teacher_ruler=1.0, external_ruler=4.0)
checkpoints = {
"step-010": FineTuneMetrics(teacher_ruler=1.02, external_ruler=2.0),
"step-020": FineTuneMetrics(teacher_ruler=1.01, external_ruler=2.1),
}
selected = select_checkpoint(
baseline=baseline,
validation=checkpoints,
heldout={"step-010": FineTuneMetrics(teacher_ruler=4.0, external_ruler=0.1)},
user22_ood={"step-010": FineTuneMetrics(teacher_ruler=0.0, external_ruler=0.0)},
)
assert selected.name == "step-010"
assert selected.selection_split == "validation"
assert selected.heldout_gate is not None
assert selected.user22_ood_report is not None
assert selected.user22_ood_report.influenced_selection is False
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